Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
  • G01N33/543—Immunoassay; Biospecific binding assay; Materials therefor with an insoluble carrier for immobilising immunochemicals
  • G01N33/54366—Apparatus specially adapted for solid-phase testing
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
  • G01N33/6854—Immunoglobulins
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
  • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
  • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
  • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/24—Immunology or allergic disorders
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N2800/00—Detection or diagnosis of diseases
  • G01N2800/28—Neurological disorders
  • G01N2800/2807—Headache; Migraine

Definitions

• Elimination of other one or more food items may be useful in at least reducing incidence and/or severity of the symptoms.
• migraine headaches are often quite diverse with respect to dietary items triggering symptoms, and no standardized test to help identify trigger food items with a reasonable degree of certainty is known, leaving such patients often to trial-and-error.
• the subject matter described herein provides systems and methods for testing food intolerance in patients diagnosed with or suspected to have migraine headaches.
• One aspect of the disclosure is a test kit with for testing food intolerance in patients diagnosed with or suspected to have migraine headaches.
• the test kit includes a plurality of distinct food preparations coupled to individually addressable respective solid carriers.
• the plurality of distinct food preparations have an average discriminatory p-value of ⁇ 0.07 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.10 as determined by FDR multiplicity adjusted p-value.
• Another aspect of the embodiments described herein includes a method of testing food intolerance in patients diagnosed with or suspected to have migraine headaches.
• the method includes a step of contacting a food preparation with a bodily fluid of a patient that is diagnosed with or suspected to have migraine headaches.
• the bodily fluid is associated with gender identification.
• the step of contacting is performed under conditions that allow IgG from the bodily fluid to bind to at least one component of the food preparation.
• the method continues with a step of measuring IgG bound to the at least one component of the food preparation to obtain a signal, and then comparing the signal to a gender-stratified reference value for the food preparation using the gender identification to obtain a result. Then, the method also includes a step of updating or generating a report using the result.
• Another aspect of the embodiments described herein includes a method of generating a test for food intolerance in patients diagnosed with or suspected to have migraine headaches.
• the method includes a step of obtaining test results for a plurality of distinct food preparations.
• the test results are based on bodily fluids of patients diagnosed with or suspected to have migraine headaches and bodily fluids of a control group not diagnosed with or not suspected to have migraine headaches.
• the method also includes a step of stratifying the test results by gender for each of the distinct food preparations. Then the method continues with a step of assigning for a predetermined percentile rank a different cutoff value for male and female patients for each of the distinct food preparations.
• Still another aspect of the embodiments described herein includes a use of a plurality of distinct food preparations coupled to individually addressable respective solid carriers in a diagnosis of migraine headache.
• the plurality of distinct food preparations are selected based on their average discriminatory p-value of ⁇ 0.07 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.10 as determined by FDR multiplicity adjusted p-value.
• Table 1 shows a list of food items from which food preparations can be prepared.
• Table 2 shows statistical data of foods ranked according to 2-tailed FDR multiplicity- adjusted p- values.
• Table 3 shows statistical data of ELISA score by food and gender.
• Table 4 shows cutpoint values of foods for a predetermined percentile rank.
• Figure 1A illustrates ELISA signal score of male migraine headache patients and control tested with cucumber.
• Figure IB illustrates a distribution of percentage of male migraine headache subjects exceeding the 90 th and 95 th percentile tested with cucumber.
• Figure 1C illustrates a signal distribution in women along with the 95 th percentile cutoff as determined from the female control population tested with cucumber.
• Figure ID illustrates a distribution of percentage of female migraine headache subjects exceeding the 90 th and 95 th percentile tested with cucumber.
• Figure 2A illustrates ELISA signal score of male migraine headache patients and control tested with tomato.
• Figure 2B illustrates a distribution of percentage of male migraine headache subjects exceeding the 90 th and 95 th percentile tested with tomato.
