# Clinical and inflammatory markers associated with metabolic dysfunction-associated fatty liver disease in a cohort of individuals with chronic schizophrenia (FACE-SZ)

Rudy Tokarski, Susana Barbosa, Baptiste Pignon, Bruno Auizerate, Christelle Andrieu, Myrtille Andre, Wahid Boukouaci, Delphine Capdevielle, Isabelle Chereau, Clément Dondé, et al.

► To cite this version:

Rudy Tokarski, Susana Barbosa, Baptiste Pignon, Bruno Auizerate, Christelle Andrieu, et al. Clinical and inflammatory markers associated with metabolic dysfunction-associated fatty liver disease in a cohort of individuals with chronic schizophrenia (FACE-SZ). Journal of Psychosomatic Research, 2025, 195, pp.112192-112192. 10.1016/j.jpsychores.2025.112192. hal-05299985

HAL Id: hal-05299985

https://hal.science/hal-05299985v1

Submitted on 27 Oct 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Check for
updates

Clinical and inflammatory markers associated with metabolic
dysfunction-associated fatty liver disease in a cohort of individuals with
chronic schizophrenia (FACE-SZ)

Rudy Tokarski<sup>b</sup>, Susana Barbosa<sup>d</sup>, Baptiste Pignon<sup>a,b,c</sup>, Bruno Aouizerate<sup>a,e,f</sup>,
Christelle Andrieu<sup>a,g</sup>, Myrtille Andre<sup>a,h</sup>, Wahid Boukouaci<sup>a,b,c</sup>, Delphine Capdevielle<sup>a,h</sup>,
Isabelle Chereau<sup>a,i</sup>, Clément Dondé<sup>a,u,v,w,x</sup>, Julie Clauss Kobayashi<sup>a,j</sup>, J.M. Dorey<sup>a,l</sup>,
Caroline Dubertret<sup>a,m,n</sup>, Eric Fakra<sup>a,o</sup>, Guillaume Fond<sup>a,g</sup>, Tudy Goze<sup>a,p</sup>, Sylvain Leignier<sup>a,k</sup>,
Pierre-Michel Llorca<sup>a,i</sup>, Jasmina Mallet<sup>a,m,n</sup>, David Misdrahi<sup>a,q,r</sup>, Nicolas Oriol<sup>a,o</sup>,
Romain Rey<sup>a,l</sup>, Paul Roux<sup>a,s,t</sup>, Franck Schürhoff<sup>a,b,c</sup>, Benoit Schorr<sup>a,j</sup>,
Andrei Szöke<sup>a,b,c</sup>, Mathieu Urbach<sup>a,s,t</sup>, Etienne Very<sup>a,p</sup>, Ching-Li Wu<sup>a,b,c</sup>, the FACE-SZ group,
Marion Leboyer<sup>a,b,c</sup>, Ryad Tamouza<sup>a,b,c</sup>, Ophelia Godin<sup>a,b,*</sup>

ARTICLE INFO

Keywords:
Schizophrenia

A B S T R A C T

Aims: Recent studies on Metabolic dysfunction-associated fatty liver (MAFLD) in schizophrenia show inconsistent findings, often based on small or specific samples. This study aims to assess the prevalence of MAFLD and its

<footnote><sup>a</sup> Fondation FondaMental, Créteil, France</footnote>

<footnote><sup>b</sup> Université Paris Est Créteil (UPEC), INSERM, Institut Henri Mondor the Recherche Biomédicale, Laboratoire Neuro-Psychiatrie Translationnelle, Créteil, France</footnote>

<footnote><sup>c</sup> Assistance Publique Hopitaux de Paris, Hôpitaux Universitaires Henri Mondor, Département Médico-Universitaire de Psychiatrie et d'Addictologie, Val-de-Marne, France</footnote>

<footnote><sup>d</sup> Université Côte d'Azur, Centre National de la Recherche Scientifique, Institut de Pharmacologie Moléculaire et Cellulaire, Valbonne, France</footnote>

<footnote><sup>e</sup> University Department of General Psychiatry, Charles Perrens Hospital, F-33076 Bordeaux, France</footnote>

<footnote><sup>f</sup> Laboratory of Nutrition and Integrative Neurobiology (UMR INRAE 1286), University of Bordeaux, Bordeaux, France</footnote>

<footnote><sup>g</sup> AP-HM, Service Universitaire de psychiatrie et d'addictologie (Pr Lançon), Hôpital Sainte Marguerite, 13009 Marseille, France</footnote>

<footnote><sup>h</sup> Service Universitaire de Psychiatrie Adulte, Hôpital la Colombière, CHRU Montpellier, Université Montpellier 1, Inserm, 1061 Montpellier, France</footnote>

<footnote><sup>i</sup> University of Clermont Auvergne, CMP-B CHU, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000 Clermont-Ferrand, France</footnote>

<footnote><sup>j</sup> Hôpitaux Universitaires de Strasbourg, Université de Strasbourg, INSERM U1329, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France</footnote>

<footnote><sup>k</sup> Centre Référent de Réhabilitation Psychosociale et de Remédiation Cognitive (C3R), CH Alpes Isère, Grenoble, France</footnote>

<footnote><sup>l</sup> INSERM U1028, CNRS UMR5292, Centre de Recherche en Neurosciences de Lyon, Université Claude Bernard Lyon 1, Equipe PSYR2, Centre Hospitalier Le Vinatier, Pole Est, 95 bd Pinel, BP 30039, 69678 Bron Cedex, France</footnote>

<footnote><sup>m</sup> AP-HP, Groupe Hospitalo-universitaire AP-HP Nord, DMU ESPRIT, Service de Psychiatrie et Addictologie, Hôpital Louis Mourier, Colombes, France</footnote>

<footnote><sup>n</sup> Université de Paris Inserm UMR1266, Sorbonne Paris Cité, Faculté de Médecine, Paris, France</footnote>

<footnote><sup>o</sup> Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France</footnote>

<footnote><sup>p</sup> Department of Psychiatry, Psychotherapies, Art-therapy, Toulouse University Hospital; Equipe de Recherche sur les Rationalités Philosophiques et les Savoirs - EA3051, Université de Toulouse - Jean Jaurès, Toulouse, France</footnote>

<footnote><sup>q</sup> University of Bordeaux, Aquitaine Institute for Cognitive and Integrative Neuroscience (CNRS UMR 5287-INCIA), Bordeaux, France</footnote>

<footnote><sup>r</sup> Department of Adult Psychiatry, Charles Perrens Hospital, Bordeaux, France</footnote>

<footnote><sup>s</sup> Versailles Hospital, Department of Adult Psychiatry and Addictology, Centre Hospitalier de Versailles, 177 rue de Versailles, 78157 Le Chesnay, France</footnote>

<footnote><sup>t</sup> MOODS Team, INSERM UMR1018, CESP, Faculté de Médecine Paris-Saclay, Université de Versailles Saint-Quentin-En-Yvelines, Université Paris-Saclay, Villejuif, France</footnote>

<footnote><sup>u</sup> Univ. Grenoble Alpes, F-38000 Grenoble, France</footnote>

<footnote><sup>v</sup> Inserm U1216, F-38000 Grenoble, France</footnote>

<footnote><sup>w</sup> Psychiatry Department, CH Alpes Isère, F-38000 Grenoble, France</footnote>

<footnote><sup>x</sup> Psychiatry Department, CHU Grenoble, F-38000 Grenoble, France</footnote>

<footnote>* Corresponding author at: Inserm U955 - IMRB (Eq Translational Neuropsychiatry), Fondation FondaMental, Hôpital Albert Chenevier - Pôle Psychiatrie - 40 rue de Mesly, 94000 Créteil, France.</footnote>

<footnote>E-mail address: ophelia.godin@inserm.fr (O. Godin).</footnote>
Liver diseases

Non-alcoholic fatty liver disease

Inflammatory markers

Metabolic abnormalities

Inflammation

NAFLD

MAFLD

sociodemographic, clinical, and treatment-related risk factors in a large cohort of chronic patients with schizophrenia. A secondary goal is to identify an immuno-inflammatory signature associated with MAFLD.

**Methods:** 1009 stable outpatients with schizophrenia were included in the cross-sectional analysis. Individuals with liver disease, hepatitis B/C, or current alcohol use disorder were excluded. MAFLD was screened using the Fatty Liver Index (FLI), with FLI > 60 indicating MAFLD. Risk factors were assessed using multivariate logistic regression, and inflammatory markers were analysed with penalized logistic regression.

**Results:** In a sample of 1009 individuals with schizophrenia, MAFLD prevalence was 36.6 %, higher in men than in women (38.3 % vs 32.0 %, *p* < 0.0001). Multivariate analysis identified male gender, older age, current smokers, mood stabilizers, antidepressant, clozapine, olanzapine and quetiapine as risk factors for MAFLD. The use of risperidone was associated with a lower risk of MAFLD (0.63 (0.42–0.94). Elevated levels of inflammatory markers such as TNF-α, CRP, and IL-12/IL-23p40 were also linked to a higher likelihood of MAFLD.

**Conclusion:** Although not generalizable to all patients with schizophrenia, this study confirms the high prevalence of MAFLD in individuals with schizophrenia. Psychiatrists should promote healthy lifestyle changes, such as weight control, improved diet, better sleep, and increased physical activity, to improve both physical health and quality of life in these patients.

