# Evidence for the absence of a relationship between inflammation and cognition in a cohort of 1565 individuals with bipolar spectrum disorders: a Bayesian analysis of network

Thomas Bastelica, Arnaud Carré, Louis-Ferdinand Lespine, Wahid Boukouaci, Ching-Lien Wu, Agnes Pelletier, Bruno Etain, Frank Bellivier, Bruno Aouizerate, Paula Martinez, et al.

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## To cite this version:

Thomas Bastelica, Arnaud Carré, Louis-Ferdinand Lespine, Wahid Boukouaci, Ching-Lien Wu, et al. Evidence for the absence of a relationship between inflammation and cognition in a cohort of 1565 individuals with bipolar spectrum disorders: a Bayesian analysis of network. Brain, Behavior, and Immunity, 2026, 131, pp.106164. 10.1016/j.bbi.2025.106164. hal-05361564

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Submitted on 12 Nov 2025

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Full-length Article

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Evidence for the absence of a relationship between inflammation and
cognition in a cohort of 1565 individuals with bipolar spectrum disorders: a
Bayesian analysis of network

Thomas Bastelica<sup>a,b,*</sup>, Arnaud Carré<sup>c</sup>, Louis-Ferdinand Lespine<sup>a,b</sup>, Wahid Boukouaci<sup>a,d</sup>, Ching-Lien Wu<sup>a,d</sup>, Agnes Pelletier<sup>a,d</sup>, Bruno Etain<sup>a,e</sup>, Frank Bellivier<sup>a,e</sup>, Bruno Aouizerate<sup>a,f</sup>, Paula Martinez<sup>a,g</sup>, Caroline Dubertret<sup>a,h</sup>, Emmanuel Haffen<sup>a,i</sup>, Emilie Olié<sup>a,j</sup>, Paul Roux<sup>a,k</sup>, Mircea Polosan<sup>a,l</sup>, Raymund Schwan<sup>a,m</sup>, Ludovic Samalin<sup>a,n</sup>, Pierre-Michel Llorca<sup>a,n</sup>, Noomane Bouaziz<sup>a,o</sup>, Antoine Lefrere<sup>a,p,q</sup>, Ryad Tamouza<sup>a,d</sup>, Thierry d'Amato<sup>a,b</sup>, FondaMental Advanced Center of Expertise for Bipolar Disorders (FACE-BD) collaborators<sup>1</sup>, Marion Leboyer<sup>a,d</sup>, Ophélia Godin<sup>a,d</sup>, Romain Rey<sup>a,b</sup>

ARTICLE INFO

Keywords:

Bipolar disorders

Cognitive function

Inflammatory markers

Peripheral inflammation

hsCRP

Cytokines

Interleukin-6

Tumor Necrosis Factor-alpha

A B S T R A C T

Previous studies have reported variable associations between peripheral inflammatory markers and cognitive functioning in individuals with bipolar spectrum disorders (BSD), with some identifying significant links and others finding no relationship. Such inconsistencies raise important questions about the role of inflammation in cognitive impairment among individuals with BSD. This study aims to investigate the relationship between peripheral inflammatory markers and cognitive function in a clinical sample of individuals with BSD using a Bayesian network analysis framework. We analyzed data from a large cohort (n = 1565) focusing on hsCRP and a subsample (n = 249) that included concurrent assessments of additional cytokines including Interleukin-6 and Tumor Necrosis Factor-alpha. A Bayesian approach was utilized to quantify uncertainty regarding the presence or absence of associations between inflammation and cognitive function. Our findings revealed no significant

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

<footnote><sup>b</sup> INSERM, U1028, CNRS, UMR5292, Lyon 1 University, Lyon Neuroscience Research Center, Psychiatric Disorders: from Resistance to Response Team, Lyon, France</footnote>

<footnote><sup>c</sup> Univ. Savoie Mont Blanc, Univ. Grenoble Alpes, LIP/PC2S, Chambéry, France</footnote>

<footnote><sup>d</sup> Université Paris-Est Créteil (UPEC), INSERM, IMRB, Translational Neuropsychiatry, AP-HP, Hôpitaux Universitaires H. Mondor, DMU IMPACT, Fédération Hospitalo-Universitaire de Médecine de Précision en Psychiatrie (FHU ADAPT), Fondation FondaMental, F-94010 Créteil, France</footnote>

<footnote><sup>e</sup> Université Paris Cité, AP-HP, GH Saint-Louis-Lariboisière-Fernand Widal, Pôle Neurosciences, and INSERM UMRS 1144 Therapeutic Optimization in Neuropharmacology Paris, France</footnote>

<footnote><sup>f</sup> Centre Hospitalier Charles Perrens, Pôle de Psychiatrie Générale et Universitaire, Laboratoire NutriNeuro (UMR INRAE 1286), Université de Bordeaux, Bordeaux, France</footnote>

<footnote><sup>g</sup> Pôle de Psychiatrie, Centre Hospitalier Princesse Grace, Monaco</footnote>

<footnote><sup>h</sup> Université de Paris, INSERM UMR1266, AP-HP, Groupe Hospitalo-Universitaire AP-HP Nord, DMU ESPRIT, service de Psychiatrie et Addictologie, Hôpital Louis Mourier, Colombes, France</footnote>

<footnote><sup>i</sup> Université Marie et Louis Pasteur, INSERM, UMR 1322 LINC, CIC-1431 INSERM et service de Psychiatrie de l'adulte, CHU de Besançon F-25000 Besançon, France</footnote>

<footnote><sup>j</sup> Department of Emergency Psychiatry and Acute Care, CHU Montpellier, IGF, Univ. Montpellier, CNRS, INSERM, Montpellier, France</footnote>

<footnote><sup>k</sup> Service Hospitalo-Universitaire de psychiatrie d'adulte et d'addictologie, Centre Hospitalier de Versailles, INSERM UMR1018, DisAP-Moods-CESP, Université de Versailles Saint-Quentin-en-Yvelines - Université Paris-Saclay, France</footnote>

<footnote><sup>l</sup> Univ. Grenoble Alpes, Inserm U1216, Grenoble Institut de Neurosciences, CHU de Grenoble F-38000 Grenoble, France</footnote>