• Figure 2C illustrates a signal distribution in women along with the 95 th percentile cutoff as determined from the female control population tested with tomato.
• Figure 2D illustrates a distribution of percentage of female migraine headache subjects exceeding the 90 th and 95 th percentile tested with tomato.
• Figure 3A illustrates ELISA signal score of male migraine headaches patients and control tested with malt.
• Figure 3B illustrates a distribution of percentage of male migraine headaches subjects exceeding the 90 th and 95 th percentile tested with malt.
• Figure 3C illustrates a signal distribution in women along with the 95 th percentile cutoff as determined from the female control population tested with malt.
• Figure 3D illustrates a distribution of percentage of female migraine headaches subjects exceeding the 90 th and 95 th percentile tested with malt.
• Figure 4A illustrates ELISA signal score of male migraine headaches patients and control tested with cauliflower.
• Figure 4B illustrates a distribution of percentage of male migraine headaches subjects exceeding the 90 th and 95 th percentile tested with cauliflower.
• Figure 4C illustrates a signal distribution in women along with the 95 th percentile cutoff as determined from the female control population tested with cauliflower.
• Figure 4D illustrates a distribution of percentage of female migraine headache subjects exceeding the 90 th and 95 th percentile tested with cauliflower.
• Figures 5A illustrates distributions of migraine headache subjects by number of foods that were identified as trigger foods at the 90 th percentile.
• Figures 5B illustrates distributions of migraine headache subjects by number of foods that were identified as trigger foods at the 95 th percentile.
• Table 5A shows raw data of migraine headache patients and control with number of positive results based on the 90 th percentile.
• Table 5B shows raw data of migraine headache patients and control with number of positive results based on the 95 th percentile.
• Table 6A shows statistical data summarizing the raw data of migraine headache patient populations shown in Table 5A.
• Table 6B shows statistical data summarizing the raw data of migraine headache patient populations shown in Table 5B.
• Table 7A shows statistical data summarizing the raw data of control populations shown in Table 5A.
• Table 7B shows statistical data summarizing the raw data of control populations shown in Table 5B.
• Table 8A shows statistical data summarizing the raw data of migraine headache patient populations shown in Table 5A transformed by logarithmic transformation.
• Table 8B shows statistical data summarizing the raw data of migraine headache patient populations shown in Table 5B transformed by logarithmic transformation.
• Table 9A shows statistical data summarizing the raw data of control populations shown in Table 5A transformed by logarithmic transformation.
• Table 9B shows statistical data summarizing the raw data of control populations shown in Table 5B transformed by logarithmic transformation.
• Table 10A shows statistical data of an independent T-test to compare the geometric mean number of positive foods between the migraine headache and non-migraine headache samples based on the 90 th percentile.
• Table 10B shows statistical data of an independent T-test to compare the geometric mean number of positive foods between the migraine headache and non-migraine headache samples based on the 95 th percentile.
• Table 11A shows statistical data of a Mann-Whitney test to compare the geometric mean number of positive foods between the migraine headache and non-migraine headache samples based on the 90 th percentile.
• Table 11B shows statistical data of a Mann-Whitney test to compare the geometric mean number of positive foods between the migraine headache and non-migraine headache samples based on the 95 th percentile.
• Figure 6A illustrates a box and whisker plot of data shown in Table 5A.
• Figure 6B illustrates a notched box and whisker plot of data shown in Table 5A.
• Figure 6C illustrates a box and whisker plot of data shown in Table 5B.
• Figure 6D illustrates a notched box and whisker plot of data shown in Table 5B.
• Table 12A shows statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5A-11 A.
• Table 12B shows statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5B-11B.
• Figure 7A illustrates the ROC curve corresponding to the statistical data shown in Table 12 A.
• Figure 7B illustrates the ROC curve corresponding to the statistical data shown in Table 12B.
• Table 13A shows a statistical data of performance metrics in predicting migraine headache status among female patients from number of positive foods based on the 90 th percentile.
