An interview with Saadet Mahmutoglu, 2016 Epilepsia prize winner for Clinical Research
Bibliographic record
Abstract
Saadet Mahmutoglu I am a biochemical geneticist and neurometabolic specialist. I completed my pediatric residency in Istanbul, Turkey, and moved to Vienna, Austria, for training in neurometabolics. Later I moved from Vienna to Vancouver (British Columbia, Canada) to complete fellowship training in biochemical genetics, which was accredited by the Canadian College of Medical Geneticists at British Columbia Children's Hospital, University of British Columbia. Following this, I also completed a 1-year pediatric neurology fellowship at the University of British Columbia. These trainings bridged my knowledge between biochemical genetics and neurology and enabled me to practice as a neurometabolic physician. Since 2011, I have been working as a neurometabolic specialist at The Hospital for Sick Children, where I established a complex epilepsy genetics clinic for the investigation of underlying genetic causes in patients with epilepsy. I have been following more than 200 patients in my clinic and performing intensive diagnostic investigations. During my training in biochemical genetics and early on in my faculty position at University of British Columbia, I had the opportunity to diagnose and treat patients with treatable metabolic epilepsies such as creatine deficiency disorders and pyridoxine dependent epilepsy. I was fascinated by seizure freedom in patients treated with oral vitamins, cofactors, and amino acid supplementations. During my work at The Hospital for Sick Children in Toronto, I found that treatable metabolic epilepsies were underdiagnosed, and I determined that expanded investigations would identify more patients and give them more opportunities for treatment. For this reason, I started an epilepsy genetics research study in newborns and children using whole-exome sequencing. At the same time, a targeted next-generation-sequencing epilepsy genetics panel was introduced at our clinical practice. This gave me opportunity to investigate my patients, and I quickly discovered that the proportion of treatable metabolic epilepsies is very small and that there are other genetic disorders causing seizures. Despite these disorders not being treatable, parents wanted to know the cause of seizures in their children. In response, I expanded my clinical practice to epilepsy genetics. My study's main question was to determine the percentage of underlying genetic causes in patients with epilepsy. I decided to undertake a retrospective cohort study including all patients seen in my complex epilepsy genetics clinic. I divided genetic causes into two groups including inherited metabolic disorders and other genetic causes. I identified an underlying genetic cause in 28% of the 110 patients including 7% with one of the inherited metabolic disorders and 21% with one of the other genetic causes. Forty-five percent of patients obtained a genetic diagnosis with use of targeted next-generation-sequencing epileptic encephalopathy panels. It is notable that 4.5% of patients had a treatable inherited metabolic disease. Targeted next-generation-sequencing panels increased the genetic diagnostic yield from <10% to >25% in patients with epileptic encephalopathy.1 Currently I have a research study looking at underlying genetic causes of epilepsy using whole-exome sequencing to identify novel epilepsy genes. I have been treating 11 patients who have pyridoxine-dependent epilepsy with lysine-restricted diet and arginine therapy in addition to pyridoxine. The accumulating metabolites do not normalize in the central nervous system despite maximum therapy. For this reason, I developed the zebra fish knockout model for drug screening of pyridoxine-dependent epilepsy. There are only two inherited metabolic disorders for which the effect of the ketogenic diet on seizure control is well known: glucose transporter 1 (GLUT1) deficiency and pyruvate dehydrogenase complex (PDHC) deficiency. We recently published a report on (mediator complex subunit 23) MED23-associated refractory epilepsy as a third genetic disorder to exhibit responsiveness to the ketogenic diet.2 I have been applying for grants to investigate patients who are being treated with the ketogenic diet using an omics approach: whole exome sequencing, proteomics, and metabolomics. This future research aims to identify biomarkers that can identify patients as responders (using positive biomarkers). I am honored to be nominated for the 2016 Epilepsia Prize, and I am thrilled to receive this prize. The Epilepsia Prize is a prestigious award that demonstrates my success in epilepsy research. It will help me to further develop my laboratory research by supporting my funding applications. Read this article online at http://onlinelibrary.wiley.com/doi/10.1111/epi.12954/abstract.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".