<footnote><sup>m</sup> Université de Lorraine, Inserm U 1114, Pôle Hospitalo-Universitaire de Psychiatrie d'Adultes et d'Addictologie CPN Laxou, France</footnote>

<footnote><sup>n</sup> CHU Clermont-Ferrand, Department of Psychiatry, University of Clermont Auvergne, UMR 6602 Institut Pascal (IP), Clermont-Ferrand, France</footnote>

<footnote><sup>o</sup> Département de recherche clinique, pôle universitaire 93G03, EPS Ville Evrard Neuilly sur Marne, Université Sorbonne Paris-Nord, Neuilly-sur Marne, France</footnote>

<footnote><sup>p</sup> Pôle de Psychiatrie, Assistance Publique Hôpitaux de Marseille, Marseille, France</footnote>

<footnote><sup>q</sup> Institut de Neurosciences de la Timone, Aix-Marseille Univ, UMR CNRS, France</footnote>

<footnote>* Corresponding author at: Fondation Fondamental, Créteil, France.</footnote>

<footnote>E-mail address: thomas.bastelica@ch-le-vinatier.fr (T. Bastelica).</footnote>

<footnote n="1">¹ List of FondaMental Advanced Center of Expertise (FACE-BD) collaborators are given at end of the article.</footnote>
Network analysis
Bayesian analysis

associations between inflammatory markers and cognitive performance in both samples. Strong evidence was found supporting the absence of association, with network analysis indicating distinct clusters for cognitive and inflammatory variables, suggesting they function as independent constructs with limited interactions. In our clinical sample of individuals with BSD, our findings do not support a direct association between some inflammatory markers and cognition, aligning with studies that found minimal or no associations. Our study emphasizes the importance of utilizing Bayesian methods to assess these relationships rigorously and suggests further exploration of individual differences and subgroup effects in future research.

# 1. Introduction

Bipolar spectrum disorders (BSD) are a major and heterogeneous mood disorders which can be associated with impairment in a broad range of cognitive domains (Bora et al., 2009; Robinson et al., 2006; Torres et al., 2007). The most affected domains include attention, verbal learning and memory, and executive functions (Bora et al., 2009; Robinson et al., 2006; Torres et al., 2007). It has been shown that these cognitive alterations predict a poorer functional status and quality of life (Baune and Malhi, 2015; Solé et al., 2017). While some cognitive alterations may ameliorate during euthymic phases, others persist during periods of symptom remission (Bora et al., 2009; Robinson et al., 2006; Torres et al., 2007), highlighting the enduring challenge posed by cognitive deficits despite conventional therapeutic approaches.

In recent decades, the understanding of the biological mechanism underlying the onset and progression of BSD has substantially increased. Accumulating evidence indicates that disturbances in the immune system may significantly contribute to the pathophysiology of the disorder (Barbosa et al., 2014). Indeed, individuals with BSD frequently exhibit elevated levels of peripheral inflammation, indicative of chronic low-grade systemic inflammation (Bauer and Teixeira, 2019; Dargél et al., 2015; Modabbernia et al., 2013; Muneer, 2016). This inflammatory state is characterized by increased concentrations of peripheral markers such as C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), and interleukins (ILs), including IL-4, IL-6, IL-10, and IL-1beta (Benedetti et al., 2020; Fries et al., 2019; Rosenblat et al., 2015) which levels can vary across depressive, manic and euthymic phases (Sayana et al., 2017).

Inflammation may disrupt neurobiological processes regulating cognitive functions and has been therefore suggested as a potential common mechanism in the development of cognitive deficits associated with BSD (Rosenblat et al., 2015). Indeed, inflammation could alter neuroplasticity mechanisms by modifying levels of monoamines, over-activating microglia, and deregulating the hypothalamic-pituitary-adrenal (HPA) axis (Fourrier et al., 2019; Van Rheenen et al., 2020). Correlations have been identified between higher CRP levels and various lower cognitive functions, including information processing speed, attentional abilities, immediate memory, longer reaction time (Dickerson et al., 2013; Milton et al., 2021) and executive functions (Millett et al., 2021). Furthermore, several studies have established an association between cognitive impairment and peripheral proinflammatory cytokines, such as IL-6 (Barbosa et al., 2018; Hamdani et al., 2015) and TNF-α (Barbosa et al., 2012; Chakrabarty et al., 2019; Doganavsargil-Baysal et al., 2013; Rahmani et al., 2021) in patients with BSD.

However, the consistency of the correlations between CRP/proinflammatory cytokines and cognitive functions remains highly variable. Such correlations are influenced by numerous factors whose precise nature remains unclear (Mario et al., 2024; Misiak et al., 2018), including environmental (i.e., education, smoking, alcohol intake, etc.), biological (i.e., sex, BMI, chronic diseases), psychopathological influences as well as variables inducing changes in blood-based inflammatory biomarkers such as mood states and time variation. Such factors contribute to the intricate interconnectedness of the relationship between inflammation and cognition, presenting a complex system.

Conventional approaches to assess clinical disorders often struggle to

capture this complexity, since they do not allow for specificity in identifying interactions among these numerous, mutually influencing factors (Fried and Nesse, 2015). The network approach shows potential in accommodating such complexities, and has therefore garnered increasing attention in psychiatric research in recent years (Borsboom and Cramer, 2013; Contreras et al., 2019). In the network analysis, variables are represented as “nodes” and the associations between them as edges. Two connected nodes are conditionally dependent given all other nodes in the network (i.e., their partial correlation is nonzero). By visualizing symptoms as interconnected nodes in a network, this approach offers a comprehensive representation of their interrelations, elucidating the conditional dependence structure of symptoms. However, most of the previous network studies have employed regularization methods to estimate the network (i.e., “the least absolute shrinkage and selection operator”; LASSO), which do not provide evidence for the null hypothesis (i.e., evidence of no effect). While this regularization method is useful for the selection of a singular optimal network structure (Epskamp and Fried, 2018), it does not allow adequate quantification of the uncertainty in favor of edge inclusion (Marsman et al., 2022). This is problematic, as the absence of an edge could be incorrectly interpreted as conditional independence. In this context, given the heterogeneity of findings in the existing inflammation/cognition literature in BSD (Mario et al., 2024; Misiak et al., 2018) and the inability of classical inference to quantify this uncertainty, Bayesian statistics represent a step forward for deriving meaningful predictions regarding the probability of an effect (Wagenmakers et al., 2018). Recently, a Bayesian approach for estimating Gaussian Graphical Models (GGM) has been introduced in the network literature (Marsman et al., 2022; Williams, 2021), enabling the assessment of the amount of evidence supporting the existence of a connection between two nodes.

