Clinical symptom improvement following modified electroconvulsive therapy is associated with modulation of peripheral inflammatory markers in schizophrenia

Abstract

BACKGROUND

Schizophrenia is a severe neuropsychiatric disorder with unclear pathogenesis, although immune-inflammatory pathways are being increasingly implicated. Elevated proinflammatory cytokines are consistently observed in patients with schizophrenia, suggesting a state of chronic low-grade inflammation. Modified electroconvulsive therapy (MECT) is an effective biological intervention for treatment-resistant schizophrenia, but its mechanisms remain incompletely understood.

AIM

To examine the association between MECT-induced changes in immunoinflammatory markers and clinical improvement in schizophrenia.

METHODS

In this prospective study, 619 patients with schizophrenia underwent MECT. Peripheral immunoinflammatory markers, including monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, and systemic immune-inflammatory index (SII), were measured before and after treatment. Clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS). Correlation and logistic regression analyses were applied to evaluate associations, and receiver operating characteristic analysis was used to assess predictive performance.

RESULTS

After MECT, significant reductions were observed in PANSS scores and most peripheral inflammatory markers (MLR, NLR and SII; all P < 0.05). The decreases in MLR, NLR, and SII showed a significant positive correlation with the PANSS score reduction rate (P < 0.05). Patients with marked clinical improvement showed greater decreases in inflammatory markers. Logistic regression identifies the change in MLR before and after treatment (ΔMLR) as a strong predictor of treatment response, with each 0.1-unit increase associated with a 57% greater probability of clinical symptom improvement (odds ratios = 1.57, P < 0.001). Receiver operating characteristic analysis demonstrated that ΔMLR had significant predictive value for MECT efficacy (area under the curve = 0.731, P < 0.001), with an optimal cutoff value of 0.075.

CONCLUSION

MECT modulates peripheral immune inflammation in schizophrenia, and these changes correlate with clinical improvement. ΔMLR may serve as a valuable predictor of MECT treatment response.

Key Words: Modified electroconvulsive therapy; Schizophrenia; Immunoinflammation; Monocyte-to-lymphocyte ratio; Positive and Negative Syndrome Scale score reduction rate

Core Tip: This study demonstrates that modified electroconvulsive therapy significantly improves clinical symptoms and reduces peripheral inflammatory markers - including monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio, and systemic immune-inflammatory indices - in patients with schizophrenia. Reductions in MLR were strongly correlated with clinical improvement and served as a robust predictor of treatment response. Specifically, each 0.1-unit increase in ΔMLR was associated with a 57% higher likelihood of significant symptom improvement. These findings suggest an immunomodulatory mechanism of modified electroconvulsive therapy and support the use of MLR as a potential biomarker for predicting treatment efficacy.

INTRODUCTION

Schizophrenia is a severe and complex neuropsychiatric disorder with a lifetime prevalence of approximately 1%, contributing substantially to the global disease burden and causing profound personal, familial, and societal challenges[1]. Its clinical presentation is characterized by positive symptoms (e.g., hallucinations and delusions), negative symptoms (e.g., avolition, social withdrawal), and cognitive impairment[2]. Although the pathogenesis of schizophrenia remains unclear, growing evidence suggests that its onset involves interactions among multiple factors, including neurodevelopmental abnormalities, neurodegenerative changes, genetic factors, and environmental factors. Among these, genetic factors play a significant role in the onset of the disease, accounting for approximately 60% to 70% of the total risk[3]. With the development of genomics and bioinformatics technologies, researchers have identified multiple susceptibility genes associated with schizophrenia that play key roles in processes such as neurodevelopment, synaptic function, and immune regulation[4,5]. Despite the considerable advances achieved in the present research, the treatment of schizophrenia continues to represent a significant challenge that necessitates further exploration.