• Table 13B shows a statistical data of performance metrics in predicting migraine headache status among male patients from number of positive foods based on the 90 th percentile.
• Table 14A shows a statistical data of performance metrics in predicting migraine headache status among female patients from number of positive foods based on the 95 th percentile.
• Table 14B shows a statistical data of performance metrics in predicting migraine headache status among male patients from number of positive foods based on the 95 th percentile.
• test kits and methods are now presented with substantially higher predictive power in the choice of food items that could be eliminated for reduction of migraine headache signs and symptoms.
• Elimination of other one or more food items may be useful in at least reducing incidence and/or severity of the symptoms.
• migraine headaches are often quite diverse with respect to dietary items triggering symptoms, and no standardized test to help identify trigger food items with a reasonable degree of certainty is known, leaving such patients often to trial-and-error.
• the numbers expressing quantities or ranges, used to describe and claim certain embodiments of the disclosure are to be understood as being modified in some instances by the term "about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the disclosure are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable.
• test kit or test panel that is suitable for testing food intolerance in a patient that is diagnosed with or suspected to have migraine headaches.
• a test kit or panel will include one or more distinct food preparations (e.g., raw or processed extract, which may include an aqueous extract with optional co-solvent, which may or may not be filtered) that are coupled to (e.g., immobilized on) individually addressable respective solid carriers (e.g., in a form of an array or a micro well plate), wherein each distinct food preparation has an average discriminatory p-value of ⁇ 0.07 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.10 as determined by FDR multiplicity adjusted p-value.
• distinct food preparations e.g., raw or processed extract, which may include an aqueous extract with optional co-solvent, which may or may not be filtered
• respective solid carriers e.g., in a form of an array or a micro well plate
• the average discriminatory p-value is determined by comparing assay values of a first patient test cohort that is diagnosed with or suspected of having migraine headaches, with assay values of a second patient test cohort that is not diagnosed with or suspected of having migraine headaches.
• the assay values can be determined by conducting assays for the first and second patient test cohorts with the distinct food preparation.
• the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the disclosure are to be understood as being modified in some instances by the term "about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some
• the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that, the numerical ranges and parameters setting forth the broad scope of some embodiments of the disclosure are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some
• exemplary food preparations include at least two, at least four, at least eight, or at least 12 food preparations prepared from foods 1-52 listed in Table 2.
• the exemplary food preparations can include at least two of cucumber, tomato, malt, cauliflower, broccoli, peach, cantaloupe, orange, egg, tea, cabbage, green pepper, safflower, grapefruit, swiss cheese, chocolate, wheat, cow milk, rye, baker's yeast, oat, honey, almond, sweet potato, onion, lemon, cheddar cheese, and butter. Still further especially contemplated food items and food additives from which food preparations can be prepared are listed in Table 1.
• the methods described herein comprise the one of one or more distinct food preparations having an average discriminatory p-value, wherein the average
• discriminatory p-value for each distinct food preparation is determined by a process that includes comparing test results of a first patient test cohort that is diagnosed with or suspected of having migraine headaches, with test results of a second patient test cohort that is not diagnosed with or suspected of having migraine headaches.
• test results e.g., ELISA
• test results for the first and second patient test cohorts are obtained for various distinct food preparations, wherein the test results are based on contacting bodily fluids (e.g., blood saliva, fecal suspension) of the first patient test cohort and the second patient test cohort with each food preparation.
• such identified food preparations will have high
• discriminatory power will have a p-value of ⁇ 0.15, ⁇ 0.10, or even ⁇ 0.05 as determined by raw p-value, and/or a p-value of ⁇ 0.10, ⁇ 0.08, or even ⁇ 0.07 as determined by False Discovery Rate (FDR) multiplicity adjusted p-value.