# 1. Introduction

Individuals with schizophrenia (SZ) have a reduced life expectancy of 10 to 20 years compared to the general population [1], mainly due to premature cardiovascular diseases (CVD), cancer or suicide [2,3]. Extensive literature has repeatedly demonstrated the high frequency of cardiovascular and metabolic disorders which are 1.5 to 3 times higher in adults with schizophrenia as compared to the general population [2,4,5]. Among non-psychiatric comorbidities, Metabolic dysfunction-associated fatty liver disease (MAFLD) may be particularly frequent in individuals with schizophrenia [6,7,8,9,10]. Indeed, not only have numerous studies highlighted that inflammation plays a key role in schizophrenia, but some have also suggested that MAFLD could represent the hepatic manifestation of metabolic syndrome. MAFLD is defined by the accumulation of triglycerides in the hepatocytes in the absence of chronic alcohol use. This condition encompasses a spectrum of diseases from non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. MAFLD has been associated with metabolic and cardiovascular disorders, such as type 2 diabetes mellitus, hypertension, dyslipidemia, and obesity, and is now regarded as the liver manifestation of the metabolic syndrome [11]. MAFLD has increased exponentially in recent decades, making it the leading cause of chronic liver disease worldwide, affecting almost 30 % of the world's population. In France, the NASH-CO Study including 118,664 individuals from the general population reported a prevalence of MAFLD of 16.7 % [12]. In individuals with schizophrenia, despite the high prevalence of cardiovascular and metabolic risk factors, few studies have focused on the prevalence of MAFLD. Furthermore, these studies were conducted in relatively small samples, or focused on subgroups of patients such as men, hospitalized individuals with schizophrenia, and individuals with a first episode of psychosis, which partly explains the wide variability in prevalence ranging from 17.63 % to 70.8 % in the literature [6,7,8,9,10]. In addition, very few studies have looked at identifying clinical and biological factors associated with MAFLD and none of them investigated inflammatory parameters whereas MAFLD is typically a pro-inflammatory state.

The recognition of the prevalent comorbidity between MAFLD and schizophrenia is necessary to inform clinical practice, and finally to improve patients' physical health and their overall quality of life.

The objective of this study was then to determine (i) the prevalence of MAFLD in a large community-dwelling sample of subjects with stabilized schizophrenia, (ii) the sociodemographic, clinical, treatment-related characteristics and immune-inflammatory markers associated with MAFLD.

# 2. Materials and methods

## 2.1. Study population

Patients included in this multicenter, cross-sectional study were recruited from the FACE-SZ (FondaMental Academic Centers of Expertise for Schizophrenia) cohort between Mars 2010 and June 2023. The FACE-SZ cohort is based on a French national network of 10 Schizophrenia Expert Centers, set up by a French scientific cooperation foundation, FondaMental and created by the French Ministry of Research to build a platform that links systematic clinical assessment to research [13,14].

The Expert Centers offer wide access for all schizophrenia subjects with few barriers for referral and no biases towards treatment-refractory cases. Their goals are to provide reliable systematic multi-dimensional assessments of subjects leading to personalized therapeutic recommendations. Exclusion criteria for referral are minimal, and all patients who purportedly meet diagnostic criteria for any schizophrenia or schizoaffective disorder can benefit from this examination. Adult participants (≥16 years) diagnosed with schizophrenia, schizoaffective disorder, who were clinically stable (i.e., without hospitalization or changes in maintenance treatment in the 4 weeks preceding the assessment) were included. For this specific research, individuals with current alcohol use disorders, known liver disease, and those with viral hepatitis B/C or drug-induced hepatitis were also excluded from the analyses.

## 2.2. Ethical approval

The study was carried following ethical principles for medical research involving humans (WMA, Declaration of Helsinki) and the French Jarde law. The assessment protocol was approved by the relevant ethical review board (CPP-Ile de France IX, January 18, 2010). As this study includes data coming from regular care assessments, a non-opposition form was signed by all participants.

## 2.3. Data collected

### 2.3.1. Definition of non-alcoholic fatty liver disease

Screening for Metabolic dysfunction-associated fatty liver disease (MAFLD) was determined using the Fatty Liver Index (FLI) [15]:

$$ \text{FLI} = \frac{\left(e^{0.953 \times \text{Ln [Trigly]}} + 0.139 \times \text{BMI} + 0.718 \times \text{Ln [GGT]}\right) + 0.053 \times \text{Waist circumference} - 15.745}{\left(1 + e^{0.953 \times \text{Ln [Trigly]}} + 0.139 \times \text{BMI} + 0.718 \times \text{Ln [GGT]}\right) + 0.053 \times \text{Waist circumference} - 15.745} \times 100 $$

The FLI can range from 0 to 100. Based on available data, the FLI has a sensitivity of 61 % and a specificity of 86 % when a threshold ≥60 is used to define the presence of hepatic steatosis. It has been validated against the diagnosis of hepatic steatosis in the United States with a proven accuracy of 0.84 (95 % CI [0.81–0.87]). Participants were classified into three groups according to FLI range: < 30 / 30–60 / ≥ 60. For
individuals with FLI < 30, the probability of not having hepatic steatosis is 91 %, those with FLI between 30 and 60 were considered as an intermediate group, while participants with FLI was ≥60 were classified as having MAFLD in the absence of other causes of chronic liver disease (i. e., history of hepatitis or cirrhosis, negative hepatitis B surface antigen, and excessive alcohol consumption [≥30 g/day])[15].

### 2.3.2. Definition of metabolic syndrome

Metabolic syndrome (MetS) was defined according to the revised criteria of the based on the modified National Cholesterol Education Program Adult Treatment Panel III criteria [16], which requires that 3 or more of the following 5 criteria be met: high waist circumference (>94 cm for men and > 80 cm for women), hypertriglyceridemia (≥1.7 mmol/L or on lipid-lowering medication), low HDL cholesterol level (<1.03 mmol/L in men and < 1.3 mmol/L in women or specific treatment), high blood pressure (≥130/85 mmHg or on antihypertensive medication), and high fasting glucose concentration (≥5.6 mmol/L or on glucose-lowering medication).

### 2.3.3. Inflammatory markers

Immune-inflammatory markers from clusters of pro- and anti-inflammatory cytokines and the regulation of inflammatory processes were measured. Serum samples were evaluated for levels of IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p70, IL-12/IL-23p40, IL-13, IL-15, IL-16, IL-17A, TNF-α, TNF-β, and IFN-γ using multiplex immunoassays based on electrochemiluminescence (ECL) (Meso Scale Discovery (MSD), Gaithersburg, MD, USA). The experiments were conducted according to the manufacturer's instructions. Of the 18 cytokines tested, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-12p70, IL-13, and TNF-β were below the lower limit of detection (LLOD) in over 50 % of samples. To avoid extensive imputation and potential bias, these cytokines were excluded from further analysis.

### 2.3.4. Sociodemographic and clinical data

Sociodemographic (sex, age, education level) and clinical variables were recording during a standardized interview. Information about illness duration, age at schizophrenia onset, the total number of psychotic episodes and duration of untreated psychosis were recorded. Associated comorbidities such as substance use (tobacco, alcohol, cannabis) were also extracted. Several clinical scales were used to assess the symptomatology of the patients, including: the Positive and Negative Syndrome Scale (PANSS) [17], the Calgary Depression Scale for Schizophrenics (CDSS) [18] to evaluate depressive symptoms, the Pittsburgh Sleep Quality Index (PSQI) [19] to assess sleep disorders. Childhood trauma was assessed using the Childhood Trauma Questionnaire (CTQ) [20], quality of life using the Quality-of-Life Scale (SQOL), and global functioning with the Global Assessment of Functioning (GAF) scale [21].

A physical examination was conducted by nurses recording body mass index (BMI), height, weight, waist circumference, as well as blood pressure.

## 2.4. Statistical analysis

Categorical variables were represented by numbers and percentages, and quantitative variables by means and standard deviations (SD).

First, sociodemographic, clinical, and biological parameters were compared within each FLI group using chi-square or Fisher's exact test for categorical variables and Wilcoxon-Mann-Whitney and Kruskal-Wallis tests for continuous variables depending on the normality of the distribution.

Then, to identify risk factors independently associated with MAFLD, we conducted multivariable multinomial logistic regression analyses. Odds ratios (OR) and 95 % confidence intervals (CI) for factors associated with MAFLD were computed after adjusting for potential adjustment factors. Results were considered significant at a 5 % threshold ($p < 0.05$).

Adjustment variables included in this model were selected based on a $p$-value threshold of ≤0.10 in univariate analyses and/or known associations with MAFLD in the literature.

Finally, in the subgroup of patients for which immune-inflammatory markers were available (330 individuals), we conducted a multivariable penalized multinomial logistic regression analysis to analyze the relationship between MAFLD and immune-inflammatory parameters. These penalized models offer several notable advantages over traditional regression models. First, they provide more robust and reliable estimators, particularly with high-dimensional data, multicollinearity among predictors, or relatively small sample sizes, as they help prevent overfitting and improve model stability. These models can effectively handle situations where explanatory variables, such as immune-inflammatory markers, are highly correlated, and they can select the most relevant variables, enhancing the precision and reliability of predictions. Penalized regression models do not provide the traditional $p$-values and confidence intervals typically used for testing the significance of coefficients. As an alternative, non-parametric Bootstrap was used for inference in penalized regression applications. The Bootstrap step involved 1000 resamplings for each of the 40 multiple imputation datasets to create 1000*40 different samples. The regression model was then fitted to each of these samples. The R packages 'caret' and 'glmnet' [22,23] were used to implement these models, adjusting for potential confounding factors. The penalized regression framework uses linear models with penalties to avoid extreme parameters likely to cause overfitting while simultaneously performing variable selection and addressing multicollinearity. Optimal hyperparameters $\lambda$ and $\alpha$ for the penalized regression algorithm were chosen via 10-fold cross-validation, with the optimal values minimizing the log-loss. Penalized regression allows the selection of a subset of variables associated with MAFLD by calculating the Variable Inclusion Probability (VIP), i.e., the percentage of times each variable is retained in the model over multiple iterations. VIP can be interpreted as the posterior probability of including a variable in the model, offering a measure of association stability. VIP indicates the weight of each variable in the model: a VIP value close to 1 suggests a strong influence of the variable on the model. After setting an appropriate threshold, VIP is used to select predictors for follow-up analyses. In our study, we set a strict VIP threshold of 70 % to identify variables stably associated with MAFLD. CI for the estimated OR of each variable were calculated using the Bootstrap percentile method. The 95 % CI provides a range of possible values for the population, offering a more nuanced approach than a simple binary decision of significance. In the absence of asymptotically valid $p$-values in high-dimensional regression, we relied solely on VIP for variable selection associated with MAFLD. If a variable is selected in more than 70 % of the Bootstrap samples, we consider it important enough to highlight, regardless of whether the corresponding 95 % CI for the odds ratio includes the value 1.

