Assessment of intestinal barrier integrity and associations with innate immune activation and metabolic syndrome in acutely ill, antipsychotic-free schizophrenia patients

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Guest, Madeleine Nussbaumer, Leon Dudeck, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7338710/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Journal of Neuroinflammation → Version 1 posted 7 You are reading this latest preprint version Abstract Background: Schizophrenia (Sz), once considered solely a brain disorder, is now recognised as a systemic illness involving immune and metabolic dysregulation. Recently, attention has focused on the intestinal barrier as a potential factor that exerts influence via gut–brain–immune interactions. However, studies on gut permeability markers in early, antipsychotic-free stages remain scarce and often neglect confounders such as smoking and metabolic syndrome. Methods: We measured two complementary markers: lipopolysaccharide-binding protein (LBP), reflecting endotoxin exposure and systemic immune activation, and intestinal fatty acid-binding protein (I-FABP), indicating gut epithelial damage and permeability changes, in blood from 96 acutely ill, antipsychotic-free Sz patients (61 first-episode, 35 relapsed) and 96 matched controls. Associations with innate immune activation, metabolic parameters, smoking, and clinical features were assessed using nonparametric statistics and random forest regression. Group differences were tested with covariate adjustment and separately in non-smokers (Sz: n=42; controls: n=84). Results: Median LBP was higher in Sz (21.96 µg/mL) vs. controls (18.10 µg/mL; FDR-adjusted p=0.021, δ=0.209), but became non-significant after adjusting for smoking (FDR-adjusted p=0.199). In contrast, I-FABP was lower in Sz (218.2 pg/mL) than controls (315.0 pg/mL; FDR-adjusted p=0.021, δ=–0.195), and remained robust across smoking-adjusted analyses. No differences were found between first-episode and relapsed patients for either marker. LBP correlated strongly with CRP (r=0.557, p<0.001) and neutrophils (r=0.468, p<0.001), regardless of smoking, and was predicted moderately by immune models (pseudo-R² = 0.354 full sample; 0.273 Sz; 0.449 controls). Links to waist circumference and blood pressure were weaker (pseudo-R²: 0.048–0.104). I-FABP was less associated with immune markers and uncorrelated with LBP (r=–0.017, FDR-adjusted p=0.819), suggesting distinct underlying mechanisms. Conclusions: Our findings reveal multifaceted gut barrier disruptions in antipsychotic-free Sz. Elevated LBP, associated with CRP and neutrophils, suggests inflammation influenced by smoking and metabolic factors. In contrast, reduced I-FABP, independent of smoking, may indicate epithelial injury. Their lack of correlation highlights distinct pathophysiological dimensions of gut dysfunction. Longitudinal studies, ideally spanning prodromal phases and integrating microbiome, dietary, and permeability assessments, are needed to clarify temporal dynamics and guide stratified treatment approaches. Schizophrenia gut–brain axis intestinal permeability lipopolysaccharide-binding protein (LBP) intestinal fatty acid-binding protein (I-FABP) innate immunity C-reactive protein (CRP) metabolic syndrome leaky gut unmedicated psychosis 1. Background Schizophrenia (Sz), once viewed solely as a brain disorder, is now recognized as a systemic illness with immune and metabolic components [ 1 – 3 ]. Meta-analyses have reported increased neutrophil and monocyte counts and slightly elevated C-reactive protein (CRP) in Sz [ 4 , 5 ]. In our previous study of 253 acutely ill, unmedicated patients and 294 controls – excluding individuals with infections, trauma, or recent surgery – we found large and medium effect size elevations in neutrophils and monocytes, respectively, even after adjusting for stress and smoking [ 6 ]. This “sterile” myeloid activation raised the question of its underlying drivers. One possible upstream mechanism is subclinical gut barrier dysfunction, which may allow translocation of microbial products such as lipopolysaccharide (LPS) into the circulation, triggering systemic inflammation. Supporting this, Gokulakrishnan et al. [ 7 ] found elevated gut permeability markers, including lipopolysaccharide-binding protein (LBP) and zonulin, especially in antipsychotic-naïve patients, suggesting intestinal barrier changes are intrinsic rather than drug-induced. Similarly, Weber et al. [ 8 ] reported elevated soluble CD14 (sCD14), a marker of monocyte activation, before Sz onset in U.S. military personnel, consistent with low-grade bacterial translocation and innate immune priming. LPS, a potent activator of neutrophils and monocytes [ 9 – 12 ]. can stimulate cytokines such as interleukin (IL)-6, which drive hepatic CRP synthesis [ 13 ]. Although human data remain scarce, animal models suggest that gut-derived inflammation can impair neurodevelopment and behavior relevant to Sz phenotypes, including social cognition [ 14 , 15 ]. These effects are mediated via the gut–microbiota–brain axis, encompassing immune signaling, the vagus nerve, neuroendocrine pathways, and microbial metabolites that influence brain function and behavior [ 15 , 16 ]. Consistent with this, Sz patients exhibit gut microbiota dysbiosis alongside immune-inflammatory abnormalities [ 17 , 18 ]. Increased intestinal permeability may also contribute to development of metabolic syndrome (MetS) 1 by enabling endotoxin-driven low-grade inflammation [ 19 – 21 ], which impairs insulin signaling and promotes insulin resistance [ 22 , 23 ]. While second-generation antipsychotics, particularly clozapine and olanzapine, contribute to weight gain and metabolic dysfunction [ 24 , 25 ], such changes also occur in antipsychotic-naïve first-episode Sz (FESz) patients, and first-degree relatives show increased MetS risk [ 26 – 31 ], suggesting intrinsic immunometabolic vulnerability. These findings suggest that immunometabolic dysfunction may precede medication effects and could reflect intrinsic vulnerability. A bidirectional relationship between MetS and gut permeability has been proposed, where systemic inflammation and oxidative stress disrupt the mucosa, creating a self-reinforcing cycle [ 32 – 35 ]. This link is particularly relevant in Sz, where both systemic inflammation and metabolic risk are elevated, even in early stages of the illness. Studies on gut permeability markers in Sz have yielded mixed results. Some reported elevated LBP, intestinal fatty acid-binding protein (I-FABP), or zonulin [ 7 , 36 , 37 ], while others find no LBP differences [ 8 , 38 , 39 ] ( Supplementary Table 1 ). LBP correlated with CRP [ 8 , 39 ], IL-6 and tumour necrosis factor (TNF)-α [ 37 ], sCD14, [ 8 , 39 ] and with body mass index (BMI) in some cohorts [ 37 , 39 ]. However, leaky gut-MetS links have not been systematically studied. Variation across studies may reflect differences in medication status, disease stage, biomarker selection, control of lifestyle factors, and geographic microbiome differences [ 7 , 8 , 37 , 39 ], highlighting the need for standardized protocols and antipsychotic-free samples. To address these gaps, we examined gut permeability markers and their immune–metabolic relationships in unmedicated acutely ill Sz patients, including both FESz and relapsed Sz (RSz) cases, to determine whether barrier dysfunction is an early, trait-like feature or develops later. We focused on LBP, a liver-derived marker of endotoxin exposure and systemic immune activation [ 40 ], and I-FABP, an enterocyte-derived marker of gut epithelial injury [ 41 ], as they are frequently used in Sz research and distinguish endotoxin-related inflammation from enterocyte stress [ 7 , 8 , 36 – 39 ]. We aimed to (1) compare LBP and I-FABP levels between Sz patients and controls, and between FESz and RSz patients; (2) examine associations with innate immune markers; (3) assess links to MetS features; and (4) evaluate relationships with clinical symptom severity. This multi-dimensional approach sought to clarify the role of gut barrier integrity in Sz immunometabolic and clinical profiles, independent of medication and illness chronicity. 2. Methods 2.1 Patients and controls The study complied with German law, the Declaration of Helsinki, and was approved by the Institutional Review Board (approval number 110/07). All participants gave written informed consent. Acutely ill inpatients with Sz (n = 96; Table 1 ), were recruited at the Department of Psychiatry, University of Magdeburg, and diagnosed per ICD-10. Table 1 Comparison of Sz patients with healthy controls. Demographic data, clinical assessments, LBP and I-FABP measurements, and immune- and metabolic syndrome-related parameters. Data are presented as median (quartile 1; quartile 3; sample size) or number of cases. Significant p-values are highlighted in bold font. Variables Sz median (Q1,Q3,n) Controls median (Q1,Q3,n) Test Test value p-value (FDR) Effect size (Cliff's delta) Demographic data & severity of clinical symptoms Age (years) 33.0 (26.3;44.5;96) 34.5 (27.0;45.8;96) U-Test W = 4471.5 0.724 (0.965) -0.030 Sex (female/male) m: 56 / f: 40 m: 56 / f: 40 Χ²-Test X²=0.0 1.000 (1.000) 0.000 BMI (kg/m 2 ) 23.56 (20.69;27.13;96) 23.69 (21.78;27.57;96) U-Test W = 4426.5 0.638 (0.965) -0.039 Tobacco smoking (yes/no) yes: 54 / no: 42 yes: 12 / no: 84 Χ²-Test X²=38.811 < 0.001 (< 0.001) 0.461 Duration of illness (years) 0.0 (0.0;3.3;94) - - - - - PANSS total corr. (score) 35.00 (28.00;48.75;96) - - - - - PANSS-P corr. (score) 12.00 (9.00;16.00;96) - - - - - PANSS-N corr. (score) 7.00 (4.00;13.00;96) - - - - - PANSS-G corr. (score) 15.00 (12.00;20.00;96) - - - - - Leaky gut markers Plasma LBP (µg/mL) 21.96 (16.66;29.60;95) 18.10 (13.89;23.48;95) U-Test W = 5457.0 0.013 (0.021) 0.209 Serum I-FABP(pg/mL) 218.2 (116.4;369.9;93) 315.0 (174.5;533.5;94) U-Test W = 3518.5 0.021 (0.021) -0.195 Immune-related parameters Neutrophils (×10⁹/L) 4.81 (3.53;6.48;96) 3.01 (2.36;3.86;94) U-Test W = 7258.0 < 0.001 (< 0.001) 0.609 Monocytes (×10⁹/L) 0.56 (0.43;0.73;96) 0.42 (0.32;0.60;94) U-Test W = 6158.0 < 0.001 (< 0.001) 0.365 CRP (mg/L) 1.35 (0.60;3.40;95) 0.90 (0.60;1.70;95) U-Test W = 5371.0 0.023 (0.054 ) 0.190 IL-18 (pg/mL) 214.6 (172.3;300.3;94) 208.3 (159.4;274.2;96) U-Test W = 5072.0 0.140 (0.245) 0.124 IL-6 (pg/mL) 35.03 (22.68;52.99;84) 30.25 (18.00;67.19;90) U-Test W = 4033.5 0.446 (0.446) 0.067 TNF-α (pg/mL) 17.05 (11.04;26.97;87) 20.50 (10.84;35.62;91) U-Test W = 3581.5 0.273 (0.319) -0.095 MCP-1 (pg/mL) 277.4 (216.7;372.6;94) 308.5 (221.2;407.0;95) U-Test W = 4026.0 0.244 (0.319) -0.098 Metabolic syndrome-related parameters MetS (yes/no) yes: 10 / no: 86 yes: 12 / no: 84 Χ²-Test X²=0.051 0.821 (0.902) 0.033 Waist circumference (cm) 88.00 (80.50;97.25;94) 90.00 (80.25;97.00;96) U-Test W = 4465.0 0.902 (0.902) -0.010 Systolic blood pressure (mmHg) 125.5 (115.0;140.0;96) 120.0 (110.0;127.5;96) U-Test W = 5799.0 0.002 (0.015) 0.258 Diastolic blood pressure (mmHg) 80.0 (70.0;90.0;96) 80.0 (70.0;80.0;96) U-Test W = 4491.0 0.754 (0.902) -0.025 Triglycerides (mmol/L) 0.89 (0.64;1.21;96) 