• FDR False Discovery Rate
• each distinct food preparations will have an average discriminatory p-value of ⁇ 0.05 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.08 as determined by FDR multiplicity adjusted p-value, or even an average discriminatory p-value of ⁇ 0.025 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.07 as determined by FDR multiplicity adjusted p-value.
• the FDR multiplicity adjusted p-value may be adjusted for at least one of age or gender, and in certain embodiments adjusted for both age and gender.
• test kit or panel is stratified for use with a single gender
• at least 50% (or 70% or all) of the plurality of distinct food preparations when adjusted for a single gender, have an average discriminatory p-value of ⁇ 0.07 as determined by raw p-value or an average discriminatory p-value of ⁇ 0.10 as determined by FDR multiplicity adjusted p- value.
• stratifications e.g., dietary preference, ethnicity, place of residence, genetic predisposition or family history, etc.
• stratifications e.g., dietary preference, ethnicity, place of residence, genetic predisposition or family history, etc.
• the solid carrier to which the food preparations are coupled may include wells of a multiwell plate, a bead (e.g., color-coded or magnetic, etc.), an adsorptive film (e.g., nitrocellulose or micro/nanoporous polymeric film, etc.), or an electrical sensor (e.g. a printed copper sensor or microchip, etc.).
• a bead e.g., color-coded or magnetic, etc.
• an adsorptive film e.g., nitrocellulose or micro/nanoporous polymeric film, etc.
• an electrical sensor e.g. a printed copper sensor or microchip, etc.
• the inventors also contemplate a method of testing food intolerance in patients that are diagnosed with or suspected to have migraine headaches. Most typically, such methods will include a step of contacting a food preparation with a bodily fluid (e.g., whole blood, plasma, serum, saliva, or a fecal suspension, etc.) of a patient that is diagnosed with or suspected to have migraine headaches, and wherein the bodily fluid is associated with a gender identification.
• a bodily fluid e.g., whole blood, plasma, serum, saliva, or a fecal suspension, etc.
• the step of contacting can be performed under conditions that allow an immunoglobulin such as IgG (or IgE or IgA or IgM) from the bodily fluid to bind to at least one component of the food preparation, and the IgG bound to the component(s) of the food preparation are then quantified/measured to obtain a signal.
• the signal is then compared against a gender-stratified reference value (e.g., at least a 90th percentile value, etc.) for the food preparation using the gender identification to obtain a result, which is then used to update or generate a report (e.g., written medical report, oral report of results from doctor to patient, written or oral directive from physician based on results, etc.).
• a gender-stratified reference value e.g., at least a 90th percentile value, etc.
• such methods will not be limited to a single food preparation, but will employ multiple different food preparations.
• suitable food preparations can be identified using various methods as described below; however, certain food preparations may include foods 1-52 listed in Table 2, and/or items of Table 1.
• at least some, or all of the different food preparations have an average discriminatory p-value of ⁇ 0.07 (or ⁇ 0.05, or ⁇ 0.025) as determined by raw p-value, and/or or an average discriminatory p-value of ⁇ 0.10 (or ⁇ 0.08, or ⁇ 0.07) as determined by FDR multiplicity adjusted p-value.
• food preparations are prepared from single food items as crude extracts, or crude filtered extracts
• food preparations can be prepared from mixtures of a plurality of food items (e.g., a mixture of citrus comprising lemon, orange, and a grapefruit, a mixture of yeast comprising baker's yeast and brewer's yeast, a mixture of rice comprising a brown rice and white rice, a mixture of sugars comprising honey, malt, and cane sugar.
• a plurality of food items e.g., a mixture of citrus comprising lemon, orange, and a grapefruit, a mixture of yeast comprising baker's yeast and brewer's yeast, a mixture of rice comprising a brown rice and white rice, a mixture of sugars comprising honey, malt, and cane sugar.
• food preparations can be prepared from purified food antigens or recombinant food antigens.
