Metabolomics Analysis Reveals Potential Biomarkers for Diffuse Axonal Injury

doi:10.21203/rs.3.rs-4618162/v1

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Research Article

Metabolomics Analysis Reveals Potential Biomarkers for Diffuse Axonal Injury

https://doi.org/10.21203/rs.3.rs-4618162/v1

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Background

Metabolism is essential for life maintenance, neurological function and injury repair, yet its role in diffuse axonal injury (DAI) is not fully understood.

Methods

30 DAI patients and 34 non-DAI patients were recruited based on the classification criteria using Magnetic Resonance Imaging (MRI) within 30 days of admission in this exploratory research. Serum samples and clinical parameters were collected upon admission, with the Glasgow Outcome Scale Extended (GOSE) at 6 months post-injury used as the neurological functional outcome. The metabolome was assayed using liquid chromatography-mass spectrometry.

Results

The DAI group and non-DAI group showed significant differences in pupillary light reflex, Glasgow Coma Scale (GCS) score, and Marshall computed tomography (CT) score, as well as in the expression levels of 27 metabolites in serum. Random forest analysis indicated that Lysophosphatidylcholine (LPC) 22:3 sn-2 and carnitine C8:1 greatly contributed to distinguishing DAI patients from non-DAI patients (MeanDecreaseGini: 3.81, 5.16). The combined prediction of DAI using these two metabolites yielded an area under the curve (AUC) of 0.944, which was higher than the combination of clinical parameters.

Conclusions

The serum metabolomics revealed potential biomarkers for DAI and has significant value for exploring pathogenesis, determining early diagnosis, and improving long-term neurological function.

Diffuse axonal injury

Metabolomics

Diagnosis

Biomarker

Diffuse axonal injury (DAI) is caused by acceleration, deceleration, or rotational forces acting on brain tissue, resulting in axonal shearing injuries and delayed axonal disconnection[1]. It has high mortality and morbidity rates in both children and adults, making it one of the most severe pathological consequences of traumatic brain injury[2,3]. The gold standard for diagnosing and grading DAI is histopathology[4]. However, for patients undergoing inpatient treatment, Magnetic Resonance Imaging (MRI) is considered the primary diagnostic tool for DAI as it can reveal subtle axonal injuries and small hemorrhages that may not be detected by computed tomography (CT) scans[5-7]. Nevertheless, the lengthy wait times for MRI examinations and the restrictions imposed by strong magnetic fields on monitoring equipment hinder its application in the acute phase of trauma[1,2]. Numerous studies have shown that early diagnosis of DAI can optimize treatment plans and improve patient outcomes[8-10]. Therefore, the early differential diagnosis of DAI in traumatic brain injury (TBI) is a pressing issue that needs to be addressed.

Metabolism is the fundamental characteristic and primary activity of life, referring to all chemical reactions and energy changes that occur within cells involving various substances[11,12]. Metabolomics was widely used in the diagnosis and mechanistic research of various diseases such as TBI, cancer, and diabetes[11,13,14]. Further research showed that the changes of N-acetyl Aspartate, propylene glycol and other metabolites in cerebrospinal fluid, serum and urine were related to the severity and prognosis of TBI[15,16]. Unfortunately, the current studies focusing on serum metabolomics in the acute phase of DAI patients were very limited, which hindered the application of biomarkers as early diagnostic factors for DAI.

This study utilized liquid chromatography-mass spectrometry (LC-MS) to detect alterations in the serum metabonomics of DAI and non-DAI patients in the acute phase of injury. Differential metabolites were screened to explore potential biomarkers and models for early DAI diagnosis.

Study population

Between April 2021 and March 2023, patients with TBI were screened in this exploratory research at emergency department (ED) from Haining People's Hospital of Zhejiang Province. This study was approved by the local Institutional Review Board and registered at the Chinese Clinical Trial Registry (ChiCTR2100044352). Written informed consent was obtained from all participants or their representatives.

