The Combined Predictive Value of LHR, NLR, and Platelet Count for First-Episode Schizophrenia

The Combined Predictive Value of LHR, NLR, and Platelet Count for First-Episode Schizophrenia | 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 The Combined Predictive Value of LHR, NLR, and Platelet Count for First-Episode Schizophrenia Chunyang Shi, Gang Zhang, Zhoubing Wang, Xianlu Chang, Zhiyun Yang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8356779/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Objective To investigate the predictive value of the combination of lymphocyte-to-high-density lipoprotein ratio (LHR), neutrophil-to-lymphocyte ratio (NLR), and platelet count for patients with first-episode schizophrenia (FES). Methods A retrospective analysis was performed on 181 inpatients with FES admitted to Zhenjiang Mental Health Center between January 2015 and February 2025.189 participants, including staff members who underwent health check-ups at the same center from July 2024 to November 2025, were prospectively recruited as the healthy control group. Both groups underwent fasting blood cell analysis. Univariate analysis, binary Logistic regression analysis, and receiver operating characteristic (ROC) curve analysis were employed to assess the predictive efficacy of the combined use of LHR, NLR, and platelet count for FES. Results Univariate analysis revealed that white blood cell count, neutrophil count, monocyte count, platelet count, high-density lipoprotein (HDL), neutrophil-to-high-density lipoprotein ratio (NHR), LHR, platelet-to-lymphocyte ratio (PLR), NLR, and systemic immune-inflammation index (SIRI) were statistically significant influencing factors associated with FES (all P < 0.05) when comparing the FES group with the healthy control group. Binary Logistic regression analysis further identified platelet count, LHR, and NLR as independent predictors of FES relative to healthy controls (all P < 0.05). The ROC curve analysis demonstrated that the combination of platelet count, LHR, and NLR yielded an area under the curve (AUC) of 0.863 for predicting FES. Conclusion The combination of, LHR, NLR and platelet count exhibits favorable predictive performance for FES, suggesting its potential as a biomarker for early identification of this population. First-episode Schizophrenia Schizophrenia LHR NLR Platelet Count Figures Figure 1 Figure 2 1. Introduction Schizophrenia is a severe mental disorder associated with high disability rates, imposing a substantial disease burden on patients, families, and society [ 1 ]. Characterized by multifaceted impairments in cognition, perception, emotion, and behavior, its chronic and protracted course often leads to profound deficits in social functioning [ 2 ]. Currently, diagnosis relies primarily on clinical psychiatric evaluations, lacking objective biological biomarkers [ 3 ]. This subjectivity may contribute to diagnostic delays or heterogeneity, particularly during the first episode—when early, accurate identification is critical for initiating timely interventions and optimizing long-term outcomes. Thus, identifying reliable, accessible objective biomarkers to facilitate early detection and risk assessment of schizophrenia, especially first-episode schizophrenia (FES), remains a pressing priority in psychiatry [ 4 ]. In recent years, the immune-inflammatory hypothesis of mental disorders has garnered widespread attention, offering a new lens for biomarker exploration [ 5 – 7 ]. Accumulating evidence links the pathogenesis of schizophrenia to activation of both central nervous system and systemic immune-inflammatory pathways [ 6 , 8 ]. Patients frequently exhibit elevated peripheral inflammatory cytokines, aberrant cellular immune responses, and dysregulated inflammation-related signaling cascades [ 9 ]. This chronic, low-grade inflammatory state may contribute to schizophrenia’s pathophysiology via mechanisms including altered neurotransmitter metabolism, impaired neuroplasticity, disrupted blood-brain barrier integrity, and dysfunctional glial cell activity [ 10 ]. Within this framework, inflammatory indices derived from complete blood counts—characterized by low cost, ease of measurement, and high reproducibility—emerge as promising biomarker candidates [ 11 ]. Neutrophil-to-lymphocyte ratio (NLR) [ 12 ]and lymphocyte to-high-density lipoprotein ratio (LHR) [ 13 ] are widely studied composite inflammatory markers. NLR reflects the balance between pro-inflammatory (neutrophil-driven) and immunoregulatory (lymphocyte-mediated) processes, with predictive value in multiple inflammation-associated somatic diseases and some mental disorders [ 14 ]. In contrast, the LHR may capture the dynamic crosstalk between the immune system (mediated by lymphocytes) and lipid metabolism coupled with anti-inflammatory defense (exerted by high-density lipoprotein) [ 15 ]. In schizophrenia, elevated NLR and altered (often increased) LHR have been documented in peripheral blood, indicative of neutrophil system activation, perturbations in lymphocyte count or function, and concomitant abnormalities in high-density lipoprotein levels or biological activity[ 13 , 16 ]. Platelets, traditionally recognized for their role in hemostasis, are increasingly acknowledged as pivotal inflammatory effectors: they secrete pro-inflammatory mediators and engage in crosstalk with circulating leukocytes. Aberrations in platelet count and activity have been documented in schizophrenia, potentially linked to concurrent inflammation, oxidative stress, and increased cardiovascular risk [ 17 ]. Most existing studies focus on single or a limited set of markers, yielding inconsistent results with modest diagnostic performance, failing to meet clinical demands for high-precision predictive tools. Given schizophrenia’s complex, multifactorial pathogenesis, combining blood indices reflecting distinct immune-inflammatory dimensions (e.g., innate immunity, adaptive immunity, and platelet activity) to construct a composite predictive model may better capture the disease’s holistic pathophysiological profile, thereby enhancing prediction accuracy and robustness. Against this backdrop, the present study aims to investigate the interrelationships and combined utility of two emerging inflammatory ratios—LHR and NLR—and platelet count. We hypothesize that drug-naive FES patients will exhibit characteristic alterations in LHR, NLR, and PLT relative to healthy controls, and that their combination will form an effective biomarker panel to improve FES discrimination. To test this, we conducted a study integrating retrospective and prospective data, systematically comparing blood parameters between FES patients and healthy controls. Multivariate statistical analyses and receiver operating characteristic (ROC) curve analyses were employed to rigorously evaluate the predictive performance of LHR, NLR, and PLT, both individually and in combination. This study represents the systematic evaluation of the combined predictive value of LHR, NLR, and PLT for FES. If validated, this simple, accessible blood-based panel could serve as a practical tool for early clinical identification of schizophrenia, advancing the objective diagnosis of mental disorders and providing novel clinical insights into the immune-inflammatory mechanisms underlying schizophrenia. 2. Methods 2.1 Study population In the present study, the study population was divided into two groups: the FES group and the healthy control (HC) group. Data collection for the FES group was retrospective. A systematic search was conducted on the electronic inpatient database of Zhenjiang Mental Health Center, with search parameters restricted to discharge dates (January 1, 2015 to February 28, 2025) and primary diagnosis of schizophrenia. Three independent investigators performed blind screening of the extracted dataset in accordance with standardized operating procedures. Following independent screening by each investigator, the research team conducted multiple rounds of cross-validation to identify FES cases that met the predefined inclusion criteria. Concurrently, patients or their family members were contacted to confirm that the individuals had a definitive diagnosis of schizophrenia and were in the first-episode, untreated stage at the time of admission. Any discrepancies in case adjudication were resolved through consultation with a fourth senior researcher until a consensus was reached. Data collection for the HC group was prospective. Eligible staff members of Zhenjiang Mental Health Center who underwent health check-ups between July 8 2024 and November 7 2025 were recruited, and their blood cell analysis results were collected as the primary outcome measure. For the HC group, the data collected by three independent investigators in compliance with standardized operating procedures were subjected to multiple rounds of verification to confirm eligibility for inclusion. Similarly, any disagreements in the determination of HC group eligibility were addressed through deliberation with a fourth senior researcher until a consensus was achieved. 2.2 Study selection The inclusion criteria for the FES group are as follows: (1) Initial hospitalization at Zhenjiang Mental Health Center with schizophrenia as the primary diagnosis in treatment-naïve patients; (2) Schizophrenia diagnosis confirmed by Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria; (3) Absence of prior antipsychotic therapy. The exclusion criteria for the FES group are as follows: (1) By reviewing the patient's past medical records and consulting the patient's family members by phone. Comorbid psychiatric disorders other than schizophrenia such as depressive disorders, and psychoactive substance abuse such as marijuana; (2) Inflammatory conditions or dyslipidemia potentially confounding hematological/biochemical analyses, such as autoimmune diseases, acute infections and so on;(3) The use of anti-inflammatory or lipid-regulating medications was required prior to hospital admission; (4) The test report or scale score is missing; (5) Previous schizophrenia diagnosis established at external healthcare institutions. The inclusion criteria for the HC group are as follows: (1) Aged between 18 and 40 years;(2) All blood cell test results fall within the normal range;(3) A fasting blood cell analysis was performed within 2 weeks prior to enrollment, with all indicators within the normal reference range of the ZhenJiang Mental Health Center laboratory;(4) Capable of understanding the study content and voluntarily signing the written informed consent form. The exclusion criteria for the HC group are as follows: (1) Diagnosis of schizophrenia or other mental disorders confirmed via the SCID-I; (2) History of acute or chronic conditions that may interfere with blood cell analysis within 2 weeks prior to enrollment;(3) Presence of severe or unstable cardiovascular, cerebrovascular, hepatic, renal, endocrine (e.g., uncontrolled diabetes mellitus, thyroid disorders), hematological, or neoplastic diseases.;(4) Use of any medications potentially affecting the immune or inflammatory systems (e.g., antibiotics, nonsteroidal anti-inflammatory drugs, corticosteroids, immunosuppressants) or any psychoactive substances/medications within 2 weeks prior to enrollment; or experience of major life stressors;(5) Pregnant, lactating, or menstruating women; (6) Engagement in vigorous physical exercise or excessive alcohol consumption within 24 hours prior to enrollment. Ultimately, 181 patients with first-episode schizophrenia and 189 healthy controls were enrolled in this study. The detailed recruitment process of the study is illustrated in Fig. 1 . Blood tests for patients in the FES group were scheduled to be performed under fasting conditions on the second day after admission. If a patient had not consumed food on the day of admission and thus met the fasting requirement, the blood test was conducted on the same day. The specific general information of the two groups of people is detailed in Table 1 . Table 1 The specific general information of the two groups Variables Categories FES group n(%) HC group n(%) Sex Male 77(42.54) 86(45.50) Female 104(57.46) 103(54.50) Age 30 years 59(32.60) 71(37.57) Education degree Illiterate 4(2.21) 0(0.00) Primary school 8(4.42) 13(6.88) Junior high school 55(30.39) 25(13.23) High school 33(18.23) 46(24.34) College or above 81(44.75) 105(55.55) marriage unmarried 128(70.72) 82(43.39) divorce 52(28.73) 21(11.11) married 1(0.55) 86(45.50) 2.3 Data extraction Three independent investigators performed blinded data extraction, encompassing the following domains: 1. Demographic characteristics: hospitalization number, chronological age, gender, educational attainment, and marital status; 2. Laboratory tests: complete blood cell count (including counts of white blood cells, neutrophils, lymphocytes, monocytes, and platelets) and comprehensive metabolic panel (with a specific focus on high-density lipoprotein cholesterol levels). 