• Each food preparation is immobilized on a solid surface (typically in an addressable manner, such that each food preparation is isolated), it is contemplated that the step of measuring the IgG or other type of antibody bound to the component of the food preparation is performed via an ELISA (enzyme-linked immunosorbent assay) test.
• exemplary solid surfaces include, but are not limited to, wells in a multiwell plate, such that each food preparation may be isolated to a separate microwell.
• the food preparation will be coupled to, or immobilized on, the solid surface.
• the food preparation(s) will be coupled to a molecular tag that allows for binding to human immunoglobulins (e.g., IgG, etc.) in solution.
• the inventors also contemplate a method of generating a test for food intolerance in patients diagnosed with or suspected to have migraine headaches. Such a test is applied to patients already diagnosed with or suspected to have migraine headaches, in certain embodiments, the authors do not contemplate that the method has a diagnostic purpose. Instead, the method is for identifying triggering food items among already diagnosed or suspected migraine headache patients.
• test kits that can be used for this method may comprise one or more distinct food preparations having an average discriminatory p-value, wherein the average discriminatory p-value for each distinct food preparation is determined by a process that includes comparing test results of a first patient test cohort that is diagnosed with or suspected of having migraine headaches, with test results of a second patient test cohort that is not diagnosed with or suspected of having migraine headaches.
• test results for the first and second patient test cohorts are obtained for various distinct food preparations, wherein the test results are based on contacting bodily fluids (e.g., blood saliva, fecal suspension, etc.) of the first patient test cohort and the second patient test cohort with each food preparation.
• bodily fluids e.g., blood saliva, fecal suspension, etc.
• the test results are then stratified by gender for each of the distinct food preparations, a different cutoff value for male and female patients for each of the distinct food preparations (e.g., cutoff value for male and female patients has a difference of at least 10% (abs) , etc.) is assigned for a predetermined percentile rank (e.g., 90th or 95th percentile).
• the distinct food preparations include at least two (or six, or ten, or fifteen) food preparations prepared from food items selected from the group consisting of foods 1-52 listed in Table 2, and/or items of Table 1.
• the distinct food preparations include a food preparation prepared from a food items other than foods 1 -52 listed in Table 2.
• each distinct food preparation will have an average discriminatory p-value of ⁇ 0.07 (or ⁇ 0.05, or ⁇ 0.025) as determined by raw p-value or an average discriminatory p- value of ⁇ 0.10 (or ⁇ 0.08, or ⁇ 0.07) as determined by FDR multiplicity adjusted p-value.
• ⁇ 0.07 or ⁇ 0.05, or ⁇ 0.025
• ⁇ 0.10 or ⁇ 0.08, or ⁇ 0.07
• a three-step procedure of generating food extracts may provide more accurate results.
• the first step is a defatting step.
• lipids from grains and nuts are extracted by contacting the flour of grains and nuts with a non-polar solvent and collecting residue.
• the defatted grain or nut flour are extracted by contacting the flour with elevated pH to obtain a mixture and removing the solid from the mixture to obtain the liquid extract.
• the liquid extract is stabilized by adding an aqueous formulation.
• the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH. This formulation allowed for long term storage at -70 °C and multiple freeze-thaws without a loss of activity.
• a two-step procedure of generating food extract may provide more accurate results.
• the first step is an extraction step.
• extracts from raw, uncooked meats or fish are generated by emulsifying the raw, uncooked meats or fish in an aqueous buffer formulation in a high impact pressure processor.
• solid materials are removed to obtain liquid extract.
• the liquid extract is stabilized by adding an aqueous formulation.
• the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH.
• a two-step procedure of generating food extract is may provide more accurate results.
• the first step is an extraction step.
• liquid extracts from fruits or vegetables are generated using an extractor (e.g., masticating juicer, etc) to pulverize foods and extract juice.
• solid materials are removed to obtain liquid extract.
• the liquid extract is stabilized by adding an aqueous formulation.
