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Abstract

Objective:

To investigate changes in serum neuroinflammatory biomarker concentrations and associated psychopathological manifestations in individuals with post-traumatic stress disorder, taking into account the presence or absence of traumatic brain injury.

Methods:

The study involved 159 male combat veterans who had been involved in combat within the year prior to their inclusion in the study, divided into four groups: veterans without post-traumatic stress disorder or traumatic brain injury (n = 43), veterans without post-traumatic stress disorder but with traumatic brain injury (n = 41), veterans with post-traumatic stress disorder but without traumatic brain injury (n = 38), and veterans with both post-traumatic stress disorder and traumatic brain injury (n = 37). Psychometric parameters (subjective stress severity, depression, avoidance, hyperarousal, intrusive thoughts, and alterations in personality traits) were evaluated. Serum concentrations of antibodies to glial fibrillary acidic protein and neuron-specific enolase were assessed using enzyme-linked immunosorbent assay, while YKL-40, migration inhibitory factor, receptor for advanced glycation end-products, interleukin-34, B-lymphocyte chemoattractant, and triggering receptor expressed on myeloid cells 2 were measured using multiplex assays.

Results:

Traumatic brain injury presence was associated with reduced severity of depression and avoidance symptoms compared to individuals with post-traumatic stress disorder alone. Elevated serum levels of glial fibrillary acidic protein and neuron-specific enolase antibodies were observed in post-traumatic stress disorder patients, with neuron-specific enolase antibodies particularly increased in the post-traumatic stress disorder + traumatic brain injury group. Notably, YKL-40 levels were decreased in the post-traumatic stress disorder + traumatic brain injury group. Migration inhibitory factor concentration was reduced in post-traumatic stress disorder, while isolated post-traumatic stress disorder was linked to higher receptor for advanced glycation end-products levels. Significant correlations were found between biomarker concentrations and psychometric indices.

Conclusion:

The findings reveal distinct clinical and biochemical profiles for isolated post-traumatic stress disorder and post-traumatic stress disorder comorbid with traumatic brain injury, reflecting differences in underlying pathophysiological processes. The data suggest involvement of neuroinflammation, blood–brain barrier disruption, and autoimmune mechanisms in post-traumatic stress disorder pathogenesis—particularly when traumatic brain injury is also present. Identified biomarkers may enhance diagnostic and prognostic tools in combat psychiatry.

Keywords

post-traumatic stress disorder traumatic brain injury neuroinflammation GFAP NSE YKL-40

Introduction

Post-traumatic stress disorder (PTSD) is a distinct psychiatric condition triggered by exposure to severe psychological trauma—particularly events involving direct threats to life or catastrophic incidents—and is characterized by a pronounced stress response. While PTSD can affect civilians, it is significantly more prevalent among individuals with combat experience.^1–3 Core clinical features include intrusive thoughts, avoidance of trauma-associated stimuli, hyperarousal, increased reactivity, and alterations in personality traits.⁴

PTSD commonly co-occurs with other psychiatric disorders,⁵ and the presence of traumatic brain injury (TBI) has a notable impact on its risk, clinical course, and diagnostic complexity.^6,7 In cases of comorbid PTSD and TBI, neuroinflammation is considered a key pathophysiological mechanism. TBI-induced neuroinflammation involves microglial activation, increased production of pro-inflammatory cytokines, and disruption of the blood–brain barrier (BBB).^8,9 A large-scale review of literature covering publications from 1990 to 2017 identified shared neuropsychiatric features and cerebral dysfunctions—such as white matter abnormalities and gray matter alterations—that contribute to emotional and cognitive impairments.¹⁰ Another important pathogenic mechanism involves the dysregulation of brain-derived neurotrophic factor, which plays a critical role in neurogenesis and the formation of synaptic connections. This dysregulation is implicated in both PTSD and TBI.¹⁰ Additionally, studies examining levels of inflammatory cytokines – interleukins (IL-1β, IL-6, IL-8, IL-12) and Tumor Necrosis Factor-alpha (TNF-α) and anti-inflammatory IL-10 in the blood and cerebrospinal fluid (CSF) of TBI patients have demonstrated that elevated cytokine levels contribute to secondary neuroinflammatory responses.¹¹

Inflammatory signaling pathways are also central to the pathophysiology of depressive disorders,^12,13 which may explain the high comorbidity between PTSD and depression in TBI populations.¹⁴ A meta-analysis by Dowlati et al.¹⁵ found significantly elevated levels of TNF-α and IL-6 in individuals with major depressive disorder compared to healthy controls. Therefore, cytokine dysregulation represents a common biological substrate for both TBI and depression, highlighting its relevance in mood disorders developing in PTSD patients with a history of brain trauma. Investigating the role of cytokines in mood regulation offers promising avenues for treatment innovation, potentially mitigating PTSD severity in TBI populations.¹⁶

Another key mechanism linking TBI and PTSD may involve disruption of the BBB. The BBB is a crucial physiological interface that separates the central nervous system from systemic circulation and regulates molecular exchange vital for brain homeostasis.¹⁷ Inflammatory processes can compromise BBB integrity, leading to a range of central nervous system disorders.¹⁷ Robust evidence indicates that TBI impairs BBB function, contributing to neuronal loss and neurological dysfunction. Post-traumatic BBB dysfunction is a major factor in injury progression and the extent of neuronal recovery.¹⁸ Stress itself also plays a significant role in BBB disruption, although its specific mechanisms—especially in PTSD—are not yet fully elucidated.¹⁹

This study aimed to explore neuroinflammatory and BBB-associated mechanisms in PTSD, particularly in the context of comorbid TBI. It also compared the intensity of inflammatory responses between isolated PTSD and PTSD with TBI by analyzing circulating biomarkers indicative of neuroinflammation and BBB integrity, based on established evidence.^20–22

Materials and methods

Patients

This study was designed as a cross-sectional observational study. The recruitment period spanned from August 2023 to December 2024. The study included 159 male combat veterans who were hospitalized within 12 months following the completion of military service. The clinical PTSD group (n = 75) consisted of patients diagnosed with F43.1 (PTSD) based on International Classification of Diseases, 10th Revision (ICD-10) criteria, either admitted emergently or presenting for elective hospitalization at the Mental-Health Clinic No. 1 named after N. A. Alexeev. The comparison group of veterans without PTSD (n = 84) included individuals without PTSD symptoms, hospitalized in the neurology department of War Veterans Hospital No. 3.