2.4 Statistical analyses Statistical analyses were performed using SPSS 27.0 software. For univariate analysis, comparisons between groups were conducted via the Mann-Whitney U test and chi-square test. Binary logistic regression analysis was employed to explore the independent influencing factors associated with first-episode schizophrenia patients and the related blood cell parameters and ratios. The receiver operating characteristic (ROC) curve method was utilized to evaluate the predictive value of the combined use of LHR, NLR, and PLT for FES, with the area under the curve (AUC) calculated accordingly. The P value of less than 0.05 was deemed statistically significant. 3. Results 3.1 Univariate analysis between the two groups A univariate analysis was performed on 181 patients in the FES Group and 189 individuals in the HC Group. Given that the data of both groups did not follow a normal distribution, a non-parametric test was employed. The results revealed that White Blood Cell Count, Neutrophil Count, Platelet Count, high-density lipoprotein (HDL), NHR, LHR, PLR, NLR, and SIRI were influencing factors associated with first-episode schizophrenia (all P < 0.05). For further details, please refer to Table 2 . Table 2 Univariate analysis between the two groups FES Group (n = 181) HC Group (n = 189) Statistics P White Blood Cell Count 7.05 ± 1.88 5.87 ± 1.46 -5.977 <0.001 Neutrophil Count 4.52 ± 1.88 3.45 ± 1.00 -5.626 <0.001 Lymphocyte Count 2.01 ± 0.74 1.96 ± 0.37 -0.169 0.866 Mononuclear cell count 0.47 ± 0.17 0.44 ± 0.15 -2.326 0.020 Platelet Count 220.64 ± 61.72 253.30 ± 57.08 -5.307 <0.001 HDL 1.38 ± 0.35 1.63 ± 0.30 -7.606 <0.001 NHR 3.44 ± 1.60 2.19 ± 0.80 -8.571 <0.001 LHR 1.56 ± 0.70 1.25 ± 0.36 -4.279 <0.001 PHR 168.64 ± 59.54 161.92 ± 53.25 -1.377 0.169 PLR 126.42 ± 69.76 132.51 ± 34.59 -3.764 <0.001 NLR 2.80 ± 2.63 1.80 ± 0.59 -4.459 <0.001 MLR 0.27 ± 0.15 0.23 ± 0.08 -1.937 0.053 SII 617.87 ± 600.16 453.95 ± 176.05 -1.449 0.147 SIRI 1.30 ± 1.27 0.82 ± 0.48 -4.783 <0.001 3.2 Analysis of independent influencing factors for patients with FES We took whether the patients were first-episode schizophrenia patients as the dependent variable and conducted binary logistic regression analysis on the parts with statistical significance in the univariate analysis of the two groups. The results revealed that Platelet Count( β = 0.022, P = 0.016), LHR( β =-5.425, P < 0.001), and NLR( β =-3.139, P = 0.003) were independent influencing factors for first-episode schizophrenia. For further details, please refer to Table 3 . Table 3 Binary logistic regression analysis was performed to compare the two groups Variables β OR 95%CI S.E P White Blood Cell Count -0.268 0.227 0.495,1.181 0.222 0.227 Neutrophil Count 0.371 1.450 0.367,5.731 0.701 0.596 Mononuclear cell count -1.015 0.362 0.002,79.395 2.750 0.712 Platelet Count 0.022 1.022 1.004,1.040 0.009 0.016 HDL 0.004 1.004 0.082,12.352 1.281 0.997 NHR 1.144 3.141 0.879,11.217 0.650 0.078 LHR -5.425 0.004 0.000,0.039 1.116 <0.001 PLR -0.012 0.988 0.957,1.021 0.017 0.473 NLR -3.139 0.043 0.005,0.355 1.073 0.003 SIRI 0.420 1.521 0.145,15.990 1.200 0.727 3.3 The predictive value of combined LHR, NLR, and platelet count for FES To facilitate readers' interpretation of the figure, the directionality of the ROC curve analysis was defined such that a smaller test result indicates a more definitive diagnostic outcome. The combined prediction model developed in this study exhibited superior performance, with the integration of LHR, NLR, and platelet count elevating the area under the ROC curve (AUC) to 0.863(Table 4 ). A notable observation is that while the individual AUC values of LHR and NLR were found to be below 0.5 in the analyses, their inclusion in the combined model significantly enhanced the overall discriminative capacity. This finding suggests that LHR and NLR are negatively correlated with the target outcome. For variables like LHR and NLR that demonstrate an inverse association with the outcome, the regression model assigns them negative coefficients. Consequently, when computing the composite score, higher values of LHR and NLR are correctly weighted as evidence supporting FES classification, whereas lower platelet counts are weighted as stronger evidence for FES. This underscores a key advantage of multivariate statistical models: they can automatically identify and adjust for the directionality of associations (positive or negative) between each predictor and the outcome. By assigning appropriate weights to each variable and integrating their complementary predictive information, a more robust composite predictor is generated—one that outperforms any single indicator. The relatively low individual AUC values of LHR and NLR do not imply they are non-informative; instead, they reflect their unique predictive patterns, which require adjustment within the model framework to unlock their full utility. For further details, please refer to Fig. 2 . Table 4 Analysis of the predictive efficacy of the combination of LHR, NLR, and platelet count for FES Variables AUC 95%CI P Platelet Count 0.660 0.604,0.715 <0.001 LHR 0.371 0.312,0.431 <0.001 NLR 0.366 0.307,0.425 <0.001 Platelet Count + LHR 0.728 0.677,0.780 <0.001 Platelet Count + NLR 0.725 0.673,0.777 <0.001 LHR + NLR 0.822 0.779,0.864 <0.001 LHR + NLR + Platelet Count 0.863 0.827,0.898 <0.001 4. Discussion This study systematically explored the predictive value of the combined application of three hematological indicators, namely LHR, NLR, and platelet count, for FES through a combination of retrospective and prospective methods. The main findings can be summarized as follows: (1) Univariate analysis showed significant differences in multiple inflammation and metabolism-related blood indicators between FES patients and healthy controls; (2) Multivariate Logistic regression analysis further confirmed that platelet count, LHR, and NLR were independent predictors for distinguishing FES from healthy controls; (3) ROC curve analysis indicated that the predictive model constructed by the combination of the above three indicators demonstrated high discriminatory efficacy (AUC = 0.863). These results suggest that the combination of LHR, NLR, and platelet count derived from routine blood tests may serve as a simple, economical, and effective auxiliary biological marker for the early identification and risk assessment of FES. The following will conduct an in-depth discussion around the results of this study, combined with existing literature, from aspects such as pathophysiological mechanisms, clinical significance, comparative analysis with previous studies, and future directions. 4.1 The pathophysiology of this study: Linking immune inflammation, metabolic disorders and platelet function The etiology of schizophrenia is complex, involving multifaceted interactions among genetic, neurodevelopmental, and environmental factors [ 18 ]. In recent years, growing attention has been paid to the roles of the immune-inflammatory hypothesis, metabolic dysregulation, and altered platelet function in the pathophysiology of schizophrenia. The three independent predictors identified in this study—LHR, NLR, and platelet count—aptly mirror these core pathological processes from distinct perspectives [ 13 , 19 , 20 ]. LHR is an emerging composite biomarker that concurrently reflects the status of the immune system (via lymphocytes) and lipid metabolism (via HDL) [ 21 ]. In the present study, LHR was found to be significantly elevated in the FES group and served as an independent predictor—findings that align with its established relevance in cardiovascular diseases and several inflammatory disorders[ 22 – 24 ]. Patients with schizophrenia frequently exhibit abnormalities in the quantity and function of lymphocytes, including T cells, B cells, and natural killer cells [ 25 ]. These alterations may be associated with autoimmune reactions and aberrant immune responses to central nervous system antigens [ 13 , 25 ]. Changes in lymphocyte count constitute one of the components of the LHR. Traditionally, HDL has been recognized as "good cholesterol" owing to its anti-inflammatory, antioxidant, and reverse cholesterol transport-promoting properties [ 26 ]. However, a growing body of evidence indicates that HDL levels may be reduced in states of chronic inflammation, such as schizophrenia [ 27 ]. Reduced HDL levels or functional abnormalities have been documented in patients with schizophrenia [ 27 , 28 ]. Thus, the elevation of LHR may concurrently capture the relative increase in lymphocytes and/or the relative reduction or functional impairment of HDL, precisely reflecting the intersection of immune activation and the dysregulation of metabolic protective mechanisms in schizophrenia. NLR has been widely employed to evaluate systemic inflammatory burden and stress status across a spectrum of somatic diseases and psychiatric disorders (e.g., major depressive disorder, bipolar disorder) [ 29 ]. The present study demonstrated that NLR was elevated in patients with FES and served as an independent predictor—findings that strengthen the evidence for a close association between schizophrenia and activation of the innate immune system[ 29 – 31 ]. An increase in neutrophils reflects the rapid, non-specific activation of the innate immune system, which may be triggered by multiple factors including infection, psychosocial stress, and microbial translocation secondary to intestinal barrier dysfunction [ 30 ]. Reactive oxygen species, proteases, and inflammatory cytokines (e.g., IL-6, IL-1β) released by neutrophils can contribute to neurotoxic processes via mechanisms such as disrupting the blood-brain barrier, activating microglia, and interfering with neurotransmitter systems [ 32 ]. Conversely, the relative reduction in lymphocytes may be associated with elevated glucocorticoid levels induced by chronic stress, immune cell redistribution, or increased apoptosis [ 31 , 33 ]. The NLR integrates these two opposing trends, providing a robust index of systemic inflammation. A higher NLR indicates a neutrophil-dominated pro-inflammatory state accompanied by potential suppression of adaptive immunity (mediated by lymphocytes), an immune profile that aligns closely with the neuroinflammation hypothesis of schizophrenia [ 34 ]. The present study identified platelet count as an independent predictor of FES. Platelets are not only central components of the coagulation cascade but also critical inflammatory effector cells and reservoirs for neuroactive substances [ 35 ]. Platelets express a diverse array of pattern recognition receptors (PRRs) and cytokine receptors on their surface, which can be activated by inflammatory signals [ 36 ]. Upon activation, platelets amplify inflammatory responses by releasing a multitude of inflammatory mediators, including platelet factor 4, transforming growth factor-β (TGF-β), and CD40 ligand [ 37 ]. Additionally, they interact with leukocytes (particularly monocytes and neutrophils) to promote the recruitment and activation of these cells [ 35 – 37 ]. Approximately 95% of serotonin in the human body is stored in platelets. The serotonin hypothesis of schizophrenia has a long-standing history, and abnormalities in platelet-mediated serotonin uptake, and release may indirectly reflect dysregulation of the central 5-hydroxytryptamine (5-HT) system [ 38 ]. Furthermore, aberrations in platelet mitochondrial function and activation markers (e.g., P-selectin) have also been reported in patients with schizophrenia [ 39 ]. The inflammatory state itself can stimulate platelet production and enhance platelet activity [ 40 ]. Accordingly, alterations in platelet count may share intrinsic pathophysiological links with the NLR and the LHR collectively forming a biological signaling network that reflects the multidimensional pathological processes of schizophrenia. 