IL-1α and CRP as key inflammatory mediators linking neuropathic pain, anxiety, and depression in major depressive disorder

IL-1α and CRP as key inflammatory mediators linking neuropathic pain, anxiety, and depression in major depressive disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article IL-1α and CRP as key inflammatory mediators linking neuropathic pain, anxiety, and depression in major depressive disorder Cuizhen Zhu, Lin Cheng, Jian Cheng, Qingrong Xia, Junwei Yan, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8823009/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: It’s common comorbidity for Major Depressive Disorder (MDD), anxiety, and pain, underpinned by shared chronic inflammatory processes, this study aimed to identify etiological inflammatory biomarkers, focusing on MDD individuals with anxiety and pain symptoms. Methods : This cross-sectional study enrolled 115 participants (75 MDD, 40 healthy controls). Depression and anxiety symptoms were assessed by Hamilton Depression Rating Scale-24 (HAMD-24)/ Beck Depression Inventory-II (BDI-II) and Hamilton Anxiety Rating Scale-14 (HAMA-14)/Beck Anxiety Inventory (BAI),respectively. Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2) and Prospective and Retrospective Memory Questionnaire (PRMQ) were to evaluate the pain feature and memory function respectively. Serum levels of 18 cytokines and chemokines, including interleukin-1α ( IL-1α) and C-reactive protein (CRP), were validated using Meso Scale Discovery. Data analysis included robust Ordinary Least Squares (OLS) regression, serial mediation (PROCESS models), Gaussian Graphical Models, and Gradient Boosted Regression (GBR) with SHapley Additive exPlanations (SHAP) values. Results: MDD patients displayed significantly higher IL-1α and CRP levels, greater pain sensitivity, and more cognitive impairment compared to controls. Multivariate regression confirmed a significant positive link between IL-1α and neuropathic pain ( β =0.25, p =0.014). Serial mediation models revealed a crucial indirect pathway: IL-1α did not directly cause depressive symptoms but acted sequentially: IL-1α → neuropathic pain → subjective anxiety → depressive symptoms. High CRP levels exacerbated the relationship between anxiety and neuropathic pain. GBR analysis confirmed IL-1α as the most significant feature predicting pain. Conclusions: The findings highlight IL-1α and CRP as key inflammatory mediators contributing to the interplay between pain, anxiety, and depressive symptoms. These results underscore the importance of targeting inflammation-related pathways in the assessment and treatment of pain in the MDD. major depressive disorder pain anxiety inflammatory cytokine IL-1α CRP Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The substantial co-prevalence of major depressive disorders (MDD), anxiety symptom and pain has been extensively documented, revealing a complicated relationship with significant therapeutic implications 1–4 . Epidemiological studies consistently demonstrate that depressive mood exacerbates pain perception, contributing to heightened pain severity, amplified pain-related functional impairment, and diminished treatment responsiveness in chronic pain management 1,5,6 . Notably, clinical studies revealed that 59.1% of MDD and anxiety patients report clinically significant pain manifestations 7 , while 69% of primary care patients meeting criteria for severe depression present primarily with somatic symptoms, particularly pain syndromes 8 . This complex comorbidity arises from a multidimensional interplay of neurobiological, psychological, and sociocultural factors 9 , biological mechanisms are well-established as primary etiological determinants, while a persistent divergence exists in elucidating the intricate link between various clinical symptom intersection states and etiology 10 . Mechanistically, pain and depression may be interconnected through multiple biological and psychosocial pathways, including shared risk factors such as lower educational attainment, obesity and physical inactivity, social isolation, genetic susceptibility, and dysregulation of serotonergic signaling. Notably, recent emerging evidence identifies elevated inflammatory markers correlating with both depressive severity and pain intensity. To address this incapacitating condition, tailored interventions must be developed by clarifying these pathways 11 . Inflammation is a particularly persuasive candidate mediator across pain and MDD. Increasing evidence have identified that dysregulation of the inflammatory system plays a role in mood disorders and chronic pain 12 , particularly some key pro-inflammatory cytokines. Numerous investigations have shown that a worse response to traditional antidepressants is preceded by higher levels of pro-inflammatory cytokines 13,14 . When compared to patients with reactive depression, those with refractory depression exhibit noticeably greater levels of pro-inflammatory mediators during treatment, specifically interleukin-6 (IL-6), C-reactive protein (CRP), monocyte chemoattractant protein-4 (MCP-4/CCL13), and thymus- and activation-regulated chemokines (TARC/CCL17) 15–18 . Additionally, it has previously been discovered that, following therapy, CRP in particular correlates with the severity of depression 19 . Furthermore, there are signs that lower levels of pro-inflammatory cytokines [(tumor necrosis factor -α, TNF-α), IL-1β, IL-6, and IL-17] and proteins that influence cellular transcription and gene expression lead to a decrease in depressive symptoms or increase in pain perception 20 . Although a few studies have shown that IL-1α release were be detected, particularly in the early stages of chronic pain in animal models, substantial research efforts have focused on identifying diagnostic or prognostic biomarkers for MDD and pain, while existing studies frequently fail to adequately address the dynamic nature of inflammatory mechanisms underlying their clinical comorbidity 21 - 22 . Indeed, clinical investigations examining inflammatory variables in relation to concurrent depression, anxiety, and pain have produced compelling yet sometimes conflicting findings, underscoring the constraints of methodological and conceptual interpretability. The bidirectional nature of the inflammation-mood–pain axis challenges causal inference, as inflammation may serve both as a consequence of and a contributor to mental and pain symptoms, establishing a feedback loop that current cross-sectional methods cannot effectively disentangle 23,24 . Further, although peripheral measurements of TNF-α, CRP and IL-6 are linked to central sensitization and disease, they may not adequately capture region-specific neuroinflammatory dynamics affecting mood dysregulation and pain amplification 25,26 . This raises concerns about the mechanistic significance of traditional computational methods for studying peripheral inflammatory markers in neuropsychiatric symptomatology 27–29 . Hence, the intricate link between inflammatory alterations in depression, co-morbid anxiety, and pain has not received much attention in observational clinical investigations, despite the substantial body of empirical data on inflammatory changes. Despite accumulating evidence, the mechanisms linking inflammatory abnormalities with anxiety, pain, and MDD remain poorly understood. Recent advances in psychiatric research have demonstrated that machine learning (ML) techniques, supported by increasingly large datasets, provide powerful tools to disentangle complex biological interactions. By identifying latent patterns within multidimensional data, ML approaches enable more precise prediction of potential etiological biomarkers and pathways. Building upon traditional analytical methods, this study introduced ML as an exploratory and supplementary step. This approach was used to help identify and prioritize potential etiological biomarkers in MDD patients with comorbid anxiety and pain, and to model the complex pathways through which aberrant inflammatory factors—especially IL-1α and CRP—may mediate the interactions among anxiety symptoms, pain symptoms, and affective symptoms. The potential clinical implications of these findings are also discussed. 2. Materials and Methods 2.1 Study population The current study was carried out at the Anhui Mental Health Center (AMHC) using a cross-sectional case-control design between January 2020 and December 2024. The study was authorized by AMHC's medical ethics committee. Prior to taking part in this study, each subject gave written consent in compliance with the Helsinki Declaration. The Mini International Neuropsychiatric Interview (C-MINI) 7.0.2 in Chinese was utilized by two qualified psychiatrists to assess subjects. Initially, 218 participants were screened: 62 healthy people were screened from the hospital's physical examination center, and 156 depressed inpatients and outpatients were screened from AMHC. Among healthy subjects, 3 individuals did not finish the evaluation, 13 did not give written informed consent, 3 did not meet the study criteria, and 3 withdrew their consent. Among the enrolled depressed inpatients and outpatients, 20 subjects did not finish the evaluation, 18 refused to sign written informed consent, 25 did not meet the study criteria, and 18 withdrew their consent. In the end, 115 participants were enrolled and split into two groups: a control group (40 healthy controls) and a depression group (75 MDD patients). (Figure 1). The following were the inclusion criteria for patients in the depression group (1) satisfied the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for MDD, as evaluated by two independent senior psychiatrists; (2) be between the ages of 18 and 65; (3) the total scores of Hamilton Depression Rating Scale-24 (HAMD-24)≥8. (4) the total scores of Beck Depression Inventory-II (BDI-II) ≥14. People who were deemed healthy by doctors enrolled the healthy control group, which was drawn from the physical examination center of AMHC. The following were the exclusion criteria for every participant: (1) severe somatic or craniocerebral trauma; (2) severe neurological, inflammatory, or tumor-related diseases; (3) substance abuse or other mental disorders; (4) serious physical disease; (5) pregnant or lactating women; (6) modified electroconvulsive therapy administered within three months prior to enrollment. 2.2 Assessment instruments Mini International Neuropsychiatric Interview (MINI) 7.0.2 MINI-7 is a widely used tool and known to have sound psychometric properties, it is valid and reliable with kappa values above 0.80 and 0.90, respectively, the inter-rater and test-retest reliabilities are excellent. MINI-7 is the gold standard for identifying depression 30 . Information on sex, marital status, income, work status, and educational achievement was collected using a self-reported questionnaire. Hamilton Depression Rating Scale-24 (HAMD-24) The HAMD-24 is the most widely used depression scale in the world because of its great specificity in determining the severity of depression symptoms. The HAMD-24 score can be used to define clinically meaningful symptom levels: mild depression is defined as 8-19, moderate depression as 20-34, and severe depression as ≥35. The HAMD-24 has a Cronbach's alpha of 0.88 and a κ score of 0.92 31 . Hamilton Anxiety Rating Scale-14 (HAMA -14 ) The Hamilton Anxiety Rating Scale-14 is now the industry standard and was one of the first viable and dependable tools to measure the degree of anxiety. It was released more than 50 years ago. The HAMA-14 was evaluated on a scale of 0 to 4, and general criteria for differentiating anxiety severity by stage were provided. Somatic and autonomic symptoms, respiratory and other bodily strain, and emotional anxiety, including concern and fear, are all included in the items 32 . Beck Depression Inventory-II (BDI-II) The 21-item Beck Depression Inventory-II is a self-report tool used to gauge how severe depression symptoms are. The overall score ranges from 0 to 63, with higher scores denoting more severe depressive symptoms. Each item is evaluated from 0 to 3 according to general guidelines. Coefficient of BDI-II is Cronbach's alpha, which has a solid internal consistency and outstanding validity with an internal consistency of 0.83 33 . Beck Anxiety Inventory (BAI) The 21-item Beck Anxiety Inventory is a self-report tool used to gauge the severity of anxiety symptoms. The overall score goes from 21 to 84, with higher scores denoting more severe anxiety symptoms. Each item is evaluated from 1 to 4 according to general norms. When it comes to anxiety disorders, BAI has outstanding discriminant validity and great internal consistency (Cronbach's alpha = 0.94) 34 . Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2) There are 22 pain descriptors and four subscales in the SF-MPQ-2, which is a self-rated scale that rates pain intensity on a scale of 0 to 10 (0 being no pain and 10 being the worst pain). Cronbach's alpha for the subscales ranges from 0.896 to 0.916, indicating its high reliability and validity. With values of 0.909, 0.973, 0.988, 0.952, and 0.927 for the complete scale and the continuous, intermittent, mostly neuropathic, and affective subscales, respectively, the test-retest results showed high reliability 35 . Prospective and Retrospective Memory Questionnaire (PRMQ) The PRMQ is a 16-item test used to assess memory loss in daily living. The PRMQ questions ask about PM in half of the cases and RM in the other half. A reliable method for assessing PM and RM deficits in gender and related age groups is the PRMQ 36 . Laboratory evaluation Following an overnight fast, venous blood samples (5 ml) were taken from each participant in the morning. Immediately, samples were forwarded to the Clinical Laboratory Department. The plasma was separated using centrifugation at 5 °C and the isolated plasma was frozen at −80◦C for further study. The MSD Platform (labservice. univ-bio.com, Shanghai, China) was used to detect 18 cytokines, namely, interleukin (IL)-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, L-12/IL-23p408, IL-13, IL-15, IL-16, IL-17A, CRP, interferon (IFN)-γ, TNF-α, TNF-β and Fms-like tyrosine kinase 1(Flt-1). The plates were analyzed with Meso Scale Diagnostics (MSD) sector 2100 A, and the data were evaluated using MSD Discovery Workbench 4.0 software. Every standard and plasma sample was examined twice, and the inflammatory cytokine detection limit was 0.01 pg/ml. 2.3 Statistical analyses The data were analyzed using the Python (version 3.12) and related scientific computing packages were used for data analysis and visualization in this work. Firstly, the main analysis dataset and the sensitivity analysis dataset were constructed independently in accordance with the research design, and the latter was subjected to sensitivity analysis. The former was transformed using the Yeo-Johnson + Z-score method, with residual heteroskedasticity using HC3 robust standard errors and sensitivity tests for high leverage points. Ordinary Least Squares (OLS) was used to build multiple linear regression models in order to investigate the relationship between the dependent variable and several independent factors. Utilizing statsmodels (version 0.14.2) and matplotlib (version 1.2.1), the regression models were subjected to standard diagnostic checks normality of residuals, influential points analysis and multicollinearity diagnostics. The two tandem mediating variables in PROCESS were utilized to evaluate if the independent variable had an indirect effect on the dependent variable using the Model 6 chain-mediated mode. To confirm the impact of the independent variable on the dependent variable and whether this effect was mediated by the serial mediation of the two chain-mediated variables, the PROCESS Model 59 moderated serial mediation model was employed. It also focuses on whether other variables have a role in moderating the two courses of serial variables. Futhermore, investigating intricate network connections between inflammation, pain, and mood measures using a Gaussian Graphical Model. In order to determine whether the primary predictor variables had a serially mediated effect on depression through pain and anxiety, a gradient boosted regression algorithm (GBR) was employed. Additionally, the contribution of each feature to the prediction was quantified using SHAP (SHapley Additive exPlanations) value interpretation, via Partial Dependence Plots (PDP). Verified were the minor impacts of the primary features on the model's predictions. The significance level was set at p <0.05 for each statistical test. 3. Results Comparison of demographic characteristics, pain intensity, memory function, and inflammation levels between depression and control groups Demographic characteristics, depression severity, pain intensity, memory performance, and inflammatory biomarkers for both groups are summarized in Supplementary Table1 and Figure 1. A total of 115 individuals were included in the final analyses. We recruited 75 MDD patients (47 females and 28 males) and 40 healthy controls (20 males and 20 females). Age, years of education, employment status, marital status, family patterns and income level did not significantly differ across the groups (p>0.05). The total scores of SF-MPQ-2 scale explored significantly higher in MDD group (Mdn = 26, IQR = 12-44) than healthy group (Mdn = 2.5, IQR = 0-5), U = 368, p ＜0.0001, with a medium effect size ( r = 0.32). As well as continuous pain subscale, intermittent pain subscale, neuropathic pain subscale and affective subscale presented significantly higher in MDD group (Mdn = 12, IQR = 6.5-19.5; Mdn = 0, IQR = 0-6; Mdn = 3, IQR = 0-7; Mdn = 8, IQR = 3.5-12.5) than the healthy group (Mdn = 0, IQR = 0-3; Mdn = 0, IQR = 0-0; Mdn = 0, IQR = 0-0; Mdn = 0, IQR = 0-0.25 ; U = 417.5, p ＜0.0001; U = 1016, p = 0 . 005; U = 800, p ＜0.0001; U = 445.5, p ＜0.0001). Moreover, memory assessment revealed significantly higher scores in the MDD group compared to the normal control group for both retrospective memory (Mdn = 30, IQR = 25.75–33 vs. Mdn = 25, IQR = 20–30; U = 1986.5, p = 0.02) and the PRMQ total score (56.52 ± 9.79 vs. 48.80 ± 15.47; t = 2.86, p =0.02). In contrast, prospective memory scores showed no significant group differences (27.38 ± 5.31 vs. 24.25 ± 8.10, t = 2.19, p = 0.08). Finally, analysis of inflammatory factors revealed significantly higher expression of CRP (Mdn = 1340864.45, IQR = 933604.76 – 1893308.92 vs. Mdn = 309308.17, IQR = 165344.84 – 945044.06; U = 2344 , p ＜0.0001 )and IL-1α (Mdn = 0.31, IQR = 0.20 – 0.55 vs. Mdn = 0.50 , IQR = 0.31 – 2.61; U = 1072 , p = 0.04) in patients with depression compared to controls, while no significant differences were observed in the remaining inflammatory factors ( p ＞ 0.05). The results represent in Figure 2. Correlation among chronic inflammation levels, cognitive decline, pain intensity, and clinical symptoms In this cohort study, we investigated the association among chronic inflammation levels, cognitive decline, pain symptoms and clinical symptoms, to determine the relationship among these characteristics, we analyzed various assessment data using Spearman’s correlation analysis. According to the findings, there are notable positive connections between the total score of SF-MPQ-2 and the scales of BDI and BAI, Specifically, subjective anxiety subscale of BAI was positively correlated with continuous pain( r s = 0.456, p < 0.001), intermittent pain( r s = 0.456, p < 0.001), neuropathic pain( r s = 0.521, p < 0.001), affective subscale( r s = 0.522, p < 0.001), SF-MPQ-2 total score ( r s = 0.5871, p < 0.001), BDI total score ( r s = 0.555, p < 0.001), HAMD total score ( r s = 0.435, p = 0.001). Moreover, neuropathic pain subscale of SF-MPQ-2 showed correlated positively with BDI total score ( r s = 0.312, p = 0.006), BAI total score ( r s = 0.542, p < 0.001) and HAMD total score ( r s =0.25, p = 0.034). Conversely, PRMQ total score exhibited negative correlations with subjective anxiety ( r s = - 0.390, p < 0.001), BAI total score ( r s = - 0.489, p < 0.001), BDI total score ( r s = - 0.303, p = 0.008), HAMD total score ( r s = - 0.277, p = 0.016). Similar patterns was found for the retrospective memory (a subscale of PRMQ). Finally, IL-1α was detected correlation positively with subjective anxiety ( r s = 0.309, p = 0.006), BAI total score ( r s = 0.327, p = 0.004), neuropathic pain ( r s = 0.264, p = 0.0219) and HAMD total score ( r s = 0.239, p = 0.038), while CRP correlated negative with core anxiety ( r s = - 0.274, p = 0.017) and cognitive/depression( r s = - 0.243, p = 0.035). The results represent in the Figure 3. Results of correlation between n europathic pain with clinical characteristics To further investigate the relationship between pain and clinical characteristics, multivariate linear regression models were constructed using OLS, incorporating the core predictor variables IL-1α, age, BMI, gender, personality_1, and personality_2 as independent variables. The models underwent parameter estimation and hypothesis testing using Heteroscedasticity-Consistent 3 (HC3) robust standard errors in order to address any potential heteroscedasticity. The Variance Inflation Factor (VIF) was used to measure multicollinearity between the independent variables, a VIF > 5 is typically regarded as the threshold for the existence of possible covariance. Results are shown in Supplementary Figure 1 and 2. The primary regression findings are displayed using partial regression plots, which display the partial correlation of a specific independent variable with the dependent variable after adjusting for other variables, and forest plots, which display the regression coefficients and their 95% CIs. There were clear correlations with neuropathic pain levels, according to multivariable analysis. IL-1α revealed a significant positive link with neuropathic pain [ β = 0.25, 95% CI (0.05, 0.46), p = 0.014], whereas age revealed a significant inverse relationship with neuropathic pain [ β = -0.29, 95% CI (-0.53 to -0.04), p = 0.021)]. In contrast, all confidence intervals for personality traits (reference categories 1 and 2), gender, and BMI did not approach statistical significance in the current model ( p > 0.05 for all). Results are shown in Figure 3. Chained mediation model to explore the relationship between IL-1α and depressive symptoms To further examine the connection between IL-1α and depressive symptoms, this study utilized a chained mediation analysis (PROCESS model 6) 37 to investigate the indirect effect of IL-1α (independent variable, X) on total scores of the HAMD (dependent variable, Y) via the sequential mediators of neuropathic pain (N.Pain; M1) and subjective anxiety (S.Anxiety; M2). First, we looked at three different mediation routes using a 5,000-sample bootstrap analysis 38 , all analyses were controlled for confounders such as age, body mass index (BMI), gender, and two personality traits (personality_1 and personality_2) (Figure 4A), the results presented N.Pain as a single mediator (M1): Through N.Pain, IL-1α (X) had an indirect influence on HAMD ratings (Y) that was not statistically significant (effect = 0.043, 95% CI [-0.011 to 0.202]). S.Anxiety alone as a single mediation (M2): This pathway similarly showed non-significant mediation (effect = 0.051, 95% CI [-0.006 to 0.147]). Chained mediation (M1→M2): Interestingly, a substantial indirect effect was found along the sequential pathway of subjective anxiety and N.Pain (effect = 0.042, 95% CI [0.004 to 0.098]). Second, the chained mediation model's key findings showed clear predictive patterns, as shown in Figure 4B. These findings include: The X-M1 pathway showed that IL-1α significantly predicted N.Pain in a positive manner ( a₁ =0.25, p =0.025). X-M2 pathway: IL-1α and S.Anxiety did not directly correlate ( a₂ =0.17, p =0.100). M1→M2 pathway: Subsequent S.Anxiety was significantly predicted by N.Pain ( d₁ =0.55, p <0.001). M1→Y pathway: N.Pain did not directly correlate with HAMD total scores after controlling for variables ( b₁ =0.17, p =0.227). M2→Y pathway: Higher HAMD scores were strongly predicted by S.Anxiety ( b₂ =0.30, p =0.029). Interestingly, after adjusting for both mediators, the direct pathway (c') from IL-1α to HAMD scores was no longer significant (c'=0.07, p=0.533). Moderated serial mediation model to explore the relationship between IL-1α and depressive symptoms To examine the impact of IL-1α (X) on depressive symptoms (HAMD total score, Y), we used a moderated sequential mediation model (PROCESS Model 59) 37 . We investigated whether S.Anxiety (M2) and N.Pain (M1) progressively mediated this effect. In particular we evaluated if the transition from N.Pain to S.Anxiety (M1 → M2) was modulated by CRP (W). Furthermore, we used the Johnson-Neyman (J-N) technique to determine the precise low, medium, and high CRP concentration ranges that affect the association between N.Pain (M1) and S.Anxiety (M2) in order to more clearly define the limits of CRP's moderating effects. The results showed that the effects of IL-1α on depression severity had non-significant simple mediation pathways, meanwhile, there was no statistically significant difference between the S.Anxiety-mediated pathway (IL-1α→S.Anxiety→HAMD; effect=0.049, 95% CI [-0.014, 0.140]) and the N.Pain-mediated pathway (IL-1α→N.Pain→HAMD; effect = 0.052, 95% CI [-0.023, 0.176]). Nonetheless, the sequential mediation pathway through S.Anxiety and N.Pain (IL-1α→N.Pain→S.Anxiety→HAMD) showed relevance that was reliant on CRP: Conditional indirect impact = 0.031 (95% CI [0.001, 0.079]) low CRP (mean -1SD). Conditional indirect impact = 0.041 (95% CI [0.001, 0.097]) indicates a moderate CRP (mean). Conditional indirect impact = 0.051 (95% CI [0.002, 0.126]) high CRP (mean +1SD). According to this dose-response pattern, the sequential pathway remained statistically detectable across the whole range of CRP levels, and the mediated effect magnitude demonstrated a positive correlation with CRP concentrations (β=+0.020 per SD increase)(Figure 4C ). Figure 4C employs a J-N plot to examine how N.Pain conditional effect on S. Anxiety varies across centered CRP values. The analysis reveals a critical threshold at -1.498 for centered CRP, where the curve represents conditional effect estimates and the shaded region