All participants, including those in the control groups (noPTSD and noPTSD + TBI), were admitted in a stable condition and did not present with acute psychiatric or somatic decompensation. Their hospitalizations were scheduled for diagnostic, rehabilitative, or follow-up purposes rather than crisis-related interventions. In the noPTSD + TBI group, admissions were often related to monitoring the long-term consequences of mild TBI (e.g., headaches, insomnia, and cognitive complaints) in the absence of PTSD symptoms. Hospitalization conditions were comparable across all groups. Blood sampling was performed within the first 3–5 days after admission to reduce the influence of hospitalization-related stress and to ensure standardization across cohorts. Although hospitalization may influence systemic inflammation, the uniform timing and clinical setting helped minimize this potential confounding effect.

Eligibility criteria included: Documented direct participation in military operations with exposure to traumatic stressors; male sex; affiliation with enlisted or junior command ranks of the Armed Forces of the Russian Federation; presence of a mental health condition requiring inpatient care; voluntary psychiatric hospitalization; and written informed consent for participation and data processing.

Exclusion criteria comprised: Absence of documented participation in military operations; officer or warrant officer military rank; absence of medical indications for psychiatric inpatient treatment; involuntary hospitalization; refusal to participate or provide informed consent; or the presence of acute somatic illnesses, active infections, recent surgeries, or major traumatic injuries that could confound inflammatory markers.

Substance use was screened during admission using structured clinical interviews and review of medical records. Participants with signs of intoxication, withdrawal, or active substance use disorders were excluded. Urine toxicology screening was performed when clinically indicated. Although standardized instruments such as Alcohol Use Disorders Identification Test or Drug Abuse Screening Test were not employed universally, screening followed real-world clinical protocols, and exclusion criteria were rigorously enforced.

Participants were stratified by the presence or absence of TBI, based on clinical records and neurologic evaluation. Only individuals with mild-to-moderate TBI were included. TBI diagnoses were initially made by military neurologists during deployment and subsequently confirmed by civilian specialists using clinical criteria and neuroimaging (computed tomography (CT)/magnetic resonance imaging (MRI)) when available. Although formal classification systems (American Congress of Rehabilitation Medicine (ACRM) or Department of Defense (DoD)) were not applied, Russian diagnostic protocols align with international guidelines and were used by experienced professionals. Medical records were anonymized and transferred in summary format; therefore, raw imaging data and exact trauma dates were not accessible. Likewise, length of stay (LOS) was not analyzed, as the study focused on symptom severity and biomarker levels at the time of inclusion.

All biomaterials were collected within the first several days of inpatient admission. All participants had returned from combat duty within the previous 12 months. This design helped reduce variance related to hospitalization duration and delayed biomarker kinetics.

This stratification yielded four groups: noPTSD, comprising veterans without PTSD or TBI (n = 43); noPTSD + TBI (n = 41); PTSD without TBI (n = 38); and PTSD + TBI (n = 37).

Only male participants were included to ensure sample homogeneity and reduce sex-based biological variability. Evidence indicates that sex hormones and genetic factors significantly influence neuroimmune regulation and biomarker expression (e.g., YKL-40, migration inhibitory factor (MIF), and IL-6).^23–25 Moreover, all participants were enlisted male veterans of combat units, and female personnel were underrepresented in these settings. Future studies will aim to address sex differences using dedicated female cohorts.

All participants underwent psychometric assessment followed by blood sample collection for biochemical analyses.

Written informed consent was obtained from all participants.

Psychometric assessment

Standardized assessment procedures were used, including clinical and anamnestic evaluation, structured interviews, and standardized clinical psychometric instruments. The following variables were recorded: age, height, weight, number of weeks since withdrawal from the combat zone, and subjective stress severity. PTSD symptomatology—including depression, alterations in personality traits, avoidance, hyperarousal, and intrusive thoughts—was rated using a five-point Likert scale, corresponding to ICD-10 diagnostic criteria.

Serum samples collection

Venous blood samples were collected from the cubital vein in fasting participants, typically before 9:00 a.m. Serum was separated immediately following collection by centrifugation at 3000 rpm for 10 min at 4°C and stored at −80°C until analysis.

Multiplex assay

To assess markers of neuroinflammation, serum levels of chitinase 3-like protein 1 (CHI3L1, also known as YKL-40), macrophage MIF, interleukin-34 (IL-34), B lymphocyte chemoattractant (BLC, CXCL13), receptor for advanced glycation end-products (RAGE), and triggering receptor expressed on myeloid cells 2 (TREM-2) were measured using commercially available multiplex immunoassay kits (ProcartaPlex™ Human Neuroinflammation Panel 6-Plex, Thermo Fisher Scientific). All assays were performed according to the manufacturer’s instructions using a Luminex™ 100/200™ analyzer. Concentrations were reported in pg/mL.

Quantification of autoantibodies to glial fibrillary acidic protein and neuron-specific enolase

The serum levels of antibodies against two neuron-specific proteins—glial fibrillary acidic protein (GFAP) and neuron-specific enolase (NSE)—were quantified via enzyme-linked immunosorbent assay. This method allows the detection of BBB disruption and neurodegeneration.