• the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component 20-50 mM of buffer from 4-9 pH. This formulation allowed for long term storage at -70 °C and multiple freeze-thaws without a loss of activity.
• the blocking buffer includes 20-50 mM of buffer from 4-9 pH, a protein of animal origin (e.g., beef, chicken, etc.) and a short chain alcohol (e.g., glycerin, etc.).
• a protein of animal origin e.g., beef, chicken, etc.
• a short chain alcohol e.g., glycerin, etc.
• Other blocking buffers including several commercial preparations, can be attempted but may not provide adequate signal to noise and low assay variability required.
• ELISA preparation and sample testing Food antigen preparations were immobilized onto respective microtiter wells following the manufacturer's instructions.
• the food antigens were allowed to react with antibodies present in the patients' serum, and excess serum proteins were removed by a wash step.
• enzyme labeled anti-IgG antibody conjugate was allowed to react with antigen-antibody complex.
• a color was developed by the addition of a substrate that reacts with the coupled enzyme. The color intensity was measured and is directly proportional to the concentration of IgG antibody specific to a particular food antigen.
• Methodology to determine ranked food list in order of ability of ELISA signals to distinguish migraine headaches from control subjects Out of an initial selection (e.g., 100 food items, or 150 food items, or even more), samples can be eliminated prior to analysis due to low consumption in an intended population.
• specific food items can be used as being representative of a larger more generic food group, especially where prior testing has established a correlation among different species within a generic group (with respect to both genders, or correlation with a single gender). For example, Swiss cheese could be dropped in favor of cheddar cheese as representative of the "cheese" food group.
• the final list foods will be shorter than 50 food items, or equal or less than of 40 food items.
• a gender-neutral food list is necessary. Since the observed sample will be at least initially imbalanced by gender (e.g. , Controls: 50% female, migraine headaches: 87% female), differences in ELISA signal magnitude strictly due to gender will be removed by modeling signal scores against gender using a two-sample t-test and storing the residuals for further analysis. For each of the tested foods, residual signal scores will be compared between migraine headache and controls using a permutation test on a two-sample t-test with a relative high number of resamplings (e.g., >1,000, or >10,000, or even >50,000).
• the Satterthwaite approximation can then be used for the denominator degrees of freedom to account for lack of homogeneity of variances, and the 2- tailed permuted p-value will represent the raw p-value for each food.
• False Discovery Rates (FDR) among the comparisons will be adjusted by any acceptable statistical procedures (e.g., Benjamini-Hochberg, Family-wise Error Rate (FWER), Per Comparison Error Rate (PCER), etc.).
• FDR False Discovery Rates
• Foods were then ranked according to their 2-tailed FDR multiplicity-adjusted p- values.
• Foods with adjusted p-values equal to or lower than the desired FDR threshold are deemed to have significantly higher signal scores among migraine headaches than control subjects and therefore deemed candidates for inclusion into a food intolerance panel.
• a typical result that is representative of the outcome of the statistical procedure is provided in Table 2.
• the ranking of foods is according to 2-tailed permutation T-test p-values with FDR adjustment.
• the final 90th and 95th percentile-based cutpoints for each food and gender will be computed as the average 90th and 95th percentiles across the 1000 samples.
• the number of foods for which each migraine headache subject will be rated as "positive” was computed by pooling data across foods. Using such method, the inventors will be now able to identify cutoff values for a predetermined percentile rank that in most cases was substantially different as can be taken from Table 4.
• Figures 1A-1D Typical examples for the gender difference in IgG response in blood with respect to cucumber is shown in Figures 1A-1D, where Figure 1A shows the signal distribution in men along with the 95 th percentile cutoff as determined from the male control population.
• Figure IB shows the distribution of percentage of male migraine headache subjects exceeding the 90 th and 95 th percentile
• Figure 1C shows the signal distribution in women along with the 95 th percentile cutoff as determined from the female control population.
