Patent Description:
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

Food sensitivity, especially as it relates to Depression (a type of mental disorder), often presents with a pervasive and persistent low mood that is accompanied by low self-esteem and by a loss of interest or pleasure in normally enjoyable activities, and underlying causes of Depression are not well understood in the medical community. Most typically, Depression is diagnosed by a mental state examination, which is an assessment of the person's current mood and thought content. Unfortunately, treatment of Depression is often less than effective and may present new difficulties due to neurochemical modulatory effects. Elimination of one or more food items has also shown promise in at least reducing incidence and/or severity of the symptoms. However, Depression is 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.

While there are some commercially available tests and labs to help identify trigger foods, the quality of the test results from these labs is generally poor as is reported by a consumer advocacy group (e.g., http://www. uk/news/<NUM>/<NUM>/food-allergy-tests-could-risk-your-health-<NUM>/). Most notably, problems associated with these tests and labs were high false positive rates, high false negative rates, high intra-patient variability, and inter-laboratory variability, rendering such tests nearly useless. Similarly, further inconclusive and highly variable test results were also reported elsewhere (<NPL>), and the authors concluded that this may be due to food reactions and food sensitivities occurring via a number of different mechanisms. For example, not all Depression patients show positive response to food A, and not all Depression patients show negative response to food B. Thus, even if a Depression patient shows positive response to food A, removal of food A from the patient's diet may not relieve the patient's Depression symptoms. In other words, it is not well determined whether food samples used in the currently available tests are properly selected based on the high probabilities to correlate sensitivities to those food samples to Depression.

In <CIT>, there is disclosed, according to its abstract, "a diet-typing system for humans, including novel methods for diagnosis and treatment of food allergies and hypersensitivities".

In a publication in the Australian & New Zealand Journal of Psychiatry titled "Treatment-Resistant Depression: When Antidepressant Drug Intolerance May Indicate Food Intolerance", there is disclosed, according to its abstract, a description of "a patient with food intolerance probably contributing to depressive symptoms, intolerance to psychotropic medication and treatment resistance".

In a publication in The New Zealand Medical Journal titled "Depressed mood associated with gluten sensitivity- resolution of symptoms with a gluten-free diet", there is disclosed a suggestion that certain mood disorders may be associated with gluten sensitivity.

In a publication in European Psychiatry dated March <NUM>, there is disclosed "The role of IgG hypersensitivity and changes in gut microbiota in the pathogenesis and therapy of depressive disorders".

<CIT> discloses compositions, devices, and methods of irritable bowel syndrome sensitivity testing.

In <CIT>, there is disclosed, according to its abstract, "a substrate to which is applied, coupled or cross-linked to at least one part of said substrate an allergen wherein said allergen is a protein derived from an extract selected from at least one of the following food groups: cereal, legume, cocoa bean, nut, fruit, vegetable, shellfish, fish, yeast, dairy product, egg and meat".

Thus, even though various tests for food sensitivities are known in the art, all or almost all of them suffer from one or more disadvantages. Therefore, there is still a need for improved compositions, devices, and methods of food sensitivity testing, especially for identification and possible elimination of trigger foods for patients identified with or suspected of having Depression.

The present invention provides a test panel for testing food intolerance in patients diagnosed with or suspected to have Depression, comprising:
a plurality of distinct food preparations, wherein each distinct food preparation is independently coupled to an individually addressable solid carrier;
wherein the plurality of distinct food preparations consists of almond, tomato, tobacco, carrot, orange, cucumber, broccoli, lettuce, malt, cantaloupe, corn, wheat, honey, chocolate, oat, avocado, rye, strawberry, cauliflower, safflower, tea, banana, squashes, green pepper, butter, buck wheat, rice, soybean, grapefruit, oyster, brewer's yeast, peach, cane sugar, cow's milk, and spinach.

The plurality of distinct food preparations have an average discriminatory p-value of ≤ <NUM> as determined by raw p-value. In some embodiments, the average discriminatory p-value is determined by a process, which includes comparing assay values of a first patient test cohort that is diagnosed with or suspected of having Depression with assay values of a second patient test cohort that is not diagnosed with or suspected of having Depression.