4.2 Advantages of Combined Indicators: From Single Indicators to Ensemble Prediction Models The most clinically insightful finding of the present study is that the combination of platelet count, LHR, and NLR yields significantly superior predictive performance (AUC = 0.863) compared to any individual indicator. This finding profoundly underscores the multi-systemic nature of schizophrenia. Individual biomarkers typically capture only a single facet of a disease’s pathophysiological network and are prone to confounding by individual variability, comorbidities, and assay fluctuations. The LHR, NLR, and platelet count respectively target immune-metabolic crosstalk, systemic inflammatory burden, and platelet-associated pathology, delivering complementary yet interconnected insights across distinct biological pathways [ 25 , 30 ]. Their integration constructs a multidimensional, multi-pathway biological "fingerprint"—a composite signature that more comprehensively and robustly captures the full spectrum of FES-related biological dysregulation. While the present study primarily focuses on FES and healthy controls, such composite indicators hold potential for future research to distinguish schizophrenia from other psychiatric disorders (e.g., bipolar disorder with psychotic features or major depressive disorder with psychotic symptoms) or to differentiate FES patients with distinct subtypes or prognostic profiles [ 34 ]. The high AUC value of the model provides a foundational basis for such applications. All the indicators of this combination are derived from routine, low-cost, and rapid complete blood count and lipid profile analysis, which is highly feasible for promotion in medical institutions at all levels, including grassroots mental health centers. This "old indicators, new combination" strategy is an efficient approach to realizing the transition of early biomarkers for schizophrenia from laboratory research to clinical practice. 4.3 Comparison with Prior Studies The findings of the present study align with the prevailing research paradigm surrounding immune-inflammatory and metabolic mechanisms in schizophrenia in recent years, while offering novel insights and deeper exploration in terms of specific indicator combinations and population focus. A growing body of research has documented elevated inflammatory ratios (e.g., NLR, PLR), abnormal HDL levels, and alterations in platelet parameters among patients with schizophrenia [ 26 , 41 ]. The present study not only replicates these findings but, more critically, employs multivariate analysis to derive a core combination of indicators with the strongest independent predictive value—incorporating the emerging LHR and emphasizing the centrality of immune-metabolic co-dysregulation. Inconsistencies have been observed in some earlier research findings—for example, reports on platelet count in schizophrenia have been conflicting, with some documenting elevations and others reductions[ 42 , 43 ]. Such discrepancies may stem from variations in study populations (e.g., first-episode status, medication exposure, disease duration), sample sizes, and control for comorbidities. The present study was strictly restricted to drug-naive patients with FES, thereby minimizing confounding effects from antipsychotic medications (many of which are known to alter hematological parameters) and secondary changes associated with disease chronicity. This design allows for a more accurate reflection of the biological underpinnings of the disease in its early stages. Our findings provide clear evidence that platelet count acts as an independent factor involved in the disease process during the acute phase of FES. 4.4 Future Directions In psychiatric outpatient settings or general hospitals, for patients presenting with first-episode psychotic symptoms without a definitive diagnosis, or during community-based screening for populations at high risk of schizophrenia, calculating the combination of LHR, NLR, and platelet count (i.e., decreased platelet count concurrent with elevated LHR and NLR) may provide an objective, quantifiable biological reference to aid clinicians in comprehensive decision-making and facilitate early intervention. Abnormalities in this indicator panel may indicate a subtype of FES characterized by prominent immune-metabolic-platelet dysfunction. This could inform the future development of biomarker-based classification of schizophrenia and offer insights for personalized treatment. 5. Conclusions and Limitations This study employed a hybrid design integrating retrospective (FES group) and prospective (HC group) approaches. The FES group, with a moderate sample size (n = 181), was explicitly defined as FES, enabling effective control of confounding factors such as medication exposure, long-term disease progression, and other covariates. The statistical workflow of this study was methodologically rigorous: starting with univariate screening, proceeding to multivariate modeling, and culminating in ROC performance evaluation, yielding robust conclusions. Ultimately, this study demonstrated that the combination of platelet count, LHR, and NLR constitutes a promising predictive panel for FES. While this study establishes associations and predictive utility, it cannot infer causal relationships. Specifically, whether alterations in these hematological indicators represent a cause, consequence, or comorbid state of schizophrenia remains to be elucidated—this requires prospective cohort studies (e.g., long-term follow-up of ultra-high-risk populations or the general population) to validate their efficacy in predicting disease onset. All participants in this study were recruited from a single medical center, which may introduce selection bias. Future research should conduct external validation across multi-center cohorts encompassing diverse ethnic and cultural backgrounds to assess the generalizability of the proposed model. Notwithstanding the need for prospective cohort studies and mechanistic investigations to deepen and validate these findings, this study undoubtedly paves a promising new avenue for the development of early, objective biological adjunctive diagnostic tools for schizophrenia. It further underscores the importance of adopting a systematic, multi-dimensional biological perspective in both schizophrenia research and clinical practice. Declarations Authors' contributions ChunYang Shi wrote the first draft of the manuscript. Xianlu Chang,Zhiyun Yang, and Yunxia Guo were three independent investigators (Responsible for collecting data). Zhoubing Wang was the fourth senior researcher (Responsible for negotiating and reaching a consensus to solve problems). Gang Zhang and Chunyang Shi performed the statistical analysis. Yangzhi Yun and Xianlu Chang did the data curation. HuiYing Xue critically revised the manuscript. All authors contributed to the final version of the manuscript. Ethics approval In accordance with the content of the Declaration of Helsinki, we strictly abide by the ethics review of the Ethics Committee of the Zhenjiang Mental Health Center. The research involving the participants was approved by the Zhenjiang Mental Health Center Ethics Review Committee (Approve the number: K2025-05). Funding This study was supported by two funding projects as follows: The 2024 Guiding Science and Technology Program for Social Development of Zhenjiang City (Project No.: FZ2024091); and the 2025 General Medical and Health Project under the Social Development Category of Zhenjiang Science and Technology Program (Project No.: SH2025064). The funders had no role in the study design, data collection, analysis, publication decision, or manuscript preparation. Declaration of competing interest All authors have no conflicts to declare. Declaration of Consenting to Participate All participants in this study consent to participate in this study. Data availability All data and model generated or used during the study appear in the submitted article, further inquiries can be directed to the first/ corresponding authors. References Luo Q, An M, Wu Y, Wang J, Mao Y, Zhang L, et al. Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population. Ann Gen Psychiatry. 2025;24(1):11. D'Addario C, Di Bartolomeo M. Epigenetic Control in Schizophrenia. Subcell Biochem. 2025;108:191–215. Lahiri A, Yigit R, Lawrie S. The Reliability, Validity and Utility of a Diagnosis in Schizophrenia. Br J Hosp Med (Lond). 2025;86(10):1–9. Tavakoli H, Rostami R, Shalbaf R, Nazem-Zadeh MR. Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning. Brain Behav. 2025;15(1):e70219. Shiwaku H. Autoantibodies and Inflammation in Schizophrenia. Psychiatry Clin Neurosci. 2025;79(10):611–8. Speers LJ, Bilkey DK. Inflammation in Schizophrenia: The Role of Disordered Oscillatory Mechanisms. Cells. 2025;14(9):650. Sun Z, Han B, Ding Q, Feng Y, Jia T, Ouyang Y, Guo X, Liang J, Huang Q, Kou C, Bai W. Systematic evaluation of the associations between schizophrenia and autoimmune diseases: An umbrella review. Autoimmun Rev. 2025;24(9):103854. Patlola SR, Donohoe G, McKernan DP. The relationship between inflammatory biomarkers and cognitive dysfunction in patients with schizophrenia: A systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;121:110668. Halstead S, Siskind D, Amft M, Wagner E, Yakimov V, Shih-Jung Liu Z, et al. Alteration patterns of peripheral concentrations of cytokines and associated inflammatory proteins in acute and chronic stages of schizophrenia: a systematic review and network meta-analysis. Lancet Psychiatry. 2023;10(4):260–71. Khandaker GM, Cousins L, Deakin J, Lennox BR, Yolken R, Jones PB. Inflammation and immunity in schizophrenia: implications for pathophysiology and treatment. Lancet Psychiatry. 2015;2(3):258–70. Ermakov E, Mednova I, Boiko A, Ivanova S. Neuroinflammation in Schizophrenia: An Overview of Evidence and Implications for Pathophysiology. J Integr Neurosci. 2025;24(7):27636. Gercek HG, Citir BG, Bukulme A. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as inflammation markers for early-onset schizophrenia. Bratisl Lek Listy. 2023;124(7):503–7. Wei Y, Wang T, Li G, Feng J, Deng L, Xu H, et al. Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder. Front Psychiatry. 2022;13:941728. Sandberg AA, Steen VM, Torsvik A. Is Elevated Neutrophil Count and Neutrophil-to-Lymphocyte Ratio a Cause or Consequence of Schizophrenia?-A Scoping Review. Front Psychiatry. 2021;12:728990. Chen H, Xiong C, Shao X, Ning J, Gao P, Xiao H, et al. Lymphocyte To High-Density Lipoprotein Ratio As A New Indicator Of Inflammation And Metabolic Syndrome. Diabetes Metab Syndr Obes. 2019;12:2117–23. Liang J, Guan X, Sun Q, Hao Y, Xiu M. Neutrophil/lymphocyte ratio and cognitive performances in first-episode patients with schizophrenia and healthy controls. Prog Neuropsychopharmacol Biol Psychiatry. 2024;135:111092. Cabello-Rangel H, Basurto-Morales M, Botello-Aceves E, Pazarán-Galicia O. Mean platelet volume, platelet count, and neutrophil/lymphocyte ratio in drug-naïve patients with schizophrenia: a cross-sectional study. Front Psychiatry. 2023;14:1150235. Jauhar S, Johnstone M, McKenna PJ. Schizophrenia Lancet. 