Characterization of the plasma metabolomic profile in infantile epileptic spasms syndrome

Introduction

Infantile epileptic spasms syndrome (IESS) is a severe, age-specific epileptic encephalopathy of early childhood characterized by epileptic spasms, hypsarrhythmia on electroencephalography (EEG), and developmental regression or stagnation (1). It represents a major cause of refractory epilepsy and neurodevelopmental impairment in infants, imposing a significant burden on affected individuals, families, and healthcare systems. Despite the recognized efficacy of first-line therapies such as adrenocorticotropic hormone (ACTH) and vigabatrin, a substantial proportion of patients remains drug resistant, and the underlying etiologies are remarkably heterogeneous, ranging from structural and genetic abnormalities to metabolic disorders (2,3). This etiological complexity, coupled with the frequent diagnostic challenges and the unpredictable therapeutic response, underscores the critical need for a deeper understanding of its molecular pathogenesis to identify novel biomarkers for early diagnosis, prognosis, and targeted intervention.

Metabolomics, a powerful systemic biology approach that provides a comprehensive snapshot of the dynamic metabolite profiles in biological systems, has emerged as a promising tool for deciphering disease-specific metabolic fingerprints (4). Unlike genomics or transcriptomics, metabolomics captures the downstream functional readout of genetic, transcriptomic, proteomic, and environmental influences, offering a more proximal reflection of the actual phenotypic state.

In the context of epilepsy and neurodevelopmental disorders, metabolomic studies have begun to reveal perturbations in key pathways, such as energy metabolism, neurotransmitter cycling, and lipid homeostasis (5). Existing clinical metabolomics studies in epilepsy have predominantly analyzed serum or plasma samples from heterogeneous cohorts, including adults with generalized or focal seizures, children with Lennox-Gastaut syndrome (LGS), or patients with drug-resistant epilepsy (DRE) (6-8). Common findings across these studies reveal disturbances in central energy metabolism including elevated lactate and decreased citrate, excitatory-inhibitory balance including altered glutamate/glutamine, and lipid metabolism including changes in fatty acids and sphingolipids. However, the application of high-throughput, untargeted metabolomics to specifically profile the plasma metabolome in IESS remains notably underexplored. Plasma, as a readily accessible biofluid, mirrors systemic metabolic alterations and may harbor crucial signatures of central nervous system dysfunction given the intricate crosstalk between the brain and periphery.

In this study, we performed an integrative untargeted metabolomic analysis of plasma samples from a well-characterized cohort of infants with IESS and age-matched healthy controls. We hypothesize that IESS is associated with a distinct plasma metabolic signature that reflects the core pathophysiological processes driving the disease. By applying ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), we aimed to (I) identify and characterize the differentially abundant metabolites between IESS patients and controls; (II) reveal the associated dysregulated metabolic pathways through functional enrichment analysis; and (III) evaluate the diagnostic potential of candidate metabolite biomarkers. Our findings provide novel insights into the metabolic basis of IESS and may pave the way for the discovery of clinically useful biomarkers, ultimately contributing to improved patient stratification and the development of novel therapeutic strategies.

Methods

Clinical characteristics of pediatric participants

Forty pediatric patients with IESS were first diagnosed in the Department of Neurology at the Children’s Hospital, Zhejiang University School of Medicine from November 2018 to December 2021. Our department serves as a National Pediatric Epilepsy Diagnosis and Treatment Center and a key national referral hub for complex pediatric neurological disorders. We receive referrals from a vast catchment area covering the entire Zhejiang Province and neighboring regions, with a serviced population of tens of millions. The inclusion criteria included the following: (I) age at onset <2 years; (II) clinical history of infantile spasms; (III) interictal EEG characterized by hypsarrhythmia or focal/multifocal epileptiform discharges; (IV) evidence of neurodevelopmental delay. The time interval from reported spasm onset to diagnosis and blood sampling varied from several days to approximately 2 months. All children were actively experiencing epileptic spasms at the time of blood sample collection. The confirmatory EEG was performed using a standard clinical protocol with a Nicolet V32 video-EEG system, recording for a minimum of 4 hours from 19 scalp electrodes (10-20 system) to capture both awake and sleep states. Importantly, to eliminate potential confounding effects of pharmacological interventions on the plasma metabolome, blood sampling was performed prior to the initiation of first-line IESS therapy (ACTH, corticosteroids, or vigabatrin). Thirty age- and sex-matched healthy children were also recruited as controls. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Children’s Hospital, Zhejiang University School of Medicine (No. 2025-IRB-0408-P-01). Written informed consent was acquired from all participants’ legal guardians. Relevant clinical data were retrospectively extracted from the hospital’s electronic medical records system.