denotes 95% confidence intervals. Results demonstrate a statistically significant positive association (entire CI above zero) when centered CRP exceeds -1.498, while this relationship becomes non-significant below this threshold. These findings indicate that N.Pain's anxiety-inducing effects manifest specifically in individuals with CRP levels above this biological cutoff point, highlighting CRP's role as a potential inflammatory moderator in pain-anxiety pathophysiology. The study's Model 59 pathway is schematically diagrammed in Figure 4F, which makes it evident how the variables (IL-1α, N.Pain, S.Anxiety, HAMD, and CRP) and their related pathway coefficients (β-values) relate to each other and to the significant levels. As depicted in the figure: asterisks denote statistical significance, and dashed lines show moderating effects. a₁ (IL-1α → N.Pain): 0.26*, a₃ (N.Pain → S.Anxiety): 0.57***, a₄ (N.Pain × CRP → S.Anxiety): 0.15, b₂ (S.Anxiety → HAMD): 0.30*, and c' (IL-1α → HAMD): 0.07(* p < 0.05, ** p < 0.01, *** p < 0.001). Network complex interrelationships among pain, mood and inflammation indicators The intricate linkages between the indicators of pain, mood, and inflammation as well as the conditional dependencies between the variables were further investigated using network analysis. The findings revealed intricate network connections between pain, CRP, IL-1α, and other clinical indicators. For instance, IL-1α (partial correlation coefficient = 0.231) and pain and anxiety (partial correlation coefficient = 0.806) were significantly positively correlated (Figure 5A). Figure 5B displays the results of additional J-N analyses of the moderating effect of CRP on the pain-anxiety relationship. The vertical axis represents the effect size of pain on anxiety (simple slope), while the solid blue line represents the point estimate of the simple slope. The light blue shaded area represents the 95% confidence intervals. The horizontal axis represents the centred CRP level (SD units). The J-N significant transition point (red dashed line) and the mean CRP and ±1 SD are indicated. The findings show that when center CRP values are between -1.763 and 2.992, the positive predictive effect of N.Pain on S.Anxiety is statistically significant (i.e., the 95% CI lies entirely above 0). Additionally, this suggests that N.Pain only significantly raises S.Anxiety levels when a person's CRP level is above a particular threshold. Sliding window investigation of how CRP levels dynamically alter the centrality of intensity and centrality of expected effects of four important nodes (IL-1α, Pain, Anxiety, and Depression). A fixed-size window (50 percent of the sample size) was moved over the data sorted by CRP in order to build networks and determine centrality for each window of data. Each window's central CRP value is shown on the horizontal axis, while the matching centrality measure is shown on the vertical axis. The interactions between key node centrality and CRP level are shown by the sliding window analysis. For instance, the centrality of the expected impact of the Anxiety and Depression nodes tended to be at a high level, indicating that the physiological inflammatory state (CRP levels) may have an impact on the relative importance of the nodes in the network. The intensity of the Pain node, on the other hand, reached its highest expected impact between CRP values of -0.42 and -0.22, and decreased with increasing CRP values. Based on this finding, it is possible that physiological inflammatory states (CRP levels) could affect the relative relevance of nodes in the network. Results are shown in Figure 5C-D. A machine learning approach to explore the sequence-mediated effects of IL-1α on depression via pain and anxiety (regulated by CRP) Lastly, the sequence-mediated effects of IL-1α on depression through pain and anxiety (controlled by CRP) were investigated using a GBR technique. By computing each feature's average absolute SHAP value, the global contribution of each feature to the model predictions is determined. The more significant the characteristic, the longer the bar. Figure 6A Findings displayed (IL-1α → Pain): With the highest mean absolute SHAP value, IL-1α was the most significant feature predicting pain (A3), followed by variables like age and BMI. According to Partial Dependency Plot (PDP) results (A2) , IL-1α levels generally showed a positive trend with predicted pain levels. Predicted pain levels were relatively low during periods of low IL-1α values and increased as IL-1α levels increased, with a curve pattern that might indicate a non-linear relationship. A1: Actual versus anticipated values in a scatterplot. The findings demonstrate that the scatter points are primarily evenly spaced around the diagonal line, suggesting that there is a strong correspondence between the actual pain values (Pain) and the model-predicted values (IL-1α) (R² = 0.795, MSE = 0.205). Furthermore, according to the results in Figure 6 B, Pain_hat is the most significant factor in predicting anxiety, followed by variables like age and BMI (B3). The overall trend between Pain_hat levels and anticipated anxiety levels was positive, according to PDP data, there is a correlation between greater expected anxiety levels and higher predicted pain levels (B2), and the curve pattern may not be linear. The model predicted anxiety more well (R 2 = 0.832, MSE = 0.168), and the scatter points also had a decent clustering pattern (B1: scatterplot of real vs. predicted values). Lastly, Figure 6 C results indicate (Anxiety_hat → Depression): The most significant characteristic in predicting depression is anxiety_hat, which is followed by variables like age and BMI (C3). According to PDP results, anxiety_hat levels and anticipated depression levels generally showed a positive trend, with greater predicted anxiety levels being linked to higher predicted depression levels (C2). The scatter distribution in C1: Actual vs. Predicted scatterplot findings shows that the model also predicted sadness at a good level (R 2 = 0.795, MSE = 0.205). 4. Discussion This study seeks to find biomarkers linked to the pathophysiology of MDD linked to inflammation, with an emphasis on instances that co-occur with anxiety disorders and persistent pain symptoms. Our results demonstrated that, compared with healthy controls, participants with depression exhibited markedly heightened pain sensitivity, greater memory impairment, and significantly higher CRP and IL-1α in MDD than controls. Additionally, we found the IL-1α correlated positively with somatic symptoms, S.Anxiety and BAI total scores. Using chained mediation and moderated serial mediation models, we found IL-1α does not act directly on depressive symptoms, but rather via the IL-1α→N.Pain→S.Anxiety→HAMD pathway. It was also shown that N.Pain significantly increased S.Anxiety levels when CRP levels exceeded a specific threshold. Finally, GBR approach confirmed the sequence-mediated effects of IL-1α on depression via pain and anxiety, with CRP serving as regulatory factor. The bidirectional association between pain and depression has been well-established. Chronic pain is a significant risk factor for MDD, and MDD often manifests with somatic complaints, including pain 39,40 . Furthermore, extensive research has demonstrated that the interconnection between anxiety symptoms, impaired pain perception, and cognitive dysfunction in patients with depression constitutes a complex clinical challenge, stemming from intricate neurobiological mechanisms 41–43 . In line with these findings, our study further validates that individuals with MDD exhibit altered pain perception compared to healthy controls. Specifically, depressed patients demonstrate more severe impairments in both neuropathic and affective pain dimensions. We also observed a negative correlation between N.Pain and age, indicating that younger individuals are more sensitive to pain 4 . Consequently, these results underscore the importance of giving special consideration to the somatic complaints of younger depressed patients. The present study reaffirms that anxiety is linked to heightened pain perception and reduced pain tolerance, and that elevated anxiety ultimately contributes to a depressed mood. In this context, while depressed mood may occasionally lower the initial sensory threshold for pain, anxiety more consistently amplifies the affective and cognitive dimensions of pain, particularly the affective component of 'slow pain' and burning sensations 44 . Compared with the extensive literature on IL-1β in depression and anxiety, research on IL-1α remains limited. Nevertheless, recent evidence points to an important role of IL-1α in MDD-related neuroinflammation 45 . Evidence notes that IL-1α, alongside IL-1β, is elevated in the peripheral blood and cerebrospinal fluid (CSF) of MDD patients, correlating with depressive symptoms such as anhedonia and fatigue 46 . This is likely due to IL-1α driven role in activating the hypothalamic-pituitary-adrenal (HPA) axis and amygdala circuits, thereby promoting stress-related neuroinflammation 47,48 . Preclinical studies show that the pharmacological blockage of IL-1α signaling with an IL-1 receptor antagonists reduces anxiety-like behaviors in chronically stressed mice, implying a potential causal role 48 . There are few human studies on IL-1α and anxiety, and most evidence is extrapolated from IL-1β or from analyses pooling IL-1α/β effects. Unlike IL-6 and CRP 49 , there was no significant IL-1α-specific correlation with anxiety symptoms in the 2021 UK Biobank project 50 . Correspondingly, our mediational analyses showed that IL-1α did not exert a direct effect on depressive symptoms, instead, its influence was transmitted via neuropathic pain, which in turn increased somatic anxiety and elevated HAMD scores. Research on pain models (such as fibromyalgia and arthritis) revealed that IL-1α was up-regulated in the spinal cord and dorsal root ganglia, causing central sensitization 51 . This suggests that IL-1α is linked to inflammatory and neuropathic pain, which causes nociceptors to become sensitized and pain signals to be amplified. This is consistent with the bidirectional relationship between depression and pain, indicating that IL-1α may worsen these two conditions. CRP is an acute-phase protein produced by the liver in response to pro-inflammatory cytokines (e.g., IL-6, IL-1α/β, TNF-α) and is a widely used as a non-specific biomarker of systemic inflammation 52 . Its levels rise in the blood when there is an inflammatory process occurring in the body. Elevated CRP levels are frequently observed in psychiatric and pain conditions associated with inflammation, particularly treatment-resistant depression and depression with somatic symptoms 49 . Similarly, in anxiety disorders, including generalized anxiety disorder（GAD）and chronic pain, elevated CRP has also been reported 53 . Research on CRP is currently somewhat varied, nevertheless, with some studies showing a lesser correlation with anxiety than with depression. According to the 2021 UK Biobank on depression and anxiety study, CRP was linked to some anxiety symptoms (such as irritation, OR = 1.06 and excessive worrying, OR = 1.03), but its impact was less pronounced than that of depression 50 . Moreover, some studies showed that the CRP-anxiety relationship attenuates after adjusting for BMI and other confounders, highlighting the influence of comorbid factors such as obesity 54 . Elevated CRP is also a hallmark of chronic pain conditions (e.g., rheumatoid arthritis, fibromyalgia), correlating with pain severity and inflammation. Even though the findings of current study did not directly link CRP to anxiety or depression symptoms, moderated serial mediation analyses revealed significant conditional indirect effects across low/mean/high CRP, supporting the interpretation that CRP indexes systemic inflammation driven by upstream cytokines such as IL-1α, which sensitize pain pathways and amplify neuroinflammatory responses, thereby linking pain to depressive outcomes. The interpretation of these results should take into account several limitations. First, this cross-sectional study relied on subjective symptom ratings, which constrains causal interpretation, biological experiments and longitudinal designs are needed to verify the mediating role of inflammatory markers in the pain-anxiety-depression relationship, and to test whether interventions targeting these pathways can reduce anxiety and depression risk attributable to pain. Second, the small sample size may limit the statistical power to detect between-group differences. In particular, given the heterogeneity in the clinical presentation of MDD patients (e.g. with or without comorbid pain), the sample was insufficient to support further subgroup stratification analyses. Third, this study was limited by the inclusion of only 18 cytokines; therefore, future research should incorporate a broader range and number of inflammatory factors and chemokines to gain a more comprehensive understanding of their relationship with MDD. Notwithstanding these drawbacks, this research offers fresh perspectives on the regulatory role of CRP in