2022;399(10323):473–86. Zhu Y, Ren J, Fu Z. Sex-specific associations between Neutrophil-Percentage-to-Albumin Ratio(NPAR) and Neutrophil-to-Lymphocyte Ratio (NLR) with positive and negative symptoms in schizophrenia. BMC Psychiatry. 2025;25(1):873. Chen Z, Wang J, Carru C, Sedda S, Nivoli AM, Li Z. Meta-analysis of peripheral mean platelet volume in patients with mental disorders: Comparisons in depression, anxiety, bipolar disorder, and schizophrenia. Brain Behav. 2023;13(11):e3240. Kulacaoglu F, Yıldırım YE, Aslan M, İzci F. Neutrophil to lymphocyte and monocyte to high-density lipoprotein ratios are promising inflammatory indicators of bipolar disorder. Nord J Psychiatry. 2023;77(1):77–82. Chuang SM, Liu SC, Chien MN, Lee CC, Lee YT, Chien KL. Neutrophil-to-High-Density Lipoprotein Ratio (NHR) and Neutrophil-to-Lymphocyte Ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study. Am J Prev Cardiol. 2024;20:100869. Zhao J, Zheng Q, Ying Y, Luo S, Liu N, Wang L, et al. Association between high-density lipoprotein-related inflammation index and periodontitis: insights from NHANES 2009–2014. Lipids Health Dis. 2024;23(1):321. Guo J, Mutailipu K, Wen X, Yin J, You H, Qu S, et al. Association between lymphocyte to high-density lipoprotein cholesterol ratio and insulin resistance and metabolic syndrome in US adults: results from NHANES 2007–2018. Lipids Health Dis. 2025;24(1):9. Zheng Y, Zhang Q, Zhou X, Yao L, Zhu Q, Fu Z. Altered levels of cytokine, T- and B-lymphocytes, and PD-1 expression rates in drug-naïve schizophrenia patients with acute phase. Sci Rep. 2023;13(1):21711. Diaz L, Bielczyk-Maczynska E. High-density lipoprotein cholesterol: how studying the 'good cholesterol' could improve cardiovascular health. Open Biol. 2025;15(2):240372. Wang J, Kockx M, Bolek M, Lambert T, Sullivan D, Chow V, et al. Triglyceride-rich lipoprotein, remnant cholesterol, and apolipoproteins CII, CIII, and E in patients with schizophrenia. J Lipid Res. 2024;65(7):100577. Herceg D, Mimica N, Herceg M, Puljić K. Aggression in Women with Schizophrenia Is Associated with Lower HDL Cholesterol Levels. Int J Mol Sci. 2022;23(19):11858. Tomioka 29SS, Mera H, Tazaki K, Nishiyama T, Yamada H. Neutrophil-Lymphocyte Ratio in Patients With Acute Schizophrenia. Cureus. 2024;16(1):e52181. Lu X, Sun Q, Wu L, Liao M, Yao J, Xiu M. The neutrophil-lymphocyte ratio in first-episode medication-naïve patients with schizophrenia: A 12-week longitudinal follow-up study. Prog Neuropsychopharmacol Biol Psychiatry. 2024;131:110959. Yüksel RN, Ertek IE, Dikmen AU, Göka E. High neutrophil-lymphocyte ratio in schizophrenia independent of infectious and metabolic parameters. Nord J Psychiatry. 2018;72(5):336–40. González-Castro TB, Tovilla-Zárate CA, Juárez-Rojop IE, Hernández-Díaz Y, López-Narváez ML, Ortiz-Ojeda RF. Effects of IL-6/IL-6R axis alterations in serum, plasma and cerebrospinal fluid with the schizophrenia: an updated review and meta-analysis of 58 studies. Mol Cell Biochem. 2024;479(3):525–37. Wang C, Zhu D, Zhang D, Zuo X, Yao L, Liu T, et al. Causal role of immune cells in schizophrenia: Mendelian randomization (MR) study. BMC Psychiatry. 2023;23(1):590. Canli D. Evaluation of systemic immune inflammation index and neutrophil-to-lymphocyte ratio in schizophrenia, bipolar disorder and depression. Bratisl Lek Listy. 2024;125(8):472–6. Maouia A, Rebetz J, Kapur R, Semple JW. The Immune Nature of Platelets Revisited. Transfus Med Rev. 2020;34(4):209–20. Kumar V, Stewart Iv JH. Platelet's plea to Immunologists: Please do not forget me. Int Immunopharmacol. 2024;143(Pt 3):113599. Koupenova M, Livada AC, Morrell CN. Platelet and Megakaryocyte Roles in Innate and Adaptive Immunity. Circ Res. 2022;130(2):288–308. De Giovanni M, Chen H, Li X, Cyster JG. GPR35 and mediators from platelets and mast cells in neutrophil migration and inflammation. Immunol Rev. 2023;317(1):187–202. Burnouf T, Chou ML, Lundy DJ, Chuang EY, Tseng CL, Goubran H. Expanding applications of allogeneic platelets, platelet lysates, and platelet extracellular vesicles in cell therapy, regenerative medicine, and targeted drug delivery. J Biomed Sci. 2023;30(1):79. Anjum A, Mader M, Mahameed S, Muraly A, Denorme F, Kliem FP, et al. Aging platelets shift their hemostatic properties to inflammatory functions. Blood. 2025;145(14):1568–82. Zhu X, Zhou J, Zhu Y, Yan F, Han X, Tan Y, et al. Neutrophil/lymphocyte, platelet/lymphocyte and monocyte/lymphocyte ratios in schizophrenia. Australas Psychiatry. 2022;30(1):95–9. Wood PL, Unfried G, Whitehead W, Phillipps A, Wood JA. Dysfunctional plasmalogen dynamics in the plasma and platelets of patients with schizophrenia. Schizophr Res. 2015;161(2–3):506–10. Zhang Y, Zheng Y, Ni P, Liang S, Li X, Yu H, et al. New role of platelets in schizophrenia: predicting drug response. Gen Psychiatr. 2024;37(2):e101347. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor assigned by journal 16 Dec, 2025 Submission checks completed at journal 16 Dec, 2025 First submitted to journal 14 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8356779","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581361128,"identity":"221339fd-df18-42bc-accc-33c6fe246f51","order_by":0,"name":"Chunyang Shi","email":"","orcid":"","institution":"ZhenJiang Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Chunyang","middleName":"","lastName":"Shi","suffix":""},{"id":581361131,"identity":"6514f8c1-0b60-4363-aa85-1e6495feaffd","order_by":1,"name":"Gang Zhang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Shenyang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zhang","suffix":""},{"id":581361134,"identity":"f5195014-03b6-442b-9e44-3b9c91510d5b","order_by":2,"name":"Zhoubing Wang","email":"","orcid":"","institution":"ZhenJiang Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Zhoubing","middleName":"","lastName":"Wang","suffix":""},{"id":581361135,"identity":"e9a2b5c6-6ad5-4575-8d9e-ee5745b97e24","order_by":3,"name":"Xianlu Chang","email":"","orcid":"","institution":"ZhenJiang Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Xianlu","middleName":"","lastName":"Chang","suffix":""},{"id":581361139,"identity":"b115fa84-5cd1-45a8-9ae9-a28de1781044","order_by":4,"name":"Zhiyun Yang","email":"","orcid":"","institution":"ZhenJiang Mental Health Center","correspondingAuthor":false,"prefix":"","firstName":"Zhiyun","middleName":"","lastName":"Yang","suffix":""},{"id":581361140,"identity":"2f619cff-d1a1-4a91-b994-47d26079f0cb","order_by":5,"name":"Huiying Xue","email":"","orcid":"","institution":"The Second Affiliated Hospital of Shenyang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Huiying","middleName":"","lastName":"Xue","suffix":""},{"id":581361142,"identity":"ac2efee3-2820-411d-a137-d1e9af6e20f6","order_by":6,"name":"Yunxia Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie2PoWrDUBSGb7hw1IEwdwojeYVbArV9jclzCES1MBlRkdJxI0aZ3WNMTl6VmQwmIxMzW+o2s7bMzeRGFnY/9XP4P36OUoHAdRL1XJ4wTg/HS9hMUrTpW53MKs4uoZmkwM1gdWYcL2bDg/b307vVQjGAVM4VpVSg4vqRR5V5VxQ9I8p2WzWdvN4qat9fxpXn/M0wkex0ZDtpQRla+xSxxMaIBQ33YrVfSSlviJkzRAA1STH4mRt2LiFCTdw26P0lrVfz4fvH4bLD6PhVbpK43ntWPvjvAUfrvytPztsJBAKB/84ZMF5MKPwFWyEAAAAASUVORK5CYII=","orcid":"","institution":"ZhenJiang Mental Health Center","correspondingAuthor":true,"prefix":"","firstName":"Yunxia","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-12-14 08:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8356779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8356779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101364607,"identity":"19eb5954-f150-4540-8ebf-062d670cd3e2","added_by":"auto","created_at":"2026-01-29 00:50:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":96962,"visible":true,"origin":"","legend":"\u003cp\u003eThe technical roadmap of this study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8356779/v1/cfc0bfb05ecd46af5670774b.png"},{"id":101364609,"identity":"9397678a-83a3-491b-b439-16b2b1571683","added_by":"auto","created_at":"2026-01-29 00:50:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294711,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curve plot of this study\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8356779/v1/6e3fd95612b1c3cfcb475d6f.png"},{"id":101398406,"identity":"54b4b604-d8d6-4798-bc9e-7642187cf801","added_by":"auto","created_at":"2026-01-29 09:41:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1248001,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8356779/v1/0198cb0f-2120-4f09-8a53-480d787838cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Combined Predictive Value of LHR, NLR, and Platelet Count for First-Episode Schizophrenia\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSchizophrenia is a severe mental disorder associated with high disability rates, imposing a substantial disease burden on patients, families, and society [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Characterized by multifaceted impairments in cognition, perception, emotion, and behavior, its chronic and protracted course often leads to profound deficits in social functioning [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, diagnosis relies primarily on clinical psychiatric evaluations, lacking objective biological biomarkers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This subjectivity may contribute to diagnostic delays or heterogeneity, particularly during the first episode\u0026mdash;when early, accurate identification is critical for initiating timely interventions and optimizing long-term outcomes. Thus, identifying reliable, accessible objective biomarkers to facilitate early detection and risk assessment of schizophrenia, especially first-episode schizophrenia (FES), remains a pressing priority in psychiatry [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In recent years, the immune-inflammatory hypothesis of mental disorders has garnered widespread attention, offering a new lens for biomarker exploration [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Accumulating evidence links the pathogenesis of schizophrenia to activation of both central nervous system and systemic immune-inflammatory pathways [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Patients frequently exhibit elevated peripheral inflammatory cytokines, aberrant cellular immune responses, and dysregulated inflammation-related signaling cascades [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This chronic, low-grade inflammatory state may contribute to schizophrenia\u0026rsquo;s pathophysiology via mechanisms including altered neurotransmitter metabolism, impaired neuroplasticity, disrupted blood-brain barrier integrity, and dysfunctional glial cell activity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Within this framework, inflammatory indices derived from complete blood counts\u0026mdash;characterized by low cost, ease of measurement, and high reproducibility\u0026mdash;emerge as promising biomarker candidates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio (NLR) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]and lymphocyte to-high-density lipoprotein ratio (LHR) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] are widely studied composite inflammatory markers. NLR reflects the balance between pro-inflammatory (neutrophil-driven) and immunoregulatory (lymphocyte-mediated) processes, with predictive value in multiple inflammation-associated somatic diseases and some mental disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In contrast, the LHR may capture the dynamic crosstalk between the immune system (mediated by lymphocytes) and lipid metabolism coupled with anti-inflammatory defense (exerted by high-density lipoprotein) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In schizophrenia, elevated NLR and altered (often increased) LHR have been documented in peripheral blood, indicative of neutrophil system activation, perturbations in lymphocyte count or function, and concomitant abnormalities in high-density lipoprotein levels or biological