Which biomarkers are most commonly used for seizures associated with Lennox-Gastaut syndrome?

The most frequent biomarkers for Lennox-Gastaut syndrome seizures are electroencephalographic measurements, specifically SSW and GPFA patterns.

Abstract

Electroencephalography metrics appear most frequently for seizures in Lennox-Gastaut syndrome. Ten studies report slow spike‐wave (SSW) patterns, and seven document generalized paroxysmal fast activity (GPFA); other EEG features (such as gamma rhythms, paroxysmal fast activity, and network indices) also support diagnosis and treatment assessment. Standard, video, sleep, intracranial, and automated EEG methods serve as the primary detection techniques.

Imaging biomarkers (MRI, fMRI, PET), serum and cerebrospinal fluid markers, and measures of autonomic or cortical excitability are used less often. Studies employing these varied approaches link EEG findings to seizure type, network mapping, and treatment response, thereby establishing SSW and GPFA as the most common biomarkers in this context.

Methods

We analyzed 34 sources from an initial pool of 998, using 6 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question.

Papers identified with Elicit search

n = 998

Papers screened using: Population - LGS Diagnosis, Biomarker Focus, Study Type and Measurements, Temporal Association, Sample Size, Study Population Type

n = 998

Papers screened out

n = 964

Papers included for extraction

n = 34

Paper search

Using your research question “Which biomarkers are most commonly used for seizures associated with Lennox-Gastaut syndrome?”, we searched across over 126 million academic papers from the Semantic Scholar corpus. We retrieved the 998 papers most relevant to the query.

Screening

We screened in sources based on their abstracts that met these criteria:

• Population - LGS Diagnosis: Does the study include patients with confirmed Lennox-Gastaut syndrome (LGS) who can be analyzed separately from patients with other epilepsy syndromes?
• Biomarker Focus: Does the study examine biological markers (molecular, cellular, imaging, or electrophysiological) in relation to seizures in LGS?
• Study Type and Measurements: Is this either a primary research study or a systematic review/meta-analysis that reports quantitative biomarker measurements?
• Temporal Association: Does the study demonstrate a clear temporal relationship between biomarker measurements and seizure activity?
• Sample Size: Does the study include 5 or more subjects?
• Study Population Type: Was the study conducted in human subjects?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper.

• Study Design:

Identify the type of study design used:

• Specify whether it is an observational study (e.g., cross-sectional, cohort, case-control).

• If an experimental study, note the specific type (e.g., randomized controlled trial, interventional study).

• If multiple study designs are present, list all types.

• If the design is not clearly stated, write “Not clearly specified”.

• Biomarkers Investigated:

List ALL biomarkers examined in the study related to Lennox-Gastaut syndrome:

• Include full names and abbreviations.

• Specify the biological source of the biomarker (e.g., serum, cerebrospinal fluid).

• Note the measurement method used (e.g., ELISA, immunoassay).

• If multiple biomarkers were studied, list them in order of appearance in the text.

• Participant Demographics:

Extract the following participant information:

• Total number of participants with Lennox-Gastaut syndrome.

• Age range or mean age.

• Gender distribution (if reported).

• Inclusion/exclusion criteria.

• Any specific subgroups within the LGS population.

• Biomarker Findings:

For each biomarker investigated:

• Report statistical significance of findings.

• Note any correlations with clinical features (e.g., seizure severity, EEG characteristics).

• Include specific numerical values (mean, standard deviation, p-values).

• Highlight any comparative findings (e.g., differences from control groups).

• Clinical Implications:

Extract the authors’ conclusions about:

• Potential diagnostic or prognostic value of identified biomarkers.
• Suggested clinical significance of biomarker findings.
• Any recommendations for future research.

Results

Characteristics of Included Studies

Key findings from included studies:

• Most frequently reported patterns: Among the included studies, slow spike-wave (SSW) was the most frequently reported biomarker pattern (10 studies). Generalized paroxysmal fast activity (GPFA) was reported in 7 studies. Other patterns: spike-wave, fast rhythms, polyspike, atypical absence, network or waveform features were each reported in 1–2 studies.
• Detection methods: Standard EEG was the most common detection method (9 studies).
• Frequency of use: 11 studies were categorized as “High”, 10 as “Moderate”, and 2 as “Low” for frequency of use.

EEG-Based Biomarkers

Clinical Applications and Validation

Diagnostic Applications

• EEG-based biomarkers: SSW and GPFA were reported as central to the diagnosis of Lennox-Gastaut syndrome in several included studies, with high sensitivity and specificity reported.
• Video-EEG monitoring: Reported as essential for distinguishing seizure types.

Treatment Monitoring

• EEG changes and treatment response: Changes in GPFA were associated with treatment response, particularly in neuromodulation contexts.

Prognostic Value

• EEG features and outcomes: Longer SSW discharges associated with poorer outcomes.

Summary

• EEG-based biomarkers: SSW and GPFA were the most commonly reported and validated biomarkers for seizures associated with Lennox-Gastaut syndrome.
• Imaging and molecular biomarkers: Emerging as important adjuncts but require further validation for routine clinical use.