Selection of participants

Patients admitted to the ED of TBI within 6 hours were enrolled in this study. Inclusion criteria were: patients age of 18 to 60 years, serum samples for metabolomics analysis have been collected along with the emergency blood testing samples. Exclusion criteria were: lack of MRI within 30 days of admission, pre-hospital sedation and intubation, progressive brain illness (parkinson disease, dementia, seizure disorder, multiple sclerosis, brain tumor), secondary brain injury (infarction, hemorrhage and intracranial infection), history of brain surgery or stroke without full recovery, severe peripheral organ complications have not been reversed, history of psychiatric disorders, or substance abuse; TBI combined with severe circulatory failure; participants or their representatives request to withdraw from the trial.

Parameters

Demographic and clinical characteristics were collected upon arrival to ED, including age, sex, body mass index (BMI), causes of trauma (road traffic accident, fall, and others), admission GCS score, pupillary light reflex (none, unilateral or bilateral), Marshall CT classification (evaluated on a scale from 1 to 6) from the first head CT scan, mean arterial blood pressure (MAP), hypotension and hypoxia.

Definitions

The clinical MRI was performed with a 1.5T scanner (Siemens Symphony, ATim). DAI was defined as TBI patients with lesions in gray-white matter junction of the cerebrum, corpus callosum, or brain stem with T2-weighted imaging (T2WI), T2-weighted fluid attenuated inversion recovery (T2 FLAIR) and diffusion-weighted imaging (DWI) in MRI[17, 18]. These lesions were defined as having hypointense focus presented on T2WI (hemorrhagic DAI), or hyperintense focus presented on DWI, T2 FLAIR and T2WI (non-hemorrhagic DAI)[17, 19]. The MRI and CT images were independently analyzed by two experienced neuroradiologists, who had access to patient clinical information but were blinded to the results of metabolomics analysis.

LC-MS based metabolomics analysis

The metabolome was determined with small modifications[20]. Briefly, 400µL of acetonitrile was added to and mixed thoroughly with 100µL of serum for extraction. Supernatant was separated after centrifugation (at 14,000rpm, 4℃, 12 min) and dried. Nontargeted metabolomics was performed using an ultra-performance liquid chromatography (UPLC) coupled to an LTQ-Orbitrap mass spectrometry system. LC separation was performed on a Waters ACQUITY UPLC system (Waters, Milford, USA).Mobile phase A and B were water with 1 mL/L formic acid and acetonitrile with 1 mL/L formic acid, respectively. The total run time was 15 min. The flow rate was set 0.35 mL/min and the column temperature was 45°C.High resolution full scan

was performed on an LTQ-Orbitrap XL mass spectrometer (Thermo Fisher, San Jose, USA) equipped with an electrospray ionization (ESI). Data was acquired in ESI positive and negative modes with scan mass range of 100–1000. High resolution MS/MS fragmentation was performed by HCD with collisional energy of 35 eV using UPLC quadrupole Orbitrap mass spectrometer (Q Exactive, Thermo Fisher, San Jose, USA). Acquired LC-MS data were processed using software SIEVE (V1.2, Thermo Fisher, San Jose, USA) for alignment and feature extraction. The differential metabolites were selected according to the variable importance in the projection (VIP) value > 1 of the orthogonal partial least squares-discriminant analysis (OPLS-DA) model and the P value < 0.05 of the Mann-Whitney U test of the normalized peak area.

Outcome assessment

Functional neurological outcome at 6 months after TBI was performed by a structured telephone survey of patients or caregivers as the secondary outcome variable. Extended Glasgow Outcome Scale (GOSE) was used to quantify 6-month outcome, it was divided into 8 levels: 8: great recovery; 7: good recovery, and accompanied by minor physical and mental deficits; 6: moderately disabled, resuming the previous job but requiring some adjustments; 5: moderately disabled, qualified for low-level work; 4: severely disabled and can do some daily activities with the help of others; 3: severely disabled and totally dependent on others for daily activities; 2: plant state; 1: death. The imputed 6-month GOSE average from the primary analysis in our study was assigned to those missing GOSE.