Despite the potential of the network approach, very few studies have explored the link between cognition and inflammation through this framework (Bastelica et al., 2023; Chalmers et al., 2022), and none have focused on the BSD population. Thus, our objective is to investigate whether specific cognitive variables are linked to peripheral inflammatory markers after controlling for all other cognitive variables, inflammatory markers, and covariates using a network analysis within a Bayesian framework. Examining these relationships is critical since cognitive impairments may stem from the interactions among these mutually influencing components. To achieve this objective, we plan to use two samples: a large one comprising measurements of high-sensitivity CRP (hsCRP), and a subsample drawn from the former, comprising fewer participants, characterized by the assessment of cytokines. Given the inconsistency of the prior literature, the primary objective of this study is exploratory in nature, as we do not have specific hypotheses regarding the relationships between cognitive mechanisms, covariates, and inflammation.

# 2. Material and methods

## 2.1. Cohort characteristics

This cross-sectional study encompassed a multicenter approach and included patients recruited into the FondaMental Academic Centers of Expertise for Bipolar Disorders (FACE-BD) cohort by a French national network. This Network integrates research and clinical practice into
community-based outpatient psychiatric care centers in France (Henry et al., 2011). The network was set up by the FondaMental Foundation (www.fondation-fondamental.org) and funded by the French Ministry of Research and the French Ministry of Health to build an infrastructure and provide resources to follow up a clinical cohort and perform comparative effectiveness research on a representative patient population (Leboyer et al., 2022).

## 2.2. Participants and inclusion/exclusion criteria

BSD was diagnosed based on the structured clinical interview assessing DSM-IV criteria. Outpatients with type I, II, or not otherwise specified (NOS, including cyclothymia) bipolar disorder who were 18-65 years of age were eligible. Only individuals who had undergone cognitive assessment were included. Exclusion criteria were: history of a central nervous system disease associated with neuropsychological problems (epilepsy, sclerosis, stroke, etc.), sensory disorders, illiteracy, non-native French language, electroconvulsive therapy in the past year.

Two samples were used in this study: Sample 1 includes 5383 participants. After applying the inclusion and exclusion criteria above-mentioned, the final sample included 1565 participants. Sample 2 is a subsample of the previous sample. It only includes patients who agreed to undergo additional blood samples. Sample 2 comprises 580 participants. After applying the inclusion and exclusion criteria mentioned above, the final sample included 249 participants. The selection criteria of the samples used for the present study are presented in Supplementary Figs. 1 and 2.

The ethics committee (Comité de Protection des Personnes Ile de France IX) approved the study protocol on January 18th, 2010.

## 2.3. Cognition

Experienced psychologists administered the tests in a fixed order. Overall, testing lasted 120 min, including a 5-10-minute break. The standardized test battery complied with the recommendations issued by the International Society for Bipolar Disorders (Yatham et al., 2010). It included 8 tests, evaluating the following five cognitive domains:

* Processing speed, using the symbol search subtests from the Wechsler Adult Intelligence Scale (WAIS) version III (Wechsler, 2000)

* Executive functions, using the colour/word condition of the Stroop test, interference score (Golden, 1978), the Trail Making Test B (TMTB), difference in completion time between parts B and A (Godefroy, 2008), and verbal fluency, total score of semantic and phonemic conditions (Godefroy, 2008)

* Verbal learning, assessed using the California Verbal Learning Test (CVLT), with the total score derived from the five initial trials of immediate recall (Delis et al., 2000)

* Working memory with the (backward) digit span subtest from the WAIS-III (Wechsler, 2000);

* Nonverbal reasoning with the matrix reasoning subtests from the WAIS-III (Wechsler, 2000)

## 2.4. Inflammatory markers

### 2.4.1. Inflammatory markers measurement

Blood samples were collected on the same day as the cognitive assessment.

For Sample 1, fasting venous blood was drawn between 7:00 and 9:00 a.m. Samples were centrifuged at 2016g for 15 min, and serum was stored at -80 °C. HsCRP concentrations were determined using the Cobas 8000 analyzer (Roche Diagnostic, Meylan, France).

For Sample 2, additional blood samples were collected concurrently. Five milliliters of peripheral blood were allowed to clot for 1 h, centrifuged at 1500g for 10 min, and serum was aliquoted (0.5 mL) and stored at -80 °C until analysis. Cytokine concentrations (CRP, 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, IFN-γ) were quantified using electrochemiluminescence-based multiplex immunoassays (Meso Scale Discovery, Gaithersburg, MD, USA).

All assays were performed according to the manufacturer's instructions. Patient samples were measured in a single replicate. Internal quality controls at three concentration levels (high, medium, and low) were included in triplicate on each plate. Intra-assay coefficients of variations (CVS), calculated from these triplicates within each plate using the formula $CV = (SD/mean) \times 100$, were <10%. Inter-assay CVs, calculated from quality controls across plates, were <15%. To preserve sample integrity, the number of freeze-thaw cycles was strictly limited to a maximum of three.

### 2.4.2. Inflammatory markers selection

We limited the number of inflammatory markers included in the analysis which were selected based on available literature. According to two recent systematic reviews examining the association between peripheral inflammatory markers and cognitive impairment in individuals with BSD (Mario et al., 2024; Misiak et al., 2018). CRP showed the most consistent associations with cognitive deficits, while TNF-α and IL-6 demonstrated moderate to significant associations with cognitive impairment in BSD. Other inflammatory markers (IL-1 Receptor Antagonist (RA), IL-2, IL-8, IL-18) showed either limited evidence due to few studies or inconsistent findings across investigations. Based on this evidence hierarchy and the requirement to limit our inflammatory panel for statistical power considerations, only hsCRP, IL-6, and TNF-α were included in our analysis as the markers with the strongest and most replicable associations with cognitive impairment in BSD. Approximately 13% of IL-6 measurements were below the assay's lower limit of detection (LLOD < 0.36 pg/ml) and were therefore assigned a value of 0.25 pg/ml (determined by the formula $LLOD/\sqrt{2}$; Moriarity et al., 2021). None of the TNF-α measurements fell below the LLOD (see Supplementary Table 1).