In recent years, immune-inflammatory pathways have attracted increasing attention in schizophrenia research. Meta-analyses have consistently demonstrated elevated levels of proinflammatory cytokines, including tumor necrosis factor alpha (TNF-α), interleukin (IL)-1β, and IL-6, in patients with schizophrenia, suggesting a state of chronic low-grade inflammation[6]. These inflammatory mediators not only are involved in neuroinflammatory processes but also may play a role in emotional changes and psychiatric symptoms by affecting neurodevelopment, synaptic plasticity, and neurotransmission pathways. For example, TNF-α plays a key role in mediating neurodegeneration, but its high expression in certain brain regions, such as the amygdala, is closely related to neuroprotective effects[7]. Notably, inflammatory markers also have potential application value in the early diagnosis of diseases and the prediction of treatment responses. Research has indicated that the levels of inflammatory markers may change prior to the onset of mental illness, thus providing potential biomarkers for early intervention[8].

Modified electroconvulsive therapy (MECT) remains a widely used biological intervention for severe and treatment-resistant psychiatric conditions, including schizophrenia. It involves the induction of generalized seizures under anesthesia and muscle relaxation, which enhances both safety and tolerability[9,10]. This therapy maximizes treatment safety and efficacy while minimizing patient discomfort by precisely controlling the intensity and duration of the electrical current and optimizing the placement of the electrodes. The mechanism of action of MECT is not yet fully understood, but research suggests that it may exert its effects through multiple neurobiological pathways, including its influence on neurotransmitters such as serotonin, norepinephrine, gamma-aminobutyric acid, and glutamate[11]. The neurotrophic hypothesis suggests that MECT can promote the restoration of neuronal plasticity and exert antipsychotic effects by regulating and increasing the levels of nerve growth factor, brain-derived neurotrophic factor, and vascular endothelial growth factor in the brains of schizophrenia patients[12]. Emerging evidence also suggests that MECT may exert immunomodulatory effects, although this remains less explored.

Recent studies have increasingly emphasized the role of inflammatory markers in the pathophysiological process of schizophrenia[13,14]. Inflammatory markers in peripheral blood, such as monocyte-to-lymphocyte ratio (MLR), the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII), have been increasingly adopted in research due to their ease of assessment, low cost, and ability to accurately reflect the body's inflammatory state. However, there are few reports on the use of peripheral blood immune markers for efficacy analysis in clinical interventions for schizophrenia. This may be because the pathological mechanisms of schizophrenia are more complex, and changes in inflammatory markers may be influenced by multiple factors, including the stage of the disease, treatment methods, and individual differences. Therefore, further research into the role of inflammatory markers in schizophrenia and their relationship with treatment response has significant clinical implications.

Although previous studies have explored the effects of MECT on inflammatory mediators in patients with schizophrenia, these studies have focused mainly on changes in single inflammatory markers and have involved relatively small sample sizes[15]. Furthermore, current studies have not sufficiently explored the relationship between changes in these inflammatory markers and improvements in patients’ clinical symptoms. Therefore, through a single-arm, pre-post observational study with a large sample size, we aim to comprehensively evaluate the regulatory effect of MECT on peripheral blood immune-inflammatory markers in schizophrenia and to analyze the association between changes in these markers and clinical symptom improvement. The results of this study will provide new insights into the immunomodulatory effects of MECT and are expected to provide clinicians with more precise treatment guidance.

MATERIALS AND METHODS

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee at Huai’an Third People’s Hospital (No. 2022-23). The study included patients who were diagnosed with schizophrenia and who were treated at Huai'an Third People’s Hospital between July 2022 and December 2023 and who received MECT treatment.

Participants

All patients were independently diagnosed by two experienced psychiatrists to ensure diagnostic reliability. The inclusion criteria for the study participants were as follows: (1) Met the diagnostic criteria for schizophrenia according to the International Classification of Diseases, 10th Revision (ICD-10); (2) Had no contraindications for MECT, such as brain tumors or cranial injuries; and (3) Provided informed consent (the patient and guardian), with written consent forms signed. The exclusion criteria were as follows: (1) History of other mental disorders or substance abuse; (2) Severe or unstable physical illnesses; (3) Pregnant or breastfeeding women; and (4) Patients unsuitable for general anaesthesia.