1.015 (0.695;1.427;96) U-Test W = 3974.5 0.100 (0.225) -0.137 HDL cholesterol (mmol/L) 1.43 (1.20;1.78;96) 1.54 (1.25;1.79;96) U-Test W = 4073.5 0.165 (0.298) -0.116 Glucose (mmol/L) 4.91 (4.49;5.53;94) 5.01 (4.71;5.30;94) U-Test W = 4212.0 0.582 (0.873) -0.047 sRAGE (pg/mL) 1349 (785;1946;95) 1538 (1042;2547;96) U-Test W = 3667.0 0.019 (0.059) -0.196 VEGF (pg/mL) 232.5 (155.2;325.7;92) 175.7 (139.9;262.1;96) U-Test W = 5287.0 0.020 (0.059) 0.197 Abbreviations: BMI=body mass index, CRP=C-reactive protein, FDR=false discover rate-corrected p-value, I-FABP=intestinal fatty acid binding protein, IL-18=interleukin 18, IL-6=interleukin 6, LBP=lipopolysaccharide binding protein, MCP-1=monocyte chemoattractant protein 1, PANSS=positive and negative syndrome scale [PANSS scores were corrected (corr.) by subtraction of minimum scores, which represented no symptoms], HDL=high density lipoprotein, sRAGE=soluble receptor for advanced glycation end products, TNF-a=tumor necrosis factor alpha, VEGF=vascular endothelial growth factor. Exclusion criteria were psychosis secondary to medical conditions, infections (respiratory, gastrointestinal, urinary tract), substance use disorders, diabetes, immune diseases, cancer, or cardiovascular disease. Screening followed German AWMF schizophrenia guidelines and included physical examination, differential blood counts, CRP, kidney function, lipids, fasting glucose, urine drug screen, MRI, and EEG. Pathological results were defined using institutional reference ranges. Symptoms were rated with the Positive and Negative Syndrome Scale (PANSS). Two patient subgroups were analyzed ( Suppl. Table 2 ): (1) FESz (n = 61) naïve to antipsychotics at baseline (T0), and (2) RSz (n = 35) patients with longer illness (median 6.0 years) but antipsychotic-free at least 6 weeks before sample collection. Healthy controls (n = 96), matched for age, sex, and smoking status (Table 1 ), were recruited from the community and underwent identical screening. Controls with psychiatric illness, substance use, infections, diabetes, immune disorders, cancer, or cardiovascular disease were excluded. 2.2 Blood samples Overnight-fasted samples were collected within 24 h of admission (8:00 a.m.). Differential white blood cell (WBC) counts were obtained from EDTA tubes within one hour after the blood take. Serum tubes were clotted for two hours, then centrifuged at 1000 g for 10 min; plasma EDTA tubes were centrifuged immediately. Supernatants were aliquoted and stored at − 80°C until analysis. 2.3 Assays Serum I-FABP and plasma LBP were measured using commercial ELISAs (Hycult Biotech, Uden, Netherlands). Neutrophil and monocyte counts were obtained with an XN-3000 counter (Sysmex). CRP was measured on a Cobas 8000 c701 analyzer (Roche Diagnostics). IL-18, IL-6, TNF-α, MCP-1, soluble receptor for advanced glycation end products (sRAGE), and vascular endothelial growth factor (VEGF) were quantified with the LegendPlex Human Neuroinflammation Panel (BioLegend, San Diego, CA, USA). 2.4 Statistics Analyses were performed in R 4.3.1, with p < 0.05 (two-tailed) considered significant. False discovery rate (FDR) correction was applied within each variable domain (Table 1 ). Data distributions were assessed by Shapiro–Wilk tests; non-parametric tests were used as most variables were non-normal. Group comparisons Demographic differences in sex and smoking status were assessed using chi-square tests. Group comparisons for continuous variables (e.g., LBP and I-FABP) were conducted using non-parametric Mann-Whitney U-tests, and included comparisons between Sz patients and controls, FESz and RSz patients, and MetS status. Additional comparisons of LBP and I-FABP levels by sex and smoking status were performed using Mann-Whitney U-tests. Cliff’s delta (δ) was used to assess effect sizes (ǀδǀ ≥ 0.147 “small,” ǀδǀ ≥ 0.330 “medium,” ǀδǀ ≥ 0.474 “large”). Correlation analyses We computed Spearman’s rank correlation coefficients between LBP/I-FABP and (a) age, BMI; (b) duration of illness, PANSS scores; (c) innate immune markers; (d) MetS-related parameters (waist circumference, blood pressure, triglycerides, HDL cholesterol, glucose, sRAGE, VEGF). 2 Analyses were run in the full sample and separately in Sz and controls. Exploratory prediction models : To complement the correlation analyses, we conducted random forest regression with backward variable selection in the full sample, the Sz and control groups to identify the most robust predictors of LBP and I-FABP levels. Case weights adjusted for unequal observation counts (e.g., repeated measures or varying subject contributions). Model selection was based on highest pseudo-R² (interpreted as: 0.40 strong predictive power). Smoking adjustment Group differences in LBP and I-FABP were retested via Aligned Rank Transform (ART) ANOVA with smoking as covariate. A subgroup analysis of non-smokers used Mann–Whitney U-tests. 3. Results 3.1 Sensitivity and validity of I-FABP and LBP blood measures Of 192 samples, 190 were within the detection range for LBP (2 below limit); 187 were within range for I-FABP (5 below limit). 3.2 Group comparisons of demographic data and severity of clinical symptoms Patients and controls were matched for age, sex, and BMI (Table 1 , Supplementary Table 2 ). Smoking was more frequent in patients (p < 0.001; Table 1 ). As expected, FESz patients had shorter illness durations than RSz patients (p < 0.001), while PANSS total scores were similar, with a nominally higher PANSS-G in FESz (p = 0.031; not significant after FDR correction) ( Supplementary Table 2 ). 3.3 Group comparisons in LPB and I-FABP levels Median LBP was higher in Sz (21.96 µg/mL) vs. controls (18.10 µg/mL; p = 0.021, FDR-adjusted = 0.021, δ = 0.209), while I-FABP was lower (218.2 vs. 315.0 pg/mL; p = 0.013, FDR-adjusted = 0.021, δ=−0.195; Table 1 ). No differences were found between FESz and RSz ( Supplementary Table 2 ). MetS status did not significantly affect LBP or I-FABP in patients (Supplementary Table 3). In controls, LBP was higher with MetS (25.78 vs. 17.24 µg/mL; FDR-adjusted < 0.001, δ = 0.610; Supplementary Table 4 ). 3.4 Group comparisons in immune- and metabolic syndrome-related parameters Compared to controls, Sz patients had elevated neutrophils (FDR < 0.001, δ = 0.609) and monocytes (FDR-adjusted < 0.001, δ = 0.365), with CRP showing a trend (FDR-adjusted = 0.054, δ = 0.190). No group differences were seen for IL-18, IL-6, TNF-α, or MCP-1, nor between FESz and RSz ( Supplementary Table 2 ). MetS prevalence was similar between groups (Table 1 ). Waist circumference, diastolic blood pressure, triglycerides, HDL, and glucose did not differ, but systolic blood pressure was higher in patients (FDR-adjusted = 0.015, δ = 0.258). VEGF and sRAGE showed trend-level differences (FDR-adjusted = 0.059 each). No metabolic differences were found between FESz and RSz ( Supplementary Table 2 ). 3.5 Correlations and prediction models for LBP and I-FABP To examine links between LBP/I-FABP and innate immune, metabolic, demographic, and clinical parameters, we used Spearman’s correlations (including Mann–Whitney U-tests for sex and smoking) and random forest regression in the full sample, and separately in Sz and control groups ( Supplementary Tables 5 and 6 ). 3.5.1 LBP findings Innate immunity : BP was significantly associated with most immune markers. CRP and neutrophil count were top predictors in all immune-based models, explaining a moderate proportion of variance (pseudo-R²: 0.354 full sample; 0.273 Sz; 0.449 controls), consistent with strong correlations (all FDR-adjusted < 0.001). Monocytes and IL-18 correlated in the full sample and/or controls but contributed less to prediction. IL-6, TNF-α, and MCP-1 showed no significant correlations and low importance. MetS- associations were weaker. Waist circumference and systolic blood pressure were the most relevant predictors, with waist circumference consistently correlated with LBP (FDR-adjusted < 0.001 full sample; p = 0.017 Sz/controls) and high importance in models. Predictive power was negligible to low (pseudo-R²: 0.048–0.104). Demographic and clinical parameters : LBP correlated with BMI (r = 0.304, p < 0.001) and smoking (p = 0.005) in the full sample; BMI was a top-ranked predictor but with low utility (pseudo-R²: 0.097 full; 0.139 controls; − 0.011 Sz). Age and sex were not associated. PANSS scores and illness duration were unrelated to LBP and explained no variance in the Sz model (pseudo-R²=–0.279). 3.5.2 I-FABP findings Innate immunity Associations were weaker than for LBP. IL-18 was the only immune marker significantly correlated with I-FABP—in the full sample (r = 0.199, FDR-adjusted = 0.046) and more strongly in Sz (r = 0.336, FDR-adjusted = 0.008). In models, IL-18 predicted I-FABP in Sz (pseudo-R²=0.167) but did not account significantly for variance in the full sample (0.024) or controls (–0.023). Monocytes and IL-6 showed moderate to high importance in Sz but there were no significant correlations. MetS : Metabolic predictors did not show significant correlations with I-FABP in any group and prediction models had negligible explanatory power (pseudo-R² range: -0.117 to 0.015). Demographic and clinical parameters : No associations with BMI, age, sex, or smoking. Models performed poorly (pseudo-R²: 0.015 full; − 0.061 Sz; − 0.042 controls). Clinical parameters were also unrelated to I-FABP, with negative pseudo-R² in Sz (–0.132). 3.5.3 Correlation between LBP and I-FABP Across all groups, no significant correlations were found between LBP and I-FABP levels. In the full sample, the correlation was near zero (r=–0.017, FDR-adjusted p = 0.819), with similarly negligible and non-significant correlations observed in controls, all Sz patients, FESz, and RSz subgroups (all FDR-adjusted p-values > 0.8; Supplementary Table 7 ). 3.6 Robustness analyses of LBP and I-FABP findings 3.6.1 Group differences adjusted for smoking and non-smoker subgroup analyses After adjusting for smoking via ART ANOVA, LBP group differences were non-significant (FDR-adjusted = 0.199), while I-FABP remained significant (FDR-adjusted = 0.033). Similarly, focusing on the subgroup of non-smoking participants, LBP differences were non-significant (FDR-adjusted = 0.211), but I-FABP remained lower in patients (FDR-adjusted = 0.003, δ=−0.350; Supplementary Table 8 ). 3.6.2 Correlation and prediction models for LBP in non-smokers In non-smokers (42 Sz, 84 controls), CRP remained the strongest LBP predictor (pseudo-R²: 0.347 Sz; 0.385 controls; Supplementary Table 9 ). Neutrophils remained significant but less predictive; monocyte associations weakened. Regarding metabolic predictors, waist circumference correlated with LBP in Sz (r = 0.448, p = 0.027) and was consistently a high-importance predictor. Systolic BP was a low-importance but significant predictor in Sz (p = 0.034). Cholesterol correlated with LBP only in controls (r = 0.333, p = 0.016). 