this intricate scenario as well as the IL-1α-mediated depressive pathways that result in anxiety prompted by pain symptoms. These results could potentially improve clinical outcomes by optimizing therapy strategies for inflammatory variables in pain in individuals with depression. Taken together, these findings delineate a pathway linking pain, anxiety and depression in which IL-1α functions as a driver and CRP as a modulator of inflammatory coupling between pain and mood, suggesting biomarker-guided targets for precision interventions in depression with pain. Prospective and experimental studies are needed to establish causality and therapeutic leverage. Declarations Acknowledgements We express our sincere gratitude to all the participants who participated in our research. Funding statement This study was supported by funding from Anhui Medical University Youth Fund Project (grant number:2023xkj114). Fund of the Fourth People's Hospital of Hefei City, Anhui Province (grant number:HFSY202101). National Clinical Key Specialty Construction Project of China (grant number: NO). Development Fund for Key Laboratory of Philosophy and Social Science in Anhui Province (grant number: SYS2023C06). Hefei City Innovative Procurement Project (grant number: NO). The funding sources were not involved in the study design, data collection, data analysis, drafting the paper, or the decision to publish the paper. Ethics approval and consent to participate The study was approved by the Medical Ethics Committee of the Anhui Mental Health Centre (AMHC). All participants provided written consent prior to study participation in accordance with the principles of the Declaration of Helsinki. The trial clinical registration number was ChiCTR2000029917. Data availability statement The data that support the findings of this study, as well as the used materials, are available from the corresponding author upon reasonable request. Author Contribution C. Z, P.J and H.G were responsible for study design and manuscript editing. C. Z was responsible for literature searches, and manuscript writing. J.C and L.C were responsible for statistical analyses, prepared figures 1-6 and all supplementary figures and tables. Q. X, J. Y, C. L, M. Z, J.Y, J.G and L.Z were responsible for clinical-scale assessment and blood data collection. The submission has had all of the authors’ approval. All clinical studies described in the manuscript were carried out following the Declaration of Helsinki promulgated by the National Institute of Health. The work presented here has not been published previously, nor is it being considered for publication elsewhere. We hope that this manuscript would be suitable for publication in BMC psychiatry. Corresponding author: Peijun Ju and Hua Gao. References Sternke EA, Abrahamson K, Bair MJ. Comorbid Chronic Pain and Depression: Patient Perspectives on Empathy. Pain Manage Nurs. 2016;17(6):363–71. 10.1016/j.pmn.2016.07.003 . Zis P, Daskalaki A, Bountouni I, Sykioti P, Varrassi G, Paladini A. Depression and chronic pain in the elderly: links and management challenges. CIA. 2017;12:709–20. 10.2147/CIA.S113576 . Bair MJ, Robinson RL, Katon W, Kroenke K. 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Supplementary Files SupplementaryTable1.docx SupplementaryFigure1.eps SupplementaryFigure2.eps SupplementaryFigureNote.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviews received at journal 16 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Editor assigned by journal 16 Feb, 2026 Submission checks completed at journal 16 Feb, 2026 First submitted to journal 08 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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16:23:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8823009/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8823009/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104179318,"identity":"d9c51459-c39d-4e25-b934-78d63e3964bc","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":664655,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/3a9e1ee08d335d8c1c4590fd.png"},{"id":104179317,"identity":"eb551731-fdd7-4500-adac-85894edc96e9","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121310,"visible":true,"origin":"","legend":"\u003cp\u003eThe comparison of demographic characteristics, pain intensity, memory function, and inflammation levels between depression and control groups\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSF-MPQ-2\u003c/strong\u003e: Short-Form Mcgill Pain Questionnaire-2; PRMQ: Prospective And Retrospective Memory Questionnaire; CRP: C-reactive protein; IL: interleukin, TNF: tumor necrosis factor; FDR: False Discovery Rate.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/13ced943b9d36e0d65eb5984.png"},{"id":104179314,"identity":"15b9dc70-954d-4b29-868d-68c63e90c001","added_by":"auto","created_at":"2026-03-08 17:03:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":105676,"visible":true,"origin":"","legend":"\u003cp\u003eThe Spearman’s correlation analysis and the multiple linear regression analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003eIn MDD group, the relationship between chronic inflammation levels, cognitive decline, pain intensity and clinical symptoms. \u003cstrong\u003eRed indicates positive correlations, while blue represents negative correlations. Darker colors signify stronger correlation magnitudes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e The forest plot and the partial regression plots (\u003cstrong\u003eB1\u003c/strong\u003e), IL-1α display the significant positive link with neuropathic pain (\u003cstrong\u003eB2\u003c/strong\u003e) and age display the significant negative link with neuropathic pain (\u003cstrong\u003eB3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSF-MPQ-2: \u003c/strong\u003eShort-Form Mcgill Pain Questionnaire-2; \u003cstrong\u003ePRMQ\u003c/strong\u003e: Prospective And Retrospective Memory Questionnaire; \u003cstrong\u003eBDI\u003c/strong\u003e: Beck Depression Inventory; \u003cstrong\u003eBAI\u003c/strong\u003e: Beck Anxiety Inventory; \u003cstrong\u003eHAMD\u003c/strong\u003e: Hamilton Depression Rating Scale; \u003cstrong\u003eHAMA\u003c/strong\u003e: Hamilton Anxiety Rating Scale; \u003cstrong\u003eCRP\u003c/strong\u003e: C-reactive protein; \u003cstrong\u003eIL\u003c/strong\u003e: interleukin; * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/8b4f0649c87ebd7047fa445c.png"},{"id":104179321,"identity":"8cd1aae5-36c4-43b6-b1e9-3e0a75bbfa13","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85803,"visible":true,"origin":"","legend":"\u003cp\u003eThe chained mediation model and moderated serial mediation model to explore the relationship between IL-1α and depressive symptoms (PROCESS model 6 and model 59)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e The IL-1α played a positive indirect role of HAMD scores via neuropathic pain → subject anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e The diagrammatic of the chained mediation model (model 6)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e The sequential mediation pathway through subjective anxiety and neuropathic pain (IL-1α→N.Pain→S.Anxiety→HAMD) showed relevance that was reliant on different CRP levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e A\u003cstrong\u003e Johnson-Neyman plot which examined the N. Pain conditional act on the S. Anxiety varies across centered CRP values, the curve represents conditional effect estimates and the shaded region denotes 95% confidence intervals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e Under different centered CRP levels, the relationship between N.Pain and S.Anxiety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e The diagrammatic of the moderated serial mediation model, \u003cstrong\u003edashed lines show moderating effects (model 59).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL\u003c/strong\u003e: interleukin; \u003cstrong\u003eHAMD\u003c/strong\u003e: Hamilton Depression Rating Scale; \u003cstrong\u003eN.Pain\u003c/strong\u003e: neuropathic pain; \u003cstrong\u003eS.Anxiety\u003c/strong\u003e: subjective anxiety; \u003cstrong\u003eCRP\u003c/strong\u003e: C-reactive protein; * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/bbf5411e74cb2eb446972516.png"},{"id":104179315,"identity":"3ad258ba-cea4-418f-9b8a-18d450f70ae5","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":74933,"visible":true,"origin":"","legend":"\u003cp\u003eThe network complex interrelationships among pain, mood and inflammation indicators\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e A GGM network. Nodes represent study variables, with their size proportional to strength centrality. Node colors are mapped according to strength centrality values. Edges denote partial correlation coefficients between variables, where blue edges indicate positive correlations and orange-red edges represent negative correlations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e The moderating effect of varying CRP levels on the strength of the association between Pain and Anxiety. The red dashed vertical line marks the Johnson-Neyman (J-N) significance transition point.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC-D\u003c/strong\u003e The strength centrality (\u003cstrong\u003eC\u003c/strong\u003e) and expected influence centrality (\u003cstrong\u003eD\u003c/strong\u003e) of four key nodes (IL-1α, Pain, Anxiety, Depress) has dynamically vary across different CRP levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGGM:\u003c/strong\u003e Gaussian Graphical Model; \u003cstrong\u003ePRMQ\u003c/strong\u003e: Prospective and Retrospective Memory Questionnaire; \u003cstrong\u003ePain tot\u003c/strong\u003e: the total score of Short-Form Mcgill Pain Questionnaire-2; \u003cstrong\u003eSom Sym\u003c/strong\u003e: somatic symptoms; \u003cstrong\u003eAnx Aff\u003c/strong\u003e: anxiety-affective factor; \u003cstrong\u003eCog Aff\u003c/strong\u003e: cognitive-affective factor; \u003cstrong\u003eCog Dep\u003c/strong\u003e: cognitive/depression; \u003cstrong\u003eSom Fac\u003c/strong\u003e: somatic factor; \u003cstrong\u003eIL 1\u003c/strong\u003e: interleukin 1α; \u003cstrong\u003eN. pain\u003c/strong\u003e: neuropathic pain; \u003cstrong\u003eS.Anxiety\u003c/strong\u003e: subjective anxiety; \u003cstrong\u003eHAMD\u003c/strong\u003e: Hamilton Depression Rating Scale; \u003cstrong\u003eCRP\u003c/strong\u003e: C-reactive protein; * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/f3f181354a2bc4f7da25dbd8.png"},{"id":104179322,"identity":"0b2c45d5-78e6-4cac-9673-b4239501f0f2","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":97919,"visible":true,"origin":"","legend":"\u003cp\u003eThe machine learning approach to explore the sequence-mediated effects of IL-1α on depression via pain and anxiety (regulated by CRP)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e IL-1α was the most significant feature predicting pain. \u003cstrong\u003eActual versus anticipated values in a scatterplot\u003c/strong\u003e (\u003cstrong\u003eA1\u003c/strong\u003e).\u003cstrong\u003eThe relationship between IL-\u003c/strong\u003e1α and pain_hat (\u003cstrong\u003eA2\u003c/strong\u003e). Each feature's mean absolute SHAP value, the longer bar, the more significant characteristic\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA3\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003ePain_hat was the most significant feature predicting anxiety. \u003cstrong\u003eActual versus anticipated values in a scatterplot\u003c/strong\u003e (\u003cstrong\u003eB1\u003c/strong\u003e)\u003cstrong\u003e. The relationship between pain_hat \u003c/strong\u003eand anxiety_hat (\u003cstrong\u003eB2\u003c/strong\u003e). Each feature's mean absolute SHAP value, the longer bar, the more significant characteristic\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eB3\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC \u003c/strong\u003eAnxiety_hat is the most important feature for predicting depression. \u003cstrong\u003eActual versus anticipated values in a scatterplot\u003c/strong\u003e (\u003cstrong\u003eC1\u003c/strong\u003e)\u003cstrong\u003e. The relationship between anxiety_hat \u003c/strong\u003eand HAMD total score (\u003cstrong\u003eC2\u003c/strong\u003e). Each feature's mean absolute SHAP value, the longer bar, the more significant characteristic\u003cstrong\u003e (C3\u003c/strong\u003e)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIL: \u003c/strong\u003einterleukin; \u003cstrong\u003eCRP\u003c/strong\u003e: C-reactive protein; \u003cstrong\u003eSHAP: \u003c/strong\u003e\u003cem\u003eSHapley Additive exPlanations; \u003c/em\u003e\u003cstrong\u003eHAMD\u003c/strong\u003e: Hamilton Depression Rating Scale; * \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; ** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/232e64dfcf7c25491bccc7de.png"},{"id":104785390,"identity":"7495413e-4fbf-4ad6-b228-caca5f236cc2","added_by":"auto","created_at":"2026-03-17 08:11:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2594695,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/04e0c394-3705-454a-8693-25da7e8f0993.pdf"},{"id":104179313,"identity":"34addbb3-a39b-4538-a4e6-2a3c1510b78c","added_by":"auto","created_at":"2026-03-08 17:03:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20326,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/ebff1249424fe2f05dde7c68.docx"},{"id":104179323,"identity":"9cb40d7c-c843-4270-9272-e68e7c606ed8","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"eps","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8193670,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.eps","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/63ebf246d273058a6f7607e5.eps"},{"id":104179320,"identity":"10d979c3-d30d-4178-82ea-9e4ff55de022","added_by":"auto","created_at":"2026-03-08 17:03:57","extension":"eps","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3702714,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.eps","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/98ad38055676ebdae9ae6f84.eps"},{"id":104779310,"identity":"f9717cf0-f150-423a-8caa-52a60ccef6f3","added_by":"auto","created_at":"2026-03-17 07:38:43","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15343,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureNote.docx","url":"https://assets-eu.researchsquare.com/files/rs-8823009/v1/7f49a18742d4eabc2d1b6841.