Clinical assessments

All schizophrenia patients were evaluated and enrolled by experienced psychiatrists, and demographic and clinical information was collected, including age, sex, and duration of illness, was collected. The study used the Positive and Negative Syndrome Scale (PANSS) to assess the severity of symptoms before and after MECT treatment, with the efficacy being evaluated by the PANSS score reduction rate, calculated using the following formula: PANSS score reduction rate = [(pre-treatment score - post-treatment score)/pre-treatment score] × 100%[16]. The PANSS score reduction rate is used as an efficacy evaluation indicator, with ≤ 25% indicating minimal improvement, > 25% and ≤ 50% indicating moderate improvement, > 50% and ≤ 75% indicating marked improvement, and > 75% indicating extensive improvement[17].

MECT treatment

MECT therapy was administered using the Thymatron System IV Integrated ECT Instrument (SOMATICS, LLC, United States). Patients fasted for 6 hours and abstained from fluids for 4 hours prior to each session. Pre-treatment management included intravenous administration of 0.5 mg atropine and continuous monitoring of electroencephalogram, electrocardiogram, blood pressure, pulse rate, and blood oxygen saturation. Anaesthesia was induced with intravenous propofol (1.0-2.0 mg/kg), and muscle relaxation was achieved with intravenous succinylcholine (0.5-1.5 mg/kg). Bilateral electrode placement was confirmed on the temporal regions. The initial stimulus dose was determined using the device’s age-based method, set at 50%-75% of the patient’s age. If a seizure failed or was incomplete, the stimulus dose was incrementally increased in subsequent treatments until a complete seizure was achieved. During the procedure, seizure activity, including duration, was monitored via electroencephalogram to ensure adequacy. Following the cessation of limb movements, assisted ventilation with a bag-valve-mask was provided until the return of spontaneous respiration. Patients received a course of MECT treatment 2-3 times per week, for a total of 6 sessions.

Blood sample collection

Peripheral venous blood samples were drawn from all participants after an overnight fast of at least 8 hours during a fixed morning time window (7:00-9:00 AM). Samples were collected at two time points: (1) Before the first MECT session (baseline); and (2) Within 24 hours after the sixth MECT session (post-treatment). Critically, in accordance with standard MECT safety protocols, all patients were clinically assessed prior to each session. The presence of an intercurrent infection (e.g., body temperature > 37.5 °C) was a temporary contraindication for MECT. Therefore, all blood samples in this study were obtained from patients confirmed to be free of acute infection. Blood was collected into vacuum tubes containing the anticoagulant ethylenediaminetetraacetic acid. Complete blood count analysis was performed within 2 hours of collection using a Mindray BC-7500 automated haematology analyser. Quality control was performed daily using the Mindray BC-6D Haematology Control (Cat No. 105-004067-00; Lot: No. MB1125AN; Shenzhen Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China) prior to sample analysis. The laboratory adhered to strict internal quality control procedures and participated in external quality assessment programs. All cell counts are reported in standard units (× 10⁹/L). The clinical reference ranges used for the key parameters in this study were: Neutrophils: 2.00-7.00 × 10⁹/L, lymphocytes: 0.80-4.00 × 10⁹/L, monocytes: 0.12-0.80 × 10⁹/L, platelets: 100-300 × 10⁹/L. The inflammatory indices were calculated from the absolute cell counts as follows: NLR = neutrophil count/Lymphocyte count; MLR = monocyte count/Lymphocyte count; PLR = platelet count/Lymphocyte count; SII = (neutrophil count × platelet count)/Lymphocyte count. The change (Δ) for each marker was defined as the pre-treatment value minus the post-treatment value.

Statistical analysis

IBM SPSS Statistics Version 26.0 (IBM Corp., Armonk, NY, United States) was used to analyze the data. The normality of the continuous variables was assessed using the Shapiro-Wilk test. Given that all the primary variables (including the PANSS score, PANSS score reduction rate, and peripheral blood inflammatory markers) did not follow a normal distribution, nonparametric statistical methods were employed for subsequent analyses. The Wilcoxon signed-rank test was used to compare changes in PANSS scores and inflammatory markers before and after MECT treatment. Spearman’s rank correlation analysis was used to assess the correlation between PANSS score reduction rates and inflammatory markers. The Mann-Whitney U test was used to compare differences in inflammatory markers between groups with different levels of efficacy. Subsequently, meaningful inflammatory markers were included in a binary logistic regression model to further assess their association with efficacy. Receiver operating characteristic (ROC) curve analysis was also performed to evaluate the value of these inflammatory markers for predicting treatment effects. The threshold for statistical significance was set at P < 0.05.