Metabolite extraction from plasma samples

A 100 µL aliquot of each plasma sample was mixed with 400 µL of prechilled 80% methanol, vortexed thoroughly, and incubated on ice for 5 minutes. The mixture was subsequently centrifuged at 15,000 ×g and 4 °C for 20 minutes to pellet the proteins. A portion of the supernatant was diluted with liquid chromatography-mass spectrometry (LC-MS)-grade water to a final concentration of 53% methanol. The diluted extract was recentrifuged at 15,000 ×g (4 °C) for 20 minutes (9,10).

UHPLC-MS/MS analysis

UHPLC-MS/MS analysis was performed on a Vanquish UHPLC system (Thermo Fisher, Bremen, Germany) coupled to an Orbitrap Q Exactive^TM HF (or HF-X) mass spectrometer (Thermo Fisher, Germany) at Novogene Co., Ltd. (Beijing, China). Separation was achieved on a Hypersil Gold column (100 mm × 2.1 mm, 1.9 µm; Thermo Fisher) maintained at 40 °C. A binary solvent system consisting of 0.1% formic acid in water (eluent A) and methanol (eluent B) was used with a flow rate of 0.2 mL/min and a 12-minute linear gradient program as follows: 0–1.5 min, 2% B; 1.5–4.5 min, 2–85% B; 4.5–10.5 min, 85–100% B; 10.5–10.6 min, 100–2% B; and 10.6–12 min, 2% B. The injection volume was 2 µL. The mass spectrometer was operated in both positive and negative ionization modes with a spray voltage of 3.5 kV. The capillary temperature was set at 320 °C, and the S-lens radio frequency (RF) level was 60%. The sheath gas and auxiliary gas flow rates were set at 35 arb and 10 arb units, respectively. The auxiliary gas heater temperature was 350 °C.

Metabolite annotation

Initial processing of the raw UHPLC-MS/MS data was performed in Compound Discoverer 3.3 (Thermo Fisher), which included peak alignment, peak picking, and quantitation. The critical parameters used were as follows: 5 ppm mass tolerance, 30% signal intensity tolerance, and quality control (QC)-based peak area correction. Three QC samples were injected prior to the analytical run to condition the system and ensure equilibrium. An additional three QC samples were analyzed using data-dependent acquisition to generate spectra for metabolite identification. Throughout the batch, QC samples were inserted at regular intervals to monitor system stability and enable data QC. After normalization to the total spectral intensity, molecular formula prediction was conducted utilizing adduct, molecular ion, and fragment ion data. Subsequent annotation was carried out by querying the mzCloud, mzVault, and MassList databases. The normalized dataset underwent rigorous QC; metabolites exhibiting a coefficient of variation (CV) exceeding 30% in QC samples were discarded. Statistical analyses were executed via R (v3.4.3) and Python (v2.7.6) in a CentOS 6.6 environment. For data deviating from normality, standardization was applied according to the following formula: relative peak area = [sample raw quantitation value/(Σ sample metabolite quantitation values/Σ QC1 metabolite quantitation values)].