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIL-1α and CRP as key inflammatory mediators linking neuropathic pain, anxiety, and depression in major depressive disorder\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe substantial co-prevalence of major depressive disorders (MDD), anxiety symptom and pain has been extensively documented, revealing a complicated relationship with significant therapeutic implications\u003csup\u003e1–4\u003c/sup\u003e. Epidemiological studies consistently demonstrate that depressive mood exacerbates pain perception, contributing to heightened pain severity, amplified pain-related functional impairment, and diminished treatment responsiveness in chronic pain management\u003csup\u003e1,5,6\u003c/sup\u003e. Notably, clinical studies revealed that 59.1% of MDD\u0026nbsp;and anxiety\u0026nbsp;patients report clinically significant pain manifestations\u003csup\u003e7\u003c/sup\u003e, while 69% of primary care patients meeting criteria for severe depression present primarily with somatic symptoms, particularly pain syndromes\u003csup\u003e8\u003c/sup\u003e. This complex comorbidity arises from a multidimensional interplay of neurobiological, psychological, and sociocultural factors\u003csup\u003e9\u003c/sup\u003e, biological mechanisms are well-established as primary etiological determinants, while\u0026nbsp;a\u0026nbsp;persistent divergence exists in elucidating the intricate link between various clinical symptom intersection states and etiology\u003csup\u003e10\u003c/sup\u003e. Mechanistically,\u0026nbsp;pain and depression may be interconnected through multiple biological and psychosocial pathways, including shared risk factors such as lower educational attainment, obesity and physical inactivity, social isolation, genetic susceptibility, and dysregulation of serotonergic signaling.\u0026nbsp;Notably, recent emerging evidence identifies\u0026nbsp;elevated inflammatory markers correlating with both depressive severity and pain intensity. To address this incapacitating condition, tailored interventions must be developed by clarifying these pathways\u003csup\u003e11\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInflammation is a particularly persuasive candidate mediator across pain and MDD. Increasing evidence have identified that dysregulation of the inflammatory system plays a role in mood disorders and chronic pain\u003csup\u003e12\u003c/sup\u003e, particularly some key pro-inflammatory cytokines. Numerous investigations have shown that a worse response to traditional antidepressants is preceded by higher levels of pro-inflammatory cytokines\u003csup\u003e13,14\u003c/sup\u003e. When compared to patients with reactive depression, those with refractory depression exhibit noticeably greater levels of pro-inflammatory mediators during treatment, specifically interleukin-6 (IL-6), C-reactive protein (CRP), monocyte chemoattractant protein-4 (MCP-4/CCL13), and thymus- and activation-regulated chemokines (TARC/CCL17)\u003csup\u003e15–18\u003c/sup\u003e.\u0026nbsp;Additionally,\u0026nbsp;it has previously been discovered that, following therapy, CRP in particular correlates with the severity of depression\u003csup\u003e19\u003c/sup\u003e. Furthermore,\u0026nbsp;there are signs that lower levels of pro-inflammatory cytokines [(tumor necrosis factor -α,\u0026nbsp;TNF-α), IL-1β, IL-6, and IL-17] and proteins that influence cellular transcription and gene expression lead to a decrease in depressive symptoms or\u0026nbsp;increase in\u0026nbsp;pain perception\u003csup\u003e20\u003c/sup\u003e.\u0026nbsp;Although\u0026nbsp;a\u0026nbsp;few studies have shown that IL-1α release were be detected, particularly in the early stages of chronic pain in animal models,\u0026nbsp;substantial research efforts have focused on identifying diagnostic or prognostic biomarkers for MDD and pain, while existing studies frequently fail to adequately address the dynamic nature of inflammatory mechanisms underlying their clinical comorbidity\u003csup\u003e21\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIndeed, clinical investigations examining inflammatory variables in relation to concurrent depression, anxiety, and pain have produced compelling yet sometimes conflicting findings, underscoring the constraints of methodological and conceptual interpretability. The bidirectional nature of the inflammation-mood–pain axis challenges causal inference, as inflammation may serve both as a consequence of and a contributor to mental and pain symptoms, establishing a feedback loop that current cross-sectional methods cannot effectively disentangle\u003csup\u003e23,24\u003c/sup\u003e. Further, although peripheral measurements of TNF-α, CRP and IL-6 are linked to central sensitization and disease, they may not adequately capture region-specific neuroinflammatory dynamics affecting mood dysregulation and pain amplification\u003csup\u003e25,26\u003c/sup\u003e. This raises concerns about the mechanistic significance of traditional computational methods for studying peripheral inflammatory markers in neuropsychiatric symptomatology\u003csup\u003e27–29\u003c/sup\u003e. Hence, the intricate link between inflammatory alterations in depression, co-morbid anxiety, and pain has not received much attention in observational clinical investigations, despite the substantial body of empirical data on inflammatory changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite accumulating evidence, the mechanisms linking inflammatory abnormalities with anxiety, pain, and MDD remain poorly understood. Recent advances in psychiatric research have demonstrated that machine learning (ML) techniques, supported by increasingly large datasets, provide powerful tools to disentangle complex biological interactions. By identifying latent patterns within multidimensional data, ML approaches enable more precise prediction of potential etiological biomarkers and pathways. Building upon traditional analytical methods, this study introduced ML as an exploratory and supplementary step. This approach was used to help identify and prioritize potential etiological biomarkers in MDD patients with comorbid anxiety and pain, and to model the complex pathways through which aberrant inflammatory factors—especially IL-1α and CRP—may mediate the interactions among anxiety symptoms, pain symptoms, and affective symptoms. The potential clinical implications of these findings are also discussed.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study was carried out at the Anhui Mental Health Center (AMHC) using a cross-sectional case-control design between January 2020 and December 2024. The study was authorized by AMHC\u0026apos;s medical ethics committee. Prior to taking part in this study, each subject gave written consent in compliance with the Helsinki Declaration. The Mini International Neuropsychiatric Interview (C-MINI) 7.0.2 in Chinese was utilized by two qualified psychiatrists to assess subjects. Initially, 218 participants were screened: 62 healthy people were screened from the hospital\u0026apos;s physical examination center, and 156 depressed inpatients and outpatients were screened from AMHC. Among healthy subjects, 3 individuals did not finish the evaluation, 13 did not give written informed consent, 3 did not meet the study criteria, and 3 withdrew their consent. Among the enrolled depressed inpatients and outpatients, 20 subjects did not finish the evaluation, 18 refused to sign written informed consent, 25 did not meet the study criteria, and 18 withdrew their consent. In the end, 115 participants were enrolled and split into two groups: a control group (40 healthy controls) and a depression group (75 MDD patients). (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe following were the inclusion criteria for patients in the depression group (1) satisfied the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for MDD, as evaluated by two independent senior psychiatrists; (2) be between the ages of 18 and 65; (3) the total scores of Hamilton Depression Rating Scale-24 (HAMD-24)\u0026ge;8. (4) the total scores of\u0026nbsp;Beck Depression Inventory-II (BDI-II)\u0026nbsp;\u0026ge;14. People who were deemed healthy by doctors\u0026nbsp;enrolled\u0026nbsp;the healthy control group, which was drawn from the physical examination center of AMHC. The following were the exclusion criteria for every participant: (1) severe somatic or craniocerebral trauma; (2) severe neurological, inflammatory, or tumor-related diseases; (3) substance abuse or other mental disorders; (4) serious physical disease; (5) pregnant or lactating women; (6) modified electroconvulsive therapy administered within three months prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssessment instruments\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMini International Neuropsychiatric Interview (MINI) 7.0.2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMINI-7 is a widely used tool and known to have sound psychometric properties, it is valid and reliable with kappa values above 0.80 and 0.90, respectively, the inter-rater and test-retest reliabilities are excellent. MINI-7 is the gold standard for identifying depression\u003csup\u003e30\u003c/sup\u003e. Information on sex, marital status, income, work status, and educational achievement was collected using a self-reported questionnaire.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHamilton Depression Rating Scale-24 (HAMD-24)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe HAMD-24 is the most widely used depression scale in the world because of its great specificity in determining the severity of depression symptoms. The HAMD-24 score can be used to define clinically meaningful symptom levels: mild depression is defined as 8-19, moderate depression as 20-34, and severe depression as \u0026ge;35. The HAMD-24 has a Cronbach\u0026apos;s alpha of 0.88 and a \u0026kappa; score of 0.92\u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHamilton Anxiety Rating Scale-14 (HAMA\u003c/strong\u003e\u003cstrong\u003e-14\u003c/strong\u003e\u003cstrong\u003e)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Hamilton Anxiety Rating Scale-14 is now the industry standard and was one of the first viable and dependable tools to measure the degree of anxiety. It was released more than 50 years ago. The HAMA-14 was evaluated on a scale of 0 to 4, and general criteria for differentiating anxiety severity by stage were provided. Somatic and autonomic symptoms, respiratory and other bodily strain, and emotional anxiety, including concern and fear, are all included in the items\u003csup\u003e32\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeck Depression Inventory-II (BDI-II)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 21-item Beck Depression Inventory-II is a self-report tool used to gauge how severe depression symptoms are. The overall score ranges from 0 to 63, with higher scores denoting more severe depressive symptoms. Each item is evaluated from 0 to 3 according to general guidelines. Coefficient of BDI-II is Cronbach\u0026apos;s alpha, which has a solid internal consistency and outstanding validity with an internal consistency of 0.83\u003csup\u003e33\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeck Anxiety Inventory (BAI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 21-item Beck Anxiety Inventory is a self-report tool used to gauge the severity of anxiety symptoms. The overall score goes from 21 to 84, with higher scores denoting more severe anxiety symptoms. Each item is evaluated from 1 to 4\u0026nbsp;according to general norms. When it comes to anxiety disorders, BAI has outstanding discriminant validity and great internal consistency (Cronbach\u0026apos;s alpha = 0.94)\u003csup\u003e34\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShort-Form McGill Pain Questionnaire-2 (SF-MPQ-2)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are 22 pain