Migraine is a neurological disorder that correlates with an increased risk of subclinical cerebral vascular lesions [1]. Subjects suffering from migraine with aura (MwA) are particularly exposed to such a vascular risk [2]. There is a trend in considering migraine as a systemic vasculopathy [3]. The relationship between migraine with aura and cerebral lesions is not fully known, but it seems that the increased vascular risk of the migraineurs is not caused by the traditional risk factors (i.e., hypertension, hyperglycemia, hypercholesterolemia, …) [2]. However, the above-mentioned factors have implications on the possible drug therapies administered to migraine sufferers.

It has been demonstrated that migraine is associated to alterations in some hematochemical parameters related to platelet aggregation [4, 5]. The biological response to some agents acting on platelet function has been found altered in women suffering from migraine: adenosine diphosphate, collagen, ristocetin, epinephrine, prostacyclin, arachidonic acid, and 5-hydro-triptamine [6]. These alterations could increase the risk of vascular diseases on a thrombotic base.

Another important alteration that was correlated to MwA is the increase in the concentration of homocysteine. This amino acid, which is linked to the metabolism of the B6 and B12 vitamins, represents a powerful risk factor for cardiovascular diseases when highly concentrated in blood serum [7, 8]. The genetic mutation T/T 677 in homozygosis of the genotype 677-MTHFR (methylenetetrahydrofolate reductase) may cause increased levels of blood homocysteine. This mutation has been correlated to MwA [9] and to an increment in the angiopathies due to oxidative stress [10]. Recent studies proved that the T/T 677 mutation correlates to hyperhomocysteinemia, peripheral arteries pathologies [11], and cardiovascular events in presence of hypertension [12]. Therefore, due to the possible vascular implications they carry, genetic mutations in MwA are being more and more studied.

Given the correlation between migraine and vascular disorders, migraine sufferers usually undergo assessment of the cerebrovascular status. The transcranial Doppler sonography (TCD) is a non-invasive and reliable technique to measure the cerebral blood flow velocity (CBFV) in the arteries of the macro-circulation [13]. Some Authors evidenced that MwA patients present altered CBFV values compared to healthy controls, both in resting conditions [14] and in reactivity maneuvers [15]. The assessment of cerebral vasomotor reactivity (i.e., the arteries’ capacity of compensating systemic blood pressure alterations) is of primary importance to assess the overall status of the artery bed. Despite this, the clinical utility of TCD in diagnosis and follow-up of migraine sufferers is still debated.

The aims of this study were: 1) to test the importance of TCD examinations in migraine sufferers by studying the correlations of CBFV with hematochemical and genetic parameters; 2) to develop a classifier for the detection of the 677-MTHFR genotype based on hematochemical parameters and instrumental data (i.e., TCD analysis).

We studied a population of female migraineurs. We applied metabonomic techniques to a database consisting of metabolic and hematochemical data, genetic data, and instrumental data coming from Doppler clinical analyses. Metabonomics, which belongs to the “-omics” sciences, quantitatively measures living systems undergoing the effects of diseases of genetic mutations. Unlike genomics and proteomics, metabonomics focuses on the multiparameter evaluation of a living complex system by studying its overall physiological profile. In this study, we combined unsupervised and supervised analysis techniques. The final result of unsupervised techniques is not guided by the maximization of externally imposed classification goals (i.e., discrimination between normal and pathologic, or placebo vs drug administration, or different classes of disease). Rather, unsupervised techniques maximize the overall perception of the data set feature by minimizing the number of variables used to represent them. Therefore, unsupervised techniques allow for an unbiased description of the natural correlation structure in the data set. Conversely, supervised methods aim at maximizing an externally imposed task, such as the separation of a-priori defined classes. Given the difficulty of the task and the biological variability we expected in this study, we combined unsupervised and supervised techniques: the unsupervised approach was used to drive the supervised analysis. We used the principal component analysis (PCA) as unsupervised technique, and the partial least squares - discriminant analysis (PLS-DA) as supervised approach.