The distinct food preparations may be crude filtered aqueous extracts or processed aqueous extracts.

The solid carrier may be a well of a multiwell plate, a bead, an electrical sensor, a chemical sensor, a microchip or an adsorptive film.

The present invention also provides an in vitro method of testing food intolerance in patients diagnosed with or suspected to have Depression using a food preparation of a plurality of distinct food preparations, comprising:.

The plurality of distinct food preparations have an average discriminatory p-value of ≤ <NUM> as determined by raw p-value.

The bodily fluid of the patient may be whole blood, plasma, serum, saliva, or a fecal suspension.

The gender-stratified reference value for each of the food preparations may be the <NUM>th percentile rank, or higher, of signals obtained by contacting bodily fluid from a control group of subjects that is not diagnosed with or suspected of having Depression with the food preparation.

Various objects, features, aspects and advantages of the embodiments described herein will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.

The inventors have discovered that food preparations used in food tests to identify trigger foods in patients diagnosed with or suspected to have Depression are not equally well predictive and/or associated with Depression/Depression symptoms. Indeed, various experiments have revealed that among a wide variety of food items certain food items are highly predictive/associated with Depression whereas others have no statistically significant association with Depression.

Even more unexpectedly, the inventors discovered that in addition to the high variability of food items, gender variability with respect to response in a test plays a substantial role in the determination of association or a food item with Depression. Consequently, based on the inventors' findings and further contemplations, 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 Depression signs and symptoms.

The following discussion provides many example embodiments of the inventive subject matter. Although each embodiment represents a single combination of inventive elements, the inventive subject matter is considered to include all possible combinations of the disclosed elements. Thus if one embodiment comprises elements A, B, and C, and a second embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

In some embodiments, the numbers expressing quantities or ranges, used to describe and claim certain embodiments of the invention 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 invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.

As used in the description herein and throughout the claims that follow, the meaning of "a," "an," and "the" includes plural reference unless the context clearly dictates otherwise Also, as used in the description herein, the meaning of "in" includes "in" and "on" unless the context clearly dictates otherwise.

The use of any and all examples, or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention 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 invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements. Moreover, and unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.

While not limiting to the inventive subject matter, food preparations will typically be drawn from foods generally known or suspected to trigger signs or symptoms of Depression. Particularly suitable food preparations may be identified by the experimental procedures outlined below.

The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. The use of any and all examples, or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

Of course, it should be noted that the particular format of the test kit or panel may vary considerably and contemplated formats include micro well plates, dip sticks, membrane-bound arrays, etc. Consequently, the solid carrier to which the food preparations are coupled may include wells of a multiwell plate, a (e.g., color-coded or magnetic) bead, or an adsorptive film (e.g., nitrocellulose or micro/nanoporous polymeric film), or an electrical sensor, (e.g., a printed copper sensor or microchip).

Consequently, the inventors also contemplate a method of testing food intolerance in patients that are diagnosed with or suspected to have Depression.

While in certain embodiments food preparations are prepared from single food items as crude extracts, or crude filtered extracts, it is contemplated that 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). In some embodiments, it is also contemplated that food preparations can be prepared from purified food antigens or recombinant food antigens.

As the food preparation is immobilized on a solid surface in an addressable manner), it is contemplated that the step of measuring the IgG bound to the component of the food preparation is performed via an ELISA 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. In certain embodiments, the food preparation will be coupled to, or immobilized on, the solid surface.

Thus, it should be appreciated that by having a high-confidence test system as described herein, the rate of false-positive and false negatives can be significantly reduced, and especially where the test systems and methods are gender stratified or adjusted for gender differences as shown below. Such advantages have heretofore not been realized and it is expected that the systems and methods presented herein will substantially increase the predictive power of food sensitivity tests for patients diagnosed with or suspected to have Depression.

General Protocol for food preparation generation: Commercially available food extracts (available from Biomerica Inc. , <NUM> Von Karman Ave, Irvine, CA <NUM>) prepared from the edible portion of the respective raw foods were used to prepare ELISA plates following the manufacturer's instructions.