## 2.5. Covariates

We selected covariates based on clearly established or theoretically justified causal pathways to both inflammation and cognition, following recommendations by Wysocki et al., (2022) and Del Giudice and Gangestad, 2021 to avoid overadjustment and collider bias. Specifically, we included age (continuous), sex (male/female), education level (years of formal education), smoking status (never smoker, former smoker, current smoker), and general disease burden, quantified as the number of currently diagnosed chronic diseases (self-reported). We also assessed body mass index (BMI) and mood symptom severity using the Montgomery-Åsberg Depression Rating Scale (MADRS; Montgomery and Asberg, 1979) for depressive symptoms and the Young Mania Rating Scale (YMRS; Young et al., 1978) for manic symptoms. Higher scores indicate greater symptom severity.

For Sample 1, primary analyses included all variables in a network approach to map conditional associations within the broader bios psychosocial system linking inflammation and cognition. This full model treats BMI and mood symptoms as integral network nodes given their theoretically complex causal relationships with both inflammation and cognition. Sensitivity analyses restricted the model to five confounders to provide conceptually clear estimates focusing specifically on inflammation-cognition associations. For Sample 2, given the smaller sample size, we restricted analyses to age, sex and education to preserve model parsimony in this exploratory framework while retaining the most theoretically essential covariates.

A detailed rationale and empirical references supporting the causal status of each covariate are provided in the Supplementary Material, together with the results of the restricted-model analyses (Supplementary Fig. 3). Excluding BMI and mood symptoms did not alter the overall pattern of results, indicating that the absence of inflammation-cognition
associations was robust across different covariate inclusion strategies.

## 2.6. Statistical analyses

The statistical software R (version 4.2.2) was used to carry out the statistical analysis (R Core Team, 2020). The R-package easybgm (Huth et al., 2023b), BDgraph (Mohammadi and Wit, 2019), and qgraph (Epskamp et al., 2012) were used for the estimation, visualization, and inferential analysis of the networks.

### 2.6.1. Data preparation

#### 2.6.1.1. Outliers' management.
The Minimum Covariance Determinant approach was used to detect outliers (Leys et al., 2018). This robust outlier-detection methodology enables the identification of multivariate outliers and yields more reliable outcomes in comparison to conventional techniques relying on means and standard deviations (Leys et al., 2019). Multivariate outliers were identified and removed from Sample 1 ($n = 220$) and Sample 2 ($n = 66$) before conducting the analyses. As recommended by Leys et al., (2019), and due to the non-preregistered nature of our study, we have additionally reported the results inclusive of outliers, which did not alter our primary conclusions (see Supplementary Figs. 4 and 5).

#### 2.6.1.2. Missing data.
Missing data were low (1.8 % in Sample 1, 1.2 % in Sample 2). Following recommended practices (Leacy et al., 2017), we conducted primary analyses under Missing At Random (MAR) assumptions, followed by sensitivity analyses for Missing Not At Random (MNAR) departures.

We examined associations between missingness and observed covariates to characterize patterns and define clinically plausible MNAR scenarios for sensitivity analyses. Logistic regressions revealed that depression severity (MADRS) and age significantly predicted missingness in cognitive variables, consistent with more severely symptomatic participants being less likely to complete demanding tasks. Primary analyses employed multiple imputation by chained equations (MICE; van Buuren and Groothuis-Oudshoorn, 2011), generating $m = 20$ datasets under MAR assumptions using predictive mean matching for continuous variables and logistic regression for binary variables. In Sample 1, all 17 network variables were imputed simultaneously; in Sample 2, 14 variables were imputed with demographic and lifestyle variables as auxiliary predictors (18 total).

Sensitivity analyses testing MNAR scenarios, where participants with missing data were systematically more impaired than predicted under MAR ($\delta = -0.5, -1.0, -1.5$ SD), showed negligible impact on conclusions (maximum Cohen's $d = 0.067$; see Supplementary Materials).

### 2.6.2. Sensitivity analysis of hsCRP in sample 1: inclusion of elevated hsCRP

While excluding participants with hsCRP values exceeding 10 mg/L from analyses is a common practice in some studies, there has been recent discussion as to the appropriateness of this as a default methodological decision (Mac Giollabhui et al., 2020). Such exclusions may not consistently signify acute infection or injury and could inadvertently limit the generalizability and robustness of findings, particularly in fields like immunopsychiatry, where understanding the interplay between immune function and psychiatric disorders is crucial (Moriarity et al., 2021). Therefore, we addressed this issue by conducting sensitivity analyses with and without the inclusion of participants with elevated hsCRP values; analyses excluding the 36 participants with hsCRP > 10 mg/L are presented in the Supplementary Fig. 6.

### 2.6.3. Network estimation

#### 2.6.3.1. Bayesian analysis of networks.
To avoid limitations of network

analyses that relies on frequentist inference, we used Bayesian network psychometric analysis that directly addresses sources of network uncertainties. This framework allows quantifying the evidence of edge presence and absence and expresses the precision of the parameters through posterior parameter distributions (Huth et al., 2023a; Marsman et al., 2022).