4. Discussion This is the first study to examine LBP and I-FABP as gut barrier markers in acutely ill, antipsychotic-free Sz patients, and to relate them to innate immune activation, metabolic features, and clinical characteristics. We found elevated LBP (δ = 0.209) and reduced I-FABP (δ=−0.195) in patients, alongside much larger increases in neutrophils (δ = 0.609) and monocytes (δ = 0.365). After smoking adjustment, only I-FABP differences remained significant. Nonetheless, LBP maintained strong correlations with CRP and neutrophils, even in non-smokers. The small effect sizes for LBP and I-FABP compared to immune cell elevations suggest gut barrier dysfunction is unlikely to be the primary driver of innate immune activation in acute psychosis. However, transient barrier disruption earlier in the illness could have initiated immune activation that persists after partial barrier recovery. Longitudinal studies, including pre-psychotic phases, are needed to clarify directionality, though such designs are logistically challenging. 4.1 LBP: Links to endotoxin burden and innate immune activation Our findings align with prior reports of elevated LBP in Sz [ 7 , 36 , 37 ] ( Supplementary Table 1 ), and its consistent correlations with CRP and neutrophils support its role as a gut-derived inflammation marker [ 40 ]. Conversely, negative results in studies such as Severance et al. [ 39 ] and Scheurink et al. [ 38 ] may reflect sample heterogeneity, chronicity, or medication effects ( Supplementary Table 1 ). Smoking emerged as a major confounder, eliminating LBP group differences when controlled for, but not its immune associations, highlighting the need for rigorous lifestyle adjustment. Smoking plausibly elevates LBP via pulmonary endotoxin exposure [ 42 , 43 ]. It can also disrupt gut barrier integrity, facilitating bacterial translocation and LPS leakage into the circulation [ 44 ]. Consequently, smoking may elevate LBP via both pulmonary and gut-derived pathways, complicating its interpretation in Sz, where smoking is common. 4.2 I-FABP: Links to enterocyte integrity and MetS I-FABP, reflecting enterocyte injury [ 41 , 45 ], was reduced in Sz and unaffected by smoking, suggesting a more stable alteration. This pattern may indicate chronic epithelial changes, e.g., mucosal atrophy from long-standing low-grade inflammation, possibly linked to shared genetic risk with inflammatory bowel disease (IBD) 3 and/or dietary patterns in Sz such as a preference for sweet and high-fat foods [ 46 – 49 ]. Unlike our results, Jensen et al. [ 36 ] found elevated I-FABP in medicated chronic Sz, and González-Blanco et al. [ 37 ] found no differences. This discrepancy could reflect differences in disease phase, antipsychotic exposure, and/or methodological designs ( Supplementary Table 1 ). 4.3 Divergent gut marker profiles and implications for pathophysiology The opposite patterns of elevated LBP and reduced I-FABP indicate that gut barrier dysfunction in Sz is multifaceted, and reflects distinct processes such as paracellular leakiness and chronic enterocyte dysfunction, respectively [ 45 , 50 ]. Their lack of correlation supports this distinction. Only LBP was consistently linked to inflammation, emphasizing the value of multi-marker approaches in capturing gut barrier complexity. 4.4 Metabolic and inflammatory interplay at the intestinal barrier LBP’s moderate associations with waist circumference and systolic BP fit with evidence that visceral adiposity and metabolic stress impair gut integrity [ 51 – 53 ]. Emerging evidence supports a bidirectional relationship between MetS and gut barrier integrity [ 51 , 53 – 55 ]: Metabolic disturbances like hyperglycemia, which may play a role in Sz [ 28 , 29 ], can compromise gut integrity. Elevated glucose levels disrupt tight junctions through GLUT2-mediated metabolic reprogramming in intestinal epithelial cells, increasing paracellular permeability [ 56 , 57 ]. Moreover, systemic inflammation driven by MetS-associated cytokines such as TNF-α and IL-6 can downregulate junctional proteins and impair epithelial structure [ 58 – 60 ]. Dyslipidemia and oxidative stress may further damage enterocytes, potentially contributing to reduced I-FABP levels [ 52 – 54 ]. In the reverse direction, increased gut permeability facilitates translocation of bacterial products like LPS, which activate innate immune responses and promote systemic inflammation. This, in turn, can exacerbate metabolic dysregulation and perpetuate intestinal barrier dysfunction, creating a self-reinforcing inflammatory-metabolic loop [ 51 , 55 , 61 ]. 4.5 Clinical and demographic correlates Neither LBP nor I-FABP correlated with PANSS scores or illness duration, nor differed between FESz and RSz, suggesting these alterations are not tied to acute symptom severity or chronicity. Age and sex showed no consistent effects. 4.6 Strengths and limitations Key strengths include a systematically recruited, antipsychotic-free cohort, matched controls, and a dual-marker strategy distinguishing endotoxin-related inflammation from enterocyte stress ( Supplementary Table 1 ). By employing a dual-marker approach, we were able to differentiate between distinct dimensions of gut dysfunction. Moreover, combining traditional correlation analyses with machine learning-based random forest models enhanced the robustness and interpretability of our findings. Limitations of the study include the cross-sectional design, which precluded causal inference. We cannot determine whether gut barrier changes preceded or followed immune alterations. Furthermore, we lacked direct permeability testing (e.g., lactulose/mannitol or zonulin), dietary and microbiome data. Additionally, smoking differences between Sz and controls limit the interpretation of LBP findings. 4.7 Conclusions and future directions We identified two dissociable gut-barrier alterations in Sz: smoking-sensitive LBP elevations linked to systemic inflammation and smoking-independent I-FABP reductions. I-FABP may be a comparatively stable marker of gut epithelial status. Future studies should determine whether these changes are state-dependent or trait-like by tracking individuals from pre-psychotic through acute and remitted phases and, critically, by assessing unaffected first-degree relatives to evaluate LBP and I-FABP as potential endophenotypes and indicators of familial vulnerability. Longitudinal designs that integrate gut-barrier markers with microbiome and dietary assessments, functional permeability testing, and targeted interventions (e.g., probiotics, anti-inflammatory agents, dietary modification) are needed to clarify mechanisms and pave the way toward more personalized treatments for Sz. Abbreviations ART Aligned Rank Transform AWMF Association of the Scientific Medical Societies in Germany (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften) BMI Body Mass Index CBBS Center for Behavioral Brain Sciences CRP C-Reactive Protein DZPG German Center for Mental Health (Deutsches Zentrum für Psychische Gesundheit) EDTA Ethylenediaminetetraacetic acid ELISA Enzyme-Linked Immunosorbent Assay FDR False Discovery Rate FESz First-Episode Schizophrenia HDL High-Density Lipoprotein I-FABP Intestinal Fatty Acid-Binding Protein IL Interleukin LBP Lipopolysaccharide-Binding Protein LPS Lipopolysaccharide MCP-1 Monocyte Chemoattractant Protein-1 MetS Metabolic Syndrome PANSS Positive and Negative Syndrome Scale pg/mL Picograms per Milliliter R² Coefficient of Determination RSz Relapsed Schizophrenia sCD14 Soluble Cluster of Differentiation 14 sRAGE Soluble Receptor for Advanced Glycation End Products Sz Schizophrenia TNF-α Tumor Necrosis Factor Alpha VEGF Vascular Endothelial Growth Factor WBC White Blood Cell. Declarations Ethics approval and consent to participate The study was approved by the Institutional Review Board (approval number 110/07). All participants gave written informed consent. Consent for publication Not applicable. Availability of data and materials Online supplementary material. Competing interests The authors declare that they have no competing interests. Funding JS and TNJ are PIs of the German Center of Mental Health (DZPG; funding code 01EE2305A “Site Halle-Jena-Magdeburg”). MN was supported by a doctoral scholarship from the Medical Faculty of the Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (Application no. 531; Funding period: April 1 to October 31, 2024). Authors' contributions KM, PCG, MN, HGB, BR, TNJ, and JS conceptualized the study and defined the research questions. GML, KB, and JS were responsible for collecting the samples and characterizing the clinical data. KM performed the LBP measures, and BR provided the I-AFBP measures. KM and JS created Supplementary Table 1. HD managed the study database, conducted all statistical analyses, and prepared all other tables under the supervision of KM, PCG, and JS. MN and LSA created the graphical abstract and verified all data tables. KM, MN, PCG, LSA, HGB, AL, and JS contributed to the literature review and synthesis. KM, PCG, and JS drafted the initial manuscript. MN, LD, HGB, AL, KS, and TNJ contributed to data interpretation. JS, KM, PCG, BR, and TNJ were responsible for methodological oversight. All authors critically revised the manuscript, contributed to its improvement, and approved the final version. Acknowledgements We thank Bianca Jerzykiewicz, Katrin Meister and Katrin Brenner for their excellent technical support. References Kirkpatrick B: Schizophrenia as a systemic disease. Schizophr Bull 2009, 35: 381-382. 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Onaolapo OJ, Onaolapo AY: Nutrition, nutritional deficiencies, and schizophrenia: An association worthy of constant reassessment. World J Clin Cases 2021, 9: 8295-8311. Wang J, Luo GY, Tian T, Zhao YQ, Meng SY, Wu JH, Han WX, Deng B, Ni J: Shared genetic basis and causality between schizophrenia and inflammatory bowel disease: evidence from a comprehensive genetic analysis. Psychol Med 2024, 54: 2658-2668. Sung KY, Zhang B, Wang HE, Bai YM, Tsai SJ, Su TP, Chen TJ, Hou MC, Lu CL, Wang YP, Chen MH: Schizophrenia and risk of new-onset inflammatory bowel disease: a nationwide longitudinal study. Aliment Pharmacol Ther 2022, 55: 1192-1201. Horowitz A, Chanez-Paredes SD, Haest X, Turner JR: Paracellular permeability and tight junction regulation in gut health and disease. Nat Rev Gastroenterol Hepatol 2023, 20: 417-432. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, Chabo C, et al: Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 2007, 56: 1761-1772. Monteiro R, Azevedo I: Chronic inflammation in obesity and the metabolic syndrome. Mediators Inflamm 2010, 2010 . Tilg H, Moschen AR: Microbiota and diabetes: an evolving relationship. Gut 2014, 63: 1513-1521. Bischoff SC, Barbara G, Buurman W, Ockhuizen T, Schulzke JD, Serino M, Tilg H, Watson A, Wells JM: Intestinal permeability--a new target for disease prevention and therapy. BMC Gastroenterol 2014, 14: 189. Camilleri M: Leaky gut: mechanisms, measurement and clinical implications in humans. Gut 2019, 68: 1516-1526. Kellett GL, Brot-Laroche E: Apical GLUT2: a major pathway of intestinal sugar absorption. Diabetes 2005, 54: 3056-3062. Thaiss CA, Levy M, Grosheva I, Zheng D, Soffer E, Blacher E, Braverman S, Tengeler AC, Barak O, Elazar M, et al: Hyperglycemia drives intestinal barrier dysfunction and risk for enteric infection. Science 2018, 359: 1376-1383. Suzuki T: Regulation of intestinal epithelial permeability by tight junctions. Cell Mol Life Sci 2013, 70: 631-659. Al-Sadi R, Guo S, Ye D, Ma TY: TNF-alpha modulation of intestinal epithelial tight junction barrier is regulated by ERK1/2 activation of Elk-1. Am J Pathol 2013, 183: 1871-1884. Al-Sadi R, Ye D, Boivin M, Guo S, Hashimi M, Ereifej L, Ma TY: Interleukin-6 modulation of intestinal epithelial tight junction permeability is mediated by JNK pathway activation of claudin-2 gene. PLoS One 2014, 9: e85345. Fasano A: Zonulin and its regulation of intestinal barrier function: the biological door to inflammation, autoimmunity, and cancer. Physiol Rev 2011, 91: 151-175. Footnotes The most widely accepted and best established criteria for metabolic syndrome are the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria, which were updated by the American Heart Association (AHA). According to these, metabolic syndrome is diagnosed when an individual exhibits at least three of the following five risk factors: 1.) increased waist circumference (≥ 102 cm in men, ≥ 88 cm in women); 2.) elevated triglycerides (≥ 150 mg/dL or receiving treatment for high triglycerides); 3.) reduced HDL cholesterol (< 40 mg/dL in men, < 50 mg/dL in women, or undergoing treatment for low HDL); 4.) elevated blood pressure (≥ 130/85 mm Hg or receiving antihypertensive treatment); 5.) elevated fasting glucose (≥ 100 mg/dL or under treatment for high glucose). Although not part of the formal diagnostic criteria for Metabolic Syndrome (MetS), sRAGE and VEGF were included as exploratory markers due to their mechanistic relevance to vascular and metabolic dysregulation. Soluble RAGE (sRAGE), a decoy receptor for advanced glycation end-products (AGEs), is inversely associated with insulin resistance, glucose intolerance, and type 2 diabetes [Yamagishi & Matsui (2016): DOI: 10.2741/e178 ; Geroldi et al. (2005): DOI: 10.1097/01.hjh.0000177535.45785.64 ]. VEGF (vascular endothelial growth factor) regulates angiogenesis and endothelial function and has been linked to visceral adiposity, adipose tissue inflammation, and insulin resistance in obesity and MetS [Silha et al. (2005): DOI: 10.1038/sj.ijo.0802987 ]. In coeliac disease and Crohn's disease, I-FABP levels increase during active mucosal injury, but may decrease in the chronic stage, correlating with the extent of enterocyte damage. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTablesLBPIFABPfinal.docx floatimage1.png Graphical abstract: Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Journal of Neuroinflammation → Version 1 posted Editorial decision: Revision requested 17 Sep, 2025 Reviews received at journal 16 Sep, 2025 Reviewers agreed at journal 26 Aug, 2025 Reviewers invited by journal 16 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 10 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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acutely ill, antipsychotic-free schizophrenia patients","fulltext":[{"header":"1. Background","content":"\u003cp\u003eSchizophrenia (Sz), once viewed solely as a brain disorder, is now recognized as a systemic illness with immune and metabolic components [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Meta-analyses have reported increased neutrophil and monocyte counts and slightly elevated C-reactive protein (CRP) in Sz [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In our previous study of 253 acutely ill, unmedicated patients and 294 controls \u0026ndash; excluding individuals with infections, trauma, or recent surgery \u0026ndash; we found large and medium effect size elevations in neutrophils and monocytes, respectively, even after adjusting for stress and smoking [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This \u0026ldquo;sterile\u0026rdquo; myeloid activation raised the question of its underlying drivers.\u003c/p\u003e\u003cp\u003eOne possible upstream mechanism is subclinical gut barrier dysfunction, which may allow translocation of microbial products such as lipopolysaccharide (LPS) into the circulation, triggering systemic inflammation. Supporting this, Gokulakrishnan et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] found elevated gut permeability markers, including lipopolysaccharide-binding protein (LBP) and zonulin, especially in antipsychotic-na\u0026iuml;ve patients, suggesting intestinal barrier changes are intrinsic rather than drug-induced. Similarly, Weber et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported elevated soluble CD14 (sCD14), a marker of monocyte activation, before Sz onset in U.S. military personnel, consistent with low-grade bacterial translocation and innate immune priming. LPS, a potent activator of neutrophils and monocytes [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. can stimulate cytokines such as interleukin (IL)-6, which drive hepatic CRP synthesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough human data remain scarce, animal models suggest that gut-derived inflammation can impair neurodevelopment and behavior relevant to Sz phenotypes, including social cognition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These effects are mediated via the gut\u0026ndash;microbiota\u0026ndash;brain axis, encompassing immune signaling, the vagus nerve, neuroendocrine pathways, and microbial metabolites that influence brain function and behavior [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Consistent with this, Sz patients exhibit gut microbiota dysbiosis alongside immune-inflammatory abnormalities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIncreased intestinal permeability may also contribute to development of \u003cem\u003emetabolic syndrome (MetS)\u003c/em\u003e\u003csup\u003e1\u003c/sup\u003e by enabling endotoxin-driven low-grade inflammation [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which impairs insulin signaling and promotes insulin resistance [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While second-generation antipsychotics, particularly clozapine and olanzapine, contribute to weight gain and metabolic dysfunction [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], such changes also occur in antipsychotic-na\u0026iuml;ve first-episode Sz (FESz) patients, and first-degree relatives show increased MetS risk [\u003cspan additionalcitationids=\"CR27 CR28 CR29 CR30\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], suggesting intrinsic immunometabolic vulnerability. These findings suggest that immunometabolic dysfunction may precede medication effects and could reflect intrinsic vulnerability. A bidirectional relationship between MetS and gut permeability has been proposed, where systemic inflammation and oxidative stress disrupt the mucosa, creating a self-reinforcing cycle [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This link is particularly relevant in Sz, where both systemic inflammation and metabolic risk are elevated, even in early stages of the illness.\u003c/p\u003e\u003cp\u003eStudies on gut permeability markers in Sz have yielded mixed results. Some reported elevated LBP, intestinal fatty acid-binding protein (I-FABP), or zonulin [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], while others find no LBP differences [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). LBP correlated with CRP [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], IL-6 and tumour necrosis factor (TNF)-α [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], sCD14, [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and with body mass index (BMI) in some cohorts [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, leaky gut-MetS links have not been systematically studied. Variation across studies may reflect differences in medication status, disease stage, biomarker selection, control of lifestyle factors, and geographic microbiome differences [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], highlighting the need for standardized protocols and antipsychotic-free samples.\u003c/p\u003e\u003cp\u003eTo address these gaps, we examined gut permeability markers and their immune\u0026ndash;metabolic relationships in unmedicated acutely ill Sz patients, including both FESz and relapsed Sz (RSz) cases, to determine whether barrier dysfunction is an early, trait-like feature or develops later. We focused on LBP, a liver-derived marker of endotoxin exposure and systemic immune activation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], and I-FABP, an enterocyte-derived marker of gut epithelial injury [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], as they are frequently used in Sz research and distinguish endotoxin-related inflammation from enterocyte stress [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe aimed to (1) compare LBP and I-FABP levels between Sz patients and controls, and between FESz and RSz patients; (2) examine associations with innate immune markers; (3) assess links to MetS features; and (4) evaluate relationships with clinical symptom severity. This multi-dimensional approach sought to clarify the role of gut barrier integrity in Sz immunometabolic and clinical profiles, independent of medication and illness chronicity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Patients and controls\u003c/h2\u003e\u003cp\u003e The study complied with German law, the Declaration of Helsinki, and was approved by the Institutional Review Board (approval number 110/07). All participants gave written informed consent. Acutely ill inpatients with Sz (n\u0026thinsp;=\u0026thinsp;96; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were recruited at the Department of Psychiatry, University of Magdeburg, and diagnosed per ICD-10.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eComparison of Sz patients with healthy controls.\u003c/b\u003e Demographic data, clinical assessments, LBP and I-FABP measurements, and immune- and metabolic syndrome-related parameters. Data are presented as median (quartile 1; quartile 3; sample size) or number of cases. Significant p-values are highlighted in bold font.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSz \u003c/p\u003e\u003cp\u003emedian (Q1,Q3,n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControls \u003c/p\u003e\u003cp\u003emedian (Q1,Q3,n)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTest value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003cp\u003e(FDR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect size (Cliff's delta)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eDemographic data \u0026amp; severity of clinical symptoms\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.0 (26.3;44.5;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.5 (27.0;45.8;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4471.