Recombinant human GFAP and NSE proteins were used as antigens, as previously described by Chekhonin et al.²⁶ and Pavlov et al.²⁷ Proteins were immobilized on 96-well plates at 4°C for 12 h in 0.1 M NaHCO₃ buffer (pH 9.2). Plates were then washed with phosphate-buffered saline containing 0.05% Tween-20 (PBS-T, pH 7.4) using an automated washer (WellWash, Thermo Fisher Scientific, USA). Non-specific binding was blocked by incubating with 3% bovine serum albumin in PBS for 1 h at room temperature. After a subsequent wash, 100 µL of diluted serum (1:50 in PBS) was added to each well and incubated for 1.5 h at 500 rpm. After further washing, biotin-conjugated secondary anti-human immunoglobulin antibodies (Thermo Fisher, 1:40,000 dilution) were added and incubated for 1 h, followed by incubation with a streptavidin–horseradish peroxidase conjugate (Thermo Fisher, 1:5,000 dilution) for 30 min. Color development was achieved using tetramethylbenzidine substrate for 10 min, and the enzymatic reaction was terminated by adding 1 M H₂SO₄. Absorbance was measured at 450 nm using a Varioskan LUX spectrophotometer (Thermo Fisher Scientific, USA).

Statistical processing

All statistical analyses were conducted using Jamovi and RStudio software. The Shapiro–Wilk test was applied to assess the normality of data distribution. Since all variables demonstrated non-normal distributions, non-parametric statistical methods were used.

Between-group comparisons were performed using the Kruskal–Wallis test followed by Dwass–Steel–Critchlow–Fligner post hoc tests. Data were expressed as median values with interquartile ranges (Q1, Q3). Statistical significance was set at p < 0.05.

To evaluate correlations between biomarker levels and psychometric indicators, Pearson’s correlation coefficients were calculated. Correlations were considered statistically significant at |r| > 0.7 with p < 0.01, corrected for multiple comparisons.

To control the family-wise error rate in the context of multiple comparisons, we applied the Benjamini–Hochberg procedure to control the false discovery rate. This method offers a compromise between limiting Type I errors and maintaining statistical power, which is particularly critical in biomarker studies involving numerous variables.

Results

Clinical characteristics of participants

Complete clinical and biochemical data were obtained for all 159 participants. Baseline demographic variables—age, height, weight, and the number of weeks since withdrawal from combat—did not significantly differ across the four experimental groups, confirming sample comparability (Table 1). However, marked differences were observed in psychometric test results.

Table 1.

Clinical and psychometric characteristics of study participants.

Parameter	noPTSD (n = 43)		noPTSD + TBI (n = 41)		PTSD (n = 38)		PTSD + TBI (n = 37)		χ²	p
Parameter	Median	Q1; Q3	Median	Q1; Q3	Median	Q1; Q3	Median	Q1; Q3	χ²	p
Age (years)	35	32; 42	36	26; 45	31,5	25; 36	35	30; 42	4.37	0.22
Weight (kg)	78	72; 83	78	72; 85	85	80; 89	79	75; 85	5.88	0.12
Height (cm)	178	172; 182	178	172; 180	178	175; 181	178.5	175.5; 183	0.95	0.81
Weeks since combat	6	4; 10	6	4.75; 9	13	4; 26	11	4; 34	5.43	0.14
Subjective stress severity	1	1; 1	1	1; 1	2	2; 3	2	2; 2	84.81	<0.001
Depression	1	1; 2	2	0; 2	2	2; 3	0	0; 2	21.74	<0.001
Alterations in personality traits	0	0; 1	0	0; 0.25	1	1; 1	1	0; 1	32.78	<0.001
Avoidance	1	0; 1	1	1; 1	1	1; 1	1	0; 1	11.74	0.008
Hyperarousal	0	0; 0	0	0; 0	1	1; 1	1	1; 1	65.05	<0.001
Intrusive thoughts	1	1; 2	2	1; 2	1	1; 1	1	0; 1	63.07	<0.001

noPTSD: veterans without PTSD and without TBI; noPTSD + TBI: veterans without PTSD and with TBI; PTSD: veterans with post-traumatic stress disorder without TBI; PTSD + TBI: veterans with PTSD and comorbid TBI; Q1: first quartile; Q3: third quartile.

The analysis was performed using the Kruskal–Wallis test. Significant p-values (<0.05) are highlighted in bold.

Participants with PTSD (both with and without TBI) exhibited higher scores for subjective stress severity, depression, alterations in personality traits, avoidance, hyperarousal, and intrusive thoughts compared to non-PTSD groups.

Multiple significant between-group differences were identified in post hoc analyses (Table 2).

Table 2.

Post hoc comparison of psychometric scale results.

Scale	p-Value
Scale	noPTSD/noPTSD + TBI	noPTSD/PTSD	noPTSD/PTSD + TBI	noPTSD + TBI/PTSD	noPTSD + TBI/PTSD + TBI	PTSD/PTSD + TBI
Subjective stress	0.26	<0.001	<0.001	<0.001	<0.001	0.16
Depression	1.0	0.007	0.39	0.03	0.52	<0.001
Alterations in personality traits	0.99	<0.001	0.004	<0.001	0.002	0.69
Avoidance	0.60	0.02	0.97	0.46	0.32	0.004
Hyperarousal	0.93	<0.001	<0.001	<0.001	<0.001	0.56
Intrusive thoughts	0.31	<0.001	<0.001	<0.001	<0.001	0.22

The analysis was performed using the Kruskal–Wallis test and subsequent multiple comparison tests (Dwass–Steel–Critchlow–Fligner). Significant p-values (<0.05) are highlighted in bold.

Veterans with PTSD (with or without TBI) demonstrated significantly greater subjective stress, hyperarousal, and alterations in personality traits than those without PTSD. In contrast, depression and avoidance scores were influenced by the presence of TBI: the PTSD + TBI group showed significantly lower depression and avoidance scores compared to the PTSD-only group.

Biochemical marker concentrations in blood

Next, we assessed serum concentrations of biochemical markers related to neuroinflammation and neurotrauma. Statistically significant differences between groups were observed for five parameters: antibodies to GFAP and NSE, YKL-40, RAGE, and MIF (Table 3).

Table 3.

Serum concentrations of biochemical markers.