#### 2.6.3.2. Evidence for edge inclusion or exclusion.
We specified an uninformative prior on the network structure (g.prior = 0.5), which assigns equal prior probability to edge inclusion and exclusion (Huth et al., 2023b). Given the heterogeneous findings in inflammation-cognition research discussed in our introduction, this choice allows the data to determine network structure without imposing strong prior beliefs that could suppress weak or context-dependent associations. Inclusion Bayes factors ($BF_{10}$) were calculated for each edge in the network by using the transformed posterior edge inclusion probability (Sekulovski et al., 2023). The Bayes Factor (BF) is used in Bayesian inference as a testing measure. Specifically, the inclusion BF indicates how much more likely the data is under the assumption that the edge is included vs. the assumption that it is excluded. To classify the strength of evidence that different edge Bayes Factors provide, we refer to Huth et al., (2023a). Therefore, a $BF_{10}$ larger than 1 indicates some evidence for edge inclusion, while a $BF_{10}$ larger than 3 is considered weak evidence and a $BF_{10}$ larger than 10 strong evidence for edge inclusion. Conversely, $BF_{10}$ smaller than 1 indicates some evidence for edge exclusion, with $BF_{10}$ smaller than 1/3 being weak and $BF_{10}$ smaller than 1/10 being strong evidence for edge exclusion. The $BF_{10}$ were then visualized as the edge evidence network, in which edges represent the corresponding inclusion BF with blue edges indicating strong evidence for inclusion (i.e., $BF_{10} > 10$), grey edges inconclusive evidence (i.e., $0.1 < BF_{10} < 10$), and red edges strong evidence for edge absence (i.e., $BF_{10} < 0.1$).

#### 2.6.3.3. Parameter estimation.
Regarding the precision estimate, we set to three the degrees of freedom of the G-Wishart prior (i.e., df.prior) on the precision matrix (Huth et al., 2023b; Mohammadi and Wit, 2019). Parameter estimate plot included edges with an inclusion probability larger than 0.5, eliminating edges with an inclusion probability < 0.5 (Marsman et al., 2022; Mohammadi and Wit, 2019). Edges represent the strength of association between two variables after having controlled for the influence of all other variables in the network (i.e., partial associations).

## 3. Results

### 3.1. Descriptive statistics

The final size of Sample 1 ($n = 1565$) included 602 men (38 %) and 963 women (62 %). Mean age was 37 years (S.D. = 29.47, range = 18–65). Mean education level was 15 years (S.D. = 12.17). Detailed descriptive statistics of the sample characteristics are provided in Table 1.

The final size of Sample 2 ($n = 249$) included 92 men (37 %) and 157 women (63 %). Mean age was 33 years (S.D. = 26.42, range = 18–65). Mean education level was 15 years (S.D. = 12.17). Detailed descriptive statistics of the sample characteristics are presented in Table 2.

A comparative analysis of Samples 1 and 2 revealed significant differences in several sociodemographic characteristics. Specifically, participants in the sample 1 were older ($p < 0.001$) and had higher BMI values ($p < 0.05$) compared to those in the sample 2. However, no significant differences were found in sex, education levels, MADRS scores, YMRS scores, chronic disease status, or smoking status.

### 3.2. Network output

Graphical results of the network analysis (partial correlation and
<table><caption>**Table 1**

Descriptive characteristic of sample 1 (n = 1565).</caption><thead><tr><th>Characteristic</th><th>Median (IQR); n (%)</th></tr></thead><tbody><tr><td><strong>AGE</strong></td><td>37 (29.47)</td></tr><tr><td><strong>SEX</strong></td><td></td></tr><tr><td>Male</td><td>602 (38 %)</td></tr><tr><td>Female</td><td>963 (62 %)</td></tr><tr><td><strong>Education</strong></td><td></td></tr><tr><td>MADRS</td><td>15 (12.17)</td></tr><tr><td>YMRS</td><td>6 (2.12)</td></tr><tr><td><strong>Number of CD</strong></td><td></td></tr><tr><td>0</td><td>969 (62 %)</td></tr><tr><td>1</td><td>445 (28 %)</td></tr><tr><td>2</td><td>130 (8.3 %)</td></tr><tr><td>3</td><td>21 (1.3 %)</td></tr><tr><td><strong>Smoking</strong></td><td></td></tr><tr><td>Never</td><td>617 (39 %)</td></tr><tr><td>Former</td><td>197 (13 %)</td></tr><tr><td>Current</td><td>751 (48 %)</td></tr><tr><td><strong>BMI</strong></td><td>24.8 (22.3, 28.3)</td></tr></tbody></table>

Note. MADRS, Montgomery-Asberg Depression Rating Scale; YMRS, Young Mania Rating Scale; CD, Chronic diseases; BMI, Body Mass Index; Cognitive items were omitted from both the MADRS and the YMRS to avoid potential overlap with cognitive assessments.

<table><caption>**Table 2**

Descriptive characteristic of sample 2 (n = 249).</caption><thead><tr><th>Characteristic</th><th>Median (IQR); n (%)</th></tr></thead><tbody><tr><td><strong>AGE</strong></td><td>33 (26.42)</td></tr><tr><td><strong>SEX</strong></td><td></td></tr><tr><td>Male</td><td>92 (37 %)</td></tr><tr><td>Female</td><td>157 (63 %)</td></tr><tr><td><strong>Education</strong></td><td></td></tr><tr><td>MADRS</td><td>15 (12.17)</td></tr><tr><td>YMRS</td><td>7 (2.13)</td></tr><tr><td><strong>Number of CD</strong></td><td></td></tr><tr><td>0</td><td>157 (63 %)</td></tr><tr><td>1</td><td>71 (28 %)</td></tr><tr><td>2</td><td>17 (6.8 %)</td></tr><tr><td>3</td><td>4 (1.6 %)</td></tr><tr><td><strong>Smoking</strong></td><td></td></tr><tr><td>Never</td><td>103 (42 %)</td></tr><tr><td>Former</td><td>37 (15 %)</td></tr><tr><td>Current</td><td>109 (44 %)</td></tr><tr><td><strong>BMI</strong></td><td>23.9 (21.4, 28.3)</td></tr></tbody></table>

Note. MADRS, Montgomery-Asberg Depression Rating Scale; YMRS, Young Mania Rating Scale; CD, Chronic diseases; BMI, Body Mass Index; Cognitive items were omitted from both the MADRS and the YMRS to avoid potential overlap with cognitive assessments.

inclusion BF) are visualized in Fig. 1 for sample 1 and in Fig. 2 for sample 2. The Highest Density Intervals (HDI) for all the parameters respective for the networks are detailed in Supplementary Figs. 7 and 8.