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.724 (0.965)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (female/male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003em: 56 / f: 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003em: 56 / f: 40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΧ\u0026sup2;-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eX\u0026sup2;=0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000 (1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.56 (20.69;27.13;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.69 (21.78;27.57;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4426.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.638 (0.965)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTobacco smoking (yes/no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes: 54 / no: 42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes: 12 / no: 84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΧ\u0026sup2;-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eX\u0026sup2;=38.811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.461\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of illness (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0 (0.0;3.3;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePANSS total corr. (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.00 (28.00;48.75;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePANSS-P corr. (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.00 (9.00;16.00;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePANSS-N corr. (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.00 (4.00;13.00;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePANSS-G corr. (score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (12.00;20.00;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eLeaky gut markers\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlasma LBP (\u0026micro;g/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.96 (16.66;29.60;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.10 (13.89;23.48;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;5457.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.013 (0.021)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum I-FABP(pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218.2 (116.4;369.9;93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e315.0 (174.5;533.5;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;3518.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.021 (0.021)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eImmune-related parameters\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophils (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.81 (3.53;6.48;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.01 (2.36;3.86;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;7258.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonocytes (\u0026times;10⁹/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.56 (0.43;0.73;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42 (0.32;0.60;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;6158.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (\u0026lt;\u0026thinsp;0.001)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.35 (0.60;3.40;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.90 (0.60;1.70;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;5371.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e (0.054\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-18 (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214.6 (172.3;300.3;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208.3 (159.4;274.2;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;5072.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.140 (0.245)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.03 (22.68;52.99;84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.25 (18.00;67.19;90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4033.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.446 (0.446)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNF-α (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.05 (11.04;26.97;87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.50 (10.84;35.62;91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;3581.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.273 (0.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCP-1 (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277.4 (216.7;372.6;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e308.5 (221.2;407.0;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4026.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.244 (0.319)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eMetabolic syndrome-related parameters\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetS (yes/no)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eyes: 10 / no: 86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eyes: 12 / no: 84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΧ\u0026sup2;-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eX\u0026sup2;=0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.821 (0.902)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e88.00 (80.50;97.25;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.00 (80.25;97.00;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4465.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.902 (0.902)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.5 (115.0;140.0;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120.0 (110.0;127.5;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;5799.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002 (0.015)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.0 (70.0;90.0;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80.0 (70.0;80.0;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4491.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.754 (0.902)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89 (0.64;1.21;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.015 (0.695;1.427;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;3974.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.100 (0.225)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL cholesterol (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.43 (1.20;1.78;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.54 (1.25;1.79;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4073.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.165 (0.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlucose (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.91 (4.49;5.53;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.01 (4.71;5.30;94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;4212.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.582 (0.873)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esRAGE (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1349 (785;1946;95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1538 (1042;2547;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;3667.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e (0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.196\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVEGF (pg/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e232.5 (155.2;325.7;92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175.7 (139.9;262.1;96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU-Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eW\u0026thinsp;=\u0026thinsp;5287.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e (0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e BMI=body mass index, CRP=C-reactive protein, FDR=false discover rate-corrected p-value, I-FABP=intestinal fatty acid binding protein, IL-18=interleukin 18, IL-6=interleukin 6, LBP=lipopolysaccharide binding protein, MCP-1=monocyte chemoattractant protein 1, PANSS=positive and negative syndrome scale [PANSS scores were corrected (corr.) by subtraction of minimum scores, which represented no symptoms], HDL=high density lipoprotein, sRAGE=soluble receptor for advanced glycation end products, TNF-a=tumor necrosis factor alpha, VEGF=vascular endothelial growth factor.\u003c/p\u003e\u003cp\u003eExclusion criteria were psychosis secondary to medical conditions, infections (respiratory, gastrointestinal, urinary tract), substance use disorders, diabetes, immune diseases, cancer, or cardiovascular disease. Screening followed German AWMF schizophrenia guidelines and included physical examination, differential blood counts, CRP, kidney function, lipids, fasting glucose, urine drug screen, MRI, and EEG. Pathological results were defined using institutional reference ranges. Symptoms were rated with the Positive and Negative Syndrome Scale (PANSS).\u003c/p\u003e\u003cp\u003eTwo patient subgroups were analyzed (\u003cb\u003eSuppl. Table\u0026nbsp;2\u003c/b\u003e): (1) FESz (n\u0026thinsp;=\u0026thinsp;61) na\u0026iuml;ve to antipsychotics at baseline (T0), and (2) RSz (n\u0026thinsp;=\u0026thinsp;35) patients with longer illness (median 6.0 years) but antipsychotic-free at least 6 weeks before sample collection. Healthy controls (n\u0026thinsp;=\u0026thinsp;96), matched for age, sex, and smoking status (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were recruited from the community and underwent identical screening. Controls with psychiatric illness, substance use, infections, diabetes, immune disorders, cancer, or cardiovascular disease were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Blood samples\u003c/h2\u003e\u003cp\u003eOvernight-fasted samples were collected within 24 h of admission (8:00 a.m.). Differential white blood cell (WBC) counts were obtained from EDTA tubes within one hour after the blood take. Serum tubes were clotted for two hours, then centrifuged at 1000 g for 10 min; plasma EDTA tubes were centrifuged immediately. Supernatants were aliquoted and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assays\u003c/h2\u003e\u003cp\u003eSerum I-FABP and plasma LBP were measured using commercial ELISAs (Hycult Biotech, Uden, Netherlands). Neutrophil and monocyte counts were obtained with an XN-3000 counter (Sysmex). CRP was measured on a Cobas 8000 c701 analyzer (Roche Diagnostics). IL-18, IL-6, TNF-α, MCP-1, soluble receptor for advanced glycation end products (sRAGE), and vascular endothelial growth factor (VEGF) were quantified with the LegendPlex Human Neuroinflammation Panel (BioLegend, San Diego, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistics\u003c/h2\u003e\u003cp\u003eAnalyses were performed in R 4.3.1, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed) considered significant. False discovery rate (FDR) correction was applied within each variable domain (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Data distributions were assessed by Shapiro\u0026ndash;Wilk tests; non-parametric tests were used as most variables were non-normal.