Marker	noPTSD (n = 43)		noPTSD + TBI (n = 41)		PTSD (n = 38)		PTSD + TBI (n = 37)		χ²	df	p
Marker	Median	Q1; Q3	Median	Q1; Q3	Median	Q1; Q3	Median	Q1; Q3	χ²	df	p
GFAP antibodies	0.64	0.50; 0.82	0.63	0.45; 0.78	0.80	0.71; 0.82	0.73	0.66; 0.82	11.95	3	0.008
NSE antibodies	0.46	0.34; 0.62	0.40	0.25; 0.58	0.66	0.61; 0.73	0.66	0.51; 0.71	29.71	3	<0.001
TREM-2	149	90; 266	142	93; 192	131	96; 174	106	74; 190	4.59	3	0.20
BLC	117	78; 166	119	87; 129	129	99; 212	119	90; 171	2.94	3	0.40
YKL-40	10,233	8380; 12,481	9627	7683; 11,404	8537	6791; 12,329	7313	5310; 9123	17.29	3	<0.001
RAGE	103	60; 146	97	55; 122	136	100; 197	120	84; 174	10.05	3	0.02
IL-34	8.5	8.5; 9.9	8.5	6.3; 9.9	8.5	8.5; 12.6	8.5	2.9; 17.1	0.45	3	0.93
MIF	483	190; 1237	387	170; 650	193	106; 618	141	96; 648	8.75	3	0.03

GFAP: glial fibrillary acidic protein; NSE: neuron-specific enolase; TREM-2: triggering receptor expressed on myeloid cells 2; BLC: B-lymphocyte chemoattractant; YKL-40: chitinase-3-like protein 1; RAGE: receptor for advanced glycation end products; IL-34: interleukin 34; MIF: macrophage migration inhibitory factor; noPTSD: veterans without PTSD and without TBI; noPTSD + TBI: veterans without PTSD and with traumatic brain injury (TBI); PTSD: veterans with post-traumatic stress disorder without TBI; PTSD + TBI: veterans with PTSD and comorbid TBI; Q1: first quartile; Q3: third quartile; df: degrees of freedom.

The analysis was performed using the Kruskal–Wallis test. Significant p-values (<0.05) are highlighted in bold.

Post hoc analysis revealed the following (Figure 1). GFAP antibodies were elevated in the PTSD group compared to the noPTSD group (p = 0.048) and the noPTSD + TBI group (p = 0.009); however, the PTSD + TBI group did not differ significantly from the other groups (Figure 1(a)). NSE antibodies were significantly increased in both the PTSD group (p < 0.001) and the PTSD + TBI group (p = 0.002) compared to the noPTSD groups (Figure 1(b)). YKL-40 levels were significantly lower in the PTSD + TBI group than in the noPTSD group (p < 0.001) and the noPTSD + TBI group (p = 0.01), while no significant difference was observed between the PTSD + TBI and PTSD groups (Figure 1(c)). RAGE levels were significantly higher in the PTSD group compared to the noPTSD + TBI group (p = 0.03; Figure 1(d)). Although post hoc comparisons of MIF levels between individual groups were not statistically significant, the presence of PTSD was associated with a significant reduction in MIF levels compared to the non-PTSD groups (p = 0.005).

Figure 1.

Concentration of biochemical parameters in serum. Data are shown as Med (Q1; Q3) in boxplots; black horizontal lines indicate medians, black dots indicate outliers. (a) Antibodies to glial fibrillary acidic protein (GFAP) were significantly higher in PTSD groups (P, PT) compared to control groups (C, CT). (b) Antibodies to neuron-specific enolase (NSE) were elevated in PTSD groups, with the highest levels observed in the PTSD + TBI group (PT). (c) Chitinase-3-like protein 1 (YKL-40) levels were significantly higher in CT and P compared to PT. (d) Receptor for advanced glycation end products (RAGE) was increased in PTSD (P) compared to PTSD + TBI (PT).

Correlation analysis

We further conducted a correlation analysis to evaluate the associations between blood biochemical marker concentrations within each experimental group (Figure 2). Significant positive correlations were identified between the neuron-specific autoantibodies to GFAP and NSE in the noPTSD group (r = 0.60, p < 0.001), the PTSD group (r = 0.76, p < 0.001), and the PTSD + TBI group (r = 0.86, p < 0.001). In the noPTSD group, a distinct cluster of positively correlated markers was observed, including YKL-40 with TREM-2 (r = 0.41, p = 0.006), YKL-40 with BLC (r = 0.52, p < 0.001), and YKL-40 with MIF (r = 0.70, p < 0.001). Additionally, in the PTSD + TBI group, a positive correlation was identified between RAGE and BLC (r = 0.66, p < 0.001).

Figure 2.

Correlation analysis of biochemical parameters in serum. Corr – Pearson correlation coefficient (r). Each cell represents the strength of correlation between two parameters (horizontally and vertically, respectively). Correlation values are color-coded: strong positive correlations are shown in red, negative in blue, and intermediate values in shades between red and blue. Crossed-out cells (X) indicate non-significant correlations (p > 0.05). Numeric values are shown only for statistically significant correlations (p ⩽ 0.05).

Next, we examined correlations between blood biochemical markers and psychometric scores within each group. Statistically significant but moderate correlations were found in the noPTSD + TBI and PTSD + TBI groups:

In noPTSD + TBI, RAGE was positively correlated with the number of weeks since combat zone withdrawal (r = 0.44, p = 0.005), when YKL-40 was positively correlated with the avoidance scale (r = 0.41, p = 0.009).

In PTSD + TBI, GFAP antibodies correlated positively with avoidance (r = 0.51, p = 0.002), when NSE antibodies correlated positively with both depression (r = 0.43, p = 0.007) and avoidance (r = 0.43, p = 0.007).

