Familial hemiplegic migraine type 2 due to a novel missense mutation in ATP1A2

Familial hemiplegic migraine type 2: a case report of an adolescent with ATP1A2 mutation

This study presents a case report of a male adolescent diagnosed with familial hemiplegic migraine type 2 (FHM2), an autosomal dominant inheritance disorder caused by ATP1A2 mutation. We report the patient who presented with headache, aphasia, and left-sided weakness. Cerebrovascular disease and various infectious agents were unremarkable during the patient's extended hospital stay. Our case revealed that brain hyperperfusion in familial hemiplegic migraine (FHM) persists over an extended duration, and despite the disease being in a state of recovery, enhanced brain magnetic resonance imaging (MRI) continues to exhibit hyperperfusion. A genetic testing was performed which revealed a mutation in the FHM2 gene (c.1133C > T). The patient has been followed for 3 years after hospital discharge. The boy suffered four episodes of hemiplegia and multiple episodes of headaches, and gradually developed seizures and cognitive impairment. It is advisable to consider FHM as a potential diagnosis for patients presenting with typical symptoms such as recurrent paroxysmal headaches and limb activity disorders.

Two pediatric patients with hemiplegic migraine presenting as acute encephalopathy: case reports and a literature review

Introduction

Hemiplegic migraine (HM) is a rare subtype of migraine. HM in children may be atypical in the initial stage of the disease, which could easily lead to misdiagnosis.

Methods

We report two cases of atypical hemiplegic migraine that onset as an acute encephalopathy. And a comprehensive search was performed using PubMed, Web of Science, and Scopus. We selected only papers that reported complete clinical information about the patients with CACNA1A or ATP1A2 gene mutation.

Results

Patient #1 showed a de novo mutation, c.674C>A (p. Pro225His), in exon 5 of the CACNA1A gene. And patient #2 showed a missense mutation (c.2143G>A, p. Gly715Arg) in exon 16 of the ATP1A2. Together with our two cases, a total of 160 patients (73 CACNA1A and 87 ATP1A2) were collected and summarized finally.

Discussion

Acute encephalopathy is the main manifestation of severe attacks of HM in children, which adds to the difficulty of diagnosis. Physicians should consider HM in the differential diagnosis of patients presenting with somnolence, coma, or convulsion without structural, epileptic, infectious, or inflammatory explanation. When similar clinical cases appear, gene detection is particularly important, which is conducive to early diagnosis and treatment. Early recognition and treatment of the disease can help improve the prognosis.

Hemiplegic Migraine

PURPOSE OF REVIEW

In this article, we review recent updates to the epidemiology, diagnostic testing, genetics, pathophysiology, and management of hemiplegic migraine.

RECENT FINDINGS

While three genes have been historically associated with hemiplegic migraine, recent studies suggest two additional genes may also be implicated including PPRT2 and SLC1A3. Hemiplegic migraine is a severe subset of migraine with aura with symptoms including reversible hemiparesis in addition to other aura symptoms such as visual, sensory, or speech. The exact pathophysiology of hemiplegic migraine is not clear, but it is thought that this phenomenon is due to neuronal and glial depolarization causing cortical spreading depression. Due to the severity of presentation as well as the numerous mimickers, it is important to know a comprehensive differential and work-up. Given the low prevalence of the disease, most studies regarding treatment are limited to case studies. There is still an important need for further and larger studies regarding management of these cases.

A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization

Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.