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGroup comparisons\u003c/strong\u003e\u003cp\u003eDemographic differences in sex and smoking status were assessed using chi-square tests. Group comparisons for continuous variables (e.g., LBP and I-FABP) were conducted using non-parametric Mann-Whitney U-tests, and included comparisons between Sz patients and controls, FESz and RSz patients, and MetS status. Additional comparisons of LBP and I-FABP levels by sex and smoking status were performed using Mann-Whitney U-tests. Cliff\u0026rsquo;s delta (δ) was used to assess effect sizes (ǀδǀ \u0026ge; 0.147 \u0026ldquo;small,\u0026rdquo; ǀδǀ \u0026ge; 0.330 \u0026ldquo;medium,\u0026rdquo; ǀδǀ \u0026ge; 0.474 \u0026ldquo;large\u0026rdquo;).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCorrelation analyses\u003c/strong\u003e\u003cp\u003eWe computed Spearman\u0026rsquo;s rank correlation coefficients between LBP/I-FABP and (a) age, BMI; (b) duration of illness, PANSS scores; (c) innate immune markers; (d) MetS-related parameters (waist circumference, blood pressure, triglycerides, HDL cholesterol, glucose, sRAGE, VEGF).\u003csup\u003e2\u003c/sup\u003e Analyses were run in the full sample and separately in Sz and controls.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eExploratory prediction models\u003c/em\u003e: To complement the correlation analyses, we conducted random forest regression with backward variable selection in the full sample, the Sz and control groups to identify the most robust predictors of LBP and I-FABP levels. Case weights adjusted for unequal observation counts (e.g., repeated measures or varying subject contributions). Model selection was based on highest pseudo-R\u0026sup2; (interpreted as: \u0026lt;0.20 negligible/weak, 0.20\u0026ndash;0.40 moderate, \u0026gt;\u0026thinsp;0.40 strong predictive power).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSmoking adjustment\u003c/strong\u003e\u003cp\u003eGroup differences in LBP and I-FABP were retested via Aligned Rank Transform (ART) ANOVA with smoking as covariate. A subgroup analysis of non-smokers used Mann\u0026ndash;Whitney U-tests.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sensitivity and validity of I-FABP and LBP blood measures\u003c/h2\u003e\u003cp\u003eOf 192 samples, 190 were within the detection range for LBP (2 below limit); 187 were within range for I-FABP (5 below limit).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Group comparisons of demographic data and severity of clinical symptoms\u003c/h2\u003e\u003cp\u003ePatients and controls were matched for age, sex, and BMI (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Smoking was more frequent in patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As expected, FESz patients had shorter illness durations than RSz patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while PANSS total scores were similar, with a nominally higher PANSS-G in FESz (p\u0026thinsp;=\u0026thinsp;0.031; not significant after FDR correction) (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Group comparisons in LPB and I-FABP levels\u003c/h2\u003e\u003cp\u003eMedian LBP was higher in Sz (21.96 \u0026micro;g/mL) vs. controls (18.10 \u0026micro;g/mL; p\u0026thinsp;=\u0026thinsp;0.021, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.021, δ\u0026thinsp;=\u0026thinsp;0.209), while I-FABP was lower (218.2 vs. 315.0 pg/mL; p\u0026thinsp;=\u0026thinsp;0.013, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.021, δ=\u0026minus;0.195; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No differences were found between FESz and RSz (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eMetS status did not significantly affect LBP or I-FABP in patients (Supplementary Table\u0026nbsp;3). In controls, LBP was higher with MetS (25.78 vs. 17.24 \u0026micro;g/mL; FDR-adjusted\u0026thinsp;\u0026lt;\u0026thinsp;0.001, δ\u0026thinsp;=\u0026thinsp;0.610; \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Group comparisons in immune- and metabolic syndrome-related parameters\u003c/h2\u003e\u003cp\u003eCompared to controls, Sz patients had elevated neutrophils (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.001, δ\u0026thinsp;=\u0026thinsp;0.609) and monocytes (FDR-adjusted\u0026thinsp;\u0026lt;\u0026thinsp;0.001, δ\u0026thinsp;=\u0026thinsp;0.365), with CRP showing a trend (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.054, δ\u0026thinsp;=\u0026thinsp;0.190). No group differences were seen for IL-18, IL-6, TNF-α, or MCP-1, nor between FESz and RSz (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eMetS prevalence was similar between groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Waist circumference, diastolic blood pressure, triglycerides, HDL, and glucose did not differ, but systolic blood pressure was higher in patients (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.015, δ\u0026thinsp;=\u0026thinsp;0.258). VEGF and sRAGE showed trend-level differences (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.059 each). No metabolic differences were found between FESz and RSz (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlations and prediction models for LBP and I-FABP\u003c/h2\u003e\u003cp\u003eTo examine links between LBP/I-FABP and innate immune, metabolic, demographic, and clinical parameters, we used Spearman\u0026rsquo;s correlations (including Mann\u0026ndash;Whitney U-tests for sex and smoking) and random forest regression in the full sample, and separately in Sz and control groups (\u003cb\u003eSupplementary Tables\u0026nbsp;5 and 6\u003c/b\u003e).\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.5.1 LBP findings\u003c/h2\u003e\u003cp\u003e\u003cem\u003eInnate immunity\u003c/em\u003e: BP was significantly associated with most immune markers. CRP and neutrophil count were top predictors in all immune-based models, explaining a moderate proportion of variance (pseudo-R\u0026sup2;: 0.354 full sample; 0.273 Sz; 0.449 controls), consistent with strong correlations (all FDR-adjusted\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Monocytes and IL-18 correlated in the full sample and/or controls but contributed less to prediction. IL-6, TNF-α, and MCP-1 showed no significant correlations and low importance.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetS-\u003c/em\u003eassociations were weaker. Waist circumference and systolic blood pressure were the most relevant predictors, with waist circumference consistently correlated with LBP (FDR-adjusted\u0026thinsp;\u0026lt;\u0026thinsp;0.001 full sample; p\u0026thinsp;=\u0026thinsp;0.017 Sz/controls) and high importance in models. Predictive power was negligible to low (pseudo-R\u0026sup2;: 0.048\u0026ndash;0.104).\u003c/p\u003e\u003cp\u003e\u003cem\u003eDemographic and clinical parameters\u003c/em\u003e: LBP correlated with BMI (r\u0026thinsp;=\u0026thinsp;0.304, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and smoking (p\u0026thinsp;=\u0026thinsp;0.005) in the full sample; BMI was a top-ranked predictor but with low utility (pseudo-R\u0026sup2;: 0.097 full; 0.139 controls; \u0026minus;\u0026thinsp;0.011 Sz). Age and sex were not associated. PANSS scores and illness duration were unrelated to LBP and explained no variance in the Sz model (pseudo-R\u0026sup2;=\u0026ndash;0.279).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.5.2 I-FABP findings\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eInnate immunity\u003c/strong\u003e\u003cp\u003eAssociations were weaker than for LBP. IL-18 was the only immune marker significantly correlated with I-FABP\u0026mdash;in the full sample (r\u0026thinsp;=\u0026thinsp;0.199, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.046) and more strongly in Sz (r\u0026thinsp;=\u0026thinsp;0.336, FDR-adjusted\u0026thinsp;=\u0026thinsp;0.008). In models, IL-18 predicted I-FABP in Sz (pseudo-R\u0026sup2;=0.167) but did not account significantly for variance in the full sample (0.024) or controls (\u0026ndash;0.023). Monocytes and IL-6 showed moderate to high importance in Sz but there were no significant correlations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eMetS\u003c/em\u003e: Metabolic predictors did not show significant correlations with I-FABP in any group and prediction models had negligible explanatory power (pseudo-R\u0026sup2; range: -0.117 to 0.015).\u003c/p\u003e\u003cp\u003e\u003cem\u003eDemographic and clinical parameters\u003c/em\u003e: No associations with BMI, age, sex, or smoking. Models performed poorly (pseudo-R\u0026sup2;: 0.015 full; \u0026minus;\u0026thinsp;0.061 Sz; \u0026minus;\u0026thinsp;0.042 controls). Clinical parameters were also unrelated to I-FABP, with negative pseudo-R\u0026sup2; in Sz (\u0026ndash;0.132).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.5.3 Correlation between LBP and I-FABP\u003c/h2\u003e\u003cp\u003eAcross all groups, no significant correlations were found between LBP and I-FABP levels. In the full sample, the correlation was near zero (r=\u0026ndash;0.017, FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.819), with similarly negligible and non-significant correlations observed in controls, all Sz patients, FESz, and RSz subgroups (all FDR-adjusted p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.8; \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Robustness analyses of LBP and I-FABP findings\u003c/h2\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1 Group differences adjusted for smoking and non-smoker subgroup analyses\u003c/h2\u003e\u003cp\u003eAfter adjusting for smoking via ART ANOVA, LBP group differences were non-significant (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.199), while I-FABP remained significant (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e\u003cp\u003eSimilarly, focusing on the subgroup of non-smoking participants, LBP differences were non-significant (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.211), but I-FABP remained lower in patients (FDR-adjusted\u0026thinsp;=\u0026thinsp;0.003, δ=\u0026minus;0.350; \u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2 Correlation and prediction models for LBP in non-smokers\u003c/h2\u003e\u003cp\u003eIn non-smokers (42 Sz, 84 controls), CRP remained the strongest LBP predictor (pseudo-R\u0026sup2;: 0.347 Sz; 0.385 controls; \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e). Neutrophils remained significant but less predictive; monocyte associations weakened.\u003c/p\u003e\u003cp\u003eRegarding metabolic predictors, waist circumference correlated with LBP in Sz (r\u0026thinsp;=\u0026thinsp;0.448, p\u0026thinsp;=\u0026thinsp;0.027) and was consistently a high-importance predictor. Systolic BP was a low-importance but significant predictor in Sz (p\u0026thinsp;=\u0026thinsp;0.034). Cholesterol correlated with LBP only in controls (r\u0026thinsp;=\u0026thinsp;0.333, p\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis is the first study to examine LBP and I-FABP as gut barrier markers in acutely ill, antipsychotic-free Sz patients, and to relate them to innate immune activation, metabolic features, and clinical characteristics. We found elevated LBP (δ\u0026thinsp;=\u0026thinsp;0.209) and reduced I-FABP (δ=\u0026minus;0.195) in patients, alongside much larger increases in neutrophils (δ\u0026thinsp;=\u0026thinsp;0.609) and monocytes (δ\u0026thinsp;=\u0026thinsp;0.365). After smoking adjustment, only I-FABP differences remained significant. Nonetheless, LBP maintained strong correlations with CRP and neutrophils, even in non-smokers.