Missing data were imputed using the multiple imputation by chained equations (mice) procedure in R software [24]. We generated 40 independent MI datasets as recommended for power considerations [25].

Statistical analyses were performed using R software (Version 2023.12.1 + 402).

## 3. Results

Among individuals with available data for MAFLD ($n = 1855$), individuals with viral hepatitis B/C ($n = 13$), drug-induced hepatitis ($n = 15$), current alcohol use disorders ($n = 176$) as well as those with a diagnosis of schizophreniform disorder ($n = 16$) were excluded, leading to a sample of 1009 individuals (Fig. 1 supplementary material). Mean age of the participants was 31.3 years (SD = 9.3), and 27.5 % were women. Most individuals were diagnosed with schizophrenia (76.7 %), with a smaller proportion suffering from schizoaffective disorder (23.3 %). The mean age at onset of the illness was 21.2 years (SD = 6.1) in our
sample.

## 3.1. Prevalence of NAFLD

The prevalence of MAFLD was estimated at 36.9 % in the overall sample. The prevalence of NAFLD was 38.5 % in men and 32.5 % in women and was significantly higher in men compared to women for each age category (under 30y: 27 % vs 19 %; 30 to 50 years: 48.9 % vs 44.3 %; over 50 years old: 61.4 % vs 42.9 %) (Fig. 1).

## 3.2. Clinical risk factors associated with NAFLD

Univariable associations (Table 1) showed that compared to individuals without MAFLD, those with MAFLD had a significantly later age of schizophrenia onset, a significantly higher mean number of lifetime psychotic episodes, and a lower quality of life. No differences between groups were observed for severity (PANSS score), and duration of untreated psychosis. Regarding comorbidities, individuals with MAFLD were significantly more likely to be smokers (50.3 % vs. 43.5 %, *p* = 0.0354), to have a higher BMI (31.4 vs. 22.4, *p* < 0.0001), and to present metabolic syndrome (58.6 % vs. 2.6 %, *p* < 0.0001). No differences were observed for alcohol and cannabis use or the frequency of eating disorders. The study of psychotropic treatments shows that the use of quetiapine, and clozapine, the use of mood stabilizer, antidepressant, anxiolytic, and hypnotic was higher in individuals with MAFLD. On the contrary, the frequency of MAFLD was significantly lower in both individuals taking aripiprazole or risperidone.

Multivariable adjusted analysis (Table 2) confirmed that men were twice as likely to have MAFLD compared to women (OR = 2.16; 95%CI = 1.52–3.06) and individuals over 40 years old were more than three times as likely to have MAFLD (OR = 3.77; 95%CI = 2.21–6.42). The prevalence of MAFLD was also significantly higher in current smokers (OR = 1.38; 95%CI = 1.01–1.88), those treated with mood stabilizers (OR = 1.64; 95%CI = 1.10–2.44), antidepressants (OR = 1.69; 95%CI = 1.20–2.37), clozapine (OR = 1.52; 95%CI = 1.00–2.29), quetiapine (OR = 1.72; 95%CI = 1.03–2.87), and olanzapine (OR = 1.64; 95%CI = 1.01–2.66). In contrast, the use of risperidone was associated with a lower risk of MAFLD (OR = 0.63; 95%CI = 0.42–0.94).

## 3.3. Association between immuno-inflammatory parameters and MAFLD

Results of the penalized multinomial logistic regression model (Table 3) showed that, after taking into account potential confounders, increased levels of TNF-α, CRP, IL-6, IL-7, IL-12/IL-23p40, and IL-16 were associated with the likelihood of MAFLD with respective mean

ORs of 1.30 (95%CI = 1.02–2.33), 1.21 (95%CI = 1.00–1.71), 1.15 (95%CI = 1.01–1.61), 0.84 (95%CI = 0.63–0.98), 1.23 (95%CI = 1.01–1.62), and 1.14 (95%CI = 1.00–1.57). Among these seven factors, TNF-α, CRP, and IL-12/IL-23p40 contributed the most to the regression model as indicated by their mean OR and VIP (>90 %). The other immune-inflammatory parameters were not associated to the prevalence of MAFLD.

## 4. Discussion

Our study, involving over 1000 subjects with schizophrenia, found a high prevalence of non-alcoholic fatty liver disease (MAFLD) of 36.9 %. The frequency was higher in men (38.5 %) than women (32.5 %) and increased with age. Our findings showed that individuals with MAFLD were more likely to be smokers, and to be treated by mood stabilizers, antidepressants, clozapine, quetiapine and olanzapine. On the contrary, they were less likely to use risperidone. In addition, our results also showed that elevated levels of inflammatory markers (TNF-α, CRP, IL-6, IL-7, IL-12/IL-23p40, and IL-16) were also linked to a higher likelihood of MAFLD.

Previous studies concerning the prevalence of MAFLD among subjects with schizophrenia reported rates between 21.4 % and 70.8 %, consistently with our study [6,7,8,9,10]. For example, at 3 years follow-up, 21.9 % individuals showed a FLI score ≥ 60 in a cohort of 160 first episode non-affective psychosis patients compared to 3 % within the control group [6,8]. In a cross-sectional study including 202 young males with schizophrenia, Yan et al. reported a prevalence of 49.5 % [9]. Li et al., reported a prevalence of MAFLD of 54.8 % in a cross-sectional study including 310 long term hospitalized patients with schizophrenia [7]. More recently, a study including 1305 hospitalized adults with schizophrenia confirmed this result and showed that prevalence of MAFLD was 32.9 % in the whole sample [10]. Although a control group was not included, the prevalence observed was notably higher than in the general French population, particularly in younger individuals. The French NASH-CO study reported a MAFLD prevalence of 16.7 % in the general population (25.8 % in men and 11.4 % in women, respectively), with a mean age of 47 years, compared to our sample’s average age of 32 years, suggesting that MAFLD is more common and occurs earlier in those with schizophrenia [12].

The study of factors associated with MAFLD highlights factors already known to be associated with MAFLD in the general population as well as more specific factors related to schizophrenia. Thus, men over 40 years old are particularly at risk, suggesting that age- and sex-related factors, such as hormonal changes, body composition differences, or cumulative exposures to risk factors, may contribute to MAFLD