Statistical analysis

Categorical data are presented as frequency or percentage and compared by Fisher exact test. Continuous data are presented as the median and interquartile range (IQR) and were compared by the Mann-Whitney U test. The receiver operating characteristic (ROC) curve analysis and random forest analysis were performed to assess the potential predictive ability of factors for DAI.Statistical analyses were performed using GraphPad Prism 9.5 (GraphPad Software, San Diego, CA, USA) and R statistical language (version R 4.3.2). A value of P < 0.05 with a two-tailed test was considered statistically significant. The raw data and details of the 110 detected metabolites in all cohorts are provided in the Supplemental Material 1.

Clinical characteristics of the participants

114 patients with TBI met all the inclusion criteria were enrolled during the study period. 50 patients were excluded due to the exclusion criteria, and a total of 64 patients were analyzed. The clinical findings, injury-related variables of eligible patients are shown in Table 1.Of the 64 eligible patients, 47(73.4%) were male, 30 (46.9%) were classified as DAI group by MRI within 30 days of admission. The GCS scores were significantly lower in patients with DAI than that of non-DAI group (P = 0.0384), as were the 6-month GOSE scores (P = 0.0003). The existence of pupillary light reflex was also different (P = 0.029). Non-DAI group had significantly lower Marshall CT scores compared to DAI group (P = 0.0007). Regarding sex, age, BMI and cause of trauma, there were no significant differences between the DAI and non-DAI groups.

The serum metabolome of DAI patients is distinct from non-DAI patients

LC-MS identified 110 metabolites from all serum samples used in this study. We compared the levels of each metabolite between the DAI and non-DAI groups, and the results showed that there were significant differences in the expression levels of 27 metabolites in serum, as demonstrated by OPLS-DA(Fig. 1A).In the DAI group, the expression levels of Glutamine-Leucine(Glu-Leu), Aspartyl-Leucine, carnitine C16:0-OH, Dihydrosphingosine, Nα-Acetyl-L-Iysine, Indolelactic acid, Lysophosphatidylcholine(LPC) 20:4 sn-2, methylglyoxal (MG) 18:1, Betaine, carnitine C16:1, carnitine C4:0, Proline, glycated leu, Pro Leu, carnitine C3:0, and carnitine C5:0 were significantly higher than those in the non-DAI group. However, the expression levels of carnitine C8:1, 7-KETOCHOLESTEROL, carnitine C10:2, bilirubin, carnitine C12:0, LPC 22:3 sn-2, Lysophosphatidylethanolamine (LPE) 20:5 sn-2, LPE 18:2 sn-2, LPC 20:3 sn-2, LPC 17:0 sn-2, and carnitine C10:0 were significantly lower in the DAI group compared to the non-DAI group (VIP > 1 and P < 0.05)(Fig. 1BCD).

Serum metabolites distinguish DAI patients from non-DAI patients

After using random forest analysis, we found two metabolites: LPC 22:3 sn-2 and carnitine C8:1, greatly contributed to distinguishing DAI patients from non-DAI patients (MeanDecreaseGini: 3.81, 5.16), with an OOB error of 0.094 (Fig. 2). The clinical predictors for DAI were composed of three items of pupillary light reflex, GCS score and Marshall CT score, which were significantly different between the DAI and non-DAI groups. ROC curves were used to compare the performance of two prediction methods for DAI (Fig. 3A). The area under the curve (AUC) of the combination of clinical indicators was 0.744, which was lower than the combination of LPC 22:3 sn-2 and carnitine C8:1 (AUC = 0.944).

The predictive ability of serum metabolites for 6-month GOSE

ROC curves were used to compare the performance of serum metabolites and clinical indicators for 6-month GOSE in TBI patients.(Fig. 3B). The AUC of the combination of LPC 22:3 sn-2 and carnitine C8:1 was 0.683, which was lower than the combination of clinical indicators (AUC = 0.887). If we combine serum metabolites with clinical indicators, the resulting AUC of 0.913 would be the highest.

This exploratory study indicated that the serum metabolome could be employed as an independent predictive factor to distinguish DAI from TBI patients. Serum metabolites (the combination of LPC 22:3 sn-2 and carnitine C8:1) presented high diagnostic accuracy for DAI (AUC = 0.944; sensitivity, 88.7%; specificity, 90.4%), but they showed relatively weak predictive ability for the 6-month GOSE in TBI patients.