Statistical analysis

Metabolite identification was conducted by matching experimental spectra against the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Metabolome Database (HMDB), and the Lipid Metabolites and Pathways Strategy (LIPIDMAPS) databases. Multivariate statistical analyses, including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were performed via the metaX software platform to assess the overall data structure and group separation. Differentially abundant metabolites were identified by applying a combination of univariate and multivariate criteria. Student’s t-test was used to calculate the statistical significance (P value). Metabolites exhibiting a variable importance in projection (VIP) score >1 from the PLS-DA model, a P value <0.05, and a fold change (FC) ≥2 or ≤0.5 were considered significantly altered. Given the exploratory discovery-phase nature of this study and the limited number of detected metabolites, additional multiple testing correction was not applied to avoid overly conservative exclusion of potentially biologically relevant metabolites, an approach consistent with current metabolomics practice (11). The results were visualized via R packages. A volcano plot was generated with ggplot2 to display the relationship between log₂(FC) and −log₁₀(P value). A clustering heatmap was created via Pheatmappackage, featuring z score-normalized intensity values of differentially abundant metabolites. The functional analysis and metabolic pathway enrichment of the differentially abundant metabolites were carried out via the KEGG database. A pathway was considered enriched if the ratio of differentially abundant metabolites mapped to it (x/n) exceeded the ratio of all identified metabolites mapped to it (y/N). A pathway with an enrichment P value <0.05 was deemed statistically significant. We calculated the effect size (Cohen’s d) as the difference between the two group means divided by the pooled standard deviation. Following conventional benchmarks, |d| values of ≥0.2, ≥0.5, and ≥0.8 were considered small, medium, and large effects, respectively (12). Additionally, statistical power was computed using the pwr.t2n.test() function from the pwr package in R (version 4.3.2), with the significance level (α) set at 0.05, the calculated Cohen’s d, and the actual sample sizes for each comparison as inputs. A power value of ≥80% is typically considered acceptable, and ≥95% indicates strong power.

Results

Clinical characteristics of the participants

The cohort consisted of 40 infants diagnosed with IESS and 30 age-matched HCs. No significant difference was observed in the mean age between the IESS group (0.73±0.35 years). There were 21 males (52.5%) and 19 females (47.5%) in the IESS group. The baseline characteristics of the study participants are presented in Table 1. The etiology of IESS was identified in 60% of the cohort, with structural causes being the most common (22.5%), followed by genetic (6%). The cases with structural brain abnormalities comprised those of: (I) congenital structural etiology (n=3), specifically malformations of cortical development including focal cortical dysplasia, pachygyria, and schizencephaly (one case each); (II) acquired structural etiology (n=4), consisting of periventricular leukomalacia secondary to perinatal brain injury (n=2), postnatal Streptococcus agalactiae meningitis leading to encephalomalacia (n=1), and a cerebral glioma (n=1); and (III) genetic-structural etiology (n=2), namely tuberous sclerosis complex and Down syndrome (Trisomy 21) (one case each). There were six cases attributed to genetic etiologies, with one case each harboring a pathogenic variant in ALG11, CASK, DEPDC5, GNAO1, SLC9A, or USH2A.

PLS-DA analysis of plasma metabolites

Total ion chromatograms of QC samples in positive and negative ionization mode are shown in Figure S1. QC samples were concentrated in the middle area of the tested samples, implying the reliability of the experimental data. Metabolites identified via mzCloud and mzVault spectral library matches are reported as Level 2, while those matched to MassList are reported as Level 3. To obtain a holistic view of the metabolic alterations associated with IESS, PLS-DA was performed to determine the contribution values of the characteristic components (Figure 1A,1B). Permutation testing of the orthogonal PLS-DA (OPLS-DA) model, as depicted in the validation plot, confirmed the robust and no overfitting nature of the model (Figure 1C,1D).

Figure 1 Multivariate analysis of plasma metabolomic profiles in IESS patients and HCs. (A,B) PLS-DA score plots in positive-ion (A) and negative-ion (B) modes. The data points represent individual samples from HCs (red circles) and IESS patients (blue triangles). (C,D) Permutation test validation plots (200 permutations) for the PLS-DA models in positive-ion (C) and negative-ion (D) modes. The blue regression line (R²) and green regression line (Q²) represent the correlations between the original and permuted Y-variables. HCs, healthy controls; IESS, infantile epileptic spasms syndrome; NEG, negative-ion mode; PC, principal component; PLS-DA, partial least squares-discriminant analysis; POS, positive-ion mode.