### 3.2.1. Sample 1

Edge weights ranged from $\theta$ = -0.254 (TMTB – Symbol search) to $\theta$ = 0.394 (Phonemic fluency – Semantic fluency). On 136 possible edges in the network, 38 showed evidence of inclusion (IBF > 10). hsCRP was associated with higher BMI ($\theta$ = 0.342) and male sex ($\theta$ = 0.119). Women had higher scores for Verbal learning ($\theta$ = 0.347) and Phonemic

fluency ($\theta$ = 0.091) compared to men. Conversely, women had lower scores for Semantic fluency ($\theta$ = -0.185) than men. Psychopathological nodes (MADRS and YMRS scores) were grouped together and poorly connected to other domains, positioned at the periphery of the network. Sixty-six edges showed sufficient evidence for exclusion (IBF < 0.1), which included every link between hsCRP and cognitive variables. In total the network structure showed 32 edges with insufficient evidence.

### 3.2.2. Sample 2

Edge weights ranged from $\theta$ = -0.309 (Sex – TNF-α) to $\theta$= 0.432 (Phonemic fluency – Semantic fluency). Among all the possible edges within the network (n = 78), 10 showed evidence for inclusion (IBF > 10). Men had higher levels of TNF-α ($\theta$ = -0.309). Women scored higher than men on the verbal learning test ($\theta$ = 0.272). Inflammatory markers were positively interrelated with high evidence supporting their relationships: IL-6 and TNF-α ($\theta$= 0.346). Thirty edges showed sufficient evidence for exclusion (IBF < 0.1), which included most of the links between inflammatory markers and cognition. The 38 edges which showed insufficient evidence (0.1 < IBF < 10) included some edges between cognition and inflammatory markers (e.g., IL-6-processing speed, IL-6-Backward digit span, TNF-α-Semantic fluency, TNF-α-flexibility, TNF-α-Matrix), precluding definitive conclusions about the absence of association. Among all inflammation-cognition links, the TNF-α – Backward span edge was the only one showing an estimated connection, although the evidence for its presence was inconclusive ($\theta$ = 0.072; BF<sub>10</sub> = 1.56).

## 4. Discussion

In the present study, we used network analysis within a Bayesian framework to explore the relationship between peripheral inflammatory markers and cognition in a clinical sample of individuals with BSD. This approach enabled us to quantify uncertainty regarding the presence or absence of connections. We conducted our analysis across two samples. The first one is a large cohort focusing solely on hsCRP measurement. The second was a subsample drawn from the former, comprising fewer participants and characterized by the assessment of cytokines IL-6 and TNF-α, which were selected based on their previously reported associations with cognitive functioning. Our findings did not reveal any significant association between any inflammatory markers and cognition in either sample. Moreover, we found strong evidence supporting the absence of association between most inflammatory markers and cognitive function. Although one edge between TNF-α and working memory showed an estimated connection, the evidence was inconclusive, in line with the overall lack of support for inflammation-cognition associations.

In both networks, all distinct domains were clustered together, indicating that they were independent constructs interacting with each other. More specifically, networks showed that cognition nodes were highly interconnected and shared edges with some of the covariables, notably sex, age, and education. Inflammatory markers shared few connections in the networks, only with age, sex and BMI.

The consistency of relationships between inflammatory markers and cognitive functions in BSD remains highly variable in studies, and the associations, when present, are often minimal and may lack clinical significance (Mario et al., 2024; Misiak et al., 2018). In individuals with BSD, our findings contrast with prior cross-sectional research that identified such a relationship (Dickerson et al., 2013; Johnsen et al., 2016; Millett et al., 2021; Milton et al., 2021), yet align with studies that found no association between cognition and CRP (Hope et al., 2015; Hoseth et al., 2016; Hua et al., 2021), TNF-α (Lotrich et al., 2014; Zazula et al., 2022), and IL-6 (Hope et al., 2015; Hoseth et al., 2016; Lotrich et al., 2014). It also partially aligns with a recent meta-analysis that showed that blood-based immune markers are not (or only very weakly) related to cognition in mood and psychotic disorders (Morrens et al., 2022).

Various hypotheses could explain the lack of association between
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Networks displaying the evidence of edges between hsCRP, cognition and covariates (top row) and the respective strength and nature of associations (bottom row). a-b) Inclusion Bayes Factor networks where edges represent inclusion Bayes Factors. Blue edges indicate evidence supporting inclusion (BF or Odds > 10), red edges indicate evidence supporting exclusion (BF or Odds < 0.1), and grey edges indicate a lack of evidence for either inclusion or exclusion (0.1 < BF or Odds < 10). For easier reading, the evidence network was divided in two plots: a) the left plot represents edges with some degree of evidence for inclusion (BF or Odds > 1), while b) the right one represents edges with some degree of evidence for exclusion (BF or Odds < 1). c) The parameter-estimate plot shows all edges with an inclusion Bayes Factor larger than 1. Blue edges indicate positive partial correlations while dashed red edges indicate negative partial correlations. The thickness of the line indicates the strength of the relationship. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

cognition and inflammation in our samples. Firstly, inflammation was assessed through peripheral blood samples. While some research reported a correlation between peripheral and central inflammatory markers (Felger et al., 2020) in patients with MDD, a recent meta-analysis reported that most studies evaluating these associations could not find any correlation (Enache et al., 2019). These results suggest that the link between periphery and central inflammation is not straightforward. Consistently, some authors argue that inflammatory mediators may be independently secreted in both compartments (Schmidt-Morgenroth et al., 2023) and question the hypothesis that central inflammation derives from an increased peripheral inflammation in MDD (Enache et al., 2019). Secondly, association might not be present due to the cross-sectional design of the study that may not capture the link between inflammation and cognition. Indeed, in the study of Mac