activity[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Platelets, traditionally recognized for their role in hemostasis, are increasingly acknowledged as pivotal inflammatory effectors: they secrete pro-inflammatory mediators and engage in crosstalk with circulating leukocytes. Aberrations in platelet count and activity have been documented in schizophrenia, potentially linked to concurrent inflammation, oxidative stress, and increased cardiovascular risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost existing studies focus on single or a limited set of markers, yielding inconsistent results with modest diagnostic performance, failing to meet clinical demands for high-precision predictive tools. Given schizophrenia\u0026rsquo;s complex, multifactorial pathogenesis, combining blood indices reflecting distinct immune-inflammatory dimensions (e.g., innate immunity, adaptive immunity, and platelet activity) to construct a composite predictive model may better capture the disease\u0026rsquo;s holistic pathophysiological profile, thereby enhancing prediction accuracy and robustness. Against this backdrop, the present study aims to investigate the interrelationships and combined utility of two emerging inflammatory ratios\u0026mdash;LHR and NLR\u0026mdash;and platelet count. We hypothesize that drug-naive FES patients will exhibit characteristic alterations in LHR, NLR, and PLT relative to healthy controls, and that their combination will form an effective biomarker panel to improve FES discrimination. To test this, we conducted a study integrating retrospective and prospective data, systematically comparing blood parameters between FES patients and healthy controls. Multivariate statistical analyses and receiver operating characteristic (ROC) curve analyses were employed to rigorously evaluate the predictive performance of LHR, NLR, and PLT, both individually and in combination.\u003c/p\u003e \u003cp\u003eThis study represents the systematic evaluation of the combined predictive value of LHR, NLR, and PLT for FES. If validated, this simple, accessible blood-based panel could serve as a practical tool for early clinical identification of schizophrenia, advancing the objective diagnosis of mental disorders and providing novel clinical insights into the immune-inflammatory mechanisms underlying schizophrenia.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.1 Study population\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the present study, the study population was divided into two groups: the FES group and the healthy control (HC) group.\u003c/p\u003e \u003cp\u003eData collection for the FES group was retrospective. A systematic search was conducted on the electronic inpatient database of Zhenjiang Mental Health Center, with search parameters restricted to discharge dates (January 1, 2015 to February 28, 2025) and primary diagnosis of schizophrenia. Three independent investigators performed blind screening of the extracted dataset in accordance with standardized operating procedures. Following independent screening by each investigator, the research team conducted multiple rounds of cross-validation to identify FES cases that met the predefined inclusion criteria. Concurrently, patients or their family members were contacted to confirm that the individuals had a definitive diagnosis of schizophrenia and were in the first-episode, untreated stage at the time of admission. Any discrepancies in case adjudication were resolved through consultation with a fourth senior researcher until a consensus was reached.\u003c/p\u003e \u003cp\u003eData collection for the HC group was prospective. Eligible staff members of Zhenjiang Mental Health Center who underwent health check-ups between July 8 2024 and November 7 2025 were recruited, and their blood cell analysis results were collected as the primary outcome measure. For the HC group, the data collected by three independent investigators in compliance with standardized operating procedures were subjected to multiple rounds of verification to confirm eligibility for inclusion. Similarly, any disagreements in the determination of HC group eligibility were addressed through deliberation with a fourth senior researcher until a consensus was achieved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study selection\u003c/h2\u003e \u003cp\u003eThe inclusion criteria for the FES group are as follows: (1) Initial hospitalization at Zhenjiang Mental Health Center with schizophrenia as the primary diagnosis in treatment-na\u0026iuml;ve patients; (2) Schizophrenia diagnosis confirmed by Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria; (3) Absence of prior antipsychotic therapy. The exclusion criteria for the FES group are as follows: (1) By reviewing the patient's past medical records and consulting the patient's family members by phone. Comorbid psychiatric disorders other than schizophrenia such as depressive disorders, and psychoactive substance abuse such as marijuana; (2) Inflammatory conditions or dyslipidemia potentially confounding hematological/biochemical analyses, such as autoimmune diseases, acute infections and so on;(3) The use of anti-inflammatory or lipid-regulating medications was required prior to hospital admission; (4) The test report or scale score is missing; (5) Previous schizophrenia diagnosis established at external healthcare institutions.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for the HC group are as follows: (1) Aged between 18 and 40 years;(2) All blood cell test results fall within the normal range;(3) A fasting blood cell analysis was performed within 2 weeks prior to enrollment, with all indicators within the normal reference range of the ZhenJiang Mental Health Center laboratory;(4) Capable of understanding the study content and voluntarily signing the written informed consent form. The exclusion criteria for the HC group are as follows: (1) Diagnosis of schizophrenia or other mental disorders confirmed via the SCID-I; (2) History of acute or chronic conditions that may interfere with blood cell analysis within 2 weeks prior to enrollment;(3) Presence of severe or unstable cardiovascular, cerebrovascular, hepatic, renal, endocrine (e.g., uncontrolled diabetes mellitus, thyroid disorders), hematological, or neoplastic diseases.;(4) Use of any medications potentially affecting the immune or inflammatory systems (e.g., antibiotics, nonsteroidal anti-inflammatory drugs, corticosteroids, immunosuppressants) or any psychoactive substances/medications within 2 weeks prior to enrollment; or experience of major life stressors;(5) Pregnant, lactating, or menstruating women; (6) Engagement in vigorous physical exercise or excessive alcohol consumption within 24 hours prior to enrollment.\u003c/p\u003e \u003cp\u003eUltimately, 181 patients with first-episode schizophrenia and 189 healthy controls were enrolled in this study. The detailed recruitment process of the study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Blood tests for patients in the FES group were scheduled to be performed under fasting conditions on the second day after admission. If a patient had not consumed food on the day of admission and thus met the fasting requirement, the blood test was conducted on the same day. The specific general information of the two groups of people is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe specific general information of the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFES group\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHC group\u003c/p\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77(42.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86(45.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104(57.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e103(54.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(25.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(13.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76(41.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93(49.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59(32.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71(37.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(2.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8(4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13(6.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55(30.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(13.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33(18.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46(24.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81(44.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105(55.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e128(70.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82(43.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edivorce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(28.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21(11.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86(45.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003e2.3\u0026nbsp;\u003c/strong\u003eData extraction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree independent investigators performed blinded data extraction, encompassing the following domains: 1. Demographic characteristics: hospitalization number, chronological age, gender, educational attainment, and marital status; 2. Laboratory tests: complete blood cell count (including counts of white blood cells, neutrophils, lymphocytes, monocytes, and platelets) and comprehensive metabolic panel (with a specific focus on high-density lipoprotein cholesterol levels).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u0026nbsp;\u003c/strong\u003eStatistical analyses\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using SPSS 27.0 software. For univariate analysis, comparisons between groups were conducted via the Mann-Whitney U test and chi-square test. Binary logistic regression analysis was employed to explore the independent influencing factors associated with first-episode schizophrenia patients and the related blood cell parameters and ratios. The receiver operating characteristic (ROC) curve method was utilized to evaluate the predictive value of the combined use of LHR, NLR, and PLT for FES, with the area under the curve (AUC) calculated accordingly. The \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was deemed statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Univariate analysis between the two groups\u003c/h2\u003e \u003cp\u003eA univariate analysis was performed on 181 patients in the FES Group and 189 individuals in the HC Group. Given that the data of both groups did not follow a normal distribution, a non-parametric test was employed. The results revealed that White Blood Cell Count, Neutrophil Count, Platelet Count, high-density lipoprotein (HDL), NHR, LHR, PLR, NLR, and SIRI were influencing factors associated with first-episode schizophrenia (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For further details, please refer to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis between the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFES Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;181)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHC Group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eStatistics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMononuclear cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e220.64\u0026thinsp;\u0026plusmn;\u0026thinsp;61.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e253.30\u0026thinsp;\u0026plusmn;\u0026thinsp;57.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-8.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e168.64\u0026thinsp;\u0026plusmn;\u0026thinsp;59.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e161.92\u0026thinsp;\u0026plusmn;\u0026thinsp;53.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e126.42\u0026thinsp;\u0026plusmn;\u0026thinsp;69.