DAI is a common primary injury in TBI, characterized histopathologically by focal axonal shearing and rupture[1, 21]. The injury mainly occurs in the corpus callosum, gray-white matter junction, upper brainstem, with disturbance of consciousness as a typical manifestation[22, 23]. Over 50 million people worldwide suffer from TBI each year, of which 25%-40% are complicated with DAI. Most DAI patients are in critical condition, with mortality rates as high as 40%-60%, and more than half of the survivors leave behind long-term sequelae or serious neurological deficits[3, 24–27]. The cognitive and other long-term neurological functions of DAI patients can be improved through early and effective treatment[8–10]. Therefore, the most crucial and challenging aspect currently is the early identification of DAI in the acute phase of traumatic brain injury.

Metabolism is the collective term for a series of orderly chemical reactions that occur within an organism to sustain life, encompassing both substance metabolism and energy metabolism[11, 12]. Metabolomics is a research approach that involves the quantitative analysis of all metabolites within an organism, aiming to identify the relative relationships between metabolites and physiological or pathological changes. It is widely applied in various fields closely related to human health, such as disease diagnosis and pharmaceutical research[11, 13].Thomas et al.[28] conducted a comprehensive metabolomics study on 716 TBI patients and non-TBI reference patients, they identified that choline phospholipids (sphingomyelins, ether phosphatidylcholines and lysophosphatidylcholines ) were negatively correlated with the severity of TBI and were among the strongest predictive factors for the prognosis of TBI patients. Banoei et al.[29] found that increased levels of glutamate, acylcarnitines, glucose, lactate, lysophosphatidylcholines and aromatic amino acids were associated with poor outcomes, and the serum metabolome on the first and fourth days post-injury showed high predictive value and accuracy for patient outcomes (AUC > 0.99). Two reviews on TBI metabolomics mentioned that changes in metabolites such as N-acetyl aspartate and propylene glycol in cerebrospinal fluid, serum, and urine are associated with the severity and prognosis of TBI[15, 16].

Current research has found that tau protein, neuron-specific enolase, S-100beta, eurofilaments, Peripherin and Hemopexin, myelin basic protein and beta-Amyloid precursor protein may be associated with DAI[2, 30–32]. However, some large molecular biomarkers face challenges in crossing the blood-brain barrier (BBB) into the bloodstream, making their collection difficult and hindering their widespread clinical application[3, 33]. The BBB consists of a lipid bilayer membrane structure of brain capillary endothelial cells that is lipophilic in nature[34]. Metabolomics can quantitatively analyze small molecules with a molecular weight under 1 kilodalton[16]. Therefore, lipid-soluble small molecule metabolites produced by damaged brain tissue are more likely to pass through the BBB, making them detectable in serum samples[15, 16].

Carnitine plays an important role in energy metabolism, participates in the process of fat catabolism in the body, and may have neuroprotective effects[35]. TBI and severe trauma may lead to a decrease in plasma free carnitine levels[36]. Other studies have shown that carnitine levels may decrease in the serum of TBI patients, possibly due to disruptions in energy metabolism and mitochondrial dysfunction associated with the injury[15, 28, 37]. Additionally, changes in carnitine levels in serum post-TBI have been linked to metabolic alterations and may serve as potential biomarkers for TBI severity and prognosis[38, 39]. Our study clearly identified significant differences in the levels of 27 metabolites in the serum of DAI and non-DAI patients, with subtypes of carnitine accounting for one-third, totaling 9. This underscores the importance of carnitine in predicting the appearance of DAI. Furthermore, our random forest analysis revealed that carnitine C8:1 had the highest MeanDecreaseGini value among the 27 differing metabolites, demonstrating its paramount importance in diagnosing DAI.