descriptors and four subscales in the SF-MPQ-2, which is a self-rated scale that rates pain intensity on a scale of 0 to 10 (0 being no pain and 10 being the worst pain). Cronbach\u0026apos;s alpha for the subscales ranges from 0.896 to 0.916, indicating its high reliability and validity. With values of 0.909, 0.973, 0.988, 0.952, and 0.927 for the complete scale and the continuous, intermittent, mostly neuropathic, and affective subscales, respectively, the test-retest results showed high reliability\u003csup\u003e35\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProspective and Retrospective Memory Questionnaire (PRMQ)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PRMQ is a 16-item test used to assess memory loss in daily living. The PRMQ questions ask about PM in half of the cases and RM in the other half. A reliable method for assessing PM and RM deficits in gender and related age groups is the PRMQ\u003csup\u003e36\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory evaluation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing an overnight fast, venous blood samples (5 ml) were taken from each participant in the morning. Immediately, samples were forwarded to the Clinical Laboratory Department. The plasma was separated using centrifugation at 5 \u0026deg;C and the isolated plasma was frozen at\u0026nbsp;\u0026minus;80◦C for further study. The MSD Platform (labservice. univ-bio.com, Shanghai, China) was used to detect 18 cytokines, namely, interleukin (IL)-1\u0026alpha;, IL-1\u0026beta;, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, L-12/IL-23p408, IL-13, IL-15, IL-16, IL-17A, CRP, interferon (IFN)-\u0026gamma;, TNF-\u0026alpha;, TNF-\u0026beta; and Fms-like tyrosine kinase 1(Flt-1). The plates were analyzed with Meso Scale Diagnostics (MSD) sector 2100 A, and the data were evaluated using MSD Discovery Workbench 4.0 software. Every standard and plasma sample was examined twice, and the inflammatory cytokine detection limit was 0.01 pg/ml.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analyzed using the Python (version 3.12) and related scientific computing packages were used for data analysis and visualization in this work. Firstly, the main analysis dataset and the sensitivity analysis dataset were constructed independently in accordance with the research design, and the latter was subjected to sensitivity analysis. The former was transformed using the Yeo-Johnson + Z-score method, with residual heteroskedasticity using HC3 robust standard errors and sensitivity tests for high leverage points. Ordinary Least Squares (OLS) was used to build multiple linear regression models in order to investigate the relationship between the dependent variable and several independent factors. Utilizing statsmodels (version 0.14.2) and matplotlib (version 1.2.1), the regression models were subjected to standard diagnostic checks normality of residuals, influential points analysis and multicollinearity diagnostics. The two tandem mediating variables in PROCESS were utilized to evaluate if the independent variable had an indirect effect on the dependent variable using the Model 6 chain-mediated mode. To confirm the impact of the independent variable on the dependent variable and whether this effect was mediated by the serial mediation of the two chain-mediated variables, the PROCESS Model 59 moderated serial mediation model was employed. It also focuses on whether other variables have a role in moderating the two courses of serial variables. Futhermore, investigating intricate network connections between inflammation, pain, and mood measures using a Gaussian Graphical Model. In order to determine whether the primary predictor variables had a serially mediated effect on depression through pain and anxiety, a gradient boosted regression algorithm (GBR) was employed. Additionally, the contribution of each feature to the prediction was quantified using SHAP (SHapley Additive exPlanations) value interpretation, via Partial Dependence Plots (PDP). Verified were the minor impacts of the primary features on the model\u0026apos;s predictions. The significance level was set at\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt;0.05 for each statistical test.\u003c/p\u003e"},{"header":"3. Results ","content":"\u003cp\u003e\u003cstrong\u003eComparison of demographic characteristics, pain intensity, memory function, and inflammation levels between depression and control groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics, depression severity, pain intensity, memory performance, and inflammatory biomarkers for both groups are summarized in Supplementary Table1 and Figure 1. A total of 115 individuals were included in the final analyses. We recruited 75 MDD patients (47 females and 28 males) and 40 healthy controls (20 males and 20 females). Age, years of education, employment status, marital status, family patterns and income level did not significantly differ across the groups (p\u0026gt;0.05). The total scores of SF-MPQ-2 scale explored significantly higher in MDD group (Mdn = 26, IQR = 12-44) than healthy group (Mdn = 2.5, IQR = 0-5), \u003cem\u003eU\u003c/em\u003e = 368, \u003cem\u003ep\u003c/em\u003e＜0.0001, with a medium effect size (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.32). As well as continuous pain subscale, intermittent pain subscale,\u0026nbsp;neuropathic pain subscale and affective subscale presented significantly higher in MDD group (Mdn = 12, IQR = 6.5-19.5; Mdn = 0, IQR = 0-6; Mdn = 3, IQR = 0-7; Mdn = 8, IQR = 3.5-12.5) than the healthy group (Mdn = 0, IQR = 0-3; Mdn = 0, IQR = 0-0; Mdn = 0, IQR = 0-0; Mdn = 0, IQR = 0-0.25 ; \u003cem\u003eU\u003c/em\u003e = 417.5, \u003cem\u003ep\u003c/em\u003e＜0.0001; \u003cem\u003eU\u003c/em\u003e = 1016, \u003cem\u003ep =\u0026nbsp;\u003c/em\u003e0\u003cem\u003e.\u003c/em\u003e005; \u003cem\u003eU\u003c/em\u003e = 800, \u003cem\u003ep\u003c/em\u003e＜0.0001; \u003cem\u003eU\u003c/em\u003e = 445.5, \u003cem\u003ep\u003c/em\u003e＜0.0001). Moreover, memory assessment revealed significantly higher scores in the MDD group compared to the normal control group for both retrospective memory (Mdn = 30, IQR = 25.75–33 \u003cem\u003evs.\u003c/em\u003e Mdn = 25, IQR = 20–30; \u003cem\u003eU\u0026nbsp;\u003c/em\u003e= 1986.5,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.02) and the PRMQ total score (56.52 ± 9.79 \u003cem\u003evs.\u003c/em\u003e 48.80 ± 15.47; \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 2.86, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e=0.02). In contrast, prospective memory scores showed no significant group differences (27.38 ± 5.31 \u003cem\u003evs.\u0026nbsp;\u003c/em\u003e24.25 ± 8.10, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 2.19, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.08). Finally, analysis of inflammatory factors revealed significantly higher expression of CRP (Mdn =\u0026nbsp;1340864.45, IQR =\u0026nbsp;933604.76\u0026nbsp;–\u0026nbsp;1893308.92\u0026nbsp;\u003cem\u003evs.\u003c/em\u003e Mdn = 309308.17, IQR = 165344.84 – 945044.06; \u003cem\u003eU\u0026nbsp;\u003c/em\u003e= 2344 ,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e＜0.0001 )and IL-1α (Mdn = 0.31, IQR = 0.20 – 0.55 \u003cem\u003evs.\u003c/em\u003e Mdn = 0.50 , IQR = 0.31 – 2.61; \u003cem\u003eU\u0026nbsp;\u003c/em\u003e= 1072 ,\u003cem\u003e\u0026nbsp;p\u0026nbsp;\u003c/em\u003e= 0.04) in patients with depression compared to controls, while no significant differences were observed in the remaining inflammatory factors ( \u003cem\u003ep\u003c/em\u003e\u003cem\u003e＞\u003c/em\u003e0.05). The results represent in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation among chronic inflammation levels, cognitive decline, pain intensity, and clinical symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this cohort study, we investigated the association among chronic inflammation levels, cognitive decline, pain symptoms and clinical symptoms, to determine the relationship among these characteristics, we analyzed various assessment data using Spearman’s correlation analysis. According to the findings, there are notable positive connections between the\u0026nbsp;total score\u0026nbsp;of\u0026nbsp;SF-MPQ-2 and the scales of BDI and BAI,\u0026nbsp;Specifically,\u0026nbsp;subjective anxiety subscale of BAI\u0026nbsp;was\u0026nbsp;positively\u0026nbsp;correlated\u0026nbsp;with continuous pain(\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.456,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), intermittent pain(\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.456,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), neuropathic pain(\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.521,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), affective subscale(\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.522,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), SF-MPQ-2 total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.5871,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), BDI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.555,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), HAMD total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.435,\u0026nbsp;\u003cem\u003ep =\u003c/em\u003e 0.001).\u0026nbsp;Moreover,\u0026nbsp;neuropathic pain\u0026nbsp;subscale of SF-MPQ-2\u0026nbsp;showed\u0026nbsp;correlated positively with BDI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.312,\u0026nbsp;\u003cem\u003ep =\u003c/em\u003e 0.006), BAI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.542,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001)\u0026nbsp;and\u0026nbsp;HAMD total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e=0.25,\u0026nbsp;\u003cem\u003ep =\u003c/em\u003e 0.034).\u0026nbsp;Conversely, PRMQ total score exhibited negative correlations with subjective anxiety (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.390,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), BAI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.489,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001), BDI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.303,\u0026nbsp;\u003cem\u003ep =\u003c/em\u003e 0.008), HAMD total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.277,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.016).\u0026nbsp;Similar patterns was\u0026nbsp;found for the retrospective memory\u0026nbsp;(a subscale of PRMQ). Finally, IL-1α\u0026nbsp;was detected\u0026nbsp;correlation\u0026nbsp;positively with subjective anxiety (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.309,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.006), BAI total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.327,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.004), neuropathic pain (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.264,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.0219) and HAMD total score (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= 0.239,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.038), while\u0026nbsp;CRP\u0026nbsp;correlated negative with core anxiety (\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.274,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.017) and cognitive/depression(\u003cem\u003er\u003csub\u003es\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e= - 0.243,\u0026nbsp;\u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.035).\u0026nbsp;The results represent in the Figure 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of correlation between n\u003c/strong\u003e\u003cstrong\u003eeuropathic pain with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eclinical characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the relationship between pain and clinical characteristics, multivariate linear regression models were constructed using OLS, incorporating the core predictor variables IL-1α, age, BMI, gender, personality_1, and personality_2 as independent variables. The models underwent parameter estimation and hypothesis testing using Heteroscedasticity-Consistent 3 (HC3) robust standard errors in order to address any potential heteroscedasticity. The Variance Inflation Factor (VIF) was used to measure multicollinearity between the independent variables, a VIF \u0026gt; 5 is typically regarded as the threshold for the existence of possible covariance. Results are shown in Supplementary Figure 1 and 2. The primary regression findings are displayed using partial regression plots, which display the partial correlation of a specific independent variable with the dependent variable after adjusting for other variables, and forest plots, which display the regression coefficients and their 95% CIs. There were clear correlations with neuropathic pain levels, according to multivariable analysis. IL-1α revealed a significant positive link with neuropathic pain [\u003cem\u003eβ\u0026nbsp;\u003c/em\u003e= 0.25, 95% CI (0.05, 0.46), \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.014], whereas\u003cem\u003e\u0026nbsp;\u003c/em\u003eage revealed a significant inverse relationship with neuropathic pain\u0026nbsp;[\u003cem\u003eβ\u0026nbsp;\u003c/em\u003e= -0.29, 95% CI (-0.53 to -0.04), \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.021)]. In contrast, all confidence intervals for personality traits (reference categories 1 and 2), gender, and BMI did not approach statistical significance in the current model (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05 for all).