Giollabhui et al., (2024) chronic inflammation was associated with cognition over time, but no significant association was observed in cross-sectional analyses. As suggested by Morrens et al., (2022), this discrepancy between studies may be due to the dimensional and dynamic nature of both processes: cognition tends to be stable and the decline tends to occur gradually, while inflammation is characterized by a high level of variability over time. Thirdly, the inconsistency of associations between inflammation and cognition could be partly attributed to the inherent biological heterogeneity of clinical populations. Indeed, diagnoses based on international classifications do not reflect the underlying pathophysiological heterogeneity (Mothi et al., 2019). Patients with the same diagnosis exhibit a variety of symptoms and underlying pathogenic mechanisms, which, when grouped into overall analyses, may obscure specific associations that could exist only within more
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Networks displaying the evidence of edges between inflammatory markers, cognition and covariates (top row) and the respective strength and nature of associations (bottom row). a-b) Inclusion Bayes Factor network where edges represent inclusion Bayes Factors. Blue edges indicate evidence supporting inclusion (BF or Odds > 10), red edges indicate evidence supporting exclusion (BF or Odds < 0.1), and grey edges indicate a lack of evidence for either inclusion or exclusion (0.1 < BF or Odds < 10). For easier reading, the evidence network was divided in two plots: a) the left plot represents edges with some degree of evidence for inclusion (BF or Odds > 1), while b) the right one represents edges with some degree of evidence for exclusion (BF or Odds < 1). c) The parameter-estimate plot shows all edges with an inclusion Bayes Factor larger than 1. Blue edges indicate positive partial correlations while dashed red edges indicate negative partial correlations. The thickness of the line indicates the strength of the relationship. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

homogeneous subgroups. Identifying homogeneous biological subgroups of individuals with BSD might unravel relationships between inflammation and cognition. Furthermore, prior research on the same cohort (Roux et al., 2017) identified several distinct cognitive profiles, with the majority of participants exhibiting normal or above-normal cognitive performance. This relatively high level of overall cognitive functioning may partly explain the lack of association observed between inflammation and cognition.

Our study extends beyond previous studies that rely on frequentist approach by employing a Bayesian framework, which facilitates the quantification of evidence ranging from the hypothesis of no relationship to the presence of a relationship (Wagenmakers et al., 2018). In addition to not observing any association between cognition and

inflammation, our results provide strong evidence of the absence of such association.

Moreover, while this is the first network analysis conducted on patients with BSD, our results are nevertheless consistent with prior network studies investigating similar relationships in healthy elderly samples that did not find a link between cognition and CRP, IL-6 (Bastelica et al., 2023; Chalmers et al., 2022) as well as TNF-α (Bastelica et al., 2023). In these previous works, the authors did not identify any connection between cognitive nodes and these inflammatory makers but could not quantify the uncertainty regarding this absence. In our larger sample, all direct hsCRP-cognition edges showed strong evidence for the absence of a relationship, further supporting the lack of association between CRP and cognition. This robustness is
underscored by the fact that our primary analysis included all partici-
pants regardless of hsCRP levels, including those with values exceeding
10 mg/L, a threshold often used to exclude participants with acute
inflammation (Felger et al., 2020; Moriarity et al., 2021). Even with this
inclusive approach that captures the full spectrum of inflammatory
states, we found strong evidence for the absence of hsCRP-cognition
associations. This demonstrates that the lack of association is not an
artifact of excluding participants with elevated inflammation, but rather
reflects a genuine absence of relationship across the entire range of
hsCRP values observed in BSD patients. Thus, our innovative approach
not only corroborates previous studies reporting no significant links
between peripheral inflammatory markers and cognition but also allows
for a more rigorous assessment of the data. In our second sample, most of
the links showed robust evidence in favor of absence of such relation-
ship. However, some edges exhibited inconclusive evidence for absence
of association between inflammatory markers and cognition, empha-
sizing the need for caution in interpreting the absence of relationship in
this context.

This study has several notable strengths. First, we used a network
analysis, a statistical approach specifically developed to uncover unique
shared associations in highly multivariate data, among a wide array of
cognitive variables and peripheral inflammatory markers. Moreover, we
used this method within the Bayesian framework which allowed us to
quantify uncertainty, facilitating more robust inferences. Second, we
used two samples, a large one including a cohort of 1565 participants
comprising measurements of hsCRP only, and a subsample drawn from
the former, with fewer participants, characterized by the concurrent
assessment of various inflammatory markers. Although employing an
independent sample would have enhanced the robustness of replicating
our findings, the consistency of our results in both samples support their
generalizability. Subsequent investigations are necessary to validate our
findings in independent samples.

Importantly, our findings should not be interpreted as a limitation
but rather as a meaningful contribution to scientific knowledge. In the
context of ongoing debates about the inflammatory hypothesis of
cognitive dysfunction in psychiatric disorders, demonstrating where
these relationships do not exist is as scientifically important as identi-
fying where they do. Our Bayesian approach provides strong evidence
that CRP, one of the most commonly studied peripheral inflammatory
markers, is not a reliable biomarker for cognitive dysfunction in BSD, at
least in cross-sectional designs. Rather than pursuing further cross-
sectional studies examining peripheral inflammatory markers and
cognition in BSD, our findings suggest researchers should redirect efforts
toward more promising avenues, such as investigating central nervous
system inflammation directly, examining longitudinal trajectories, or
identifying specific biological subgroups. Embracing such robust null
findings is essential for refining theoretical models and preventing the
perpetuation of potentially misleading research directions.

There are some limitations to our study. First, as discussed above, an
association might not be present due to the cross-sectional design of the
study that may not capture the link between inflammation and cogni-
tion. Thus, future investigations should adopt a longitudinal approach to
assess the temporal dynamics of the relationship, which may reveal
previously undetected associations. Second, while our sample might be
representative of patients in various phases of BSD, in euthymic state or
with depressive symptoms, most of our participants had none or very
few (hypo)manic symptoms. Therefore, our results might not be
generalizable to patients with actively manic episodes. Third, our sec-
ond sample size may have limited detection of weaker network con-
nections. However, our Bayesian approach provided transparent
uncertainty quantification, distinguishing edges with strong evidence
from those requiring additional investigation. Fourth, although our re-
sults do not indicate any association between inflammatory markers and
our cognitive variables, a potential role of inflammation on cognitive
functions cannot be entirely excluded. Indeed, the values for all cyto-
kines were not available or included in the network analysis due to

power issues. Especially, IL-1RA was not available although an associ-
ation between IL and 1RA and cognitive impairment was identified by
two previous systematic reviews (Mario et al., 2024; Misiak et al., 2018).