\u003c/p\u003e\u003cp\u003eThe small effect sizes for LBP and I-FABP compared to immune cell elevations suggest gut barrier dysfunction is unlikely to be the primary driver of innate immune activation in acute psychosis. However, transient barrier disruption earlier in the illness could have initiated immune activation that persists after partial barrier recovery. Longitudinal studies, including pre-psychotic phases, are needed to clarify directionality, though such designs are logistically challenging.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.1 LBP: Links to endotoxin burden and innate immune activation\u003c/h2\u003e\u003cp\u003eOur findings align with prior reports of elevated LBP in Sz [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e), and its consistent correlations with CRP and neutrophils support its role as a gut-derived inflammation marker [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Conversely, negative results in studies such as Severance et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and Scheurink et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] may reflect sample heterogeneity, chronicity, or medication effects (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eSmoking emerged as a major confounder, eliminating LBP group differences when controlled for, but not its immune associations, highlighting the need for rigorous lifestyle adjustment. Smoking plausibly elevates LBP via pulmonary endotoxin exposure [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It can also disrupt gut barrier integrity, facilitating bacterial translocation and LPS leakage into the circulation [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Consequently, smoking may elevate LBP via both pulmonary and gut-derived pathways, complicating its interpretation in Sz, where smoking is common.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.2 I-FABP: Links to enterocyte integrity and MetS\u003c/h2\u003e\u003cp\u003eI-FABP, reflecting enterocyte injury [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], was reduced in Sz and unaffected by smoking, suggesting a more stable alteration. This pattern may indicate chronic epithelial changes, e.g., mucosal atrophy from long-standing low-grade inflammation, possibly linked to shared genetic risk with inflammatory bowel disease (IBD)\u003csup\u003e3\u003c/sup\u003e and/or dietary patterns in Sz such as a preference for sweet and high-fat foods [\u003cspan additionalcitationids=\"CR47 CR48\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Unlike our results, Jensen et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] found elevated I-FABP in medicated chronic Sz, and Gonz\u0026aacute;lez-Blanco et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] found no differences. This discrepancy could reflect differences in disease phase, antipsychotic exposure, and/or methodological designs (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Divergent gut marker profiles and implications for pathophysiology\u003c/h2\u003e\u003cp\u003eThe opposite patterns of elevated LBP and reduced I-FABP indicate that gut barrier dysfunction in Sz is multifaceted, and reflects distinct processes such as paracellular leakiness and chronic enterocyte dysfunction, respectively [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Their lack of correlation supports this distinction. Only LBP was consistently linked to inflammation, emphasizing the value of multi-marker approaches in capturing gut barrier complexity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Metabolic and inflammatory interplay at the intestinal barrier\u003c/h2\u003e\u003cp\u003eLBP\u0026rsquo;s moderate associations with waist circumference and systolic BP fit with evidence that visceral adiposity and metabolic stress impair gut integrity [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Emerging evidence supports a \u003cem\u003ebidirectional relationship\u003c/em\u003e between MetS and gut barrier integrity [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan additionalcitationids=\"CR54\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003eMetabolic disturbances like hyperglycemia, which may play a role in Sz [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], can compromise gut integrity. Elevated glucose levels disrupt tight junctions through GLUT2-mediated metabolic reprogramming in intestinal epithelial cells, increasing paracellular permeability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Moreover, systemic inflammation driven by MetS-associated cytokines such as TNF-α and IL-6 can downregulate junctional proteins and impair epithelial structure [\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Dyslipidemia and oxidative stress may further damage enterocytes, potentially contributing to reduced I-FABP levels [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the reverse direction, increased gut permeability facilitates translocation of bacterial products like LPS, which activate innate immune responses and promote systemic inflammation. This, in turn, can exacerbate metabolic dysregulation and perpetuate intestinal barrier dysfunction, creating a self-reinforcing inflammatory-metabolic loop [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Clinical and demographic correlates\u003c/h2\u003e\u003cp\u003eNeither LBP nor I-FABP correlated with PANSS scores or illness duration, nor differed between FESz and RSz, suggesting these alterations are not tied to acute symptom severity or chronicity. Age and sex showed no consistent effects.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Strengths and limitations\u003c/h2\u003e\u003cp\u003eKey strengths include a systematically recruited, antipsychotic-free cohort, matched controls, and a dual-marker strategy distinguishing endotoxin-related inflammation from enterocyte stress (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). By employing a dual-marker approach, we were able to differentiate between distinct dimensions of gut dysfunction. Moreover, combining traditional correlation analyses with machine learning-based random forest models enhanced the robustness and interpretability of our findings.\u003c/p\u003e\u003cp\u003eLimitations of the study include the cross-sectional design, which precluded causal inference. We cannot determine whether gut barrier changes preceded or followed immune alterations. Furthermore, we lacked direct permeability testing (e.g., lactulose/mannitol or zonulin), dietary and microbiome data. Additionally, smoking differences between Sz and controls limit the interpretation of LBP findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Conclusions and future directions\u003c/h2\u003e\u003cp\u003eWe identified two dissociable gut-barrier alterations in Sz: smoking-sensitive LBP elevations linked to systemic inflammation and smoking-independent I-FABP reductions. I-FABP may be a comparatively stable marker of gut epithelial status.\u003c/p\u003e\u003cp\u003eFuture studies should determine whether these changes are state-dependent or trait-like by tracking individuals from pre-psychotic through acute and remitted phases and, critically, by assessing unaffected first-degree relatives to evaluate LBP and I-FABP as potential endophenotypes and indicators of familial vulnerability. Longitudinal designs that integrate gut-barrier markers with microbiome and dietary assessments, functional permeability testing, and targeted interventions (e.g., probiotics, anti-inflammatory agents, dietary modification) are needed to clarify mechanisms and pave the way toward more personalized treatments for Sz.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eART\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAligned Rank Transform\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eAWMF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAssociation of the Scientific Medical Societies in Germany (Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCBBS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCenter for Behavioral Brain Sciences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-Reactive Protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eDZPG\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGerman Center for Mental Health (Deutsches Zentrum f\u0026uuml;r Psychische Gesundheit)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eEDTA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEthylenediaminetetraacetic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eELISA\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEnzyme-Linked Immunosorbent Assay\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eFDR\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFalse Discovery Rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eFESz\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFirst-Episode Schizophrenia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eHDL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eI-FABP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntestinal Fatty Acid-Binding Protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eIL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterleukin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLBP\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLipopolysaccharide-Binding Protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eLPS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLipopolysaccharide\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMCP-1\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMonocyte Chemoattractant Protein-1\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eMetS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMetabolic Syndrome\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003ePANSS\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePositive and Negative Syndrome Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003epg/mL\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePicograms per Milliliter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCoefficient of Determination\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eRSz\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRelapsed Schizophrenia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003esCD14\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSoluble Cluster of Differentiation 14\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003esRAGE\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSoluble Receptor for Advanced Glycation End Products\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eSz\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSchizophrenia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eTNF-α\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTumor Necrosis Factor Alpha\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eVEGF\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eVascular Endothelial Growth Factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003e\u003cb\u003eWBC\u003c/b\u003e\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite Blood Cell.