For some food extracts, the inventors expect that food extracts prepared with specific procedures to generate food extracts provides more superior results in detecting elevated IgG reactivity in Depression patients compared to commercially available food extracts. For example, for grains and nuts, a three-step procedure of generating food extracts is preferred. The first step is a defatting step. In this step, lipids from grains and nuts are extracted by contacting the flour of grains and nuts with a non-polar solvent and collecting residue. Then, 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. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component <NUM>-<NUM> of buffer from <NUM>-<NUM> pH. This formulation allowed for long term storage at -<NUM> and multiple freeze-thaws without a loss of activity.

For another example, for meats and fish, a two step procedure of generating food extract is preferred. The first step is an extraction step. In this 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. Then, solid materials are removed to obtain liquid extract. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component <NUM>-<NUM> of buffer from <NUM>-<NUM> pH. This formulation allowed for long term storage at -<NUM> and multiple freeze-thaws without a loss of activity.

For still another example, for fruits and vegetables, a two step procedure of generating food extract is preferred. The first step is an extraction step. In this step, liquid extracts from fruits or vegetables are generated using an extractor (e.g., masticating juicer, etc) to pulverize foods and extract juice. Then, solid materials are removed to obtain liquid extract. Once the liquid extract is generated, the liquid extract is stabilized by adding an aqueous formulation. In a preferred embodiment, the aqueous formulation includes a sugar alcohol, a metal chelating agent, protease inhibitor, mineral salt, and buffer component <NUM>-<NUM> of buffer from <NUM>-<NUM> pH. This formulation allowed for long term storage at -<NUM> and multiple freeze-thaws without a loss of activity.

Blocking of ELISA plates: To optimize signal to noise, plates will be blocked with a proprietary blocking buffer. In a preferred embodiment, the blocking buffer includes <NUM>-<NUM> of buffer from <NUM>-<NUM> pH, a protein of animal origin and a short chain alcohol. 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. For the assays, the food antigens were allowed to react with antibodies present in the patients' serum, and excess serum proteins were removed by a wash step. For detection of IgG antibody binding, 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 Depression from control subjects: Out of an initial selection (e.g., <NUM> food items, or <NUM> food items, or even more), samples can be eliminated prior to analysis due to low consumption in an intended population. In addition, specific food items can be used as being representative of the a larger more generic food group, especially where prior testing has established a correlation among different species within a generic group (most preferably in both genders, but also suitable for correlation for a single gender). For example, green pepper could be dropped in favor of chili pepper as representative of the "pepper" food group, or sweet potato could be dropped in favor of potato as representative of the "potato" food group. In further preferred aspects, the final list foods will be shorter than <NUM> food items, and more preferably equal or less than of <NUM> food items.

Since the foods ultimately selected for the food intolerance panel will not be specific for a particular gender, a gender-neutral food list is necessary. Since the observed sample will be at least initially imbalanced by gender (e.g., Controls: <NUM>% female, Depression: <NUM>% 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 Depression and controls using a permutation test on a two-sample t-test with a relative high number of resamplings (e.g., ><NUM>,<NUM>, more preferably ><NUM>,<NUM>, even more preferably ><NUM>,<NUM>). The Satterthwaite approximation can then be used for the denominator degrees of freedom to account for lack of homogeneity of variances, and the <NUM>-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.).

Foods were then ranked according to their <NUM>-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 Depression 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 <NUM>. Here the ranking of foods is according to <NUM>-tailed permutation T-test p-values with FDR adjustment.

Based on earlier experiments (data not shown here, see <CIT>), the inventors contemplate that even for the same food preparation tested, the ELISA score for at least several food items will vary dramatically, and exemplary raw data are provided in Table <NUM>. As should be readily appreciated, data unstratified by gender will therefore lose significant explanatory power where the same cutoff value is applied to raw data for male and female data. To overcome such disadvantage, the inventors therefore contemplate stratification of the data by gender as described below.

Statistical Method for Cutpoint Selection for each Food: The determination of what ELISA signal scores would constitute a "positive" response can be made by summarizing the distribution of signal scores among the Control subjects. For each food, Depression subjects who have observed scores greater than or equal to selected quantiles of the Control subject distribution will be deemed "positive". To attenuate the influence of any one subject on cutpoint determination, each food-specific and gender-specific dataset will be bootstrap resampled <NUM> times. Within each bootstrap replicate, the 90th and 95th percentiles of the Control signal scores will be determined. Each Depression subject in the bootstrap sample will be compared to the 90th and <NUM>% percentiles to determine whether he/she had a "positive" response. 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 <NUM> samples. The number of foods for which each Depression 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 <NUM>.