RESULTS

As shown in Table 1, a total of 619 patients, with 323 (52.18%) males and 296 (47.82%) females, were included in this study. Their mean ± SD age was 36.03 ± 10.69 years, and the mean disease duration was 9.24 ± 8.15 years.

Table 1 Characteristics of the study participants (n = 619), n (%).

Characteristic	Value
Age, years, mean ± SD	36.03 ± 10.69
Male	323 (52.18)
Female	296 (47.82)
Illness duration, years, mean ± SD	9.24 ± 8.15
Baseline PANSS total score, mean ± SD	94.85 ± 14.02
Atypical antipsychotics	587 (94.83)
Typical antipsychotics	32 (5.17)

PANSS: Positive and Negative Syndrome Scale.

Comparison of PANSS scores and inflammatory markers

As shown in Table 2, the PANSS scores, MLR, NLR, PLR, and SII were significantly lower after MECT treatment than before treatment, indicating a marked improvement in schizophrenia symptoms and a significant reduction in peripheral blood inflammatory markers.

Table 2 Comparison of Positive and Negative Syndrome Scale scores and inflammatory markers.

	Before MECT	After MECT	Statistical analysis
	mean ± SD	mean ± SD	Z	P value
PANSS	94.85 (14.02)	57.05 (12.89)	21.557	< 0.001
MLR	0.30 (0.20)	0.24 (0.18)	8.486	< 0.001
NLR	3.42 (2.27)	2.59 (2.23)	10.089	< 0.001
PLR	161.67 (116.03)	167.59 (124.73)	3.303	< 0.001
SII	763.01 (595.37)	586.36 (470.14)	8.291	< 0.001

MECT: Modified electroconvulsive therapy; PANSS: Positive and Negative Syndrome Scale; MLR: Monocyte-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; SII: Systemic immune-inflammatory index.

Correlation analysis between PANSS score reduction rate and changes in inflammatory markers

As shown in Figure 1, the ΔMLR showed a significant positive correlation with the PANSS score reduction rate (r = 0.207, P < 0.001). Similarly, the ΔNLR (r = 0.192, P < 0.001) and ΔSII (r = 0.171, P < 0.001) were also significantly correlated with greater clinical improvement. In contrast, the change in ΔPLR was not significantly associated with PANSS score reduction rate (r = -0.032, P = 0.458). These findings indicate that reductions in ΔMLR, ΔNLR, and ΔSII following MECT are significantly, albeit weakly, associated with the degree of clinical symptom improvement, with the change in ΔMLR demonstrating the strongest correlation.

Figure 1 Scatter plot illustrating the correlation between Positive and Negative Syndrome Scale score reduction rate and Δmonocyte-to-lymphocyte ratio, Δneutrophil-to-lymphocyte ratio, and Δsystemic immune-inflammatory index. PANSS: Positive and Negative Syndrome Scale; MLR: Monocyte-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; SII: Systemic immune-inflammatory index.

Comparison between the minimal improvement group and the effective group

Patients with a PANSS score reduction rate ≤ 0.25 were included in the therapeutic minimal improvement group, while those with a PANSS score reduction rate > 0.5 were included in the therapeutic effective group. Changes in inflammatory markers before and after treatment were subsequently compared between the two groups. As shown in Figure 2, the analysis revealed that the effective group exhibited significantly greater values for all markers compared to the minimal improvement group. Specifically, for ΔMLR, the mean rank was significantly greater in the effective group (124.81, n = 98) than in the minimal improvement group (78.35, n = 103; U = 2714.00, Z = -5.662, P < 0.001). This pattern was consistent for ΔNLR (effective group mean rank = 123.41 vs minimal improvement group = 79.67; U = 2850.50, Z = -5.329, P < 0.001) and ΔSII (effective group mean rank = 119.70 vs minimal improvement group = 83.21; U = 3214.50, Z = -4.446, P < 0.001). These findings indicate that the greater the reduction in these inflammatory markers after treatment, the better the clinical symptom improvement attained through MECT treatment.

