Abstract
Martins H, Mills JD, Pagni S, Gulcebi MI, Vakrinou A, Moloney PB, Clayton LM, Bellampalli R, Stamberger H, Weckhuysen S, Striano P, Zara F, Bagnall RD, Harris RV, Lawrence KM, Sadleir LG, Crompton DE, Friedman D, Laze J, Li L, Berkovic SF, Semsarian C, Scheffer IE, Devinsky O, Kuchenbaecker K, Balestrini S, Sisodiya SM. EBioMedicine. 2025 Aug;118:105841. doi: 10.1016/j.ebiom.2025.105841. Epub 2025 Jul 28. PMID: 40731221; PMCID: PMC12368338. Background: Sudden Unexpected Death in Epilepsy (SUDEP) is a rare and tragic outcome in epilepsy, identified by those with the condition as their most serious concern. Although several clinical factors are associated with elevated SUDEP risk, mechanisms underlying SUDEP are poorly understood, making individual risk prediction challenging, especially early in the disease course. We hypothesised that common genetic variation contributes to SUDEP risk. Methods: Genetic data from people who had succumbed to SUDEP was compared to data from people with epilepsy who had not succumbed to SUDEP and from healthy controls. Polygenic risk scores (PRSs) for longevity, intelligence and epilepsy were compared across cohorts. Reactome pathways and gene ontology terms implicated by the contributing single nucleotide polymorphisms (SNPs) were explored. In the subset of SUDEP cases with the necessary data available, a risk score was calculated using an existing risk prediction tool (SUDEP-3); the added value to this prediction of SNP-based genomic information was evaluated. Findings: Only European-ancestry participants were included. 161 SUDEP cases were compared to 768 cases with epilepsy and 1153 healthy controls. PRS for longevity was significantly reduced in SUDEP cases compared to disease (P =0.0096) and healthy controls (P=0.0016), as was PRS for intelligence (SUDEP cases compared to disease (P=0.0073) and healthy controls (P=0.00024)). The PRS for epilepsy did not differ between SUDEP cases and disease controls (P =0.76). SNP-determined pathway and gene ontology analysis highlighted those related to inter-neuronal communication as amongst the most enriched in SUDEP. Addition of PRS for longevity and intelligence to SUDEP-3 scores improved risk prediction in a subset of cases (38) and controls (703), raising the area-under-the-curve in a receiver-operator characteristic from 0·699 using SUDEP-3 alone to 0·913 when PRSs were added. Interpretation: Common genetic variation contributes to SUDEP risk, offering new approaches to improve risk prediction and to understand underlying mechanisms.
Commentary
Sudden unexpected death in epilepsy (SUDEP) is a devastating complication of epilepsy. While the exact mechanisms remain unknown, SUDEP potentially occurs due to seizure-related disruption of normal cardiorespiratory patterns. 1 SUDEP occurs in 1 in 1000 adults and children with epilepsy per year, 2 with known risk factors including chronic, refractory epilepsy, generalized tonic-clonic seizures, seizures in sleep, male sex, and neurodevelopmental disability.1,3
Predicting an individual's risk for SUDEP can be challenging. Risk assessments, such as SUDEP-7 and SUDEP-3 inventories, have been designed to help identify those at risk, focusing on factors such as frequent generalized tonic-clonic seizures, seizures in the past year, and intellectual/developmental disability. 4 Polygenic risk scoring (PRS) is a method of quantifying the potential risk of developing a disease based on the evaluation of specific genomic variants associated with that disease state. This technique can aid in prediction, diagnosis, and potentially disease prevention. 5 The rationale behind PRS is that the cumulative effects of common genetic variants are important for disease development and collating those variants into a disease-specific PRS can be helpful for risk prediction, particularly in disorders where there are otherwise only rare monogenic causes typically found. 6
Martins et al evaluated genetic factors associated with SUDEP by examining PRS for longevity and intelligence, as markers of “genomically-mediated resilience” to negative health outcomes, hypothesizing that lower PRS for longevity and intelligence are associated with a higher SUDEP risk for individuals with epilepsy. 7 This multinational, multicenter study looked at individuals of only European ancestry, with the SUDEP cohort including those with definite, probable, or possible SUDEP from three different locations: Europe, Australia, and the United States. The two control cohorts, including individuals with epilepsy and healthy controls, were participants in the Genomics England (GEL) 100,000 genomes project. Given the associations between Apolipoprotein E alleles, longevity, and disease development, Martins and colleagues also evaluated single-nucleotide polymorphisms (SNPs) in ApoE, to assess if there was an impact independent of the longevity PRS. Finally, the authors also looked at the performance of the SUDEP-3 inventory and evaluated the impact of the incorporation of longevity and intelligence PRSs into the performance of this battery.