Lipids are essential components of brain structure and function. Lipidomics analysis revealed that LPC and cholesterol ester (CE) were the lipid families most affected by TBI in the hippocampal region. Additionally, LPC (16:0) was associated with hippocampal-dependent memory function[40]. Lipidomics analysis of a mouse model of repetitive mild TBI showed a significant increase in total LPC levels, leading to neuropathological and biochemical consequences[41]. Pasvogel et al.[42] dynamically monitored changes in phospholipid concentrations in the cerebrospinal fluid of TBI patients. The results showed that the median concentration of LPC was highest on the first day after brain injury, demonstrating the significant role of LPC in further membrane phospholipid breakdown in the central nervous system following TBI. In the 27 differing metabolites identified in the serum of DAI and non-DAI patients, there were 4 subtypes of LPC, which aligned with the findings of the aforementioned researchers, indicating that LPC may also serve as a potential biomarker for predicting DAI. Furthermore, our random forest analysis revealed that LPC 22:3 sn-2 had the second-highest MeanDecreaseGini value among the 27 differing metabolites, suggesting its significant role in diagnosing DAI.

Early diagnosis of DAI during the acute phase of TBI and the implementation of targeted treatment strategies could improve the long-term neurological function of DAI patients[8–10]. Therefore, many researchers were dedicated to exploring early diagnostic models for DAI[1, 2, 30, 32, 43–47]. Tomita et al.[45] measured serum tau levels in 40 suspected DAI patients within 6 hours of TBI. The ROC curve was used to evaluate its diagnostic ability for DAI, with an AUC of 0.690, sensitivity of 74.1%, and specificity of 69.2%. The results suggested that tau protein could be used as a biomarker for early diagnosis of DAI, but its accuracy was not high enough. Compared to the non-DAI group, DAI patients showed a significant decrease in serum high-density lipoprotein cholesterol (HDLc) levels one week after injury. Multivariate analysis indicated that high-density lipoprotein cholesterol was an independent predictive factor for DAI. The study suggested that plasma HDLc levels may be a feasible biomarker for predicting the presence and prognosis of DAI after TBI[43]. The ratio of serum NSE level to GCS score at admission may also be a useful model for early diagnosis of DAI[1]. However, in clinical patients, the limitations of low serum concentrations of large molecular biomarkers and the challenges in collecting cerebrospinal fluid restricted the application of biomarkers as early diagnostic indicators for DAI[2, 33].

Researchers were increasingly exploring the use of small molecule metabolites in TBI for this purpose[15, 28, 37, 39]. Oresic et al.[37] developed an algorithm based on metabolite concentrations of brain injury patients during hospitalization, which accurately predicts patient outcomes (with an AUC of 0.84 in the validation cohort). The addition of metabolites to the established clinical model (CRASH), including clinical and CT data, significantly improved the prediction of patient outcomes. Thomas et al.[28] conducted a comprehensive metabolomics study on 716 TBI patients and non-brain-injured reference patients. They identified a set of metabolites strongly associated with the severity of TBI and patient prognosis, which could be used to predict the outcomes of traumatic brain injury patients. Most current studies focus on TBI patients, lacking specific models for early diagnosis using serum metabolites in DAI patients. The early diagnostic model established using the two metabolites most correlated with DAI (LPC 22:3 sn-2 and carnitine C8:1) outperformed traditional clinical indicator prediction models (AUC: 0.944 vs. 0.744). However, its ability to predict 6-month neurological function was not as strong as the latter. Adding metabolites to the clinical model significantly improves the prediction of outcomes for DAI patients (AUC = 0.913).

Limitations

There are several limitations in our present study. First, the sample size was not large enough to evaluate the predictive value of metabolites for DAI and further research is needed in larger, confirmatory studies. Second, while our cohort included both DAI and non-DAI patients, the absence of a healthy control group limited our understanding of DAI, and including them in future studies would enhance our insights. Third, the blood samples we used, although easily accessible, primarily reflect overall metabolic changes in patients, but may not directly reflect metabolic disturbances in the brain. Forth, the results obtained through LC-MS methods were subject to inherent limitations of the technique, particularly in terms of sensitivity, separation, and/or extraction efficiency, which may introduce biases in the results[48].