Discussion

A central aim of our study was to assess the extent to which TBI influences the severity of PTSD symptomatology. We analyzed clinical data from four experimental groups: veterans without PTSD and without traumatic exposure, veterans without PTSD but with TBI, veterans with PTSD, and veterans with comorbid PTSD and TBI (PTSD + TBI). Demographic variables, including age, weight, height, and time since deployment, did not differ significantly between the groups, suggesting sample comparability. However, we observed significant differences in psychometric parameters. The most pronounced disparities emerged in measures of subjective stress severity, depression, personality traits, avoidance, hyperarousal, and intrusive thoughts. Participants in the PTSD and PTSD + TBI groups reported significantly higher levels of subjective stress, reflecting increased emotional burden. This finding is consistent with literature highlighting the unique stressors of military service, which differ substantially from civilian experiences.²⁸ For instance, Bray et al.²⁹ reported that both male and female service members experience nearly double the occupational stress of civilian counterparts. Similar trends were evident in depression scores, with the highest values found in PTSD groups—consistent with prior findings on the elevated prevalence of depressive disorders among military personnel.^30,31 Veterans with comorbid PTSD and TBI exhibited the most significant deviations in nearly all psychometric dimensions, suggesting a synergistic interaction between traumatic exposure and brain injury. These results align with evidence indicating greater vulnerability to psychopathological symptoms among individuals with comorbid disorders.^32,33

Post hoc analysis further supported significant intergroup differences, underscoring the nuanced role of TBI in the etiology of mental health disorders in military populations. Notable differences were found in the levels of subjective severity of stress, depression, personality characteristics, as well as in the severity of symptoms associated with PTSD, including avoidance, excitability, and intrusive thoughts. PTSD and PTSD + TBI groups showed elevated scores in subjective stress, personality alterations, and hyperarousal. Notably, the PTSD + TBI group exhibited comparatively lower intrusion scores than the PTSD group, potentially reflecting differences in cognitive processing in comorbid states. This is in line with studies suggesting that PTSD may distort memory processing, emphasizing trauma-related content and contributing to the persistence of intrusive thoughts.³⁴ Conversely, depression scores were significantly lower in the PTSD + TBI group compared to the PTSD-only group. This may indicate neuropsychological changes associated with comorbidity. Supporting this interpretation, Simonovic et al.³⁵ found that once PTSD severity was controlled for, group differences in depressive symptoms diminished, implying that heightened depression in the PTSD + TBI group is likely a function of more severe PTSD rather than TBI itself. These findings highlight the complex interaction between PTSD and TBI in shaping clinical presentations. TBI may both intensify and mitigate specific PTSD symptoms, emphasizing the need for refined methodologies to distinguish the direct effects of TBI from those mediated by PTSD severity.

We also conducted a peripheral blood biomarker analysis to explore neuroinflammatory activity and BBB integrity. Based on prior research, we selected indicators reflective of inflammation, neurotrophic regulation, and BBB function to assess mechanistic differences in PTSD with and without TBI.^20–22

To further contextualize our biomarker findings, we incorporated current neurocircuitry models of PTSD involving the amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC).^36,37 The amygdala is central to fear expression and threat detection, while the vmPFC provides top-down inhibitory regulation over the amygdala, facilitating extinction of fear responses. PTSD is often associated with vmPFC hypoactivity, leading to unchecked amygdala hyperreactivity and exaggerated anxiety.³⁷ The hippocampus, which supports contextual memory and discrimination between safe and threatening stimuli, is frequently compromised in PTSD, contributing to impaired fear extinction.³⁶

Neuroinflammatory processes may disrupt functional connectivity among these regions.³⁸ Specifically, YKL-40 and RAGE, known markers of inflammation and oxidative stress, may reflect immune-mediated alterations in vmPFC-amygdala circuitry. Elevated GFAP and NSE autoantibodies could additionally signal astrocytic and neuronal injury in areas critical for emotional regulation and memory, consistent with the neuropsychological manifestations of PTSD.³⁹ This integrated framework strengthens the translational significance of our findings by linking peripheral biomarkers to neural substrates of PTSD.

Studies suggest that BBB disruption may underlie both PTSD and cognitive dysfunction, with microglial activation contributing to early BBB remodeling during single prolonged stress.⁴⁰ While an intact BBB may protect the brain from PTSD-related neuropathology, its breakdown may increase susceptibility to traumatic stress.^19,41 Animal models support a correlation between hippocampal BBB integrity and resilience to psychological trauma.^19,41

We used a novel approach to assess BBB compromise—quantifying serum autoantibodies against neuron-specific proteins. Because the brain is immunoprivileged, its proteins are typically sequestered from the immune system. However, when BBB integrity is compromised, neuron-specific proteins may enter circulation, triggering autoimmune responses. Such autoantibodies have been reported in neurological and neurodegenerative diseases, including Alzheimer’s, stroke, epilepsy, spinal cord injury, and paraneoplastic syndromes.⁴² For example, myelin basic protein, myelin oligodendrocyte glycoprotein, and other intracellular myelin proteins in the spinal cord are attacked by the immune system in multiple sclerosis and other demyelinating diseases. As far as we know, no studies have been conducted on the content of antibodies to neurospecific proteins in mental illnesses such as schizophrenia, depression, and PTSD.

The study of this biomarker is due to the fact that TBI is a situation where high concentrations of neurospecific proteins are temporarily released into the bloodstream and become available to the immune system. Marchi et al.⁴³ demonstrated that the levels of autoantibodies to the S100β antiglial protein are elevated in football players with repeated concussions.

Our study focused on GFAP and NSE, both brain-specific proteins. NSE, a glycolytic enzyme isoform, is normally confined to the cytoplasm of neurons and glia. Cell damage results in its release into CSF and then the bloodstream.⁴⁴

GFAP, a marker of astrocytes, is released in both intact (50 kDa) and fragmented (18–44 kDa) forms upon astrocytic injury.^45–47 Models have been proposed to explain its transport across a disrupted BBB.⁴⁸ GFAP autoantibody levels rise within 24 h after TBI, peaking by days 5–7 and persisting for months or years.^42,49 Wang et al.⁴² also found acute-phase GFAP autoantibodies associated with TBI history, while Needham et al.⁴⁹ observed sustained antibody elevation for up to 6 months post-injury, declining by 6–13 years.