<figure><img src="image_3.png" /><figcaption>Fig. 1. Estimated prevalence of MAFLD by age and sex in individuals with schizophrenia.</figcaption></figure>
<table><caption>**Table 1**
Demographic and clinical factors associated with Metabolic dysfunction-associated fatty liver disease (MAFLD).</caption><thead><tr><th rowspan="2"></th><th rowspan="2">All<br/>n = 1009</th><th colspan="3">Fatty Liver Index (FLI)</th><th rowspan="2">P<sup>a</sup></th><th rowspan="2">P &lt; 30 vs &gt; 60</th></tr><tr><th>&lt;30<br/>n = 421</th><th>30-60<br/>n = 216</th><th>&gt;60<br/>n = 372</th></tr></thead><tbody><tr><td>Sex, n (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>277 (27.5)</td><td>144 (34.2)</td><td>43 (19.9)</td><td>90 (24.2)</td><td>&lt;0.0001</td><td>0.0026</td></tr><tr><td>Male</td><td>732 (72.5)</td><td>277 (65.8)</td><td>173 (80.1)</td><td>282 (75.8)</td><td></td><td></td></tr><tr><td>Age, mean (SD)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>≤30, n (%)</td><td>31.3 (9.3)</td><td>28.8 (8.5)</td><td>31.1 (9)</td><td>34.2 (9.6)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>30-50, n (%)</td><td>530 (52.5)</td><td>282 (67)</td><td>115 (53.2)</td><td>133 (35.8)</td><td></td><td></td></tr><tr><td>≥50, n (%)</td><td>444 (44)</td><td>132 (30.7)</td><td>93 (42.8)</td><td>219 (58.4)</td><td></td><td></td></tr><tr><td>Education level (years), mean (SD)</td><td>12.7 (2.6)</td><td>12.8 (2.5)</td><td>12.5 (2.4)</td><td>12.7 (2.7)</td><td>0.4685</td><td>0.7553</td></tr><tr><td>Illness characteristics</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Diagnosis, n (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Schizophrenia</td><td>774 (76.7)</td><td>335 (79.6)</td><td>167 (77.3)</td><td>272 (73.1)</td><td>0.0972</td><td>0.0397</td></tr><tr><td>Schizoaffective disorder</td><td>235 (23.3)</td><td>86 (20.4)</td><td>49 (22.7)</td><td>100 (26.9)</td><td></td><td></td></tr><tr><td>Age of first psychotic episode (years), mean (SD)</td><td>21.2 (6.1)</td><td>20.6 (5.8)</td><td>20.7 (5.4)</td><td>22.2 (6.6)</td><td>0.0006</td><td>0.0004</td></tr><tr><td>Duration of illness (years), mean (SD)</td><td>10 (8)</td><td>7.9 (6.9)</td><td>10.6 (8.3)</td><td>12.1(8.5)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Number of psychotic episodes, mean (SD)</td><td>3.1 (3.2)</td><td>2.7 (2.7)</td><td>2.7 (2.2)</td><td>3.8 (4.0)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Duration of untreated psychosis (months), mean (SD))</td><td>22.9 (39.5)</td><td>22.1 (35.2)</td><td>25.3 (35.5)</td><td>22.6 (46)</td><td>0.7587</td><td>0.9089</td></tr><tr><td>PANSS, mean (SD)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Total</td><td>69.6 (19.2)</td><td>70 (19.6)</td><td>67.9 (17.3)</td><td>70.1 (19.7)</td><td>0.3599</td><td>0.9826</td></tr><tr><td>Positive factor</td><td>9 (4.4)</td><td>9.1 (4.4)</td><td>8.8 (4.2)</td><td>9.1 (4.5)</td><td>0.7546</td><td>0.9541</td></tr><tr><td>Negative factor</td><td>17.3 (6.7)</td><td>17.5 (7)</td><td>16.9 (6.2)</td><td>17.3 (6.7)</td><td>0.6409</td><td>0.7252</td></tr><tr><td>Disorganization factor</td><td>7.9 (3.5)</td><td>7.6 (3.3)</td><td>7.8 (3.3)</td><td>8.2 (3.9)</td><td>0.0696</td><td>0.0268</td></tr><tr><td>Excitation factor</td><td>5.6 (2.3)</td><td>5.5 (2.2)</td><td>5.5 (2.1)</td><td>5.7 (2.6)</td><td>0.3628</td><td>0.2034</td></tr><tr><td>Depression factor</td><td>7.2 (3.1)</td><td>7.2 (3)</td><td>7 (3.1)</td><td>7.4 (3.3)</td><td>0.3398</td><td>0.3355</td></tr><tr><td>Medication Adherence Rating Scale, mean (SD)</td><td>6.2 (3.2)</td><td>6.1 (2.4)</td><td>6.4 (2.2)</td><td>6.2 (2.2)</td><td>0.3917</td><td>0.8106</td></tr><tr><td>Depressive symptoms, mean (SD)</td><td>4 (4.1)</td><td>4 (4.1)</td><td>4 (4.1)</td><td>4 (4.1)</td><td>0.9959</td><td>0.9284</td></tr><tr><td>Global functioning assessment, mean (SD)</td><td>49.9 (13.6)</td><td>50.2 (13.7)</td><td>50.7 (12.8)</td><td>49 (13.8)</td><td>0.2732</td><td>0.2199</td></tr><tr><td>Childhood trauma, mean (SD)</td><td>41.3 (12.1)</td><td>41.2 (12.7)</td><td>40.2 (11)</td><td>42.2 (12.1)</td><td>0.1883</td><td>0.2802</td></tr><tr><td>Comorbidities, n (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current smokers, n (%)</td><td>490 (49.2)</td><td>183 (43.5)</td><td>115 (53.2)</td><td>187 (50.3)</td><td>0.0486</td><td>0.0354</td></tr><tr><td>Lifetime alcohol use disorder, n (%)</td><td>187 (18.2)</td><td>67 (15.9)</td><td>37 (17.1)</td><td>79 (21.2)</td><td>0.1207</td><td>0.0610</td></tr><tr><td>Lifetime cannabis use disorder, n (%)</td><td>264 (25.8)</td><td>113 (26.8)</td><td>57 (26.4)</td><td>88 (23.4)</td><td>0.6318</td><td>0.4628</td></tr><tr><td>Metabolic syndrome, n (%)</td><td>268 (26.6)</td><td>11 (2.6)</td><td>39 (18.1)</td><td>218 (58.6)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>BMI, mean (SD)</td><td>26.5 (5.3)</td><td>22.4 (2.7)</td><td>26 (2.4)</td><td>31.4 (4.6)</td><td></td><td></td></tr><tr><td>&lt;25</td><td>449 (44.5)</td><td>357 (84.8)</td><td>79 (36.6)</td><td>13 (3.5)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>25-30</td><td>335 (33.2)</td><td>60 (14.3)</td><td>122 (56.5)</td><td>153 (41.1)</td><td></td><td></td></tr><tr><td>&gt;30</td><td>225 (22.3)</td><td>4 (1)</td><td>15 (6.9)</td><td>206 (55.4)</td><td></td><td></td></tr><tr><td>Sleep disorder, n (%)</td><td>282 (27.5)</td><td>105 (24.9)</td><td>55 (25.5)</td><td>119 (32)</td><td>0.1155</td><td>0.0728</td></tr><tr><td>Quality of life, mean (SD)</td><td>51.9 (18.3)</td><td>53.7 (17.8)</td><td>51.5 (18.1)</td><td>50.1 (18.8)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Psychotropic treatments, n (%)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Antipsychotic</td><td>945 (93.7)</td><td>374 (88.8)</td><td>204 (94.4)</td><td>367 (98.7)</td><td>0.2912</td><td>0.1328</td></tr><tr><td>Olanzapine</td><td>110 (10.9)</td><td>33 (7.8)</td><td>29 (13.4)</td><td>48 (12.9)</td><td>0.0620</td><td>0.0550</td></tr><tr><td>Quetiapine</td><td>88 (8.7)</td><td>25 (5.9)</td><td>17 (7.9)</td><td>46 (12.4)</td><td>0.0012</td><td>0.0060</td></tr><tr><td>Clozapine</td><td>154 (15.3)</td><td>52 (12.4)</td><td>23 (10.6)</td><td>79 (21.2)</td><td>&lt;0.0001</td><td>0.0050</td></tr><tr><td>Aripiprazole</td><td>200 (19.8)</td><td>95 (22.6)</td><td>46 (23.1)</td><td>59 (15.9)</td><td>0.0130</td><td>0.0050</td></tr><tr><td>Risperidone</td><td>190 (18.8)</td><td>95 (22.6)</td><td>38 (17.6)</td><td>57 (15.3)</td><td>0.0060</td><td>0.0020</td></tr><tr><td>Mood stabilizer</td><td>159 (15.8)</td><td>45 (10.7)</td><td>30 (13.9)</td><td>84 (22.6)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Antidepressant</td><td>271 (26.9)</td><td>86 (20.4)</td><td>62 (28.7)</td><td>123 (33.1)</td><td>0.0020</td><td>&lt;0.0001</td></tr><tr><td>Anxiolytics/Hypnotics</td><td>265 (30.9)</td><td>86 (24.9)</td><td>65 (34.9)</td><td>114 (34.9)</td><td>0.0082</td><td>0.0049</td></tr><tr><td>Biological parameters, mean (SD)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GGT</td><td>33.7 (36.7)</td><td>20.4 (11.7)</td><td>30.8 (17.4)</td><td>50.5 (53.3)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>ASAT</td><td>23.2 (10.5)</td><td>20.4 (7.6)</td><td>22.6 (8.3)</td><td>26.5 (13.1)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>ALAT</td><td>31.6 (24.2)</td><td>22.3 (13.7)</td><td>29.9 (17.6)</td><td>42.8 (30.9)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>Triglycerides (mmol/L)</td><td>1.5 (1.2)</td><td>0.9 (0.4)</td><td>1.4 (0.7)</td><td>2.3 (1.5)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>LDL (mmol/L)</td><td>3.1 (0.9)</td><td>2.7 (0.8)</td><td>3.2 (0.9)</td><td>3.4 (0.9)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr><tr><td>HDL (mmol/L)</td><td>1.3 (0.4)</td><td>1.4 (0.4)</td><td>1.2 (0.3)</td><td>1.1 (0.5)</td><td>&lt;0.0001</td><td>&lt;0.0001</td></tr></tbody></table>

pathogenesis. Furthermore, as expected, the prevalence of MAFLD was significantly higher in overweight individuals and those with metabolic syndrome. Associations between male gender, age, metabolic syndrome and MAFLD have been widely described in the general population [26].

Interestingly, NAFLD was also significantly associated with smoking status and the use of mood stabilizers, antidepressants, and different second-generation antipsychotics independently of other potential confounding factors. More particularly, the use of clozapine, quetiapine, and olanzapine was higher in MAFLD whereas risperidone was associated with a lower risk of MAFLD. Results on psychotropic treatments are

consistent with the existing literature, highlighting the impact of antipsychotic treatments on the metabolic and cardiovascular health of individuals with schizophrenia. Indeed, numerous meta-analyses of therapeutic trials have shown that antipsychotic treatments, particularly second-generation ones, lead to significant weight gain, lipid, and glucose abnormalities, which are recognized as major risk factors for MAFLD [27,28]. Clozapine, olanzapine, and quetiapine seem to be the most metabolically detrimental molecules. Besides antipsychotics, some studies also highlight the role of certain antidepressants (particularly tricyclics) and mood stabilizers in the occurrence of metabolic and

<footnote><sup>a</sup> Khi2 for categorical variables and analysis of variance (ANOVA) or Kruskal-Willis for continuous variables. PANSS: Positive And Negative Syndrome Scale; BARS: Brief Adherence Rating Scale; MADRS: Montgomery Asberg Depressive Rating Scale; CTQ: Childhood trauma questionnaire; BMI: Body Mass Index; PSQI: Pittsburgh Sleep questionnaire Inventory; SQOL: The Schizophrenia Quality Of Life.</footnote>
<caption>**Table 2**</caption>

Multivariable analysis with imputation of missing values for demographic and clinical factors associated with Metabolic dysfunction-associated fatty liver disease (MAFLD).