Table 3 reports the result of the multidimensional ANOVA analysis performed on the hematochemical and platelet-related data of Table 2 when considering the left and right CBFV as dependent variables. The principal factors were considered the presence of mutation and of aura. The CBFVs are influenced by response to ADP3 and collagen (p < 0.001). The left CBFV is also related to the respone to ristocetin (p < 0.001), whereas right CBFV is not. Between the principal factors, only mutation is related to blood velocity, whereas the presence of aura is not. The significant variables were used in the following unsupervised step to confirm relations between instrumental and biochemical data.

PCA was performed on a data set consisting of the 50 subjects (rows) and the following 7 observations: ADP3, collagen, ristocetin, right CBFV, left CBFV, right BHI and left BHI. All the variables were standardized. We extracted 3 PCs; their combination explained 79.9% of the total variance of the data. Fig. (1) reports the graph of the seven eigenvalues. The eigenvalues are proportional to the percent of variability in the data they explain. We chose the eigenvalues greater than 1 (horizontal line in Fig. 1), since eigenvalues lower than 1 explain the same variance of the original variables, so they are not useful for data analysis in a reduced domain.

Fig. (1). Eigenvalues of the PCA applied to the data set consisting of 7 variables (ADP3, collagen, ristocetin, right and left CBFV, right and left BHI). The first three eigenvalues are higher than 1 (horizontal dashed line). Therefore, these three eigenvalues were used to represent the subjects in a reduced domain of three principal components.

Table 4 reports the weights of the seven variables on the three components. Both the CBFVs have a high weight in the first component, whereas the BHIs in second and third. Also, ristocetin, collagen, and ADP3 responses have a negative high weight in the third component.

Fig. (2) reports the subjects’ distribution on the hyperplanes formed by component 1 and 2 (uppermost graph), component 2 and 3 (central graph), and component 1 and 3 (bottom graph). Black circles represent the subjects in the T/T677 group, gray circles those in the C/T677 group, and white circles those without mutation. The black continuous lines represent the projection of the original variables on the hyperplanes. The first component separates the subjects with T/T677 mutation from the subjects with C/T677 mutation or no mutation (Fig. 2, upper panel in the top right quadrant and bottom panel in the upper part of the chart). Component 2 separates the T/T677 mutation in the upper part of the chart (Fig. 2, middle panel). Table 5 reports the result of the t-test performed on the mutation subgroups. Table 5 confirms that Component 1 differentiates T/T677 from C/T677, whereas Component 2 separates T/T677 from C/T677 and from the subjects without mutation.

Fig. (2). PCA representation of the subjects in the three hyperplanes. Black circles represent the subjects in the T/T677 group, gray circles those in the C/T677 group, and white circles those without mutation. The dashed lines represent the zero level of the principal components. The black lines represent the projection of the original variables on the hyperplanes. Upper panel: PC1 vs PC2. Central panel: PC3 vs PC2. Lower panel: PC3 vs PC1.

The overall result of PCA was that subjects with T/T677 mutation might be distinguished from the other subjects because of their PC values. However, such PCs are mainly correlated to the CBFVs (first component), BHI (second and third component), collagen, ADP3, and ristocetin (third component). This result confirms the importance of the vascular parameters alterations carried by genetic mutations in migraine.

The analysis of the subjects with respect to mutation was refined by using PLS as supervised approach. Independent variables were same as PCA, dependent variables were mutation and aura. Table 6 summarizes the weights of the dependent and independent components. Fig. (3) reports the PLS discrimination of the subjects in the hyperplane (factor 2, factor 4). Factor 2, in particular, is discriminant of the T/T677 (black circles – left part of the graph) and C/T677 mutations (gray circles – right part of the graph). In particular, collagen responseis higher in the subjects without mutation with respect to T/T677 (t-test, p < 0.001) and in the subjects with C/T677 mutation with respect to T/T677 (t-test, p < 0.002).

Fig. (3). Sample PLS representation of the subjects in the hyperplane defined by Factor 2 and Factor 4 (factors are numerically reported by table VI). Black circles represent the subjects in the T/T677 group, gray circles those in the C/T677 group, and white circles those without mutation. The dashed lines represent the zero level of the principal components. It can be observed that Factor 2 separates the genetic mutation T/T677 (left part of the chart – black circles) from the C/T677 (right part of the chart – gray circles).