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Review Board (approval number 110/07). All participants gave written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOnline supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJS and TNJ are PIs of the German Center of Mental Health (DZPG; funding code 01EE2305A \u0026ldquo;Site Halle-Jena-Magdeburg\u0026rdquo;). MN was supported by a doctoral scholarship from the Medical Faculty of the Otto-von-Guericke-University Magdeburg, Magdeburg, Germany (Application no. 531; Funding period: April 1 to October 31, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKM, PCG, MN, HGB, BR, TNJ, and JS conceptualized the study and defined the research questions. GML, KB, and JS were responsible for collecting the samples and characterizing the clinical data. KM performed the LBP measures, and BR provided the I-AFBP measures. KM and JS created Supplementary Table 1. HD managed the study database, conducted all statistical analyses, and prepared all other tables under the supervision of KM, PCG, and JS. MN and LSA created the graphical abstract and verified all data tables. KM, MN, PCG, LSA, HGB, AL, and JS contributed to the literature review and synthesis. KM, PCG, and JS drafted the initial manuscript. MN, LD, HGB, AL, KS, and TNJ contributed to data interpretation. JS, KM, PCG, BR, and TNJ were responsible for methodological oversight. All authors critically revised the manuscript, contributed to its improvement, and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Bianca Jerzykiewicz, Katrin Meister and Katrin Brenner for their excellent technical support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKirkpatrick B: \u003cstrong\u003eSchizophrenia as a systemic disease.\u003c/strong\u003e \u003cem\u003eSchizophr Bull \u003c/em\u003e2009, \u003cstrong\u003e35:\u003c/strong\u003e381-382.\u003c/li\u003e\n\u003cli\u003eKhandaker GM, Cousins L, Deakin J, Lennox BR, Yolken R, Jones PB: \u003cstrong\u003eInflammation and immunity in schizophrenia: implications for pathophysiology and treatment.\u003c/strong\u003e \u003cem\u003eLancet Psychiatry \u003c/em\u003e2015, \u003cstrong\u003e2:\u003c/strong\u003e258-270.\u003c/li\u003e\n\u003cli\u003eSarnyai Z, Ben-Shachar D: 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permeability by tight junctions.\u003c/strong\u003e \u003cem\u003eCell Mol Life Sci \u003c/em\u003e2013, \u003cstrong\u003e70:\u003c/strong\u003e631-659.\u003c/li\u003e\n\u003cli\u003eAl-Sadi R, Guo S, Ye D, Ma TY: \u003cstrong\u003eTNF-alpha modulation of intestinal epithelial tight junction barrier is regulated by ERK1/2 activation of Elk-1.\u003c/strong\u003e \u003cem\u003eAm J Pathol \u003c/em\u003e2013, \u003cstrong\u003e183:\u003c/strong\u003e1871-1884.\u003c/li\u003e\n\u003cli\u003eAl-Sadi R, Ye D, Boivin M, Guo S, Hashimi M, Ereifej L, Ma TY: \u003cstrong\u003eInterleukin-6 modulation of intestinal epithelial tight junction permeability is mediated by JNK pathway activation of claudin-2 gene.\u003c/strong\u003e \u003cem\u003ePLoS One \u003c/em\u003e2014, \u003cstrong\u003e9:\u003c/strong\u003ee85345.\u003c/li\u003e\n\u003cli\u003eFasano A: \u003cstrong\u003eZonulin and its regulation of intestinal barrier function: the biological door to inflammation, autoimmunity, and cancer.\u003c/strong\u003e \u003cem\u003ePhysiol Rev \u003c/em\u003e2011, \u003cstrong\u003e91:\u003c/strong\u003e151-175.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The most widely accepted and best established criteria for metabolic syndrome are the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria, which were updated by the American Heart Association (AHA). According to these, metabolic syndrome is diagnosed when an individual exhibits at least three of the following five risk factors: 1.) increased waist circumference (\u0026ge;\u0026thinsp;102 cm in men, \u0026ge;\u0026thinsp;88 cm in women); 2.) elevated triglycerides (\u0026ge;\u0026thinsp;150 mg/dL or receiving treatment for high triglycerides); 3.) reduced HDL cholesterol (\u0026lt;\u0026thinsp;40 mg/dL in men, \u0026lt;\u0026thinsp;50 mg/dL in women, or undergoing treatment for low HDL); 4.) elevated blood pressure (\u0026ge;\u0026thinsp;130/85 mm Hg or receiving antihypertensive treatment); 5.) elevated fasting glucose (\u0026ge;\u0026thinsp;100 mg/dL or under treatment for high glucose).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Although not part of the formal diagnostic criteria for Metabolic Syndrome (MetS), sRAGE and VEGF were included as exploratory markers due to their mechanistic relevance to vascular and metabolic dysregulation. Soluble RAGE (sRAGE), a decoy receptor for advanced glycation end-products (AGEs), is inversely associated with insulin resistance, glucose intolerance, and type 2 diabetes [Yamagishi \u0026amp; Matsui (2016): DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2741/e178\u003c/span\u003e\u003cspan address=\"10.2741/e178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Geroldi et al. (2005): DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/01.hjh.0000177535.45785.64\u003c/span\u003e\u003cspan address=\"10.1097/01.hjh.0000177535.45785.64\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. VEGF (vascular endothelial growth factor) regulates angiogenesis and endothelial function and has been linked to visceral adiposity, adipose tissue inflammation, and insulin resistance in obesity and MetS [Silha et al. (2005): DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/sj.ijo.0802987\u003c/span\u003e\u003cspan address=\"10.1038/sj.ijo.0802987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In coeliac disease and Crohn's disease, I-FABP levels increase during active mucosal injury, but may decrease in the chronic stage, correlating with the extent of enterocyte damage.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Schizophrenia, gut–brain axis, intestinal permeability, lipopolysaccharide-binding protein (LBP), intestinal fatty acid-binding protein (I-FABP), innate immunity, C-reactive protein (CRP), metabolic syndrome, leaky gut, unmedicated psychosis","lastPublishedDoi":"10.21203/rs.3.rs-7338710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7338710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground:\u003c/em\u003e Schizophrenia (Sz), once considered solely a brain disorder, is now recognised as a systemic illness involving immune and metabolic dysregulation. Recently, attention has focused on the intestinal barrier as a potential factor that exerts influence via gut–brain–immune interactions. However, studies on gut permeability markers in early, antipsychotic-free stages remain scarce and often neglect confounders such as smoking and metabolic syndrome.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods:\u003c/em\u003e We measured two complementary markers: lipopolysaccharide-binding protein (LBP), reflecting endotoxin exposure and systemic immune activation, and intestinal fatty acid-binding protein (I-FABP), indicating gut epithelial damage and permeability changes, in blood from 96 acutely ill, antipsychotic-free Sz patients (61 first-episode, 35 relapsed) and 96 matched controls. Associations with innate immune activation, metabolic parameters, smoking, and clinical features were assessed using nonparametric statistics and random forest regression. Group differences were tested with covariate adjustment and separately in non-smokers (Sz: n=42; controls: n=84).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults:\u003c/em\u003e Median LBP was higher in Sz (21.96 µg/mL) vs. controls (18.10 µg/mL; FDR-adjusted p=0.021, δ=0.209), but became non-significant after adjusting for smoking (FDR-adjusted p=0.199). In contrast, I-FABP was lower in Sz (218.2 pg/mL) than controls (315.0 pg/mL; FDR-adjusted p=0.021, δ=–0.195), and remained robust across smoking-adjusted analyses. No differences were found between first-episode and relapsed patients for either marker.\u003c/p\u003e\n\u003cp\u003eLBP correlated strongly with CRP (r=0.557, p\u0026lt;0.001) and neutrophils (r=0.468, p\u0026lt;0.001), regardless of smoking, and was predicted moderately by immune models (pseudo-R² = 0.354 full sample; 0.273 Sz; 0.449 controls). Links to waist circumference and blood pressure were weaker (pseudo-R²: 0.048–0.104). I-FABP was less associated with immune markers and uncorrelated with LBP (r=–0.017, FDR-adjusted p=0.819), suggesting distinct underlying mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions:\u003c/em\u003e Our findings reveal multifaceted gut barrier disruptions in antipsychotic-free Sz. Elevated LBP, associated with CRP and neutrophils, suggests inflammation influenced by smoking and metabolic factors. In contrast, reduced I-FABP, independent of smoking, may indicate epithelial injury. Their lack of correlation highlights distinct pathophysiological dimensions of gut dysfunction. Longitudinal studies, ideally spanning prodromal phases and integrating microbiome, dietary, and permeability assessments, are needed to clarify temporal dynamics and guide stratified treatment approaches.\u003c/p\u003e","manuscriptTitle":"Assessment of intestinal barrier integrity and associations with innate immune activation and metabolic syndrome in acutely ill, antipsychotic-free schizophrenia patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 09:13:42","doi":"10.21203/rs.3.rs-7338710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-17T12:40:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-16T20:23:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28676083960364642700733392246782988961","date":"2025-08-26T10:33:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-17T01:49:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T13:40:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T09:42:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Neuroinflammation","date":"2025-08-10T12:00:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-neuroinflammation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jneu","sideBox":"Learn more about [Journal of Neuroinflammation](http://jneuroinflammation.biomedcentral.com)","snPcode":"12974","submissionUrl":"https://submission.nature.com/new-submission/12974/3","title":"Journal of Neuroinflammation","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2aca1def-1d04-4726-9b15-cd14efdcab49","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:02:36+00:00","versionOfRecord":{"articleIdentity":"rs-7338710","link":"https://doi.org/10.1186/s12974-025-03584-3","journal":{"identity":"journal-of-neuroinflammation","isVorOnly":false,"title":"Journal of Neuroinflammation"},"publishedOn":"2025-10-13 15:57:54","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2025-08-25 09:13:42","video":"","vorDoi":"10.1186/s12974-025-03584-3","vorDoiUrl":"https://doi.org/10.1186/s12974-025-03584-3","workflowStages":[]},"version":"v1","identity":"rs-7338710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7338710","identity":"rs-7338710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}