• Figure ID shows the distribution of percentage of female migraine headache subjects exceeding the 90 th and 95 th percentile.
• Figures 2A-2D exemplarily depict the differential response to tomato
• Figures 3A-3D exemplarily depict the differential response to malt
• Figures 4A-4D exemplarily depict the differential response to cauliflower.
• Figures 5A-5B show the distribution of migraine headache subjects by number of foods that were identified as trigger foods at the 90 th percentile (5 A) and 95 th percentile (5B). Inventors contemplate that regardless of the particular food items, male and female responses were notably distinct.
• one or more of a patient's food specific IgG results can be normalized to the patient's total IgG.
• the normalized value of the patient's IgG specific to cucumber can be 0.1 and the normalized value of the patient's IgG specific to tomato can be 0.3.
• the relative strength of the patient's response to tomato is three times higher compared to cucumber. Then, the patient's sensitivity to grapefruit and malt can be indexed as such.
• one or more of a patient's food specific IgG results can be normalized to the global mean of that patient's food specific IgG results.
• the global means of the patient's food specific IgG can be measured by total amount of the patient's food specific IgG.
• the patient's specific IgG to shrimp can be normalized to the mean of patient's total food specific IgG
• the global means of the patient's food specific IgG can be measured by the patient's IgG levels to a specific type of food via multiple tests. If the patient has been tested for his sensitivity to shrimp five times and to pork seven times previously, the patient's new IgG values to shrimp or to pork are normalized to the mean of five-times test results to shrimp or the mean of seven-times test results to pork.
• the normalized value of the patient's IgG specific to shrimp can be 6.0 and the normalized value of the patient's IgG specific to pork can be 1.0. In this scenario, the patient has six times higher sensitivity to shrimp at this time compared to his average sensitivity to shrimp, but substantially similar sensitivity to pork. Then, the patient's sensitivity to shrimp and pork can be indexed based on such comparison.
• Average and median number of positive foods was computed for migraine headache and non- migraine headache patients. From the raw data shown in Table 5 A and Table 5B, average and standard deviation of the number of positive foods was computed for migraine headache and non-migraine headache patients. Additionally, the number and percentage of patients with zero positive foods was calculated for both migraine headache and non-migraine headache. The number and percentage of patients with zero positive foods in the migraine population is almost half of the percentage of patients with zero positive foods in the non-migraine population (1 1.3% vs. 20.4%, respectively) based on 90 th percentile value (Table 5A), and the percentage of patients in the migraine population with zero positive foods is also less than half of that seen in the non-migraine headache population (17.9% vs.
• Table 6A and Table 7A show exemplary statistical data summarizing the raw data of two patient populations shown in Table 5A.
• the statistical data includes normality, arithmetic mean, median, percentiles and 95% confidence interval (CI) for the mean and median representing number of positive foods in the migraine headache population and the non-migraine headache population.
• Table 6B and Table 7B show exemplary statistical data summarizing the raw data of two patient populations shown in Table 5B.
• the statistical data includes normality, arithmetic mean, median, percentiles and 95% confidence interval (CI) for the mean and median representing number of positive foods in the migraine headache population and the non-migraine headache population.
• Table 8A and Table 9A show exemplary statistical data summarizing the raw data of two patient populations shown in Table 5A.
• the raw data was transformed by logarithmic transformation to improve the data interpretation.
• Table 8B and Table 9B show another exemplary statistical data summarizing the raw data of two patient populations shown in Table 5B.
• the raw data was transformed by logarithmic transformation to improve the data interpretation.
• Table 10A and Table 11A show exemplary statistical data of an independent T- test (Table 10A, logarithmically transformed data) and a Mann-Whitney test (Table 1 1 A) to compare the geometric mean number of positive foods between the migraine headache and non- migraine headache samples.