Identification of differential metabolites between the IESS and HC groups

After data preprocessing and metabolite identification, 221 metabolites were identified from 875 metabolite features extracted from the raw data acquired in positive ionization mode, whereas 125 metabolites were identified from 518 metabolite features extracted from the raw data acquired in negative ionization mode (Figure 2; Figure S2). Among 221 altered metabolites in positive ionization mode, 144 were upregulated, whereas 71 were downregulated in the IESS group. Moreover, 105 upregulated and 20 downregulated differentially abundant metabolites (DEMs) were identified among the detected metabolites in negative ionization mode. The lollipop chart displays the top 20 significantly upregulated and downregulated metabolites ranked by FC (Figure 3).

Figure 2 Identification of the DEMs between IESS patients and HCs. Volcano plots showing the significance and magnitude of metabolic changes in positive-ion (A) and negative-ion (B) modes. The red dots represent significantly upregulated metabolites, and the blue dots represent significantly downregulated metabolites. The size of the points corresponds to the VIP score derived from the OPLS-DA model. DEMs, differentially expressed metabolites; HCs, healthy controls; IESS, infantile epileptic spasms syndrome; NEG, negative-ion mode; OPLS-DA, orthogonal partial least squares-discriminant analysis; POS, positive-ion mode; VIP, variable importance in projection.

Figure 3 Lollipop chart of the top 20 DEMs between IESS patients and HCs. (A) Positive ion mode. (B) Negative ion mode. Red: upregulated metabolites; blue: downregulated metabolites. DEMs, differentially expressed metabolites; HCs, healthy controls; IESS, infantile epileptic spasms syndrome; NEG, negative-ion mode; POS, positive-ion mode; VIP, variable importance in projection.

KEGG enrichment analysis of differentially abundant metabolites between the IESS and HC groups

Functional enrichment analysis based on the KEGG database revealed distinct molecular signatures between the two comparison groups. The KEGG classification demonstrated that the differentially expressed metabolites were predominantly enriched within metabolism pathways (Figure 4A,4B). The key metabolites driving each enriched pathway with metabolite class, effect size and statistical power were presented in the Table 2 and Table 3. Subsequent enrichment analysis further identified several key pathways with high statistical significance. Notably, pathways such as glutathione metabolism, drug metabolism and prolactin signaling pathway were significantly enriched in the positive ion mode group, whereas pathways such as phenylalanine metabolism, valine, leucine and isoleucine biosynthesis and pyruvate metabolism were enriched in the negative ion mode group (Figure 5A,5B).

Figure 4 KEGG pathway analysis of dysregulated metabolic pathways. (A,B) KEGG pathway classification of the DEMs identified in positive-ion (A) and negative-ion (B) modes. DEMs, differentially expressed metabolites; KEGG, Kyoto Encyclopedia of Genes and Genomes; NEG, negative-ion mode; POS, positive-ion mode.

Figure 5 Scatter plots of the top 20 dysregulated metabolic pathways. The top 20 enriched KEGG pathways for DEMs in positive-ion (A) and negative-ion (B) modes. DEMs, differentially expressed metabolites; KEGG, Kyoto Encyclopedia of Genes and Genomes; NEG, negative-ion mode; POS, positive-ion mode.

Pathway enrichment analysis further revealed specific dysregulated biological processes in IESS (Figure 5). Notably, the neuroactive ligand-receptor interaction pathway was significantly enriched, alongside key metabolic pathways, including steroid hormone biosynthesis, arachidonic acid metabolism, and fatty acid degradation. The coordinated enrichment of these pathways, particularly the involvement of neuroendocrine signaling and inflammatory lipid mediators, suggests a multifactorial pathophysiology in IESS, which aligns with its known etiological heterogeneity.