<table><thead><tr><th rowspan="2"></th><th colspan="2">Risk of having MAFLD (ref = FLI &lt; 30)</th></tr><tr><th>FLI 30-60<br>OR (95 % CI)</th><th>FLI ≥ 60<br>OR (95 % CI)</th></tr></thead><tbody><tr><td>Sex</td><td></td><td></td></tr><tr><td>Women</td><td>1 (ref)</td><td>1 (ref)</td></tr><tr><td>Men</td><td>2.29 (1.52-3.44) ***</td><td>2.13 (1.50-3.02) ***</td></tr><tr><td>Age</td><td></td><td></td></tr><tr><td>&lt;30</td><td>1 (ref)</td><td>1 (ref)</td></tr><tr><td>30-40</td><td>1.56 (1.00-2.43) *</td><td>3.30 (2.24-4.87) ***</td></tr><tr><td>≥40</td><td>1.40 (0.69-2.86)</td><td>4.55 (2.51-8.23) ***</td></tr><tr><td>Duration of illness (years)</td><td>1.03 (1.00-1.07) *</td><td>1.00 (0.98-1.03)</td></tr><tr><td>Number of psychotic episodes</td><td>0.95 (0.89-1.01)</td><td>1.01 (0.97-1.06)</td></tr><tr><td>Sleep disorder (PSQI)</td><td>0.83 (0.58-1.19)</td><td>1.12 (0.82-1.54)</td></tr><tr><td>Current smokers</td><td>1.45 (1.02-2.05) *</td><td>1.38 (1.01-1.88) *</td></tr><tr><td>Mood stabilizers</td><td>1.11 (0.69-1.80)</td><td>1.64 (1.10-2.44) *</td></tr><tr><td>Anxiolytics + Hypnotics</td><td>1.23 (0.84-1.81)</td><td>1.00 (0.71-1.41)</td></tr><tr><td>Antidepressants</td><td>1.63 (1.12-2.39) *</td><td>1.69 (1.20-2.37) **</td></tr><tr><td>Clozapine</td><td>0.69 (0.41-1.15)</td><td>1.52 (1.00-2.29) *</td></tr><tr><td>Quetiapine</td><td>0.91 (0.49-1.68)</td><td>1.72 (1.03-2.87) *</td></tr><tr><td>Olanzapine</td><td>1.32 (0.78-2.25)</td><td>1.64 (1.01-2.66) *</td></tr><tr><td>Aripiprazole</td><td>0.98 (0.64-1.49)</td><td>0.79 (0.54-1.17)</td></tr><tr><td>Risperidone</td><td>0.68 (0.44-1.06)</td><td>0.63 (0.42-0.94) *</td></tr></tbody></table>

FLI: Fatty Liver Disease; OR = Odds Ratio; CI = Confidence Interval, PSQI: Pittsburgh Sleep Quality Index, MAFLD = Metabolic dysfunction-associated fatty liver disease.

cardiovascular alterations [29,30]. Our study also explored associations between immuno-inflammatory markers and MAFLD and identified an immuno-inflammatory signature of individuals with MAFLD, characterized by elevated levels of CRP, TNF-α, IL-6, IL-7, IL-12/IL-23p40, and IL-16. These pro-inflammatory cytokines are well known for their role in mediating chronic inflammation and metabolic disturbances, supporting the hypothesis of an immuno-inflammatory link between schizophrenia and MAFLD. The metaflammation is characterized by an increase in the serum concentration of pro-inflammatory cytokines, mainly interleukin-1 β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α), originating from the chronically inflamed adipose tissue and associated with oxidative stress. Elevated CRP levels have been repeatedly associated, in general population, with an increased prevalence of MAFLD or metabolic syndrome, a major risk factor for developing MAFLD [31,32,33]. Implication of CRP in schizophrenia has been also well documented in the literature. Higher CRP levels in patients with schizophrenia correlate with worst symptoms, resistance to treatment, cognitive decline as well as with comorbid cardiovascular and metabolic syndrome [34,35,36,37,38]. The elevation of TNF-α, produced by dysregulated adipocytes, is involved in the progression of MAFLD to more severe forms (NASH), partly by contributing to insulin resistance and cholesterol metabolism alterations, all of which are processes disrupted in metabolic syndrome [39,40]. Elevated IL-6 levels have been repeatedly associated with schizophrenia compared to healthy controls. It is well known that IL-6 activates CRP production by the liver. Additionally, the frequently reported association of IL-6 with metabolic syndrome may stem from its status as an adipokine, its increased production by adipocytes, and its role in lipid metabolism, insulin sensitivity, and blood pressure control [41]. Furthermore, it has been found that IL-6 interacts with the severity of metabolic syndrome [42] and is associated with obesity and visceral fat accumulation [43]. Studies in the general population exploring association between IL7 and MAFLD have shown inconsistent results. In a recent large meta-analysis investigating the association between 19 different inflammatory cytokines and MAFLD based on 36,074 patients and 47,052 controls, no association was observed between MAFLD and IL7 [44]. Interestingly, a recent study revealed increased IL-7 levels in patients with schizophrenia, metabolic syndrome, and treated with atypical antipsychotics [45].

Previous data have shown an increase in IL-12/23 p40 levels in schizophrenia compared to healthy controls. IL-12 is well known to play central roles in T cell-mediated responses in inflammation, such as naive T-cell differentiation and NKT cell activation. More recently, studies have suggested that IL-12 is involved in the progression of hepatitis delta virus infection and sclerosing cholangitis by activating MAIT cells and inducing Treg dysfunction. IL-12 acts as a proinflammatory cytokine that bridges innate and adaptive immunity, reprograms T cell suppressive functions, and exerts antitumor immune effects [46]. To date, only one study has reported that elevated IL-16 levels were associated with metabolic syndrome and type 2 diabetes in the general population, but without adjustment for other variables [47]. Although considered a pro-inflammatory cytokine, the relative rarity of studies on IL-16 only allows hypothesizing that its increase could be part of a general pro-inflammatory response during metabolic syndrome and schizophrenia [48]. However, even if some cytokines were significantly associated with MAFLD in schizophrenia, as no control group was included in our cohort, no conclusion could be drawn as to the specificity of these findings. Furthermore, the association between MAFLD and inflammatory markers suggests that MAFLD may indeed confound to some extent the association between low-grade inflammation and schizophrenia.

This study has some limitations. Due to the cross-sectional nature of the study, no causal conclusions can be drawn, and longitudinal studies are needed. We used a non-invasive index (FLI) to estimate the prevalence of MAFLD rather than imaging for MAFLD diagnosis. Additionally, the cohort may not be fully representative of the schizophrenia population, particularly because institutionalized, hospitalized or unstable patients (making thorough assessment difficult) were not referred to the Expert Centers. In addition, we observed a slightly higher sex ratio for men in our cohort compared to what have been described so far in literature, although in recent years studies have highlighted a higher incidence among men. Despite including many potential confounders, factors like physical exercise and diet were not measured. Nevertheless, this is the first large-scale study investigating risk factors associated with MAFLD and identifying an immune-inflammatory signature in individuals with chronic schizophrenia.

In terms of implications, beyond the increased cardiovascular risk induced by the presence of MAFLD, studies in the general population have suggested that MAFLD could have a significant impact on cognitive functions. A systematic review found that MAFLD was frequently associated with lower overall cognitive performance, (including memory and learning disorders, mental speed, attention, and psychomotor speed) [49]. Moreover, studies have also shown impaired cognitive performance in individuals with schizophrenia presenting with metabolic syndrome [50,51].

There is currently no curative treatment for MAFLD. Management focuses on reducing cardiovascular risk through a healthy lifestyle: balanced diet, regular exercise, avoiding alcohol, and maintaining good sleep. The Mediterranean diet, rich in fruits, vegetables, whole grains, and healthy fats, has been shown to reduce inflammation, improve metabolic health, and protect against cardiovascular disease [52]. This is especially relevant for individuals with schizophrenia, who often have poor dietary habits [53]. A normo-caloric ketogenic diet also shows promise in reducing liver fat and promoting weight loss by inhibiting de novo lipogenesis and inducing fatty acid oxidation, although it requires medical supervision [54,55].

New therapeutic approaches targeting the inflammatory and oxidative mechanisms of MAFLD could play a crucial role in reducing the prevalence and severity of MAFLD, and by extension, metabolic syndrome. Treatments such as SGLT2 inhibitors and GLP-1 receptor agonists, used for type 2 diabetes, have shown benefits in reducing liver inflammation and fibrosis [56]. Additionally, antioxidants like vitamin E and polyphenols have demonstrated protective effects against liver diseases, improving serum markers of liver health in patients with MAFLD [57]. Further exploration and rigorous clinical trials, particularly in patients with schizophrenia, are needed to confirm these
<caption>**Table 3**</caption>

Multivariate Penalized Logistic regression analysis of inflammatory parameters associated with Metabolic dysfunction-associated fatty liver disease (MAFLD).