\u0026nbsp;Results are shown in Figure 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChained mediation model to explore the relationship between IL-1α and depressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further examine the connection between IL-1α and depressive symptoms, this study utilized a chained mediation analysis (PROCESS model 6)\u003csup\u003e37\u003c/sup\u003e to investigate the indirect effect of IL-1α (independent variable, X) on total scores of the HAMD (dependent variable, Y) via the sequential mediators of neuropathic pain (N.Pain; M1) and subjective anxiety (S.Anxiety; M2). First, we looked at three different mediation routes using a 5,000-sample bootstrap analysis\u003csup\u003e38\u003c/sup\u003e, all analyses were controlled for confounders such as age, body mass index (BMI), gender, and two personality traits (personality_1 and personality_2) (Figure 4A), the results presented N.Pain as a single mediator (M1): Through N.Pain, IL-1α (X) had an indirect influence on HAMD ratings (Y) that was not statistically significant (effect = 0.043, 95% CI [-0.011 to 0.202]). S.Anxiety alone as a single mediation (M2): This pathway similarly showed non-significant mediation (effect = 0.051, 95% CI [-0.006 to 0.147]). Chained mediation (M1→M2): Interestingly, a substantial indirect effect was found along the sequential pathway of subjective anxiety and N.Pain (effect = 0.042, 95% CI [0.004 to 0.098]).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond, the chained mediation model's key findings showed clear predictive patterns, as shown in Figure 4B. These findings include: The X-M1 pathway showed that IL-1α significantly predicted N.Pain in a positive manner (\u003cem\u003ea₁\u003c/em\u003e=0.25, \u003cem\u003ep\u003c/em\u003e=0.025). X-M2 pathway: IL-1α and S.Anxiety did not directly correlate (\u003cem\u003ea₂\u003c/em\u003e=0.17, \u003cem\u003ep\u003c/em\u003e=0.100). M1→M2 pathway: Subsequent S.Anxiety was significantly predicted by N.Pain (\u003cem\u003ed₁\u003c/em\u003e=0.55, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). M1→Y pathway: N.Pain did not directly correlate with HAMD total scores after controlling for variables (\u003cem\u003eb₁\u003c/em\u003e=0.17, \u003cem\u003ep\u003c/em\u003e=0.227). M2→Y pathway: Higher HAMD scores were strongly predicted by S.Anxiety (\u003cem\u003eb₂\u003c/em\u003e=0.30, \u003cem\u003ep\u003c/em\u003e=0.029). Interestingly, after adjusting for both mediators, the direct pathway (c') from IL-1α to HAMD scores was no longer significant (c'=0.07, p=0.533).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerated serial mediation model to explore the relationship between IL-1α and depressive symptoms\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the impact of IL-1α (X) on depressive symptoms (HAMD total score, Y), we used a moderated sequential mediation model (PROCESS Model 59)\u003csup\u003e37\u003c/sup\u003e. We investigated whether S.Anxiety (M2) and N.Pain (M1) progressively mediated this effect. In particular we evaluated if the transition from N.Pain to S.Anxiety (M1 → M2) was modulated by CRP (W). Furthermore, we used the Johnson-Neyman (J-N) technique to determine the precise low, medium, and high CRP concentration ranges that affect the association between N.Pain (M1) and S.Anxiety (M2) in order to more clearly define the limits of CRP's moderating effects. The results showed that the effects of IL-1α on depression severity had non-significant simple mediation pathways, meanwhile, there was no statistically significant difference between the S.Anxiety-mediated pathway (IL-1α→S.Anxiety→HAMD; effect=0.049, 95% CI [-0.014, 0.140]) and the N.Pain-mediated pathway (IL-1α→N.Pain→HAMD; effect = 0.052, 95% CI [-0.023, 0.176]). Nonetheless, the sequential mediation pathway through S.Anxiety and N.Pain (IL-1α→N.Pain→S.Anxiety→HAMD) showed relevance that was reliant on CRP: Conditional indirect impact = 0.031 (95% CI [0.001, 0.079]) low CRP (mean -1SD). Conditional indirect impact = 0.041 (95% CI [0.001, 0.097]) indicates a moderate CRP (mean). Conditional indirect impact = 0.051 (95% CI [0.002, 0.126]) high CRP (mean +1SD). According to this dose-response pattern, the sequential pathway remained statistically detectable across the whole range of CRP levels, and the mediated effect magnitude demonstrated a positive correlation with CRP concentrations (β=+0.020 per SD increase)(Figure 4C ). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4C employs a J-N plot to examine how N.Pain conditional effect on S. Anxiety varies across centered CRP values. The analysis reveals a critical threshold at -1.498 for centered CRP, where the curve represents conditional effect estimates and the shaded region denotes 95% confidence intervals. Results demonstrate a statistically significant positive association (entire CI above zero) when centered CRP exceeds -1.498, while this relationship becomes non-significant below this threshold. These findings indicate that N.Pain's anxiety-inducing effects manifest specifically in individuals with CRP levels above this biological cutoff point, highlighting CRP's role as a potential inflammatory moderator in pain-anxiety pathophysiology. The study's Model 59 pathway is schematically diagrammed in Figure 4F, which makes it evident how the variables (IL-1α, N.Pain, S.Anxiety, HAMD, and CRP) and their related pathway coefficients (β-values) relate to each other and to the significant levels. As depicted in the figure: asterisks denote statistical significance, and dashed lines show moderating effects. a₁ (IL-1α → N.Pain): 0.26*, a₃ (N.Pain → S.Anxiety): 0.57***, a₄ (N.Pain × CRP → S.Anxiety): 0.15, b₂ (S.Anxiety → HAMD): 0.30*, and c' (IL-1α → HAMD): 0.07(* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork complex interrelationships among pain, mood and inflammation indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intricate linkages between the indicators of pain, mood, and inflammation as well as the conditional dependencies between the variables were further investigated using network analysis. The findings revealed intricate network connections between pain, CRP, IL-1α, and other clinical indicators. For instance, IL-1α (partial correlation coefficient = 0.231) and pain and anxiety (partial correlation coefficient = 0.806) were significantly positively correlated (Figure 5A). Figure 5B displays the results of additional J-N analyses of the moderating effect of CRP on the pain-anxiety relationship. The vertical axis represents the effect size of pain on anxiety (simple slope), while the solid blue line represents the point estimate of the simple slope. The light blue shaded area represents the 95% confidence intervals. The horizontal axis represents the centred CRP level (SD units). The J-N significant transition point (red dashed line) and the mean CRP and ±1 SD are indicated. The findings show that when center CRP values are between -1.763 and 2.992, the positive predictive effect of N.Pain on S.Anxiety is statistically significant (i.e., the 95% CI lies entirely above 0). Additionally, this suggests that N.Pain only significantly raises S.Anxiety levels when a person's CRP level is above a particular threshold.\u003c/p\u003e\n\u003cp\u003eSliding window investigation of how CRP levels dynamically alter the centrality of intensity and centrality of expected effects of four important nodes (IL-1α, Pain, Anxiety, and Depression). A fixed-size window (50 percent of the sample size) was moved over the data sorted by CRP in order to build networks and determine centrality for each window of data. Each window's central CRP value is shown on the horizontal axis, while the matching centrality measure is shown on the vertical axis. The interactions between key node centrality and CRP level are shown by the sliding window analysis. For instance, the centrality of the expected impact of the Anxiety and Depression nodes tended to be at a high level, indicating that the physiological inflammatory state (CRP levels) may have an impact on the relative importance of the nodes in the network. The intensity of the Pain node, on the other hand, reached its highest expected impact between CRP values of -0.42 and -0.22, and decreased with increasing CRP values. Based on this finding, it is possible that physiological inflammatory states (CRP levels) could affect the relative relevance of nodes in the network. Results are shown in Figure 5C-D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA machine learning approach to explore the sequence-mediated effects of IL-1α on depression via pain and anxiety (regulated by CRP)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, the sequence-mediated effects of IL-1α on depression through pain and anxiety (controlled by CRP) were investigated using a GBR technique. By computing each feature's average absolute SHAP value, the global contribution of each feature to the model predictions is determined. The more significant the characteristic, the longer the bar. Figure 6A Findings displayed (IL-1α\u0026nbsp;→\u0026nbsp;Pain): With the highest mean absolute SHAP value, IL-1α was the most significant feature predicting pain (A3), followed by variables like age and BMI. According to Partial Dependency Plot (PDP) results (A2) , IL-1α levels generally showed a positive trend with predicted pain levels. Predicted pain levels were relatively low during periods of low IL-1α values and increased as IL-1α levels increased, with a curve pattern that might indicate a non-linear relationship. A1: Actual versus anticipated values in a scatterplot. The findings demonstrate that the scatter points are primarily evenly spaced around the diagonal line, suggesting that there is a strong correspondence between the actual pain values (Pain) and the model-predicted values (IL-1α) (R² = 0.795, MSE = 0.205). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, according to the results in Figure 6 B, Pain_hat is the most significant factor in predicting anxiety, followed by variables like age and BMI (B3). The overall trend between Pain_hat levels and anticipated anxiety levels was positive, according to PDP data, there is a correlation between greater expected anxiety levels and higher predicted pain levels (B2), and the curve pattern may not be linear. The model predicted anxiety more well (R\u003csup\u003e2\u003c/sup\u003e = 0.832, MSE = 0.168), and the scatter points also had a decent clustering pattern (B1: scatterplot of real vs. predicted values). Lastly, Figure 6 C results indicate (Anxiety_hat\u0026nbsp;→\u0026nbsp;Depression): The most significant characteristic in predicting depression is anxiety_hat, which is followed by variables like age and BMI (C3). According to PDP results, anxiety_hat levels and anticipated depression levels generally showed a positive trend, with greater predicted anxiety levels being linked to higher predicted depression levels (C2). The scatter distribution in C1: Actual vs. Predicted scatterplot findings shows that the model also predicted sadness at a good level (R\u003csup\u003e2\u003c/sup\u003e = 0.795, MSE = 0.205).\u003c/p\u003e"},{"header":"4. Discussion ","content":"\u003cp\u003eThis study seeks to find biomarkers linked to the pathophysiology of MDD linked to inflammation, with an emphasis on instances that co-occur with anxiety disorders and persistent pain symptoms. Our results demonstrated that, compared with healthy controls, participants with depression exhibited markedly heightened pain sensitivity, greater memory impairment, and significantly higher CRP and IL-1α in MDD than controls. Additionally, we found the\u0026nbsp;IL-1α correlated positively with\u0026nbsp;somatic\u0026nbsp;symptoms,\u0026nbsp;S.Anxiety\u0026nbsp;and\u0026nbsp;BAI total scores.