Typical examples for the gender difference in IgG response in blood with respect to almond is shown in <FIG>, where <FIG> shows the signal distribution in men along with the <NUM>th percentile cutoff as determined from the male control population. <FIG> shows the distribution of percentage of male Depression subjects exceeding the <NUM>th and <NUM>th percentile, while <FIG> shows the signal distribution in women along with the <NUM>th percentile cutoff as determined from the female control population <FIG> shows the distribution of percentage of female Depression subjects exceeding the <NUM>th and <NUM>th percentile. In the same fashion, <FIG> exemplarily depict the differential response to tomato, <FIG> exemplarily depict the differential response to tobacco, and <FIG> exemplarily depict the differential response to carrot. <FIG> show the distribution of Depression subjects by number of foods that were identified as trigger foods at the <NUM>th percentile (SA) and <NUM>th percentile (5B). Inventors contemplate that regardless of the particular food items, male and female responses will be notably distinct.

It should be noted that nothing in the art have provided any predictable food groups related to Depression that is gender-stratified. Thus, a discovery of food items that show distinct responses by gender is a surprising result, which could not be obviously expected in view of all previously available arts. In other words, selection of food items based on gender stratification provides an unexpected technical effect such that statistical significances for particular food items as triggering food among male or female Depression patients have been significantly improved.

Normalization of IgG Response Data: While the raw data of the patient's IgG response results can be used to compare strength of response among given foods, it is also contemplated that the IgG response results of a patient are normalized and indexed to generate unit-less numbers for comparison of relative strength of response to a given food. For example, one or more of a patient's food specific IgG results (e.g., IgG specific to orange and IgG specific to malt) can be normalized to the patient's total IgG. The normalized value of the patient's IgG specific to orange can be <NUM> and the normalized value of the patient's IgG specific to malt can be <NUM>. In this scenario, the relative strength of the patient's response to malt is three times higher compared to orange. Then, the patient's sensitivity to malt and orange can be indexed as such.

In other examples, one or more of a patient's food specific IgG results (e.g., IgG specific to shrimp and IgG specific to pork) 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. In this scenario, the patient's specific IgG to shrimp can be normalized to the mean of patient's total food specific IgG (e.g., mean of IgG levels to shrimp, pork, Dungeness crab, chicken, peas, etc.). However, it is also contemplated that 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 <NUM> and the normalized value of the patient's IgG specific to pork can be <NUM>. 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.

Methodology to determine the subset of Depression patients with food sensitivities that underlie Depression: While it is suspected that food sensitivities plays a substantial role in signs and symptoms of Depression, some Depression patients may not have food sensitivities that underlie Depression Those patients would not be benefit from dietary intervention to treat signs and symptoms of Depression. To determine the subset of such patients, body fluid samples of Depression patients and non- Depression patients can be tested with ELISA test using test devices with up to <NUM> food samples.

Table 5A and Table 5B provide exemplary raw data. As should be readily appreciated, the data indicate number of positive results out of <NUM> sample foods based on <NUM>th percentile value (Table <NUM>A) or <NUM>th percentile value (Table 5B). The first column is Depression (n=<NUM>); second column is non-Depression (n=<NUM>) by ICD-<NUM> code. Average and median number of positive foods was computed for Depression and non-Depression patients. From the raw data shown in Table SA and Table 5B, average and standard deviation of the number of positive foods was computed for Depression and non-Depression patients. Additionally, the number and percentage of patients with zero positive foods was calculated for both Depression and non-Depression. The number and percentage of patients with zero positive foods in the Depression population is approximately <NUM>% lower than the percentage of patients with zero positive foods in the non-Depression population (<NUM>% vs. <NUM>%, respectively) based on <NUM>th percentile value (Table 5A), and the percentage of patients in the Depression population with zero positive foods is also significantly lower (i.e. approximately <NUM>% lower) than that seen in the non-Depression population (<NUM> % vs. <NUM>%, respectively) based on <NUM>th percentile value (Table 5B). Thus, it can be easily appreciated that the Depression patient having sensitivity to zero positive foods is unlikely to have food sensitivities underlying their signs and symptoms of Depression.