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e132.51\u0026thinsp;\u0026plusmn;\u0026thinsp;34.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e617.87\u0026thinsp;\u0026plusmn;\u0026thinsp;600.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e453.95\u0026thinsp;\u0026plusmn;\u0026thinsp;176.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of independent influencing factors for patients with FES\u003c/h2\u003e \u003cp\u003eWe took whether the patients were first-episode schizophrenia patients as the dependent variable and conducted binary logistic regression analysis on the parts with statistical significance in the univariate analysis of the two groups. The results revealed that Platelet Count(\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), LHR(\u003cem\u003eβ\u003c/em\u003e=-5.425,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NLR(\u003cem\u003eβ\u003c/em\u003e=-3.139,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were independent influencing factors for first-episode schizophrenia. For further details, please refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary logistic regression analysis was performed to compare the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495,1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367,5.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMononuclear cell count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002,79.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.004,1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082,12.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879,11.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000,0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.957,1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005,0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.145,15.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The predictive value of combined LHR, NLR, and platelet count for FES\u003c/h2\u003e \u003cp\u003eTo facilitate readers' interpretation of the figure, the directionality of the ROC curve analysis was defined such that a smaller test result indicates a more definitive diagnostic outcome. The combined prediction model developed in this study exhibited superior performance, with the integration of LHR, NLR, and platelet count elevating the area under the ROC curve (AUC) to 0.863(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A notable observation is that while the individual AUC values of LHR and NLR were found to be below 0.5 in the analyses, their inclusion in the combined model significantly enhanced the overall discriminative capacity. This finding suggests that LHR and NLR are negatively correlated with the target outcome. For variables like LHR and NLR that demonstrate an inverse association with the outcome, the regression model assigns them negative coefficients. Consequently, when computing the composite score, higher values of LHR and NLR are correctly weighted as evidence supporting FES classification, whereas lower platelet counts are weighted as stronger evidence for FES. This underscores a key advantage of multivariate statistical models: they can automatically identify and adjust for the directionality of associations (positive or negative) between each predictor and the outcome. By assigning appropriate weights to each variable and integrating their complementary predictive information, a more robust composite predictor is generated\u0026mdash;one that outperforms any single indicator. The relatively low individual AUC values of LHR and NLR do not imply they are non-informative; instead, they reflect their unique predictive patterns, which require adjustment within the model framework to unlock their full utility. For further details, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of the predictive efficacy of the combination of LHR, NLR, and platelet count for FES\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.604,0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312,0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307,0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u0026thinsp;+\u0026thinsp;LHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.677,0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count\u0026thinsp;+\u0026thinsp;NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.673,0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u0026thinsp;+\u0026thinsp;NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779,0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLHR\u0026thinsp;+\u0026thinsp;NLR\u0026thinsp;+\u0026thinsp;Platelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.827,0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study systematically explored the predictive value of the combined application of three hematological indicators, namely LHR, NLR, and platelet count, for FES through a combination of retrospective and prospective methods. The main findings can be summarized as follows: (1) Univariate analysis showed significant differences in multiple inflammation and metabolism-related blood indicators between FES patients and healthy controls; (2) Multivariate Logistic regression analysis further confirmed that platelet count, LHR, and NLR were independent predictors for distinguishing FES from healthy controls; (3) ROC curve analysis indicated that the predictive model constructed by the combination of the above three indicators demonstrated high discriminatory efficacy (AUC\u0026thinsp;=\u0026thinsp;0.863). These results suggest that the combination of LHR, NLR, and platelet count derived from routine blood tests may serve as a simple, economical, and effective auxiliary biological marker for the early identification and risk assessment of FES. The following will conduct an in-depth discussion around the results of this study, combined with existing literature, from aspects such as pathophysiological mechanisms, clinical significance, comparative analysis with previous studies, and future directions.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The pathophysiology of this study: Linking immune inflammation, metabolic disorders and platelet function\u003c/h2\u003e \u003cp\u003eThe etiology of schizophrenia is complex, involving multifaceted interactions among genetic, neurodevelopmental, and environmental factors [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In recent years, growing attention has been paid to the roles of the immune-inflammatory hypothesis, metabolic dysregulation, and altered platelet function in the pathophysiology of schizophrenia. The three independent predictors identified in this study\u0026mdash;LHR, NLR, and platelet count\u0026mdash;aptly mirror these core pathological processes from distinct perspectives [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLHR is an emerging composite biomarker that concurrently reflects the status of the immune system (via lymphocytes) and lipid metabolism (via HDL) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the present study, LHR was found to be significantly elevated in the FES group and served as an independent predictor\u0026mdash;findings that align with its established relevance in cardiovascular diseases and several inflammatory disorders[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Patients with schizophrenia frequently exhibit abnormalities in the quantity and function of lymphocytes, including T cells, B cells, and natural killer cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These alterations may be associated with autoimmune reactions and aberrant immune responses to central nervous system antigens [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Changes in lymphocyte count constitute one of the components of the LHR. Traditionally, HDL has been recognized as \"good cholesterol\" owing to its anti-inflammatory, antioxidant, and reverse cholesterol transport-promoting properties [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, a growing body of evidence indicates that HDL levels may be reduced in states of chronic inflammation, such as schizophrenia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Reduced HDL levels or functional abnormalities have been documented in patients with schizophrenia [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Thus, the elevation of LHR may concurrently capture the relative increase in lymphocytes and/or the relative reduction or functional impairment of HDL, precisely reflecting the intersection of immune activation and the dysregulation of metabolic protective mechanisms in schizophrenia.\u003c/p\u003e \u003cp\u003eNLR has been widely employed to evaluate systemic inflammatory burden and stress status across a spectrum of somatic diseases and psychiatric disorders (e.g., major depressive disorder, bipolar disorder) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The present study demonstrated that NLR was elevated in patients with FES and served as an independent predictor\u0026mdash;findings that strengthen the evidence for a close association between schizophrenia and activation of the innate immune system[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. An increase in neutrophils reflects the rapid, non-specific activation of the innate immune system, which may be triggered by multiple factors including infection, psychosocial stress, and microbial translocation secondary to intestinal barrier dysfunction [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Reactive oxygen species, proteases, and inflammatory cytokines (e.g., IL-6, IL-1β) released by neutrophils can contribute to neurotoxic processes via mechanisms such as disrupting the blood-brain barrier, activating microglia, and interfering with neurotransmitter systems [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, the relative reduction in lymphocytes may be associated with elevated glucocorticoid levels induced by chronic stress, immune cell redistribution, or increased apoptosis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The NLR integrates these two opposing trends, providing a robust index of systemic inflammation. A higher NLR indicates a neutrophil-dominated pro-inflammatory state accompanied by potential suppression of adaptive immunity (mediated by lymphocytes), an immune profile that aligns closely with the neuroinflammation hypothesis of schizophrenia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study identified platelet count as an independent predictor of FES. Platelets are not only central components of the coagulation cascade but also critical inflammatory effector cells and reservoirs for neuroactive substances [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Platelets express a diverse array of pattern recognition receptors (PRRs) and cytokine receptors on their surface, which can be activated by inflammatory signals [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Upon activation, platelets amplify inflammatory responses by releasing a multitude of inflammatory mediators, including platelet factor 4, transforming growth factor-β (TGF-β), and CD40 ligand [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Additionally, they interact with leukocytes (particularly monocytes and neutrophils) to promote the recruitment and activation of these cells [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Approximately 95% of serotonin in the human body is stored in platelets. The serotonin hypothesis of schizophrenia has a long-standing history, and abnormalities in platelet-mediated serotonin uptake, and release may indirectly reflect dysregulation of the central 5-hydroxytryptamine (5-HT) system [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Furthermore, aberrations in platelet mitochondrial function and activation markers (e.g., P-selectin) have also been reported in patients with schizophrenia [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe inflammatory state itself can stimulate platelet production and enhance platelet activity [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Accordingly, alterations in platelet count may share intrinsic pathophysiological links with the NLR and the LHR collectively forming a biological signaling network that reflects the multidimensional pathological processes of schizophrenia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Advantages of Combined Indicators: From Single Indicators to Ensemble Prediction Models\u003c/h2\u003e \u003cp\u003eThe most clinically insightful finding of the present study is that the combination of platelet count, LHR, and NLR yields significantly superior predictive performance (AUC\u0026thinsp;=\u0026thinsp;0.863) compared to any individual indicator. This finding profoundly underscores the multi-systemic nature of schizophrenia. Individual biomarkers typically capture only a single facet of a disease\u0026rsquo;s pathophysiological network and are prone to confounding by individual variability, comorbidities, and assay fluctuations. The LHR, NLR, and platelet count respectively target immune-metabolic crosstalk, systemic inflammatory burden, and platelet-associated pathology, delivering complementary yet interconnected insights across distinct biological pathways [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Their integration constructs a multidimensional, multi-pathway biological \"fingerprint\"\u0026mdash;a composite signature that more comprehensively and robustly captures the full spectrum of FES-related biological dysregulation. While the present study primarily focuses on FES and healthy controls, such composite indicators hold potential for future research to distinguish schizophrenia from other psychiatric disorders (e.g., bipolar disorder with psychotic features or major depressive disorder with psychotic symptoms) or to differentiate FES patients with distinct subtypes or prognostic profiles [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The high AUC value of the model provides a foundational basis for such applications. All the indicators of this combination are derived from routine, low-cost, and rapid complete blood count and lipid profile analysis, which is highly feasible for promotion in medical institutions at all levels, including grassroots mental health centers. This \"old indicators, new combination\" strategy is an efficient approach to realizing the transition of early biomarkers for schizophrenia from laboratory research to clinical practice.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Comparison with Prior Studies\u003c/h2\u003e \u003cp\u003eThe findings of the present study align with the prevailing research paradigm surrounding immune-inflammatory and metabolic mechanisms in schizophrenia in recent years, while offering novel insights and deeper exploration in terms of specific indicator combinations and population focus. A growing body of research has documented elevated inflammatory ratios (e.g., NLR, PLR), abnormal HDL levels, and alterations in platelet parameters among patients with schizophrenia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The present study not only replicates these findings but, more critically, employs multivariate analysis to derive a core combination of indicators with the strongest independent predictive value\u0026mdash;incorporating the emerging LHR and emphasizing the centrality of immune-metabolic co-dysregulation. Inconsistencies have been observed in some earlier research findings\u0026mdash;for example, reports on platelet count in schizophrenia have been conflicting, with some documenting elevations and others reductions[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Such discrepancies may stem from variations in study populations (e.g., first-episode status, medication exposure, disease duration), sample sizes, and control for comorbidities. The present study was strictly restricted to drug-naive patients with FES, thereby minimizing confounding effects from antipsychotic medications (many of which are known to alter hematological parameters) and secondary changes associated with disease chronicity. This design allows for a more accurate reflection of the biological underpinnings of the disease in its early stages. Our findings provide clear evidence that platelet count acts as an independent factor involved in the disease process during the acute phase of FES.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Future Directions\u003c/h2\u003e \u003cp\u003eIn psychiatric outpatient settings or general hospitals, for patients presenting with first-episode psychotic symptoms without a definitive diagnosis, or during community-based screening for populations at high risk of schizophrenia, calculating the combination of LHR, NLR, and platelet count (i.e., decreased platelet count concurrent with elevated LHR and NLR) may provide an objective, quantifiable biological reference to aid clinicians in comprehensive decision-making and facilitate early intervention. Abnormalities in this indicator panel may indicate a subtype of FES characterized by prominent immune-metabolic-platelet dysfunction. This could inform the future development of biomarker-based classification of schizophrenia and offer insights for personalized treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and Limitations","content":"\u003cp\u003eThis study employed a hybrid design integrating retrospective (FES group) and prospective (HC group) approaches. The FES group, with a moderate sample size (n\u0026thinsp;=\u0026thinsp;181), was explicitly defined as FES, enabling effective control of confounding factors such as medication exposure, long-term disease progression, and other covariates. The statistical workflow of this study was methodologically rigorous: starting with univariate screening, proceeding to multivariate modeling, and culminating in ROC performance evaluation, yielding robust conclusions. Ultimately, this study demonstrated that the combination of platelet count, LHR, and NLR constitutes a promising predictive panel for FES. While this study establishes associations and predictive utility, it cannot infer causal relationships. Specifically, whether alterations in these hematological indicators represent a cause, consequence, or comorbid state of schizophrenia remains to be elucidated\u0026mdash;this requires prospective cohort studies (e.g., long-term follow-up of ultra-high-risk populations or the general population) to validate their efficacy in predicting disease onset. All participants in this study were recruited from a single medical center, which may introduce selection bias. Future research should conduct external validation across multi-center cohorts encompassing diverse ethnic and cultural backgrounds to assess the generalizability of the proposed model. Notwithstanding the need for prospective cohort studies and mechanistic investigations to deepen and validate these findings, this study undoubtedly paves a promising new avenue for the development of early, objective biological adjunctive diagnostic tools for schizophrenia. It further underscores the importance of adopting a systematic, multi-dimensional biological perspective in both schizophrenia research and clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChunYang Shi wrote the first draft of the manuscript. Xianlu Chang,Zhiyun Yang, and Yunxia Guo were three independent investigators (Responsible for collecting data). Zhoubing Wang was the fourth senior researcher (Responsible for negotiating and reaching a consensus to solve problems). Gang Zhang and Chunyang Shi\u0026nbsp;performed the statistical analysis. Yangzhi Yun and Xianlu Chang did the data curation. HuiYing Xue\u0026nbsp;critically revised the manuscript.\u0026nbsp;All authors contributed to the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the content of the Declaration of Helsinki, we strictly abide by the ethics review of the Ethics Committee of the Zhenjiang Mental Health Center. The research involving the participants was approved by the Zhenjiang Mental Health Center Ethics Review Committee (Approve the number: K2025-05).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by two funding projects as follows: The 2024 Guiding Science and Technology Program for Social Development of Zhenjiang City (Project No.: FZ2024091); and the 2025 General Medical and Health Project under the Social Development Category of Zhenjiang Science and Technology Program (Project No.: SH2025064). The funders had no role in the study design, data collection, analysis, publication decision, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflicts to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDeclaration of Consenting to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in this study consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data and model generated or used during the study appear in the submitted article, further inquiries can be directed to the first/ corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLuo Q, An M, Wu Y, Wang J, Mao Y, Zhang L, et al. Genetic overlap between schizophrenia and constipation: insights from a genome-wide association study in a European population. Ann Gen Psychiatry. 2025;24(1):11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD'Addario C, Di Bartolomeo M. Epigenetic Control in Schizophrenia. Subcell Biochem. 2025;108:191\u0026ndash;215.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahiri A, Yigit R, Lawrie S. The Reliability, Validity and Utility of a Diagnosis in Schizophrenia. Br J Hosp Med (Lond). 2025;86(10):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavakoli H, Rostami R, Shalbaf R, Nazem-Zadeh MR. Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning. Brain Behav. 2025;15(1):e70219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiwaku H. Autoantibodies and Inflammation in Schizophrenia. Psychiatry Clin Neurosci. 2025;79(10):611\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpeers LJ, Bilkey DK. Inflammation in Schizophrenia: The Role of Disordered Oscillatory Mechanisms. Cells. 2025;14(9):650.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Z, Han B, Ding Q, Feng Y, Jia T, Ouyang Y, Guo X, Liang J, Huang Q, Kou C, Bai W. Systematic evaluation of the associations between schizophrenia and autoimmune diseases: An umbrella review. Autoimmun Rev. 2025;24(9):103854.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatlola SR, Donohoe G, McKernan DP. The relationship between inflammatory biomarkers and cognitive dysfunction in patients with schizophrenia: A systematic review and meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2023;121:110668.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalstead S, Siskind D, Amft M, Wagner E, Yakimov V, Shih-Jung Liu Z, et al. Alteration patterns of peripheral concentrations of cytokines and associated inflammatory proteins in acute and chronic stages of schizophrenia: a systematic review and network meta-analysis. Lancet Psychiatry. 2023;10(4):260\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandaker GM, Cousins L, Deakin J, Lennox BR, Yolken R, Jones PB. Inflammation and immunity in schizophrenia: implications for pathophysiology and treatment. Lancet Psychiatry. 2015;2(3):258\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErmakov E, Mednova I, Boiko A, Ivanova S. Neuroinflammation in Schizophrenia: An Overview of Evidence and Implications for Pathophysiology. J Integr Neurosci. 2025;24(7):27636.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGercek HG, Citir BG, Bukulme A. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios as inflammation markers for early-onset schizophrenia. Bratisl Lek Listy. 2023;124(7):503\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Y, Wang T, Li G, Feng J, Deng L, Xu H, et al. Investigation of systemic immune-inflammation index, neutrophil/high-density lipoprotein ratio, lymphocyte/high-density lipoprotein ratio, and monocyte/high-density lipoprotein ratio as indicators of inflammation in patients with schizophrenia and bipolar disorder. Front Psychiatry. 