<table><thead><tr><th rowspan="2">Clinical/Sociodemographic Variables</th><th colspan="3">FLI &lt; 30</th><th colspan="3">30 &lt; FLI &lt; 60</th><th colspan="3">FLI &gt; 60</th></tr><tr><th>Median OR</th><th>IC95%</th><th>VIP</th><th>Median OR</th><th>IC95%</th><th>VIP</th><th>Median OR</th><th>IC95%</th><th>VIP</th></tr></thead><tbody><tr><td>Sex (Female)</td><td>1.47</td><td>[1.06-3.58]</td><td>0.97</td><td>0.90</td><td>[0.52-1.03]</td><td>0.39</td><td>0.85</td><td>[0.43-0.99]</td><td>0.60</td></tr><tr><td>Sex (Male)</td><td>0.77</td><td>[0.55-1.00]</td><td>0.93</td><td>1.07</td><td>[0.98-1.42]</td><td>0.37</td><td>1.11</td><td>[1.00-1.57]</td><td>0.57</td></tr><tr><td>&lt;30 years</td><td>1.85</td><td>[1.21-4.26]</td><td>0.99</td><td>0.87</td><td>[0.56-0.99]</td><td>0.34</td><td>0.78</td><td>[0.44-0.98]</td><td>0.71</td></tr><tr><td>30-40 years</td><td>0.80</td><td>[0.54-0.99]</td><td>0.50</td><td>1.08</td><td>[0.81-1.56]</td><td>0.26</td><td>1.15</td><td>[0.96-1.67]</td><td>0.51</td></tr><tr><td>&gt;40 years</td><td>0.69</td><td>[0.31-0.98]</td><td>0.67</td><td>1.20</td><td>[0.74-2.03]</td><td>0.43</td><td>1.23</td><td>[0.90-2.44]</td><td>0.42</td></tr><tr><td>Duration of illness (years)</td><td>0.97</td><td>[0.75-1.43]</td><td>0.50</td><td>1.02</td><td>[0.72-1.34]</td><td>0.46</td><td>1.01</td><td>[0.69-1.30]</td><td>0.49</td></tr><tr><td>Number of psychotic episodes</td><td>0.95</td><td>[0.71-1.24]</td><td>0.51</td><td>0.93</td><td>[0.66-1.17]</td><td>0.48</td><td>1.13</td><td>[0.91-1.52]</td><td>0.71</td></tr><tr><td>Sleep disorder (No)</td><td>1.15</td><td>[0.89-2.11]</td><td>0.60</td><td>1.05</td><td>[0.72-1.61]</td><td>0.40</td><td>0.83</td><td>[0.44-1.04]</td><td>0.73</td></tr><tr><td>Sleep disorder (Yes)</td><td>0.93</td><td>[0.67-1.07]</td><td>0.58</td><td>0.98</td><td>[0.74-1.22]</td><td>0.39</td><td>1.12</td><td>[0.98-1.56]</td><td>0.70</td></tr><tr><td>Current smokers (No)</td><td>1.11</td><td>[0.88-1.82]</td><td>0.58</td><td>0.95</td><td>[0.64-1.34]</td><td>0.45</td><td>0.94</td><td>[0.62-1.24]</td><td>0.44</td></tr><tr><td>Current smokers (Yes)</td><td>0.94</td><td>[0.73-1.09]</td><td>0.56</td><td>1.02</td><td>[0.84-1.30]</td><td>0.43</td><td>1.03</td><td>[0.87-1.29]</td><td>0.43</td></tr><tr><td>Clozapine (No)</td><td>0.89</td><td>[0.46-1.23]</td><td>0.42</td><td>1.33</td><td>[1.01-3.92]</td><td>0.76</td><td>0.87</td><td>[0.52-1.11]</td><td>0.56</td></tr><tr><td>Clozapine (Yes)</td><td>1.07</td><td>[0.87-1.49]</td><td>0.42</td><td>0.84</td><td>[0.53-1.00]</td><td>0.74</td><td>1.09</td><td>[0.94-1.48]</td><td>0.54</td></tr><tr><td>Quetiapine (No)</td><td>1.20</td><td>[0.97-2.67]</td><td>0.46</td><td>1.30</td><td>[1.00-4.93]</td><td>0.50</td><td>0.55</td><td>[0.14-0.89]</td><td>0.98</td></tr><tr><td>Quetiapine (Yes)</td><td>0.89</td><td>[0.54-1.02]</td><td>0.44</td><td>0.84</td><td>[0.44-1.00]</td><td>0.48</td><td>1.51</td><td>[1.00-2.58]</td><td>0.94</td></tr><tr><td>Olanzapine (No)</td><td>1.29</td><td>[1.01-2.99]</td><td>0.78</td><td>0.93</td><td>[0.58-1.45]</td><td>0.37</td><td>0.81</td><td>[0.33-1.05]</td><td>0.57</td></tr><tr><td>Olanzapine (Yes)</td><td>0.86</td><td>[0.56-1.00]</td><td>0.75</td><td>1.03</td><td>[0.79-1.44]</td><td>0.35</td><td>1.13</td><td>[0.97-1.70]</td><td>0.56</td></tr><tr><td>Mood stabilizer (No)</td><td>1.16</td><td>[0.87-2.30]</td><td>0.61</td><td>1.02</td><td>[0.69-1.68]</td><td>0.38</td><td>0.86</td><td>[0.47-1.08]</td><td>0.64</td></tr><tr><td>Mood stabilizer (Yes)</td><td>0.93</td><td>[0.65-1.08]</td><td>0.59</td><td>1.00</td><td>[0.72-1.25]</td><td>0.37</td><td>1.08</td><td>[0.96-1.49]</td><td>0.62</td></tr><tr><td>Antidepressant (No)</td><td>1.18</td><td>[0.99-2.07]</td><td>0.77</td><td>0.88</td><td>[0.51-1.09]</td><td>0.54</td><td>0.94</td><td>[0.63-1.25]</td><td>0.39</td></tr><tr><td>Antidepressant (Yes)</td><td>0.90</td><td>[0.68-1.00]</td><td>0.75</td><td>1.08</td><td>[0.94-1.43]</td><td>0.53</td><td>1.03</td><td>[0.87-1.32]</td><td>0.37</td></tr><tr><td>Anxiolytics + Hypnotics (No)</td><td>1.19</td><td>[0.98-2.04]</td><td>0.76</td><td>0.85</td><td>[0.43-1.07]</td><td>0.62</td><td>0.98</td><td>[0.69-1.48]</td><td>0.37</td></tr><tr><td>Anxiolytics + Hypnotics (Yes)</td><td>0.91</td><td>[0.68-1.01]</td><td>0.72</td><td>1.10</td><td>[0.95-1.49]</td><td>0.61</td><td>1.00</td><td>[0.81-1.28]</td><td>0.35</td></tr><tr><td>Aripiprazole (No)</td><td>0.88</td><td>[0.51-1.20]</td><td>0.61</td><td>1.04</td><td>[0.69-1.78]</td><td>0.41</td><td>1.11</td><td>[0.78-1.78]</td><td>0.56</td></tr><tr><td>Aripiprazole (Yes)</td><td>1.07</td><td>[0.89-1.45]</td><td>0.59</td><td>0.99</td><td>[0.72-1.27]</td><td>0.40</td><td>0.95</td><td>[0.70-1.15]</td><td>0.54</td></tr><tr><td>Risperidone (No)</td><td>0.78</td><td>[0.39-0.99]</td><td>0.83</td><td>1.20</td><td>[1.00-2.56]</td><td>0.57</td><td>1.06</td><td>[0.71-1.65]</td><td>0.37</td></tr><tr><td>Risperidone (Yes)</td><td>1.16</td><td>[1.00-1.65]</td><td>0.80</td><td>0.90</td><td>[0.61-1.00]</td><td>0.56</td><td>0.97</td><td>[0.73-1.20]</td><td>0.35</td></tr><tr><td>Clinical CRP &lt;3</td><td>1.64</td><td>[1.10-4.90]</td><td>0.98</td><td>0.93</td><td>[0.65-1.15]</td><td>0.26</td><td>0.75</td><td>[0.37-0.97]</td><td>0.91</td></tr><tr><td>Clinical CRP &gt;3</td><td>0.71</td><td>[0.49-1.00]</td><td>0.94</td><td>1.06</td><td>[0.91-1.29]</td><td>0.25</td><td>1.21</td><td>[1.00-1.71]</td><td>0.87</td></tr><tr><td>Log_TNF alpha</td><td>0.98</td><td>[0.76-1.21]</td><td>0.27</td><td>0.79</td><td>[0.43-0.98]</td><td>0.88</td><td>1.30</td><td>[1.02-2.33]</td><td>0.91</td></tr><tr><td>Log_IFN gamma</td><td>0.94</td><td>[0.74-1.18]</td><td>0.54</td><td>1.11</td><td>[0.89-1.64]</td><td>0.60</td><td>0.95</td><td>[0.74-1.14]</td><td>0.45</td></tr><tr><td>Log_IL_6</td><td>0.85</td><td>[0.60-0.99]</td><td>0.75</td><td>1.00</td><td>[0.75-1.36]</td><td>0.37</td><td>1.15</td><td>[1.01-1.61]</td><td>0.77</td></tr><tr><td>Log_IL_10</td><td>0.98</td><td>[0.76-1.24]</td><td>0.50</td><td>1.04</td><td>[0.80-1.42]</td><td>0.49</td><td>0.99</td><td>[0.74-1.26]</td><td>0.45</td></tr><tr><td>Log_IL_7</td><td>0.84</td><td>[0.63-0.98]</td><td>0.89</td><td>1.05</td><td>[0.92-1.28]</td><td>0.37</td><td>1.13</td><td>[1.00-1.49]</td><td>0.67</td></tr><tr><td>Log_IL_8</td><td>1.13</td><td>[0.92-1.63]</td><td>0.55</td><td>1.04</td><td>[0.81-1.70]</td><td>0.37</td><td>0.84</td><td>[0.44-1.03]</td><td>0.57</td></tr><tr><td>Log_IL_12_IL_23p40</td><td>0.90</td><td>[0.64-1.03]</td><td>0.54</td><td>0.93</td><td>[0.71-1.14]</td><td>0.48</td><td>1.23</td><td>[1.01-1.62]</td><td>0.93</td></tr><tr><td>Log_IL_16</td><td>0.98</td><td>[0.79-1.17]</td><td>0.36</td><td>0.88</td><td>[0.63-1.01]</td><td>0.64</td><td>1.14</td><td>[1.00-1.57]</td><td>0.75</td></tr></tbody></table>

The median odds ratios [OR], 95% confidence intervals [CI], and variable inclusion probabilities [VIP] are displayed. Variables stably associated with MAFLD were determined based on a VIP > 0.70 and are highlighted in grey.

promising findings and support integration into standard care.

In conclusion, this study confirms the high prevalence of MAFLD in individuals with schizophrenia and explores the associated sociodemographic and clinical factors, as well as immune-inflammatory aspects of MAFLD in this population. In clinical follow-up, it is recommended to integrate additional MAFLD screening using a non-invasive technique, in addition to regular metabolic syndrome screening. To prevent the onset of hepatic steatosis and reduce cardiovascular risks and beyond, psychiatrists should promote a healthy lifestyle, including weight control, restriction of sugar and fat intake, improvement of sleep, and increased physical activities. This holistic approach could not only improve patients' physical health but also their overall quality of life.