Because autoantibodies reflect long-term immunological memory, they may serve as diagnostic markers of chronic TBI, especially when patient history is incomplete.⁴⁹ Additionally, GFAP antibody levels may reflect individual immune variability or antibody clearance capacity, rather than antigen quantity alone. In vitro studies show that GFAP antibodies can enter live astrocytes, potentially impairing glial cell function. Stronger immune responses to GFAP in the subacute phase have been associated with worse TBI outcomes.⁴⁶

We hypothesized that GFAP and NSE autoantibody levels would correlate with PTSD symptoms and TBI status.

Consistent with this, NSE antibodies were elevated in PTSD and PTSD + TBI groups, suggesting neural injury in PTSD even without physical brain trauma. This observation may be of particular significance given the absence of prior evidence for autoantibody elevation in PTSD. One proposed mechanism involves hypothalamic–pituitary–adrenal (HPA) axis dysregulation, leading to impaired glucocorticoid signaling, astrocytic dysfunction, impaired glutamate uptake, and subsequent excitotoxic damage.⁵⁰

GFAP antibodies were significantly elevated in PTSD participants relative to non-PTSD veterans, indicating astrocytic injury and autoimmune involvement. However, this increase was not observed in the PTSD + TBI group. The elevated GFAP antibody levels in PTSD may represent a persistent autoimmune response, as described by Wang et al.,⁴² in which GFAP autoantibodies enhance neuroinflammation and contribute to chronic PTSD/TBI pathogenesis.

This apparent discrepancy between GFAP and NSE patterns may reflect differences in biomarker kinetics as well as astrocytic phenotype transitions. According to Papa et al.,⁵¹ GFAP concentrations typically peak around 20 h post-injury and decrease gradually within the first week, whereas NSE (or its analog UCH-L1) peaks earlier—approximately 8 h post-injury—but may persist under chronic neuronal stress. Because blood sampling in this study occurred days or weeks after trauma, GFAP levels in the PTSD + TBI group may have already normalized, whereas NSE could remain elevated, reflecting ongoing injury, or stress.

Moreover, astrocytic reactivity is known to vary. A1 astrocytes are neurotoxic, highly GFAP-expressive, and activated during acute inflammation; A2 astrocytes, in contrast, are neuroprotective, GFAP-low, and support tissue repair.^52,53 It is plausible that chronic PTSD promotes a shift toward the A2 phenotype, resulting in reduced GFAP synthesis and lower anti-GFAP immune reactivity.^54-56 This may explain the absence of elevated GFAP in PTSD + TBI despite the presence of trauma-related pathology.

Based on the provided data, brain injury in TBI leads to the production of autoantibodies GFAP, which enhances neuroinflammation and contributes to the persistence of symptoms of PTSD and TBI. As the study has shown that the levels of these autoantibodies correlate with a history of TBI and remain elevated during the chronic phase, the increased levels of GFAP in our study may indicate a sustained autoimmune response to brain antigens following injury.⁴² Therefore, the observed changes in the concentrations of neurospecific markers and inflammatory proteins, in conjunction with the literature on autoimmune processes associated with TBI, suggest a complex interplay between neuroinflammation, autoimmune activity, and the development and consequences of PTSD following TBI.

Furthermore, our assessment of brain-specific autoantibodies revealed evidence of neural injury not only in TBI but also in PTSD. Increased serum concentrations of both GFAP and NSE antibodies were observed in PTSD patients, even in the absence of diagnosed TBI. Strong positive correlations between GFAP and NSE antibody levels were identified across all groups—noPTSD, PTSD, and PTSD + TBI—indicating a shared immunological response involving neuron-specific proteins. This response was particularly pronounced in the PTSD + TBI group. Importantly, GFAP antibody levels were positively correlated with avoidance symptoms, while NSE antibodies were associated with both depression and avoidance. These associations point to a link between central nervous system damage and specific clinical manifestations, reinforcing the relevance of autoantibody profiling in PTSD and TBI research.^42,57–60

We also investigated peripheral biomarkers of neuroinflammation including YKL-40, MIF, IL-34, BLC, RAGE, and TREM-2. A significant and unexpected finding was a decrease in YKL-40 in the PTSD+TBI group. YKL-40 is a highly conserved glycoprotein that non-enzymatically binds to heparin and chitin, and may be involved in apoptosis, inflammation, as well as the remodeling or degradation of the extracellular matrix and is elevated in Alzheimer’s, multiple sclerosis, stroke, and TBI.^61–63 YKL-40 has been recognized as a well-established neuroinflammatory molecule and a potent biomarker that is present at the early stages of disease pathogenesis and can distinguish between various brain-related conditions.⁶³ While considered a key early neuroinflammation marker, no previous data exist on its behavior in PTSD, so this study was the first to raise this issue and yielded somewhat contradictory results. Specifically, we found that patients with PTSD and TBI had lower levels of YKL-40 compared to groups of veterans who did not have PTSD.

Our findings contrast with prior studies showing increased YKL-40 post-TBI and its correlation with severity and outcome.⁶¹ We propose that the observed decrease may reflect altered inflammatory dynamics in comorbid PTSD + TBI, different from isolated TBI. In the PTSD-only group, YKL-40 levels were unchanged. These findings parallel results by Olsson et al., who reported reduced YKL-40 in the CSF of patients with Parkinson’s disease and synucleinopathies, attributing it to diminished glial activity.⁶⁴

The observed decrease in YKL-40 levels in the PTSD + TBI group compared to the other cohorts may reflect a complex neuroimmune interaction arising from the combined impact of TBI and PTSD. YKL-40 is secreted primarily by reactive astrocytes during neuroinflammatory responses, particularly in the acute phase of injury, and its elevation is generally associated with worse outcomes.^65–67 However, PTSD is characterized by HPA axis dysregulation and chronic cortisol exposure, both of which may suppress immune activation and reduce expression of inflammatory mediators such as YKL-40.⁶⁵

Furthermore, astrocytic YKL-40 production is modulated by microglial activity. While acute TBI leads to activated microglia that promote cytokine release and synaptic remodeling,^66,68 chronic stress or comorbid PTSD may result in a phenotypic shift toward dystrophic or senescent microglia, leading to reduced glial responsiveness.^67,69 It is also possible that compartmental differences between CSF and plasma and altered biomarker kinetics in chronic neuroinflammation account for the observed decrease in circulating YKL-40.⁷⁰

The significance of the YKL-40 biomarker in neuroinflammatory conditions associated with TBI is further supported by the presence of correlations between this biomarker and other biochemical and psychological measures. In the noPTSD group, YKL-40 showed strong correlations with TREM-2, BLC, and MIF, reinforcing its role as a central inflammatory mediator and potential preclinical biomarker of neurodegeneration.^62,71,72 The positive correlation between YKL-40 and avoidance in the noPTSD + TBI group may reflect chronic inflammatory responses to combat stress and their behavioral manifestations.