• Table 10A and Table 1 1A indicate statistically significant differences in the geometric mean of positive number of foods between the migraine headache population and the non-migraine headache population. In both statistical tests, it is shown that the number of positive responses with 90 food samples is significantly higher in the migraine headache population than in the non-migraine headache population with an average discriminatory p-value of ⁇ 0.0001.
• These statistical data is also illustrated as a box and whisker plot in Figure 6A, and a notched box and whisker plot in Figure 6B.
• Table 10B and Table 11B show exemplary statistical data of an independent T- test (Table 10A, logarithmically transformed data) and a Mann-Whitney test (Table 1 IB) to compare the geometric mean number of positive foods between the migraine headache and non-migraine headache samples.
• Table 10B and Table 11B indicate statistically significant differences in the geometric mean of positive number of foods between the migraine headache population and the non-migraine headache population. In both statistical tests, it is shown that the number of positive responses with 90 food samples is significantly higher in the migraine headache population than in the non-migraine headache population with an average discriminatory p-value of ⁇ 0.0001.
• Table 12A shows exemplary statistical data of a Receiver Operating
• ROC Characteristic
• the above test can be used as another 'rule in' test to add to currently available clinical criteria for diagnosis for migraine headache.
• the number of positive foods seen in migraine headache vs. non-migraine headache subjects is significantly different whether the geometric mean or median of the data is compared.
• the number of positive foods that a person has is indicative of the presence of migraine headaches in subjects.
• the test has discriminatory power to detect migraine headache with -46% sensitivity and -78% specificity.
• the absolute number and percentage of subjects with 0 positive foods is also very different in migraine headache vs. non-migraine headache subjects, with a far lower percentage of migraine headache subjects (1 1%) having 0 positive foods than non-migraine headache subj ects (20%).
• the data suggests a subset of migraine headache patients may have migraine headaches due to other factors than diet, and may not benefit from dietary restriction.
• Table 12B shows exemplary statistical data of a Receiver Operating
• ROC Characteristic
• the above test can be used as another 'rule in' test to add to currently available clinical criteria for diagnosis for migraine headaches.
• Tables 5B-12B, and Figure 7B based on 95 percentile data, the number of positive foods seen in migraine headache vs. non-migraine headache subjects is significantly different whether the geometric mean or median of the data is compared.
• the number of positive foods that a person has is indicative of the presence of migraine headaches in subjects.
• the test has discriminatory power to detect migraine headaches with -70% sensitivity and -60% specificity. Additionally, the absolute number and percentage of subjects with 0 positive foods is also very different in migraine headache vs. non-migraine headache subjects, with a far lower percentage of migraine headache subjects (18%) having 0 positive foods than non- migraine headache subjects (39%).
• the data suggests a subset of migraine headache patients may have migraine headache due to other factors than diet, and may not benefit from dietary restriction.
• Method for determining distribution of per-person number of foods declared "positive” To determine the distribution of number of "positive” foods per person and measure the diagnostic performance, the analysis was performed with 90 food items from the Table 1 , which shows most positive responses to migraine headache patients.
• the 90 food items includes chocolate, grapefruit, honey, malt, rye, baker's yeast, brewer's yeast, broccoli, cola nut, tobacco, mustard, green pepper, buck wheat, avocado, cane sugar, cantaloupe, garlic, cucumber, cauliflower, sunflower seed, lemon, strawberry, eggplant, wheat, olive, halibut, cabbage, orange, rice, safflower, tomato, almond, oat, barley, peach, grape, potato, spinach, sole, and butter.
• each food-specific and gender-specific dataset was bootstrap resampled 1000 times. Then, for each food item in the bootstrap sample, sex-specific cutpoint was determined using the 90th and 95th percentiles of the control population. Once the sex-specific cutpoints were determined, the sex-specific cutpoints was compared with the observed ELISA signal scores for both control and migraine headache subjects. In this comparison, if the observed signal is equal or more than the cutpoint value, then it is determined "positive" food, and if the observed signal is less than the cutpoint value, then it is determined "negative" food.

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