CRediT authorship contribution statement

**Rudy Tokarski:** Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. **Susana Barbosa:** Writing – review & editing, Writing – original draft, Validation, Methodology, Formal

analysis, Conceptualization. **Baptiste Pignon:** Writing – review & editing, Validation, Conceptualization. **Bruno Auizerate:** Writing – review & editing, Validation, Resources, Investigation, Funding acquisition, Conceptualization. **Christelle Andrieu:** Writing – review & editing, Validation, Resources, Data curation. **Myrtille Andre:** Writing – review & editing, Validation, Resources, Investigation, Data curation. **Wahid Boukouaci:** Writing – review & editing, Validation, Resources, Methodology. **Delphine Capdevielle:** Writing – review & editing, Validation, Resources, Funding acquisition. **Isabelle Chereau:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Clément Donde:** Writing – review & editing, Validation, Resources, Funding acquisition, Data curation. **Julie Claus** **Kobayashi:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **J.M. Dorey:** Writing – review & editing, Validation, Resources, Conceptualization. **Caroline Dubertret:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Eric Fakra:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Guillaume Fond:**
Writing – review & editing, Validation, Resources, Data curation, Conceptualization. **Tudy Goze:** Writing – review & editing, Validation, Resources, Conceptualization. **Sylvain Leignier:** Writing – review & editing, Validation, Resources, Conceptualization. **Pierre-Michel Llorca:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Jasmina Mallet:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **David Misdrahi:** Writing – review & editing, Validation, Resources, Funding acquisition, Data curation, Conceptualization. **Nicolas Oriol:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Romain Rey:** Writing – review & editing, Validation, Resources, Data curation, Conceptualization. **Paul Roux:** Writing – review & editing, Validation, Resources, Funding acquisition, Conceptualization. **Franck Schürhoff:** Writing – review & editing, Validation, Resources, Conceptualization. **Benoit Schorr:** Writing – review & editing, Validation, Resources, Conceptualization. **Andrei Szöke:** Writing – review & editing, Validation, Resources, Data curation, Conceptualization. **Mathieu Urbach:** Writing – review & editing, Validation, Resources, Conceptualization. **Etienne Very:** Writing – review & editing, Validation, Resources, Conceptualization. **Ching-Li Wu:** Writing – review & editing, Validation, Resources, Methodology, Data curation. **Marion Leboyer:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Funding acquisition, Conceptualization. **Ryad Tamouza:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Data curation, Conceptualization. **Ophelia Godin:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization.

## Ethical standards

The study was carried following ethical principles for medical research involving humans (WMA, Declaration of Helsinki) and the French Jarde law. The assessment protocol was approved by the relevant ethical review board (CPP-Ile de France IX, January 18, 2010). All data were collected anonymously. As this study includes data coming from regular care assessments, a non-opposition form was signed by all participants.

## Funding

This research was supported by the Foundation FondaMental, Institut National de la Santé et de la Recherche Médicale (INSERM), and by the Investissements d'Avenir program managed by the Agence National de la Recherche (ANR) under reference ANR-11-IDEX-0004-02 and ANR-10-COHO-10-01. This funding source had no role in the study design, data collection, analysis, preparation of the manuscript, or decision to submit the manuscript for publication.

## Declaration of competing interest

All authors reported no biomedical financial interests or potential conflicts of interest.

## Acknowledgments

We thank the FondaMental Foundation that promotes scientific cooperation in mental health and that is developing a new model for translational research in psychiatry in France. We express our thanks to the individuals who were included in the present study. We thank Umar Saleem, and his team (Seif Ben Salem, Victor Barteau, Stephane Beaufort, Yecodji Dansou, Yanisse Abbad, Denis Chanet and Mohamed Laaidi) for the development of the FACE-SZ computer interface, data management, quality control and regulatory aspects.

List of FondaMental Advanced Center of Expertise (FACE-SZ) collaborators: Andre M, Andrieu-Haller C, Aouizerate B, Berna F, Blanc O,

Capdevielle D, Cléry M, Chereau-Boudet I, Clauss-Kobayashi, J, Coulon N, Dassing R, Dorey JM, Dubertret C, D'Amato T, Esselin A, Fakra E, Fond G, Gabayet F, Goze T, Jarroir M, Lacelle D, Leboyer M, Leignier S, Llorca PM, Mallet J, Metairie E, Michel T, Misdrahi D, Oriol N, Passereux C, Pignon B, Peri P, Portalier C, Rey R, Roman C, Roux P, Schorr B, Schürhoff F, Szöke A, Tessier A, Urbach M, Very E, Zinetti-Bertschy A.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpsychores.2025.112192.

## Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

## References

[1] C.-K. Chang, Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London - PubMed [web site]. https://pubmed.ncbi.nlm.nih.gov/21611123/, 2011 accessed 6 February 2024.

[2] C. Kowal, H. Peyre, A. Amad, A. Pelissolo, M. Leboyer, F. Schürhoff, et al., Psychotic, mood, and anxiety disorders and venous thromboembolism: a systematic review and meta-analysis, Psychosom. Med. 82 (9) (2020) 838-849.

[3] T.M. Laursen, T. Munk-Olsen, M. Vestergaard, Life expectancy and cardiovascular mortality in persons with schizophrenia, Curr. Opin. Psychiatry 25 (2) (2012) 83-88.

[4] A. Szoke, B. Pignon, O. Godin, A. Ferchiou, R. Tamouza, M. Leboyer, et al., Multimorbidity and the etiology of schizophrenia, Curr. Psychiatry Rep. 26 (5) (2024) 253-263.

[5] R.R. Veeneman, J.M. Vermeulen, A. Abdellaoui, E. Sanderson, R.E. Wootton, R. Tadros, et al., Exploring the relationship between schizophrenia and cardiovascular disease: a genetic correlation and multivariable Mendelian randomization study, Schizophr. Bull. 48 (2) (2022) 463-473.

[6] S. Galiano Rus, Ortiz García, V. de la Foz, M.T. Arias-Loste, P. Iruzubieta, M. Gómez-Revuelta, M. Juncal-Ruiz, et al., Elevated risk of liver steatosis in first-episode psychosis patients: results from a 3-year prospective study, Schizophr. Res. 246 (2022) 30-38.

[7] X. Li, Y. Gao, Y. Wang, Y. Wang, Q. Wu, Prevalence and influence factors for non-alcoholic fatty liver disease in long-term hospitalized patients with schizophrenia: a cross-sectional retrospective study, Neuropsychiatr. Dis. Treat. 19 (2023) 379-389.

[8] M.J. Morlán-Coarasa, M.T. Arias-Loste, V. Ortiz-García de la Foz, O. Martínez-García, C. Alonso-Martín, J. Crespo, et al., Incidence of non-alcoholic fatty liver disease and metabolic dysfunction in first episode schizophrenia and related psychotic disorders: a 3-year prospective randomized interventional study, Psychopharmacology (Berl) 233 (23-24) (2016) 3947-3952.

[9] J. Yan, C. Hou, Y. Liang, The prevalence and risk factors of young male schizophrenics with non-alcoholic fatty liver disease, Neuropsychiatr. Dis. Treat. 13 (2017) 1493-1498.

[10] W. Yi, H. Wu, W. Fu, H. Feng, J. Huang, H. Li, et al., Prevalence and risk factors of non-alcoholic fatty liver disease (NAFLD) in non-obese patients with schizophrenia: a retrospective study, Diabetes Metab. Syndr. Obes. Targets Ther. 17 (2024) 841-849.

[11] Z.M. Younossi, A.B. Koenig, D. Abdelatif, Y. Fazel, L. Henry, M. Wymer, Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes, Hepatol. Baltim. Md 64 (1) (2016) 73-84.

[12] O. Nabi, K. Lacombe, J. Boursier, P. Mathurin, M. Zins, L. Serfaty, Prevalence and risk factors of nonalcoholic fatty liver disease and advanced fibrosis in general population: the French nationwide NASH-CO study, Gastroenterology 159 (2) (2020) 791-793.e2.

[13] F. Schürhoff, G. Fond, F. Berna, E. Bulzacka, J. Vilain, D. Capdevielle, et al., A national network of schizophrenia expert centres: an innovative tool to bridge the research-practice gap, Eur. Psychiatry J. Assoc. Eur. Psychiatr. 30 (6) (2015) 728-735.

[14] A. Szoke, J.-R. Richard, G. Fond, D. Misdrahi, M. Lajnef, B. Aouizerate, et al., Clinical and pharmacological correlates of caffeine consumption in subjects with schizophrenia - data from the FACE-SZ cohort, J. Psychiatr. Res. 161 (2023) 206-212.

[15] G. Bedogni, S. Bellentani, L. Miglioli, F. Masutti, M. Passalacqua, A. Castiglione, et al., The fatty liver index: a simple and accurate predictor of hepatic steatosis in the general population, BMC Gastroenterol. 6 (2006) 33.

[16] National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III), Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in
adults (adult treatment panel III) final report, Circulation 106 (25) (2002) 3143-3421.

[17] S.R. Kay, A. Fiszbein, L.A. Opler, The positive and negative syndrome scale (PANSS) for schizophrenia, Schizophr. Bull. 13 (2) (1987) 261-276.

[18] D. Bernard, C. Lançon, P. Auquier, G. Reine, D. Addington, Calgary depression scale for schizophrenia: a study of the validity of a French-language version in a population of schizophrenic patients, Acta Psychiatr. Scand. 97 (1) (1998) 36-41.

[19] D.J. Buyse, C.F. Reynolds 3rd, T.H. Monk, S.R. Berman, D.J. Kupfer, The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research, Psychiatry Res. 28 (2) (1989) 193-213.