Identification of potential metabolic biomarkers for IESS

A panel of six differentially abundant metabolites, including glutathione disulfide, 2-amino-8-mercapto-9H-purin-6-ol, and 5,8-dihydroxy-10-methyl-5,8,9,10-tetrahydro-2H-oxecin-2-one, demonstrated significant alterations in the serum metabolome of IESS patients compared with HCs (Figure 6A-6F, all P<0.001 and absolute value of Log2FC ≥1.0). The distinct separation in the distribution of these metabolites, as visualized by violin and box plots, suggests robust dysregulation in IESS. Moreover, receiver operating characteristic (ROC) curve analysis confirmed the high diagnostic value of these metabolites, with area under the curve (AUC) values ranging from 0.801–0.894 for distinguishing IESS patients from HCs (Figure 6G-6L). We further performed Gene set enrichment analysis (GSEA), the results of which are presented in Figure S3 as the ten most significant GSEA set profiles.

Figure 6 Violin plot and ROC curves of DMEs between IESS patients and HCs. (A-G) Violin plots showing the distribution of six plasma metabolite levels in positive-ion mode (A-C) and negative-ion mode (E-G). The data are presented as violin outlines (kernel density), box plots (interquartile ranges and medians), and individual data points. (H-N) ROC curves were used to evaluate the diagnostic performance of each metabolite for discriminating IESS patients from HCs in positive-ion mode (H-K) and negative-ion mode (L-N). AUC, area under the curve; DEMs, differentially expressed metabolites; HCs, healthy controls; IESS, infantile epileptic spasms syndrome; ROC, receiver operating characteristic.

Discussion

IESS is a devastating early-life epileptic encephalopathy of which its underlying pathophysiology remains incompletely understood, hampering the development of targeted therapies and objective biomarkers. This study presents comprehensive plasma metabolomic profiling of IESS infants, revealing a distinct metabolic signature characterized by widespread dysregulation and identifying a panel of metabolites with high diagnostic potential. Our findings not only provide novel insights into the metabolic perturbations associated with IESS but also open new avenues for diagnostic and therapeutic strategies.

The most salient finding of our study is the pronounced dysregulation of pathways central to redox homeostasis, most notably the significant upregulation of glutathione disulfide (GSSG). A key question arising from the finding of elevated glutathione disulfide is whether oxidative stress is a primary driver or a secondary consequence of IESS. Our cross-sectional study design cannot definitively resolve this temporal relationship. It is plausible that the intense neuronal firing during spasms itself generates excessive reactive oxygen species, making oxidative stress a downstream effect of the epileptic activity. However, considerable evidence from other epilepsy models indicates that oxidative stress can precede and directly contribute to neuronal hyperexcitability and epileptogenesis (13,14). The developing brain is exceptionally vulnerable to oxidative damage due to its high oxygen consumption and abundance of oxidizable substrates (15). An imbalance in the GSH/GSSG ratio can disrupt neuronal signaling, impair mitochondrial function, and promote neuroinflammation, all of which are established contributors to epileptogenesis (16,17). The concurrent enrichment of pathways such as phenylalanine metabolism and valine, leucine, and isoleucine biosynthesis further underscores a profound disruption in amino acid metabolism, which is critical for neurotransmitter synthesis and energy production. This suggests a dual hit of increased neuronal excitability and compromised cellular resilience, creating a permissive environment for spasms (18). Therefore, we propose that these mechanisms may not be mutually exclusive but likely form a vicious cycle: an initial precipitant, such as genetic, structural, could induce both network instability and mitochondrial oxidative stress, which then mutually reinforce each other, culminating in the vicious cycle of electrical-chemical dysfunction characteristic of IESS. This model underscores oxidative stress as a critical, and potentially targetable, node in the pathophysiology, whether as an initiating factor or a disease-sustaining amplifier.