The supervised PLS-DA classifier was built by coding by a number (0 = no mutation; 1 = C/T677 mutation; 2 = T/T677 mutation) the dummy variable Y. To build the PLS-DA classifier we relied on the previously performed ANOVA analysis and removed all the variables with a correlation coefficient higher than 0.6. This lead to remove the following four variables for the X matrix: antithrombin, PTinr, hematocrit, and erythrocytes. The classifier was developed on a set of 40 subjects (corpus set). Fig. (4) shows a sample of the discrimination of a corpus set performed by the first two functions. The black “+” (plus sign) mark the centroids of the clusters. In this specific case, sensitivity is 100% and specificity is 100% (on the corpus set); the Wilks’ lambda parameter is equal to 0.071, leading to p < 0.003.

Fig. (4). Sample PLS-DA representation of the 40 subjects of a corpus set in the hyperplane defined by the discriminant Functions 1 and 2. Black circles represent the subjects in the T/T677 group, gray circles those in the C/T677 group, and white circles those without mutation. The black “+” (plus sign) mark the centroids of the clusters.

The PLS-DA classifier was then used to cluster the remaining 10 subjects (validation set). Corpus and validation sets were randomly selected. We tested 100 combinations leading to corpus and validation sets containing at least 2 subjects for each group. Hence, we classified 1000 subjects, 363 of which belonged to the T/T677 group, 364 to the C/T677 group, and 273 to the no mutation group.

Each element of the validation set was assigned to a group on the basis of its distance from the centroids. For all the considered classifiers, the discriminant function p-value were always lower than 0.01. The classification performance were (between brackets we indicated the range of variation):

• 100% sensitivity (95.6% - 100%)and 97.6% specificity (94.1% - 98.3%) for the T/T677 mutation;
• 91.8% sensitivity (87.5% - 95.5%) sensitivity and 97.6% specificity (92.3% - 99.0%) for the C/T677 mutation;
• 89.0% sensitivity (84.7% - 92.4%) and 95.9% specificity (90.2% - 98.8%) for the group without mutation.

Overall classifier performance was 95.9% sensitivity, 89.0% specificity, and 94.0% efficiency.

Migraine is a complex neurological disorder involving different aspects, ranging from metabolic imbalances to neuronal and vascular anomalies. In this study, we profiled different groups of women suffering from migraine. The independent observation we considered in this study was the genetic mutation of the 677-MTHFR gene. We believe that, though preliminary, this study could be important in the clinical management of migraineurs.

We built a mixed database incorporating instrumental data (the CBFVs in the middle cerebral arteries and the cerebral reactivity index to BH), genetic data (the mutation type: C/T677 or T/T677, or the absence of mutation), and some biochemical parameters (including hematochemical variables and data related to platelet aggregation. The complete list is reported by Table 2).

PCA and multivariate ANOVA essentially pointed out that, in our sample population of migraine sufferers, 677-MTHFR mutations are associated to altered platelet response to collagen, ristocetin, and ADP3. However, we also documented that the MTHFR mutations in migraine correlate with the instrumental measures of CBFVs and BHI. PLS results confirmed the importa-nce of the CBFVs and BHI patterns in the different subgroups of the genetic mutations (last four rows of Table 6). When compared to C/T677 subgroup, T/T677 subjects showed increased BHIs (t-test, p < 5×10-6 for left and p < 6×10-7 for the right side).

The PLS-DA offered satisfactory classification performance when classifying a population of migraineurs on the basis of their genetic mutation. The overall classification performance was 95.9% sensitivity and 89.0% specificity. The most important variables for classification were the left and right CBFVs, the BHI, and, again, the platelet response to ADP3, collagen, and ristocetin. This finding confirms the importance of assessing the vasculature-related variables in migraine. Therefore, we believe that TCD could be of primary importance also in the follow-up of migraine sufferers.