Figure 2 Box plots illustrating the differences between the minimal improvement group and the effective group in terms of Δmonocyte-to-lymphocyte ratio, Δneutrophil-to-lymphocyte ratio, and Δsystemic immune-inflammatory index. PANSS: Positive and Negative Syndrome Scale; MLR: Monocyte-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; SII: Systemic immune-inflammatory index.

Predictive value of inflammatory marker changes for treatment outcomes

To evaluate the predictive value of changes in peripheral blood inflammatory markers for the clinical efficacy of MECT, three univariate logistic regression analyses were conducted. The response to treatment (defined as “effective” for a PANSS score reduction rate > 50% and “minimal improvement” for ≤ 25%) served as the dependent variable, while the ΔMLR, ΔNLR, and ΔSII were used as independent variables. As shown in Table 3, ΔMLR exhibited the most robust predictive capacity. For every 0.1 unit increase in ΔMLR, the odds of a patient achieving significant clinical symptom improvement increased by 57% [odds ratio (OR) = 1.572, 95% confidence interval (CI): 1.300-1.900]. The presence of ΔNLR was also identified as a significant predictor, albeit with a more modest effect size (OR = 1.044 per 0.1 unit, 95%CI: 1.026-1.062). Although the association for ΔSII was statistically significant, it was minimal, with a 10-unit increase being required to show a 1% increase in the odds of improvement (OR = 1.010, 95%CI: 1.005-1.015). In summary, the reduction in all three peripheral inflammatory markers following MECT was significantly associated with clinical symptom improvement. However, among the inflammatory markers studied, changes in ΔMLR represent the most reliable and clinically relevant predictor of treatment efficacy.

Table 3 Logistic regression analysis of inflammatory marker changes as predictors of clinical symptom improvement after modified electroconvulsive therapy.

	Increment	OR	95%CI	P value
ΔMLR	per 0.1 unit	1.572	1.300-1.900	< 0.001
ΔNLR	per 0.1 unit	1.044	1.026-1.062	< 0.001
ΔSII	per 10 unit	1.010	1.005-1.015	< 0.001

OR: Odds ratio; CI: Confidence interval; MLR: Monocyte-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; SII: Systemic immune-inflammatory index.

Predictive power of ΔMLR for treatment outcomes

In order to evaluate the predictive efficacy of ΔMLR for MECT treatment outcomes, we conducted further ROC analysis. As shown in Figure 3, the area under the curve (AUC) for the ability of the ΔMLR to predict treatment efficacy was 0.731 (95%CI: 0.662-0.800, P < 0.001). In accordance with the prevailing standards within the academic community (where an AUC between 0.7 and 0.9 is indicative of moderate accuracy), this outcome substantiates the notion that the ΔMLR has considerable predictive value with regard to the clinical efficacy of MECT[18]. Furthermore, the optimal cutoff value for ΔMLR was determined to be 0.075 through calculation using Youden’s index. This value provides a preliminary quantitative reference for the utilization of the ΔMLR as a potential biomarker in clinical decision-making.

Figure 3 Receiver operating characteristic curve for Δmonocyte-to-lymphocyte ratio in predicting treatment outcomes of modified electroconvulsive therapy. The area under the curve was 0.73 (95% confidence interval: 0.66-0.80). The marked point in the figure indicates the optimal cutoff value (0.075) determined by the Youden index, which corresponds to both high sensitivity and specificity. The diagonal line (dashed) denotes the reference line, which possesses no predictive value (area under the curve = 0.5). MLR: Monocyte-to-lymphocyte ratio; AUC: Area under the curve.

DISCUSSION

This study, which was based on large-sample data analysis, revealed that PANSS scores significantly decreased in schizophrenia patients following MECT treatment, which was accompanied by marked reductions in multiple peripheral blood immune-inflammatory markers (including MLR, NLR and SII). Correlation analysis further revealed a significant positive correlation between the degree of decrease in MLR, NLR, and SII with the rate of PANSS score reduction, suggesting that improvements in inflammatory levels are concomitant with clinical symptom improvement. To validate the regulatory effect of MECT on peripheral blood inflammatory markers, patients were divided into two groups based on treatment efficacy: A minimal improvement group and an effective group. The findings demonstrated that the decrease in the aforementioned inflammatory markers was considerably more pronounced in the effective group compared to the minimal improvement group. This observation signifies that patients who exhibited greater clinical symptom improvement following MECT treatment also demonstrated a more significant decrease in inflammation levels.