In addition to metabolic pathways, the significant enrichment of the neuroactive ligand‒receptor interaction pathway is of paramount importance. This pathway involves signaling for a wide array of neurotransmitters and neuromodulators [e.g., gamma-aminobutyric acid (GABA), glutamate, and dopamine]. Its disturbance likely reflects the core electrophysiological dysfunction in IESS, directly contributing to the network hyperexcitability underlying hypsarrhythmia and spasms. Furthermore, the enrichment of pathways such as steroid hormone biosynthesis and the prolactin signaling pathway offers a compelling link to the known efficacy of hormonal therapy in IESS (19-21). These findings suggest that the therapeutic effect of hormone may be mediated, in part, by correcting underlying dysregulation of neuroendocrine signaling, a hypothesis that merits further investigation. The involvement of arachidonic acid metabolism points toward a role for neuroinflammatory processes (22), as this pathway generates proinflammatory eicosanoids that can modulate neuronal excitability (23). Experimental studies employing cellular or animal models are necessary to determine whether these notable findings play a direct mechanistic role in IESS.

The robust separation in the OPLS-DA model and the high diagnostic accuracy (AUC 0.801–0.894) of the six-metabolite panel are highly promising. Currently, IESS diagnosis relies heavily on clinical assessment and EEG findings, which can be subjective and delayed. The identification of a plasma-based metabolic biomarker signature offers the potential for a complementary, objective tool to aid in earlier and more accurate diagnosis, which is crucial for initiating prompt treatment and improving neurodevelopmental outcomes (24). Whether this metabolite panel can differentiate IESS from other epileptic encephalopathies with overlapping clinical features remains to be determined. Future studies incorporating appropriate disease control groups are essential to establish the specificity of these candidate biomarkers for IESS before any clinical application can be considered.

Several limitations of our study should be acknowledged. The sample size, while well matched, is moderate, and validation in a larger, multicenter cohort, including other infantile epilepsies as disease controls, is essential to confirm its specificity and generalizability. Additionally, while plasma provides a readily accessible window into systemic metabolism, the precise cellular origins of these metabolites, whether neuronal, glial, or peripheral, remain to be elucidated. Future studies integrating cerebrospinal fluid metabolomics or utilizing animal models will be critical for delineating central nervous system-specific contributions. Furthermore, while we identified a plasma metabolite panel with high diagnostic accuracy in distinguishing IESS patients from HCs, its specificity for IESS compared to other infantile-onset epilepsies, such as Dravet syndrome, LGS, or epilepsy of infancy with migrating focal seizure, remains unknown. The inclusion of such disease control groups in future studies is critical to determine whether the observed metabolic alterations are unique to IESS or represent shared pathophysiological features across different epileptic encephalopathies. Finally, the findings may have been influenced by confounding factors, such as underlying etiology, seizure burden and comorbidities. Future validation studies should collect detailed information on etiology classification, quantitative seizure burden metrics, and comorbid conditions to enable stratified analyses or multivariate modeling that can account for these variables.

Conclusions

In conclusion, our integrated metabolomic analysis provides compelling evidence for a multifactorial pathophysiology in IESS involving interconnected disturbances in oxidative balance, amino acid and lipid metabolism, neuroendocrine signaling, and neuroinflammation. We identified a specific plasma metabolite signature with robust diagnostic potential. These findings in this discovery-phase study contribute novel metabolic insights to our understanding of IESS and lay the groundwork for future research aimed at developing metabolic biomarkers for clinical use and exploring novel therapeutic targets, such as antioxidants or modulators of neuroendocrine pathways, to improve the prognosis of affected infants. However, the results are hypothesis-generating observations derived from enrichment analysis. The translational gap between metabolic profiling and clinical intervention is substantial.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-951/dss

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-951/prf

Funding: This study was funded by STI 2030-Major Projects (No. 2021ZD0201700) and the Special Fund for the Incubation of Young Clinical Scientist from the Children’s Hospital of Zhejiang University School of Medicine (No. CHZJU2023YS001).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-1-951/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of the Children’s Hospital, Zhejiang University School of Medicine (No. 2025-IRB-0408-P-01). Written informed consent was acquired from all participants’ legal guardians.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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