We found that the BHI is altered in subjects with genetic mutations (t-test, p < 5×10-6). This result confirms that vascular reactivity may be impaired in migraineurs [24]. In 2008, Vernieri et al. showed that subjects suffering from MwA presented an increased BHI in the predominant migraine side with respect to non predominant and to control subjects [25], suggesting a possible deficit in the cerebrovascular autonomic control.

Our results are confirmatory of previously published studies. Novelty resides in two results that we obtained thanks to the applied multidimensional approach. First, we found that aura is not a major significant factor when related to mutation. In other words, the presence of aura was not correlated to the overall profile of the genetic mutation. This result is somehow new, since many studies pointed out that migraine with and without aura are characterized by different pathogenesis. Also, some pathologies have a greater prevalence in MwA than in migraine without aura (i.e., the patent foramen ovale [26]. We did not find any significant correlation between aura and the instrumental or biochemical variables. PCA and PLS showed that the metabolic profile of subjects with C/T677and T/T677 mutations is significantly different than subjects without mutation, even if aura is discarded among the classification variables.

The second important and novel result is that biochemical data alone are insufficient to perform classification. We tested a PLS-DA classifier based only on the variables of Table 2. Basically, we removed the instrumental data from the data matrix X. Performance dropped to 54% sensitivity and 58% specificity (p > 0.05). Mutation, therefore, induces a global alteration of the metabolic profile of the subject that is better described by the joint analysis of hematochemical and vascular parameters.

In developing the PLS-DA classifier, we removed all the collinear variables with correlation coefficient higher than 0.6. We used this as stopping criterion for PLS regression. We tried to remove the variables with correlation coefficient higher than 0.5. In this case we deleted from the database five variables: antithrombin, PTinr, hematocrit, erythrocytes, and C protein. Classification performance was lower than in the former version: sensitivity was 90.1% and specificity was 85.2%. Therefore, we adopted 0.6 as threshold for assessing collinearity and for removing the co-variables. This result demonstrates the importance of a complete metabolic profiling for clustering MTHFR genetic mutations in migraine sufferers.

Finally, we did not perform outlier rejection. Being they outliers either for the biochemical variables or for the instrumental variables, outliers were correctly classified in all the tests we did. Therefore, we assumed that outliers might be representative of the variability of the data that can be obtained when working with a complex pathology like migraine.

The principal limitation of this study relies in the relative paucity of the sample database. However, results seem very promising since even the first multivariate analysis we performed (unsupervised PCA, Fig. 1) showed a natural tendency in clustering T/T677 mutations. The application of this method corresponds to depicting a sort of ‘natural normalization’ of the studied data set, given that principal components correspond to the eigenvectors of the correlation matrix, which in turn corresponds by definition to the covariance matrix of the standardized variables. This is particularly convenient when dealing with heterogeneous variables defined by completely different measurement units, ruling out all questionable a priori defined standardization processes. Another limitation of the study is the small number of hematochemical data inserted into the database. We decided to develop a study based on exams that are routinely done. Therefore, we did not specifically test any hematochemical variable outside the list in Table 2 (first column), which somehow reflects is the list of blood examinations requested by the Institution where the patients are followed.

To the very best of our knowledge, this is the first attempt to use metabonomic statistical techniques applied to a clinical data set consisting of metabolic and instrumental data. We believe that this approach could constitute a pilot experience to bring metabolomic (and, in general, data mining) techniques into clinical practice, when tuned for specific and well-defined groups of patients.

By using a metabonomic approach, we developed a classifier to detect genetic mutations of the 677-MTHFR gene in a population of women suffering from migraine. Our database consisted biochemical and instrumental data. Transcranial Doppler ultrasonography was used to measure the baseline values of the cerebral blood flow and their variation during voluntary breath – holding. Unsupervised and supervised approaches were used to extract the variables correlating to mutation. Our classifier showed overall satisfactory performance: 95.9% sensitivity and 89.0% specificity. We found that cerebral blood flow velocities and response to breath – holding are important variables in characterizing mutations in migraine.

This pilot study adapting metabonomic techniques to the profiling of pathologic subjects could be a basis for the development of a clinically applicable methodology devoted to the profiling, classification, and evolutionary modeling of migraine patients.