To further advance the translation of research findings into clinical practice, we employed logistic regression analysis to evaluate the predictive value of inflammatory marker changes for predicting treatment response. The present study identified ΔMLR as a prominent predictor, with each 0.1-unit increase in ΔMLR being associated with a 57% greater probability of achieving significant clinical improvement. ROC analysis revealed an AUC value of 0.731 for ΔMLR with respect to predicting MECT efficacy, indicating its strong predictive performance. Additionally, the Youden index determines the optimal cutoff value for ΔMLR to be 0.075. If the result exceeds 0.075, it indicates that MECT is likely to yield favorable outcomes for the patient, thereby providing robust objective evidence to support subsequent treatment decisions. This study not only corroborates the systemic anti-inflammatory effects of MECT but also reveals a close association between inflammatory markers and clinical symptom improvement. The demonstration of excellent predictive value by ΔMLR provides an objective and quantifiable reference for the evaluation of treatment in schizophrenia patients. It should be emphasized that this study primarily employed univariate analysis, which established the independent predictive value and robust effect size of ΔMLR, thereby laying a clear and solid evidential foundation for its clinical application. Future research should focus on developing predictive models that integrate multiple inflammatory markers or clinical variables for further exploration.

First, the core findings of this study provide robust human clinical evidence for the anti-inflammatory effects of MECT. The substantial decrease in inflammatory markers after MECT treatment indicates that its therapeutic effects may extend beyond mere neurophysiological modulation. It is hypothesized that this systemic decrease in inflammation is not coincidental, but rather a consequence of MECT intervention within the intricate immune-neuro-endocrine network. The potential mechanisms may involve multiple pathways, such as the regulation of the hypothalamic-pituitary-adrenal (HPA) axis. MECT has been demonstrated to function as a highly efficacious physiological stressor, with the capacity to precisely regulate the HPA axis, thereby suppressing excessive glucocorticoid release[19]. We hypothesize that this effect disrupts the glucocorticoid resistance commonly observed in schizophrenia, thereby restoring the axis's inhibitory function on innate immune responses[20,21]. As a result, the proliferation and activation of proinflammatory cells (e.g. neutrophils and monocytes) are hindered. Additionally, MECT-induced seizures may activate vagal pathways. Acetylcholine released from the terminals of the vagus nerve binds to α7 nicotinic acetylcholine receptors on immune cells, including tissue macrophages. The nuclear translocation of key proinflammatory transcription factors, such as nuclear factor kappa-light-chain-enhancer of activated B cells, is directly inhibited by this process, thereby suppressing the release of core proinflammatory cytokines, including TNF-α, IL-1β, and IL-6[22,23]. This may represent the core mechanism underlying the reduced proportion of monocytes (the primary source of cytokine production).

Our findings demonstrate that clinical improvement following MECT is associated with a reduction in peripheral inflammation, most reliably reflected by a decrease in ΔMLR. The strong predictive value of ΔMLR establishes it as a robust peripheral marker of treatment response. This association between peripheral immunomodulation and central symptom improvement invites the hypothesis that MECT may engage integrated body-brain pathways. For instance, it has been proposed that peripheral immune changes can influence brain function through various pathways[24,25], and MECT is known to upregulate neurotrophic factors such as BDNF[26,27]. However, these proposed mechanisms are not addressed by our peripheral measures and remain to be tested. Future studies directly assessing central immune activity (e.g., via neuroimaging) alongside peripheral markers are essential for exploring this possibility. The lack of decrease in the PLR after MECT likely reflects platelets’ dual role in inflammation and coagulation. As a physiological stressor, MECT may temporarily alter platelet and lymphocyte kinetics. Consequently, the distinct PLR response may capture a unique, MECT-induced immune-coagulation interaction. This represents an intriguing finding that merits further investigation in future studies.