2022;13:941728.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandberg AA, Steen VM, Torsvik A. Is Elevated Neutrophil Count and Neutrophil-to-Lymphocyte Ratio a Cause or Consequence of Schizophrenia?-A Scoping Review. Front Psychiatry. 2021;12:728990.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H, Xiong C, Shao X, Ning J, Gao P, Xiao H, et al. Lymphocyte To High-Density Lipoprotein Ratio As A New Indicator Of Inflammation And Metabolic Syndrome. Diabetes Metab Syndr Obes. 2019;12:2117\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang J, Guan X, Sun Q, Hao Y, Xiu M. Neutrophil/lymphocyte ratio and cognitive performances in first-episode patients with schizophrenia and healthy controls. Prog Neuropsychopharmacol Biol Psychiatry. 2024;135:111092.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabello-Rangel H, Basurto-Morales M, Botello-Aceves E, Pazar\u0026aacute;n-Galicia O. Mean platelet volume, platelet count, and neutrophil/lymphocyte ratio in drug-na\u0026iuml;ve patients with schizophrenia: a cross-sectional study. Front Psychiatry. 2023;14:1150235.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJauhar S, Johnstone M, McKenna PJ. Schizophrenia Lancet. 2022;399(10323):473\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu Y, Ren J, Fu Z. Sex-specific associations between Neutrophil-Percentage-to-Albumin Ratio(NPAR) and Neutrophil-to-Lymphocyte Ratio (NLR) with positive and negative symptoms in schizophrenia. BMC Psychiatry. 2025;25(1):873.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Z, Wang J, Carru C, Sedda S, Nivoli AM, Li Z. Meta-analysis of peripheral mean platelet volume in patients with mental disorders: Comparisons in depression, anxiety, bipolar disorder, and schizophrenia. Brain Behav. 2023;13(11):e3240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulacaoglu F, Yıldırım YE, Aslan M, İzci F. Neutrophil to lymphocyte and monocyte to high-density lipoprotein ratios are promising inflammatory indicators of bipolar disorder. Nord J Psychiatry. 2023;77(1):77\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuang SM, Liu SC, Chien MN, Lee CC, Lee YT, Chien KL. Neutrophil-to-High-Density Lipoprotein Ratio (NHR) and Neutrophil-to-Lymphocyte Ratio (NLR) as prognostic biomarkers for incident cardiovascular disease and all-cause mortality: A comparison study. Am J Prev Cardiol. 2024;20:100869.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Zheng Q, Ying Y, Luo S, Liu N, Wang L, et al. Association between high-density lipoprotein-related inflammation index and periodontitis: insights from NHANES 2009\u0026ndash;2014. Lipids Health Dis. 2024;23(1):321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo J, Mutailipu K, Wen X, Yin J, You H, Qu S, et al. Association between lymphocyte to high-density lipoprotein cholesterol ratio and insulin resistance and metabolic syndrome in US adults: results from NHANES 2007\u0026ndash;2018. Lipids Health Dis. 2025;24(1):9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Zhang Q, Zhou X, Yao L, Zhu Q, Fu Z. Altered levels of cytokine, T- and B-lymphocytes, and PD-1 expression rates in drug-na\u0026iuml;ve schizophrenia patients with acute phase. Sci Rep. 2023;13(1):21711.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiaz L, Bielczyk-Maczynska E. High-density lipoprotein cholesterol: how studying the 'good cholesterol' could improve cardiovascular health. Open Biol. 2025;15(2):240372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Kockx M, Bolek M, Lambert T, Sullivan D, Chow V, et al. Triglyceride-rich lipoprotein, remnant cholesterol, and apolipoproteins CII, CIII, and E in patients with schizophrenia. J Lipid Res. 2024;65(7):100577.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerceg D, Mimica N, Herceg M, Puljić K. Aggression in Women with Schizophrenia Is Associated with Lower HDL Cholesterol Levels. Int J Mol Sci. 2022;23(19):11858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomioka 29SS, Mera H, Tazaki K, Nishiyama T, Yamada H. Neutrophil-Lymphocyte Ratio in Patients With Acute Schizophrenia. Cureus. 2024;16(1):e52181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu X, Sun Q, Wu L, Liao M, Yao J, Xiu M. The neutrophil-lymphocyte ratio in first-episode medication-na\u0026iuml;ve patients with schizophrenia: A 12-week longitudinal follow-up study. Prog Neuropsychopharmacol Biol Psychiatry. 2024;131:110959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY\u0026uuml;ksel RN, Ertek IE, Dikmen AU, G\u0026ouml;ka E. High neutrophil-lymphocyte ratio in schizophrenia independent of infectious and metabolic parameters. Nord J Psychiatry. 2018;72(5):336\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-Castro TB, Tovilla-Z\u0026aacute;rate CA, Ju\u0026aacute;rez-Rojop IE, Hern\u0026aacute;ndez-D\u0026iacute;az Y, L\u0026oacute;pez-Narv\u0026aacute;ez ML, Ortiz-Ojeda RF. Effects of IL-6/IL-6R axis alterations in serum, plasma and cerebrospinal fluid with the schizophrenia: an updated review and meta-analysis of 58 studies. Mol Cell Biochem. 2024;479(3):525\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Zhu D, Zhang D, Zuo X, Yao L, Liu T, et al. Causal role of immune cells in schizophrenia: Mendelian randomization (MR) study. BMC Psychiatry. 2023;23(1):590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCanli D. Evaluation of systemic immune inflammation index and neutrophil-to-lymphocyte ratio in schizophrenia, bipolar disorder and depression. Bratisl Lek Listy. 2024;125(8):472\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaouia A, Rebetz J, Kapur R, Semple JW. The Immune Nature of Platelets Revisited. Transfus Med Rev. 2020;34(4):209\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar V, Stewart Iv JH. Platelet's plea to Immunologists: Please do not forget me. Int Immunopharmacol. 2024;143(Pt 3):113599.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoupenova M, Livada AC, Morrell CN. Platelet and Megakaryocyte Roles in Innate and Adaptive Immunity. Circ Res. 2022;130(2):288\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Giovanni M, Chen H, Li X, Cyster JG. GPR35 and mediators from platelets and mast cells in neutrophil migration and inflammation. Immunol Rev. 2023;317(1):187\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnouf T, Chou ML, Lundy DJ, Chuang EY, Tseng CL, Goubran H. Expanding applications of allogeneic platelets, platelet lysates, and platelet extracellular vesicles in cell therapy, regenerative medicine, and targeted drug delivery. J Biomed Sci. 2023;30(1):79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnjum A, Mader M, Mahameed S, Muraly A, Denorme F, Kliem FP, et al. Aging platelets shift their hemostatic properties to inflammatory functions. Blood. 2025;145(14):1568\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Zhou J, Zhu Y, Yan F, Han X, Tan Y, et al. Neutrophil/lymphocyte, platelet/lymphocyte and monocyte/lymphocyte ratios in schizophrenia. Australas Psychiatry. 2022;30(1):95\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood PL, Unfried G, Whitehead W, Phillipps A, Wood JA. Dysfunctional plasmalogen dynamics in the plasma and platelets of patients with schizophrenia. Schizophr Res. 2015;161(2\u0026ndash;3):506\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zheng Y, Ni P, Liang S, Li X, Yu H, et al. New role of platelets in schizophrenia: predicting drug response. Gen Psychiatr. 2024;37(2):e101347.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-general-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agps","sideBox":"Learn more about [Annals of General Psychiatry](http://annals-general-psychiatry.biomedcentral.com/)","snPcode":"12991","submissionUrl":"https://submission.nature.com/new-submission/12991/3","title":"Annals of General Psychiatry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"First-episode Schizophrenia, Schizophrenia, LHR, NLR, Platelet Count","lastPublishedDoi":"10.21203/rs.3.rs-8356779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8356779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the predictive value of the combination of lymphocyte-to-high-density lipoprotein ratio (LHR), neutrophil-to-lymphocyte ratio (NLR), and platelet count for patients with first-episode schizophrenia (FES).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A retrospective analysis was performed on 181 inpatients with FES admitted to Zhenjiang Mental Health Center between January 2015 and February 2025.189 participants, including staff members who underwent health check-ups at the same center from July 2024 to November 2025, were prospectively recruited as the healthy control group. Both groups underwent fasting blood cell analysis. Univariate analysis, binary Logistic regression analysis, and receiver operating characteristic (ROC) curve analysis were employed to assess the predictive efficacy of the combined use of LHR, NLR, and platelet count for FES.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate analysis revealed that white blood cell count, neutrophil count, monocyte count, platelet count, high-density lipoprotein (HDL), neutrophil-to-high-density lipoprotein ratio (NHR), LHR, platelet-to-lymphocyte ratio (PLR), NLR, and systemic immune-inflammation index (SIRI) were statistically significant influencing factors associated with FES (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) when comparing the FES group with the healthy control group. Binary Logistic regression analysis further identified platelet count, LHR, and NLR as independent predictors of FES relative to healthy controls (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The ROC curve analysis demonstrated that the combination of platelet count, LHR, and NLR yielded an area under the curve (AUC) of 0.863 for predicting FES.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe combination of, LHR, NLR and platelet count exhibits favorable predictive performance for FES, suggesting its potential as a biomarker for early identification of this population.\u003c/p\u003e","manuscriptTitle":"The Combined Predictive Value of LHR, NLR, and Platelet Count for First-Episode Schizophrenia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:50:52","doi":"10.21203/rs.3.rs-8356779/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T12:08:38+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"212034153738922706177329940140182908849","date":"2026-04-07T07:18:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T00:25:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T03:46:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332008870967677432695673488874458410318","date":"2026-04-02T18:41:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163713580194666939894893639246988762548","date":"2026-04-02T01:09:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T19:16:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143120037223567846144304828399196141431","date":"2026-01-26T08:51:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T17:05:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-16T07:53:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-16T07:51:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of General Psychiatry","date":"2025-12-14T08:20:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-general-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agps","sideBox":"Learn more about [Annals of General Psychiatry](http://annals-general-psychiatry.biomedcentral.com/)","snPcode":"12991","submissionUrl":"https://submission.nature.com/new-submission/12991/3","title":"Annals of General Psychiatry","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa02fe74-d554-420f-b083-5c1e1c078b98","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T06:12:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:50:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8356779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8356779","identity":"rs-8356779","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}