[20] D.P. Bernstein, T. Ahluvalia, D. Pogge, L. Handelsman, Validity of the childhood trauma questionnaire in an adolescent psychiatric population, J. Am. Acad. Child Adolesc. Psychiatry 36 (3) (1997) 340-348.

[21] M. Startup, M.C. Jackson, S. Bendix, The concurrent validity of the global assessment of functioning (GAF), Br. J. Clin. Psychol. 41 (Pt 4) (2002) 417-422.

[22] J. Friedman, T. Hastie, R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, J. Stat. Softw. 33 (1) (2010) 1-22.

[23] M. Kuhn, Building predictive models in R using the caret package, J. Stat. Softw. 28 (2008) 1-26.

[24] S. van Buuren, Multiple imputation of multilevel data, in: Handbook for Advanced Multilevel Analysis. European Association for Methodology Series, Routledge/ Taylor & Francis Group, New York, NY, US, 2011, pp. 173-196.

[25] J.W. Graham, A.E. Olchowski, T.D. Gilreath, How many imputations are really needed? Some practical clarifications of multiple imputation theory, Prev. Sci. Off. J. Soc. Prev. Res. 8 (3) (2007) 206-213.

[26] Z.M. Younossi, P. Golabi, J.M. Paik, A. Henry, C. Van Dongen, L. Henry, The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review, Hepatol. Baltim. Md 77 (4) (2023) 1335-1347.

[27] D. Grajales, V. Ferreira, Á.M. Valverde, Second-generation antipsychotics and dysregulation of glucose metabolism: beyond weight gain, Cells 8 (11) (2019) 1336.

[28] T. Pillinger, R.A. McCutcheon, L. Vano, Y. Mizuno, A. Arumuhah, G. Hindley, et al., Comparative effects of 18 antipsychotics on metabolic function in patients with schizophrenia, predictors of metabolic dysfunction, and association with psychopathology: a systematic review and network meta-analysis, Lancet Psychiatry 7 (1) (2020) 64-77.

[29] R. Gafoor, H.P. Booth, M.C. Gulliford, Antidepressant utilisation and incidence of weight gain during 10 years' follow-up: population based cohort study, BMJ 361 (2018) k1951.

[30] A. Serretti, L. Mandelli, Antidepressants and body weight: a comprehensive review and meta-analysis, J. Clin. Psychiatry 71 (10) (2010) 1259-1272.

[31] J. Huang, M. Wang, Y. Wu, R. Kumar, S. Lin, Serum high-sensitive C-reactive protein is a simple indicator for all-cause among individuals with MAFLD, Front. Physiol. 13 (2022) 1012887.

[32] Q. Xue, X. Yang, Y. Huang, D. Zhu, Y. Wang, Y. Wen, et al., Association between baseline and changes in high-sensitive C-reactive protein and metabolic syndrome: a nationwide cohort study and meta-analysis, Nutr. Metab. 19 (1) (2022) 2.

[33] C. Zhu, D. Huang, H. Ma, C. Qian, H. You, L. Bu, et al., High-sensitive CRP correlates with the severity of liver steatosis and fibrosis in obese patients with metabolic dysfunction associated fatty liver disease, Front. Endocrinol. 13 (2022) 848937.

[34] E. Bulzacka, L. Boyer, F. Schürhoff, O. Godin, F. Berna, L. Brunel, et al., Chronic peripheral inflammation is associated with cognitive impairment in schizophrenia: results from the multicentric FACE-SZ dataset, Schizophr. Bull. 42 (5) (2016) 1290-1302.

[35] G. Fond, O. Godin, L. Boyer, F. Berna, M. Andrianarisoa, N. Coulon, et al., Chronic low-grade peripheral inflammation is associated with ultra resistant schizophrenia. Results from the FACE-SZ cohort, Eur. Arch. Psychiatry Clin. Neurosci. 269 (8) (2019) 985-992.

[36] O. Godin, M. Leboyer, F. Schürhoff, L. Boyer, M. Andrianarisoa, L. Brunel, et al., Predictors of rapid high weight gain in schizophrenia: longitudinal analysis of the French FACE-SZ cohort, J. Psychiatr. Res. 94 (2017) 62-69.

[37] V. Lestra, B. Romeo, C. Martelli, A. Benyamina, N. Hamdani, Could CRP be a differential biomarker of illness stages in schizophrenia? A systematic review and meta-analysis, Schizophr. Res. 246 (2022) 175-186.

[38] V. Mondelli, S. Ciufolini, M. Belvederi Murri, S. Bonaccorso, M. Di Forti, A. Giordano, et al., Cortisol and inflammatory biomarkers predict poor treatment response in first episode psychosis, Schizophr. Bull. 41 (5) (2015) 1162-1170.

[39] O. Erbaş, H.S. Akseki, H. Aktuğ, D. Taşkuran, Low-grade chronic inflammation induces behavioral stereotypy in rats, Metab. Brain Dis. 30 (3) (2015) 739-746.

[40] T.C. Wascher, J.H.N. Lindeman, H. Sourij, T. Kooistra, G. Pacini, M. Roden, Chronic TNF-α neutralization does not improve insulin resistance or endothelial function in “healthy” men with metabolic syndrome, Mol. Med. 17 (3) (2011) 189-193.

[41] A.R. Aroor, S. McKarns, V.G. Demarco, G. Jia, J.R. Sowers, Maladaptive immune and inflammatory pathways lead to cardiovascular insulin resistance, Metabolism 62 (11) (2013) 1543-1552.

[42] F. Lamers, Y. Milaneschi, C.H. Vinkers, R.A. Schoevers, E.J. Giltay, B.W.J. H. Penninx, Depression profilers and immuno-metabolic dysregulation: longitudinal results from the NESDA study, Brain Behav. Immun. 88 (2020) 174-183.

[43] P. Trayhurn, I.S. Wood, Adipokines: inflammation and the pleiotropic role of white adipose tissue, Br. J. Nutr. 92 (3) (2004) 347-355.

[44] Y. Duan, X. Pan, J. Luo, X. Xiao, J. Li, P.L. Bestman, et al., Association of inflammatory cytokines with non-alcoholic fatty liver disease, Front. Immunol. 13 (2022) 880298.

[45] A.S. Boiko, I.A. Mednova, E.G. Kornetova, V.I. Gerasimova, A.N. Kornetov, A.J. M. Loonen, et al., Cytokine level changes in schizophrenia patients with and without metabolic syndrome treated with atypical antipsychotics, Pharm. Basel Switz. 14 (5) (2021) 446.

[46] Y. He, S. Hwang, Y.A. Ahmed, D. Feng, N. Li, M. Ribeiro, et al., Immunopathobiology and therapeutic targets related to cytokines in liver diseases, Cell. Mol. Immunol. 18 (1) (2021) 18-37.

[47] K.P. Zak, I.N. Kondratskaia, S.V. Mel'nichenko, V.V. Popova, Circulating interleukin-16 in blood of patients with metabolic syndrome and type 2 diabetes mellitus, Lik. Sprava 5-6 (2007) 46-49.

[48] D. Frydecka, M. Krzystek-Korpacka, A. Lubeiro, F. Stramecki, B. Stańczykiewicz, J. A. Beszlej, et al., Profiling inflammatory signatures of schizophrenia: a cross-sectional and meta-analysis study, Brain Behav. Immun. 71 (2018) 28-36.

[49] E.S. George, S. Sood, R.M. Daly, S.-Y. Tan, Is there an association between non-alcoholic fatty liver disease and cognitive function? A systematic review, BMC Geriatr. 22 (1) (2022) 47.

[50] A.S. Goughari, S. Mazhari, A.M. Pourrahimi, M.M. Sadeghi, N. Nakhae, Associations between components of metabolic syndrome and cognition in patients with schizophrenia, J. Psychiatr. Pract. 21 (3) (2015) 190-197.

[51] J.P. Lindenmayer, A. Khan, S. Kaushik, A. Thanju, R. Praveen, L. Hoffman, et al., Relationship between metabolic syndrome and cognition in patients with schizophrenia, Schizophr. Res. 142 (1-3) (2012) 171-176.

[52] E.S. George, E.N. Georgopoulos, D.D. Mellor, C. Chrysohou, C. Pitsavos, D. B. Panagiotakos, Exploring the path of mediterranean diet, non-alcoholic fatty liver disease (NAFLD) and inflammation towards 10-year cardiovascular disease (CVD) risk: the ATTICA study 10-year follow-up (2002-2012), Nutrients 14 (12) (2022) 2367.

[53] S. Dipasquale, C.M. Pariante, P. Dazzan, E. Aguglia, P. McGuire, V. Mondelli, The dietary pattern of patients with schizophrenia: a systematic review, J. Psychiatr. Res. 47 (2) (2013) 197-207.

[54] S. De Nucci, C. Bonfiglio, R. Donvito, M. Di Chito, N. Cerabino, R. Rinaldi, et al., Effects of an eight week very low-calorie ketogenic diet (VLCKD) on white blood cell and platelet counts in relation to metabolic dysfunction-associated steatotic liver disease (MASLD) in subjects with overweight and obesity, Nutrients 15 (20) (2023) 4468.

[55] R.G.R. Mooli, S.K. Ramakrishnan, Emerging role of hepatic ketogenesis in fatty liver disease, Front. Physiol. 13 (2022), https://doi.org/10.3389/fphys.2022.946474.

[56] B. Filipovic, S. Lukic, D. Mijac, M. Marjanovic-Haljilji, M. Vojnovic, J. Bogdanovic, et al., The new therapeutic approaches in the treatment of non-alcoholic fatty liver disease, Int. J. Mol. Sci. 22 (24) (2021) 13219.

[57] X. Qi, J. Guo, Y. Li, C. Fang, J. Lin, X. Chen, et al., Vitamin E intake is inversely associated with NAFLD measured by liver ultrasound transient elastography, Sci. Rep. 14 (1) (2024) 2592.