The most translational medicine-oriented discovery in this study is the advancement of ΔMLR and other indicators from associative biomarkers to predictive biomarkers. Binary logistic regression confirmed ΔMLR as the strongest predictor of treatment response (OR = 1.57 per 0.1 unit increase), while ROC analysis further quantified its predictive value (AUC = 0.731, cutoff value = 0.075). The underlying mechanism for the predictive value of ΔMLR may be attributable to the fact that monocytes are among the fastest-responding and most plastic innate immune cells in the body[28,29]. Alterations in their numbers can serve as a reliable indicator of shifts in the body’s overall inflammatory state. Consequently, patients exhibiting favourable treatment responses may demonstrate a more expeditious immune response to the “anti-inflammatory signal” emitted by MECT, manifesting as a precipitous decline in MLR. Conversely, patients exhibiting poor responses may have an immune system entrenched in a more intractable, less modifiable inflammatory state. Consequently, ΔMLR is not merely a numerical value but a sensitive quantitative indicator of the modifiability of a patient’s immune system. ΔMLR is a promising biomarker, and its predictive performance warrants further validation in an independent prospective cohort.

In summary, this study demonstrated that clinical improvement following MECT is associated with a concurrent reduction in peripheral inflammatory activity, as evidenced by decreases in MLR, NLR, and SII. The strong correlation and predictive capacity of ΔMLR establish it as a promising, readily accessible peripheral biomarker for predicting treatment response in schizophrenia. We propose that future research should test a model in which MECT-induced peripheral immunomodulation, potentially via mechanisms such as HPA axis regulation and cholinergic signalling, synergizes with neurotrophic upregulation to promote clinical recovery. The validation of this model, however, requires direct investigation of central nervous system immune activity in conjunction with peripheral markers. Our work highlights the clinical relevance of peripheral immune profiling and positions ΔMLR as a candidate biomarker to guide personalized MECT treatment strategies.

This study has several limitations. First, the single-arm observational design, which lacked a control group (e.g., patients receiving only pharmacological treatment), prevents us from fully attributing the observed clinical improvements and reductions in inflammatory markers to MECT. These changes may have been influenced by the natural course of the illness or concomitant medication. Furthermore, we did not statistically adjust for other clinical covariates such as specific psychotropic co-medications (e.g., benzodiazepines, mood stabilizers), anti-inflammatory drugs, corticosteroids, smoking status, body mass index, metabolic markers, or menstrual status. Future prospective studies that systematically collect and control for these covariates will help further validate the independence of the predictive value of ΔMLR. Second, we could not rule out the possibility that the observed decrease in inflammatory markers resulted from non-specific effects of repeated anesthesia or procedural interventions rather than specific effects of MECT itself - an aspect that warrants further investigation in future studies. Simultaneously, future research should aim to systematically analyze the dose-response relationship between MECT technical parameters (such as stimulus dosage and seizure quality) and neuroimmune changes within more heterogeneous treatment parameter contexts, so as to further elucidate its underlying mechanisms. Furthermore, although the fixed six-session treatment protocol facilitated standardized measurements, it limited our ability to delineate the precise trajectory of immune changes, leaving it unclear whether the decline in inflammation occurred abruptly after the initial sessions or gradually throughout the course of treatment. On the measurement level, the currently employed inflammatory markers remain inadequate for reflecting specific alterations in immune cell subsets and their underlying molecular mechanisms. Future research should incorporate control groups and utilize flow cytometry to analyze lymphocyte and monocyte composition, alongside simultaneous monitoring of plasma cytokines and BDNF levels, to validate proposed immunoregulatory pathways. Moreover, the application of microglia-specific PET imaging could enable direct observation of MECT’s impact on central nervous system immunity. By correlating peripheral and central indicators, a systematic evidence chain linking peripheral and central mechanisms can be established.

CONCLUSION

In conclusion, our large-sample study demonstrated that MECT significantly improved clinical symptoms and reduced peripheral inflammatory indices (MLR, NLR, PLR, and SII) in patients with schizophrenia. Notably, the reduction in MLR strongly correlated with clinical symptom improvement and served as a robust predictor of treatment response. These findings suggest that the therapeutic effects of MECT may be partially mediated through regulation of the immune-inflammatory system. MLR represents a promising peripheral inflammatory marker for predicting treatment efficacy, highlighting the potential for immune profiling to guide personalized therapeutic strategies in schizophrenia.

ACKNOWLEDGEMENTS

We are grateful to all the people who took part in this study.