The study found that the PRS for longevity was significantly lower in the SUDEP cohort as compared to the Epilepsy (adjusted P = .0096, with P threshold (PT) of 10−3, Tukey's test) and Healthy control groups (adjusted P = .0016 at PT = 10−3, Tukey's test) while no difference was seen between the Epilepsy and Healthy control groups. ApoE data was only available in a subset of subjects but did not demonstrate a significant difference between groups, nor did exclusion of ApoE SNPs from the PRS estimation affect their findings, indicating that factors outside of ApoE contributed to the impact of the longevity PRS. Evaluating the PRS for intelligence, this was significantly lower in the SUDEP group compared to the Epilepsy (adjusted P = .0073, at PT = 10−3, Tukey's test) and Healthy controls (adjusted P = .00024, at PT = =10−3, Tukey's test), while the Epilepsy and Healthy controls did not differ. The epilepsy PRS did not differ between SUDEP cases and Epilepsy controls but was significantly higher in both SUDEP cases and Epilepsy controls compared to Healthy controls. The risk of SUDEP on the liability scale was considered modest at 5%; with approximately 1% of the risk explained by the longevity PRS and approximately 4% explained by the intelligence PRS.
When evaluating the performance of the SUDEP-3 scale to distinguish individuals from the SUDEP cohort and the Epilepsy controls, the area under the curve was 0.691 (95% CI: 0.62221-0.7761). As compared to both the longevity and intelligence PRS as univariate classifiers, the SUDEP-3 underperformed the intelligence PRS (AUC = 0.827; 95% CI: 0.776-0.8776) and longevity PRS (AUC = 0.785; 95% CI: 0.7251-0.84519). The authors looked at all combinations of these three variables and ultimately found that incorporating all three variables demonstrated the best performance (AUC = 0.913; 95% CI: 0.879-0.974), while any combination of variables outperformed the SUDEP-3 alone.
The results of this study are important because they highlight that the risk of SUDEP is not limited to rare monogenic causes of epilepsy but rather has contributions from common genetic variation. These findings suggest that there is an inherent baseline risk beyond epilepsy features, such as frequency of tonic-clonic seizures, which underlie the risk of SUDEP. Additionally, the improved performance of the SUDEP-3, when augmented by the PRSs for intelligence and longevity, indicates that incorporation of inherent risk with clinical prediction tools is an area for future research and hopefully one day, clinical use to provide better prognostication and guidance for patients with epilepsy. While this innate risk cannot be modified, this type of information could aid providers in identifying who would benefit most from seizure detection devices or nighttime monitoring. 3
There are several limitations to the current study, which the authors acknowledge. First, there were a limited number of SUDEP cases to include for PRS analysis and even fewer with all the available information needed for SUDEP-3 assessment. Additionally, only individuals of European ancestry were included in each of the three groups analyzed, which limits the generalizability of the findings. This study did not look at rare genetic variants which could also impact SUDEP risk, as the focus was on common variants within the studied populations. Finally, more research is needed before it is known how well these predictive tools work in the real-world setting.
Discussing SUDEP is uncomfortable and a topic that unfortunately can be neglected during epilepsy care, based on assessments of clinician practices and opinions. 8 However, patients and families/caregivers want to know more about SUDEP, its risks, and ways to modify that risk. 9 While information, such as the risk identified through PRS, is still incomplete, it is possible that having this information available would increase and improve provider communication to patients, caregivers, and families on an individual's risk of SUDEP. This in turn may improve factors such as medication compliance and follow-up, as well as improve trust in the provider.
Future research could aim to expand the assessment from individuals of European ancestry to a more diverse cohort. Additionally, looking at more granular details of epilepsy phenotypes and the genetic risk for certain epilepsy features, such as drug-resistance or specific epilepsy syndromes, could help in further identifying risk factors or predictors of SUDEP. Finally, as the authors suggest, evaluation of PRS in other sudden death syndromes, such as sudden infant death syndrome and sudden unexplained death in childhood, can potentially help identify common pathways and risks related to SUDEP and sudden death.