5. Conclusion

Using network analysis within a Bayesian framework, we provide
robust evidence of the absence of association between peripheral in-
flammatory markers and cognitive variables, challenging the hypothe-
sized link between inflammation and cognition in a clinical sample of
individuals with BSD. Despite utilizing a comprehensive approach and
analyzing data from a substantial cohort, we found no significant asso-
ciations between hsCRP and cognitive function. This finding held
consistent across both our primary large sample and a subsample with
concurrent IL-6, and TNF-α assessments. Our study emphasizes the
importance of using Bayesian methods to assess these relationships
rigorously and suggests further exploration of individual differences and
subgroup effects in future research.

FACE-BD Clinical Coordinating Center (Fondation FondaMental); B. Etain, E. Olié, M. Leboyer, E. Haffen and PM Llorca;

FACE-BD Data Coordinating Center (Fondation FondaMental); V. Barteau, S. Bensalem, O. Godin, H. Laouamri, and K. Souryis;

FACE-BD Clinical Sites and Principal Collaborators in France;

AP-HP, DHU PePSY, Pôle de Psychiatrie et d'Addictologie des
Hôpitaux Universitaires H Mondor, Créteil; S. Hotier, A. Pelletier, N.
Drancourt, JP. Sanchez, E. Saliou, C. Hebbache, J. Petrucci, L. Willaume
and E. Bourdin;

AP-HP, GH Saint-Louis-Lariboisière-Fernand Widal, Pôle Neurosci-
ences, Paris; F. Bellivier, M. Carminati, B. Etain, J. Maruani, E. Marlinge,
J. Meheust, and M. Meyrel;

Hôpital Charles Perrens, Centre Expert Trouble Bipolaire, Pôle de
Psychiatrie Générale et Universitaire, Bordeaux; Sébastien Gard, Claire
Elkael, Katia M'bailara, Isabelle Minois, Nathalie Da Ros, Jessica Spor-
tich and Fanny Hoorelbeke.

Département d'Urgence et Post Urgence Psychiatrique, CHRU
Montpellier, Montpellier; A. Auxilia, M. Benramdane, L. Boukhobza, P.
Courtet, B. Deffinis, D. Ducasse, M. Gachet, E. Michel, E. Olié, E. Palmeri
and G. Tarquini;

Pôle de Psychiatrie, addictologie et pédopsychiatrie, Hôpital Sainte
Marguerite, Marseille; J. M. Azorin, R. Belzeaux, N. Correard, J. L.
Consoloni, F. Groppi, L. Lescalier, M. Rebattu, N. Viglianese, I. Mur-
accioli, and A. Suray;

Centre Psychothérapique de Nancy, Pôle Hospitalo-Universitaire de
Psychiatrie d'Adultes du Grand Nancy; R. Schwan, T. Schwitzer, G.
Gross, R. Cohen, and O. Wajsbrot-Elgrabli;

Service Universitaire de Psychiatrie, CHU de Grenoble et des Alpes,
Grenoble; Bertrand A, Pouchon A, Pastol J, Fredembach B, Winiarski E,
Denoual Q, Polosan M;

Centre Hospitalier de Versailles, Service Universitaire de Psychiatrie
d'adultes et d'addictologie, Le Chesnay; L. Brehon, L. Durand, V. Feuga,
S. Frileux, P. Roux, E. Vaux;

Centre Hospitalier Princesse Grace, Monaco; V. Aubin, I. Cussac, M.A
Dupont, J. Loftus, I. Medecin Paula Martinez, and Anamaria Bogdan;

APHP, Groupe Hospitalo-universitaire AP-HP Nord, DMU ESPRIT
Service de psychiatrie et addictologie, Hôpital Louis Mourier, Colombes,
France: C. Dubertret, N. Mazer, P. Laurent.

Service de Psychiatrie de l'adulte B, Centre Expert Trouble Bipolaire,
CHU de Clermont-Ferrand, Clermont-Ferrand, France: PM. Llorca, L.
Samalin, L., M. Gambier, T. Bonnet, C. Garcin, M. Vayssié, C. Beal, and
O. Blanc.

CRediT authorship contribution statement

**Thomas Bastelica:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Formal analysis, Conceptualization.

Arnaud Carré: Writing – review & editing, Methodology,
Conceptualization. **Louis-Ferdinand Lespine:** Writing – review & editing, Methodology, Conceptualization. **Wahid Boukouaci:** Writing – review & editing, Methodology, Investigation. **Ching-Lien Wu:** Writing – review & editing, Methodology, Investigation. **Agnes Pelletier:** Writing – review & editing, Methodology, Investigation. **Bruno Etain:** Writing – review & editing, Investigation. **Frank Bellivier:** Writing – review & editing, Investigation. **Bruno Aouizerate:** Writing – review & editing, Investigation. **Paula Martinez:** Writing – review & editing, Investigation. **Caroline Dubertret:** Writing – review & editing, Investigation. **Emmanuel Haffen:** Writing – review & editing, Investigation. **Emilie Olié:** Writing – review & editing, Investigation. **Paul Roux:** Writing – review & editing, Investigation. **Mircea Polosan:** Writing – review & editing, Investigation. **Raymund Schwan:** Writing – review & editing, Investigation. **Ludovic Samalin:** Writing – review & editing, Investigation. **Pierre-Michel Llorca:** Writing – review & editing, Investigation. **Noomane Bouaziz:** Writing – review & editing, Investigation. **Antoine Lefrere:** Writing – review & editing, Investigation. **Ryad Tamouza:** Writing – review & editing, Methodology, Investigation. **Thierry d'Amato:** Writing – review & editing, Investigation. **Marion Leboyer:** Writing – review & editing, Supervision, Funding acquisition. **Ophélia Godin:** Writing – review & editing, Methodology, Formal analysis, Conceptualization. **Romain Rey:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Investigation, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This research was supported by Foundation FondaMental, Créteil, France and by the Investissements d'Avenir Programs managed by the ANR under references ANR-11--IDEX--0004--02 and ANR-10--COHO-10--01. We thank Karoline Huth for helpful comments during the revision process, particularly regarding network methodology and Bayesian estimation.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbi.2025.106164.

## Data availability

Due to ethical and legal restrictions, clinical participants' data are not publicly available. All relevant data are available upon request to the Fondation FondaMental for researchers who meet the criteria for access to confidential data.

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