\u0026nbsp;Using chained mediation\u0026nbsp;and moderated serial mediation models, we found IL-1α does not act directly on depressive symptoms, but rather via the IL-1α→N.Pain→S.Anxiety→HAMD pathway. It was also shown that N.Pain significantly increased S.Anxiety levels when CRP levels exceeded a specific threshold. Finally, GBR approach confirmed the sequence-mediated effects of IL-1α on depression via pain and anxiety, with CRP serving as regulatory factor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe bidirectional association between pain and depression has been well-established. Chronic pain is a significant risk factor for MDD, and MDD often manifests with somatic complaints, including pain\u003csup\u003e39,40\u003c/sup\u003e. Furthermore, extensive research has demonstrated that the interconnection between anxiety symptoms, impaired pain perception, and cognitive dysfunction in patients with depression constitutes a complex clinical challenge, stemming from intricate neurobiological mechanisms\u003csup\u003e41–43\u003c/sup\u003e. In line with these findings, our study further validates that individuals with MDD exhibit altered pain perception compared to healthy controls. Specifically, depressed patients demonstrate more severe impairments in both neuropathic and affective pain dimensions. We also observed a negative correlation between N.Pain and age, indicating that younger individuals are more sensitive to pain\u003csup\u003e4\u003c/sup\u003e. Consequently, these results underscore the importance of giving special consideration to the somatic complaints of younger depressed patients. The present study reaffirms that anxiety is linked to heightened pain perception and reduced pain tolerance, and that elevated anxiety ultimately contributes to a depressed mood. In this context, while depressed mood may occasionally lower the initial sensory threshold for pain, anxiety more consistently amplifies the affective and cognitive dimensions of pain, particularly the affective component of 'slow pain' and burning sensations\u003csup\u003e44\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared with the extensive literature on IL-1β in depression and anxiety, research on IL-1α remains limited. Nevertheless, recent evidence points to an important role of IL-1α in MDD-related neuroinflammation\u003csup\u003e45\u003c/sup\u003e. Evidence notes that IL-1α, alongside IL-1β, is elevated in the peripheral blood and cerebrospinal fluid (CSF) of MDD patients, correlating with depressive symptoms such as anhedonia and fatigue\u003csup\u003e46\u003c/sup\u003e. This is likely due to IL-1α driven role in activating the hypothalamic-pituitary-adrenal (HPA) axis and amygdala circuits, thereby promoting stress-related neuroinflammation\u003csup\u003e47,48\u003c/sup\u003e. Preclinical studies show that the pharmacological blockage of IL-1α signaling with an IL-1 receptor antagonists reduces anxiety-like behaviors in chronically stressed mice, implying a potential causal role\u003csup\u003e48\u003c/sup\u003e. There are few human studies on IL-1α and anxiety, and most evidence is extrapolated from IL-1β or from analyses pooling IL-1α/β effects. Unlike IL-6 and CRP\u003csup\u003e49\u003c/sup\u003e, there was no significant IL-1α-specific correlation with anxiety symptoms in the 2021 UK Biobank project\u003csup\u003e50\u003c/sup\u003e. Correspondingly, our mediational analyses showed that IL-1α did not exert a direct effect on depressive symptoms, instead, its influence was transmitted via neuropathic pain, which in turn increased somatic anxiety and elevated HAMD scores. Research on pain models (such as fibromyalgia and arthritis) revealed that IL-1α was up-regulated in the spinal cord and dorsal root ganglia, causing central sensitization\u003csup\u003e51\u003c/sup\u003e. This suggests that IL-1α is linked to inflammatory and neuropathic pain, which causes nociceptors to become sensitized and pain signals to be amplified. This is consistent with the bidirectional relationship between depression and pain, indicating that IL-1α may worsen these two conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP is an acute-phase protein produced by the liver in response to pro-inflammatory cytokines (e.g., IL-6, IL-1α/β, TNF-α) and is a widely used as a non-specific biomarker of systemic inflammation\u003csup\u003e52\u003c/sup\u003e. Its levels rise in the blood when there is an inflammatory process occurring in the body. Elevated CRP levels are frequently observed in psychiatric and pain conditions associated with inflammation, particularly treatment-resistant depression and depression with somatic symptoms\u003csup\u003e49\u003c/sup\u003e. Similarly, in anxiety disorders, including generalized anxiety disorder（GAD）and chronic pain, elevated CRP has also been reported\u003csup\u003e53\u003c/sup\u003e. Research on CRP is currently somewhat varied, nevertheless, with some studies showing a lesser correlation with anxiety than with depression. According to the 2021 UK Biobank on depression and anxiety study, CRP was linked to some anxiety symptoms (such as irritation, OR = 1.06 and excessive worrying, OR = 1.03), but its impact was less pronounced than that of depression\u003csup\u003e50\u003c/sup\u003e. Moreover, some studies showed that the CRP-anxiety relationship attenuates after adjusting for BMI and other confounders, highlighting the influence of comorbid factors such as obesity\u003csup\u003e54\u003c/sup\u003e. Elevated CRP is also a hallmark of chronic pain conditions (e.g., rheumatoid arthritis, fibromyalgia), correlating with pain severity and inflammation. Even though the findings of current study did not directly link CRP to anxiety or depression symptoms, moderated serial mediation analyses revealed significant conditional indirect effects across low/mean/high CRP, supporting the interpretation that CRP indexes systemic inflammation driven by upstream cytokines such as IL-1α, which sensitize pain pathways and amplify neuroinflammatory responses, thereby linking pain to depressive outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe interpretation of these results should take into account several limitations. \u0026nbsp;First, this cross-sectional study relied on subjective symptom ratings, which constrains causal interpretation, biological experiments and longitudinal designs are needed to verify the mediating role of inflammatory markers in the pain-anxiety-depression relationship, and to test whether interventions targeting these pathways can reduce anxiety and depression risk attributable to pain.\u0026nbsp;Second, the small sample size may limit the statistical power to detect between-group differences. In particular, given the heterogeneity in the clinical presentation of MDD patients (e.g. with or without comorbid pain), the sample was insufficient to support further subgroup stratification analyses. Third, this study was limited by the inclusion of only 18 cytokines; therefore, future research should incorporate a broader range and number of inflammatory factors and chemokines to gain a more comprehensive understanding of their relationship with MDD. Notwithstanding these drawbacks, this research offers fresh perspectives on the regulatory role of CRP in this intricate scenario as well as the IL-1α-mediated depressive pathways that result in anxiety prompted by pain symptoms. These results could potentially improve clinical outcomes by optimizing therapy strategies for inflammatory variables in pain in individuals with depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these findings delineate a pathway linking pain, anxiety and depression in which IL-1α functions as a driver and CRP as a modulator of inflammatory coupling between pain and mood, suggesting biomarker-guided targets for precision interventions in depression with pain. Prospective and experimental studies are needed to establish causality and therapeutic leverage.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to all the participants who participated in our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by funding from Anhui Medical University Youth Fund Project (grant number:2023xkj114). Fund of the Fourth People\u0026apos;s Hospital of Hefei City, Anhui Province (grant number:HFSY202101). National Clinical Key Specialty Construction Project of China (grant number: NO). Development Fund for Key Laboratory of Philosophy and Social Science in Anhui Province (grant number: SYS2023C06). Hefei City Innovative Procurement Project (grant number: NO). The funding sources were not involved in the study design, data collection, data analysis, drafting the paper, or the decision to publish the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethics Committee of the Anhui Mental Health Centre (AMHC). All participants provided written consent prior to study participation in accordance with the principles of the Declaration of Helsinki. The trial clinical registration number was ChiCTR2000029917.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study, as well as the used materials, are available from the corresponding author upon reasonable request.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC. Z, P.J and H.G were responsible for study design and manuscript editing. C. Z was responsible for literature searches, and manuscript writing. J.C and L.C were responsible for statistical analyses, prepared figures 1-6 and all supplementary figures and tables. Q. X, J. Y, C. L, M. Z, J.Y, J.G and L.Z were responsible for clinical-scale assessment and blood data collection. The submission has had all of the authors\u0026rsquo; approval. All clinical studies described in the manuscript were carried out following the Declaration of Helsinki promulgated by the National Institute of Health. The work presented here has not been published previously, nor is it being considered for publication elsewhere. We hope that this manuscript would be suitable for publication in BMC psychiatry. Corresponding author: Peijun Ju and Hua Gao.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSternke EA, Abrahamson K, Bair MJ. 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Psychoneuroendocrinology. 2024;167:107094. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.psyneuen.2024.107094\u003c/span\u003e\u003cspan address=\"10.1016/j.psyneuen.2024.107094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"major depressive disorder, pain, anxiety, inflammatory cytokine, IL-1α, CRP","lastPublishedDoi":"10.21203/rs.3.rs-8823009/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8823009/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt’s common comorbidity for Major Depressive Disorder (MDD), anxiety, and pain, underpinned by shared chronic inflammatory processes, this study aimed to identify etiological inflammatory biomarkers, focusing on MDD individuals with anxiety and pain symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study enrolled 115 participants (75 MDD, 40 healthy controls). Depression and anxiety symptoms were assessed by Hamilton Depression Rating Scale-24 (HAMD-24)/ Beck Depression Inventory-II (BDI-II) and Hamilton Anxiety Rating Scale-14 (HAMA-14)/Beck Anxiety Inventory (BAI),respectively. Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2) and Prospective and Retrospective Memory Questionnaire (PRMQ) were to evaluate the pain feature and memory function respectively. Serum levels of 18 cytokines and chemokines, including interleukin-1α ( IL-1α) and C-reactive protein (CRP), were validated using Meso Scale Discovery. Data analysis included robust Ordinary Least Squares (OLS) regression, serial mediation (PROCESS models), Gaussian Graphical Models, and Gradient Boosted Regression (GBR) with SHapley Additive exPlanations (SHAP) values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\n MDD patients displayed significantly higher IL-1α and CRP levels, greater pain sensitivity, and more cognitive impairment compared to controls. Multivariate regression confirmed a significant positive link between IL-1α and neuropathic pain (\u003cem\u003eβ\u003c/em\u003e=0.25, \u003cem\u003ep\u003c/em\u003e=0.014). Serial mediation models revealed a crucial indirect pathway: IL-1α did not directly cause depressive symptoms but acted sequentially: IL-1α → neuropathic pain → subjective anxiety → depressive symptoms. High CRP levels exacerbated the relationship between anxiety and neuropathic pain. GBR analysis confirmed IL-1α as the most significant feature predicting pain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003cbr\u003e\nThe findings highlight IL-1α and CRP as key inflammatory mediators contributing to the interplay between pain, anxiety, and depressive symptoms. These results underscore the importance of targeting inflammation-related pathways in the assessment and treatment of pain in the MDD.\u003c/p\u003e","manuscriptTitle":"IL-1α and CRP as key inflammatory mediators linking neuropathic pain, anxiety, and depression in major depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:03:52","doi":"10.21203/rs.3.rs-8823009/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T06:30:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T13:12:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T12:17:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143361510404067998019618547348878757859","date":"2026-03-16T07:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154452365535322764537557331328126104961","date":"2026-03-12T07:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209870226119301621067102792919388867895","date":"2026-03-09T13:03:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T04:18:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T12:22:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T12:22:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-02-08T16:18:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2587bfcb-33c6-4bd2-9221-b596523eed4e","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T01:38:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:03:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8823009","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8823009","identity":"rs-8823009","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}