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Biomedical subjects

Jangsup Moon

Publications and source records attributed to Jangsup Moon.

2 recordsLinked to original sources

Genotype-structure-phenotype correlations define divergent natural history in early-onset spastic paraplegia type 4.

Hereditary spastic paraplegia type 4 (SPG4), caused by variants in SPAST, is the most common form of HSP and exhibits a remarkable phenotypic heterogeneity ranging from late-onset pure presentations to severe, early-onset complex disease. Robust genotype-phenotype correlations and detailed natural history data are lacking, limiting clinical trial readiness. We analyzed 206 patients with genetically confirmed SPG4 enrolled across seven international centers, complemented by high-quality literature-derived cases. Deep phenotyping included standardized motor scales, spasticity ratings, developmental milestones, and patient-reported outcomes. We developed an extended essentiality-mapping framework to classify SPAST missense variants by integrating in silico pathogenicity predictions, evolutionary constraint, physicochemical residue connectivity, and variant enrichment within the human spastin hexamer structure. Plasma neurofilament light chain (pNfL) using was quantified using Simoa in 26 patients and 101 controls. We identified 136 distinct SPAST variants, including 10 novel variants. Variant class segregated strongly by inheritance, with de novo cases enriched for missense variants and inherited cases showing a variety of variant classes with enrichment for truncating variants. Longitudinal analysis revealed two latent trajectories: a rapidly progressive severe subgroup enriched for de novo missense variants, and a biphasic moderate subgroup enriched for inherited truncating variants. Patient stratification integrating spastin essentiality mapping (missense variants affecting essential, neutral, or context-dependent residues) with established genetic modifiers (biallelic pathogenic variants or modifier variants in trans) classified patients into predicted severe and moderate subgroups with divergent age at onset and clinical disease progression. The severe subgroup showed early developmental delays, rapid loss of ambulation, and declining quality of life, while the moderate subgroup displayed delayed but accelerating disease progression. pNfL levels were elevated in both subgroups, most pronounced in severe early disease. This study provides the most detailed natural history of SPG4 to date and introduces a biologically informed stratification framework that links variant class and location to divergent clinical trajectories. These data establish clinically meaningful benchmarks and offer a genotype-based framework to improve anticipatory care and optimize trial design for SPG4.

SPAST

Prediction of personalized antiseizure medications response based on clinical signatures in epilepsy.

Despite the increasing number of available antiseizure medications (ASMs), optimal medical therapy is still a process of trial and error. We aimed to predict the responsiveness of various ASMs based on initial tests using artificial intelligence. The study consisted of 2586 patients fulfilling the following criteria: (1) first visit to the epileptologists from 2008 to 2017, (2) a diagnosis of epilepsy, and 3) ≥ three years of follow-up duration. The clinical characteristics, ASM history, seizure frequency, laboratory, EEG, and MRI results, were collected. Machine algorithms were utilized to predict the responsiveness of specific regimens. Valproate showed the highest area under curve (AUC), 0.636. The AUCs of levetiracetam, oxcarbazepine, and lamotrigine were 0.614, 0.633, and 0.674. The AUCs of common dual regimens were 0.543 for levetiracetam + oxcarbazepine, 0.454 for levetiracetam + valproate, and 0.583 for levetiracetam + lamotrigine. Levetiracetam + carbamazepine showed the highest AUC, 0.686. In Shapley Additive exPlanations analysis, seizure type significantly impacted prediction performance for valproate responsiveness, and onset age and disease duration for lamotrigine. The prediction performances for the response based on initial data differ according to ASMs. An enormous dataset from a multicenter would improve the prediction power of ASM responsiveness in the future.

Humans