RAGE, a multi-ligand pattern-recognition receptor, binds stress-related ligands and plays roles in immune response and oxidative stress.⁷³ RAGE and its soluble forms, collectively referred to as sRAGE, play a role in protecting the body against infections and inflammation. Additionally, some data suggest that the activation of RAGE leads to an increase in reactive oxygen species production and oxidative stress.⁷³ Overall, elevated levels of stage may indicate chronic inflammation and comorbidities rather than a state of health.⁷³

We observed significantly elevated RAGE in PTSD patients compared to noPTSD + TBI participants, suggesting increased inflammatory and oxidative stress responses in PTSD. This is the first study, to our knowledge, to document elevated RAGE in PTSD without TBI; therefore, these findings require further validation. Prior studies show that sRAGE levels increase rapidly post-injury within 30 min, and these increases correlate with the severity of the injury.⁷⁴ We anticipated that our research would also reveal an increase in RAGE levels following TBI; however, this was not the case. This may be due to the fact that blood samples were collected at a relatively long time point following injury. This is supported by a study which has shown that the level of sRAGE decreases within a few days of the injury occurring.⁷⁵

The increased RAGE levels in PTSD may also reflect persistent immune dysregulation in response to psychological trauma. RAGE activation is known to occur via interaction with Damage-Associated Molecular Pattern ligands such as High-Mobility Group Box 1 and S100B, which activate Mitogen-Activated Protein Kinase and NF-κB pathways, leading to sustained expression of IL-1β, IL-6, and TNF-α—even in the absence of acute peripheral triggers. This mechanism could underlie the observed proinflammatory and oxidative stress profile in chronic PTSD. The RAGE–BLC correlation in our PTSD + TBI group further supports the notion of coordinated neuroimmune activation across chemokine and redox pathways.

Although differences in TREM-2 and BLC were not statistically significant, the trends suggest their potential relevance in PTSD and TBI pathophysiology. Interestingly, MIF—typically a proinflammatory mediator—was found at lower levels in PTSD and PTSD + TBI groups. According to the literature, MIF has been identified as a potential predictive and diagnostic marker for early detection and management of complications in patients with severe TBI and psychiatric disorders.⁷⁶ While this seems paradoxical, it may reflect altered MIF regulation in PTSD, which remains poorly understood.^76,77 In general, the mechanisms underlying the molecular processes associated with MIF have only been partially explored, and it may have both pro-inflammatory and anti-inflammatory roles in diseases.⁷⁷

The decrease in MIF in the PTSD(+TBI) group may also indicate a chronic exhaustion of innate immune mechanisms following prolonged neuroinflammatory stimulation. MIF is a potent upstream activator of proinflammatory cytokines such as IL-6 and TNF-α through NF-κB signaling and microglial activation.⁷⁸ Interestingly, similar reductions in MIF have been reported in patients with coronary artery disease and severe emotional exhaustion, where the authors proposed a phenomenon of immune “burnout” secondary to chronic stress.⁷⁹ A comparable mechanism may be at play in PTSD—particularly when combined with TBI—where prolonged neuroendocrine and immunological stress may downregulate MIF as part of a maladaptive regulatory loop.

Therefore, the phenomenon of decreased MIF levels in PTSD requires further investigation. We note that including canonical cytokines (e.g., IL-1β, IL-6, and TNF-α) in future study phases would enhance the characterization of neuroimmune shifts and clarify the functional implications of the observed MIF–RAGE imbalance.

Finally, we observed correlations between RAGE and BLC (CXCL13) in the PTSD + TBI group. This may reflect coordinated activation of oxidative and inflammatory pathways in comorbid states. RAGE is a known mediator of neuroinflammation and neurodegeneration⁸⁰ by activating pro-inflammatory pathways in response to tissue injury, while BLC (CXCL13) recruits immune cells to CNS inflammatory sites.^81,82 Prior studies confirm elevated oxidative and inflammatory markers in veterans with PTSD and TBI,⁸³ supporting a synergistic effect on immune burden. Thus, the RAGE–BLC correlation may indicate heightened immune activation in the brain exacerbated by oxidative stress.

Our findings may also contribute to the broader translational efforts in PTSD biomarker discovery. Despite growing interest in objective markers for PTSD, progress remains limited, impeding early detection, staging, stratification, and treatment personalization.⁸⁴ Recently developed multi-omic strategies, including a 28-marker blood-based panel integrating DNA methylation, metabolomics, microRNA, and proteomics, demonstrated approximately 81% diagnostic accuracy for warzone-related PTSD across veteran cohorts.⁸⁴ Our data on neuroinflammatory and neuronal injury markers (e.g., GFAP, NSE, YKL-40, and RAGE) may complement such panels by capturing neuroimmune dimensions of PTSD pathophysiology.

Furthermore, our study aligns with the objectives of the INTRuST Clinical Consortium (Injury and Traumatic Stress), a multi-institutional initiative uniting neuroimaging, biomarker profiling, and clinical data from veteran populations to facilitate reproducible diagnostic strategies and accelerate therapeutic development.⁸⁵ Embedding our results in this framework underscores their relevance for future validation and integration into composite diagnostic tools.