Our study demonstrated that there were specific changes in serum metabolism in patients with DAI, reflecting metabolic disruptions in brain tissue following extensive axonal injury. Diagnostic models based on combinations of metabolites could potentially be used for early DAI diagnosis. These findings are significant for enhancing our understanding of the pathogenesis of DAI and for early diagnosis, prognosis prediction, and treatment of DAI.

Acknowledgements

The authors thank the doctors and nurses in the emergency department of Haining people's Hospital in Zhejiang Province for their help, and Professor Zhou Qinghua, College of Environment, Zhejiang University of Technology, for providing guidance on serum metabonomics analysis.

Funding

This study was supported by the Haining Science and Technology Plan Project（2021058）

Availability of data and materials

The datasets used in the current study are available from the corresponding authors on reasonable request.

Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki and approved by the local Institutional Review Board and registered at the Chinese Clinical Trial Registry (ChiCTR2100044352).

Declaration of competing interest

The authors declare no conflicts of interest.

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a) The manuscript complies with all instructions to authors

b) All authorship requirements have been met and the final manuscript was approved by all authors

c) A list of each author’s contributions

W.C.: conceptualization, investigation, data collection, data curation, review and editing. J.W. and S.L.: conceptualization, investigation, resources, data curation, and editing. G.W.: conceptualization, supervision, project administration and writing-review. C.Y. and R.C.: conceptualization, investigation, resources, data curation, writing-review and project administration. W.S.: conceptualization, investigation, resources, data curation.

d) This manuscript has not been published elsewhere and is not under consideration by another journal

e) Confirmation of adherence to ethical guidelines and indicate ethical approvals (IRB) and use of informed consent, as appropriate. Retrospective studies require a statement regarding IRB approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Haining people's Hospital.

Trial registration: ISRCTN, ChiCTR2100044352. Registered 17 March 2021, https://www.chictr.org.cn/

f) Conflict of Interest statement for all authors: The authors declare no conflicts of interest.

g) Report Checklist not used.

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Table.1 Demographic and clinical characteristics of eligible patients with DAI and non- DAI.

Variables	DAI (n = 30)	Non-DAI (n = 34)	P value
Male, n (%)	23(76.7)	24(70.6)	0.7774
Age (years), median (IQR)	47(34-55)	50.5(42-55.8)	0.3292
BMI, median (IQR)	24(22.7-25.3)	23.3(21.5-24.8)	0.2476
Cause of trauma, n (%)
Road traffic accident	14(46.7)	14(41.2)	0.8013
Fall	14(46.7)	17(50)	0.8076
Others	2(6.6)	3(8.8)	＞0.9999
Pupillary light reflex, n (%)			0.0290
None pupillary light reflex	3(10)	1(2.9)	0.3334
Unilateral pupillary light reflex	12(40)	7(20.6)	0.1071
Bilateral pupillary light reflex	15(50)	26(76.5)	0.0378
GCS, median (IQR)	7(5-11.8)	12(9.3-13.8)	0.0384
Marshall CT score, median (IQR)	5(4-5)	4(2.25-4)	0.0007
6-month GOSE, median (IQR)	5(3.3-6)	7(6-7.8)	0.0003

BMI, Body Mass Index; GCS, Glasgow Coma Scale, GOSE, Extended Glasgow Outcome Scale, IQR = (25th percentile–75th percentile).

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Reviewers agreed at journal
28 Aug, 2024
Reviewers invited by journal
09 Aug, 2024
Editor invited by journal
26 Jun, 2024
Editor assigned by journal
25 Jun, 2024
First submitted to journal
24 Jun, 2024

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Metabolomics Analysis Reveals Potential Biomarkers for Diffuse Axonal Injury

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

Study population

Selection of participants

Parameters

Definitions

LC-MS based metabolomics analysis

Outcome assessment

Statistical analysis

Results

Clinical characteristics of the participants

The serum metabolome of DAI patients is distinct from non-DAI patients

Serum metabolites distinguish DAI patients from non-DAI patients

The predictive ability of serum metabolites for 6-month GOSE

Discussion

Limitations

Conclusions

Declarations

References

Table 1

Supplementary Files

Status:

Version 1