Joint Metabonomic and Instrumental Analysis for the Classification of Migraine Patients with 677-MTHFR Mutations

  • Giustetto P
  • Liboni W
  • Mana O
  • et al.
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Abstract

Migraine is a neurological disorder that correlates with an increased risk of cerebrovascular lesions. Genetic mutations of the MTHFR gene are correlated to migraine and to the increased risk of artery pathologies. Also, migraine patients show altered hematochemical parameters, linked to an impaired platelet aggregation mechanism. Hence, the vascular assessment of migraineurs is of primary importance.Transcranial Doppler sonography (TCD) is used to measure cerebral blood flow velocity (CBFV) and vasomotor reactivity (by an index measured during breath-holding – BHI). Aim of this study was the metabolic profiling of migraine subjects with T/T677-MTHFR and C/T677-MTHFR mutations and its correlation with CBFV and BHI.Metabonomic multidimensional techniques were used to describe and cluster subjects. Fifty women suffering from migraine (age: 18-64; 21 with aura) underwent TCD examination, hematochemical blood analysis, Born test, and genetic tests for MTHFR mutation. Fourteen (7 with aura) had T/T677, 18 (8 with aura) had C/T677, and 18 (6 with aura) had no mutation. The total number of variables was 24.Unsupervised and supervised techniques_showed the correlation between CBFV and BHI with mutation. Discriminant analysis allowed for classifying the subjects with 95.9% sensitivity and 89.0% specificity. Aura was not correlated to mutation or variations of instrumental data.Our study showed that metabonomics could be effectively applied in clinical problems to show the overall correlation structure of complex systems in pathology. Specifically, our results confirmed the importance of TCD in the metabolic profiling and follow-up of migraine patients.

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Giustetto, P., Liboni, W., Mana, O., Allais, G., Benedetto, C., & Molinari, F. (2010). Joint Metabonomic and Instrumental Analysis for the Classification of Migraine Patients with 677-MTHFR Mutations. The Open Medical Informatics Journal, 4(1), 23–30. https://doi.org/10.2174/1874431101004020023

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