Therefore, our biomarker analyses revealed significant group differences that help elucidate the pathophysiology of PTSD and TBI. Nonetheless, this study has several limitations that should be acknowledged. First, a civilian control group was not included. While the comparison group comprised military personnel without PTSD, which is consistent with common practice in combat PTSD research, this approach may limit the generalizability of our findings to non-military populations. Second, the time interval between TBI and biomaterial sampling was not standardized, which is particularly important for biomarkers with dynamic kinetics (e.g., GFAP). Although all participants were hospitalized within 12 months after combat, the precise timing of injury and sample collection could not always be determined due to restricted access to military medical records. This temporal variability may have affected biomarker concentrations. Third, the study population consisted entirely of inpatients, which may reflect more severe clinical presentations and may not be representative of individuals managed in outpatient settings. Fourth, only male participants were included to ensure sample homogeneity; thus, sex-based differences were not examined and should be explored in future research. Fifth, formal grading of PTSD and TBI severity using standardized scales (e.g., CAPS for PTSD or ACRM/DoD criteria for TBI) was not conducted. Diagnostic and severity classifications were based on clinical judgment by treating physicians and available documentation. Individuals with severe TBI were excluded. Sixth, neuroimaging data (CT/MRI) were not accessible, limiting our ability to correlate serum biomarkers with structural brain pathology. Seventh, we had limited access to comprehensive clinical records, including the duration of hospitalization, which could have provided an additional indicator of clinical severity. Eighth, no standardized laboratory protocol was employed for substance use screening. While participants with active substance use disorders were excluded based on clinical assessment, biochemical verification was not conducted systematically across all cases. These factors should be addressed in future research.

Conclusions

This study demonstrated a significant impact of PTSD and TBI—both individually and in combination—on the psychological state and biochemical parameters of patients, particularly in the context of combat exposure. Based on psychometric assessments, statistically significant differences were identified among study groups in levels of subjective stress, depression, PTSD-related symptoms (avoidance, hyperarousal, and intrusive thoughts), and personality traits. Comorbidity of PTSD and TBI was associated with the most severe disturbances, suggesting a potential synergistic effect of the two conditions.

Biochemical analyses corroborated these findings, revealing alterations in serum concentrations of autoantibodies against neuron-specific proteins (GFAP, NSE) as well as inflammatory markers (RAGE, YKL-40). These results underscore the involvement of neuroinflammatory and autoimmune mechanisms in the pathophysiology of PTSD and TBI.

We conducted a novel quantitative assessment of serum autoantibodies to GFAP and NSE, a method that, to our knowledge, had not previously been applied to PTSD research. The results showed elevated GFAP and NSE antibody levels in veterans with isolated PTSD, and increased NSE antibodies in those with comorbid PTSD and TBI. These alterations suggest possible neuronal and astrocytic injury and impaired BBB integrity in PTSD—even in the absence of direct physical trauma. These findings are of particular importance, as such biomarker changes were expected primarily in TBI cases, yet our data provide new evidence of their presence in PTSD without TBI, a phenomenon previously unreported. We hypothesize that neuronal and glial autoantibody biomarkers may serve not only as indicators of chronic TBI but also as tools to evaluate individual differences in immune reactivity. This novel observation warrants further systematic investigation.

In addition, our study is the first to report a reduction in the level of YKL-40, a critical inflammatory marker, in the blood of patients with comorbid PTSD and TBI. This decrease may reflect dysregulation of the inflammatory response under dual CNS pathology—distinct from the response observed in isolated PTSD or TBI. We also demonstrated, for the first time, that isolated PTSD is associated with elevated RAGE levels in peripheral blood. This elevation may be linked to inflammatory and oxidative stress-related processes that characterize PTSD pathophysiology.

Taken together, our findings reveal clear distinctions between isolated PTSD and PTSD comorbid with TBI in both clinical presentation and serum biomarker profiles, highlighting differential features of the underlying pathological mechanisms. These results underscore the necessity of a multidimensional approach to the diagnosis and treatment of PTSD and TBI, one that integrates both psychopathological assessment and biochemical evaluation. The biomarker differences identified herein may open new directions for elucidating the pathomechanisms of these disorders and developing more effective tools for their diagnosis and management. Furthermore, these insights hold promise for the advancement of personalized therapeutic strategies and monitoring systems, tailored to the individual biochemical profiles of patients. In addition, the findings may contribute to the design of novel preventive and rehabilitative interventions aimed at reducing the risk of long-term neuropsychiatric consequences of PTSD and TBI—an increasingly critical issue in light of the rising global incidence of trauma-related mental and neurological disorders.

Footnotes

Acknowledgements

The English translation of the manuscript was produced with the assistance of the AI-based language model ChatGPT (OpenAI, GPT-4 architecture) to ensure linguistic accuracy and adherence to academic style. All content was subsequently reviewed and validated by the authors for scientific accuracy and integrity.

ORCID iDs

Riabinina Daria

Khamidova Alina

Abramova Olga

Zorkina Yana

Mukhin Vladimir

Morozova Irina

Andreyuk Denis

Ethical considerations

This research was conducted in accordance with the guidelines of the Helsinki Declaration. Experiments involving human participants were conducted in accordance with ethical guidelines (Protocol No. 6/11.08.2023 approved by the Local Ethics Committee of Mental Health Clinic No. 1 named after N. A. Alexeev within the Department of Health of Moscow).

Consent to participate

Written informed consent to participate was obtained from all subjects before the study.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Moscow Centre for Innovative Technologies in Healthcare. Grant No. 2102-11/23 (Agreement No. 2102-11/23). The publication of this manuscript was sponsored by the Moscow Center for Innovative Technologies in Healthcare.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Pathophysiological pathways of post-traumatic stress disorder in relation to traumatic brain injury: An observational cross-sectional study

Abstract

Objective:

Methods:

Results:

Conclusion:

Keywords

Introduction

Materials and methods

Patients

Psychometric assessment

Serum samples collection

Multiplex assay

Quantification of autoantibodies to glial fibrillary acidic protein and neuron-specific enolase

Statistical processing

Results

Clinical characteristics of participants

Biochemical marker concentrations in blood

Correlation analysis

Discussion

Conclusions

Footnotes

Acknowledgements

ORCID iDs

Ethical considerations

Consent to participate

Funding

Declaration of conflicting interests

References