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 <NUM>% confidence interval (CI) for the mean and median representing number of positive foods in the Depression population and the non-Depression 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 <NUM>% confidence interval (CI) for the mean and median representing number of positive foods in the Depression population and the non-Depression population.

Table 8A and Table 9A show exemplary statistical data summarizing the raw data of two patient populations shown in Table 5A. In Tables 8A and 9A, 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. In Tables 8B and 9B, 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 11A) to compare the geometric mean number of positive foods between the Depression and non-Depression samples. The data shown in Table 10A and Table 11A indicate statistically significant differences in the geometric mean of positive number of foods between the Depression population and the non-Depression population. In both statistical tests, it is shown that the number of positive responses with <NUM> food samples is significantly higher in the Depression population than in the non-Depression population with an average discriminatory p-value of ≤ <NUM>. These statistical data is also illustrated as a box and whisker plot in <FIG>, and a notched box and whisker plot in <FIG>.

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 11B) to compare the geometric mean number of positive foods between the Depression and non-Depression samples. The data shown in Table 10B and Table 11B indicate statistically significant differences in the geometric mean of positive number of foods between the Depression population and the non-Depression population. In both statistical tests, it is shown that the number of positive responses with <NUM> food samples is significantly higher in the Depression population than in the non-Depression population with an average discriminatory p-value of ≤ <NUM>. These statistical data is also illustrated as a box and whisker plot in <FIG>, and a notched box and whisker plot in <FIG>.

Table 12A shows exemplary statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5A-11A to determine the diagnostic power of the test used in Table <NUM> at discriminating Depression from non- Depression subjects. When a cutoff criterion of more than <NUM> positive foods is used, the test yields a data with <NUM>% sensitivity and <NUM>% specificity, with an area under the curve (AUROC) of <NUM>. The p-value for the ROC is significant at a p-value of <<NUM>. <FIG> illustrates the ROC curve corresponding to the statistical data shown in Table 12A. Because the statistical difference between the Depression population and the non-Depression population is significant when the test results are cut off to a positive number of <NUM>, the number of foods for which a patient tests positive could be used as a confirmation of the primary clinical diagnosis of Depression, and whether it is likely that food sensitivities underlies on the patient's signs and symptoms of Depression Therefore, the above test can be used as another 'rule in' test to add to currently available clinical criteria for diagnosis for Depression.

As shown in Tables 5A-12A, and <FIG>, based on <NUM>th percentile data, the number of positive foods seen in Depression vs. non-Depression 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 Depression in subjects. The test has discriminatory power to detect Depression with <NUM>% sensitivity and <NUM>% specificity. Additionally, the absolute number and percentage of subjects with <NUM> positive foods is also very different in Depression vs. non-Depression subjects, with a far lower percentage of Depression subjects (<NUM>%) having <NUM> positive foods than non-Depression subjects (<NUM>%). The data suggests a subset of Depression patients may have Depression due to other factors than diet, and may not benefit from dietary restriction.

Table 12B shows exemplary statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5B-11B to determine the diagnostic power of the test used in Table <NUM> at discriminating Depression from non-Depression subjects. When a cutoff criterion of more than <NUM> positive foods is used, the test yields a data with <NUM>% sensitivity and <NUM>% specificity, with an area under the curve (AUROC) of <NUM>. The p-value for the ROC is significant at a p-value of <<NUM>. <FIG> illustrates the ROC curve corresponding to the statistical data shown in Table 12B. Because the statistical difference between the Depression population and the non-Depression population is significant when the test results are cut off to positive number of ><NUM>, the number of foods that a patient tests positive could be used as a confirmation of the primary clinical diagnosis of Depression, and whether it is likely that food sensitivities underlies on the patient's signs and symptoms of Depression. Therefore, the above test can be used as another 'rule in' test to add to currently available clinical criteria for diagnosis for Depression.

As shown in Tables 5B-12B, and <FIG>, based on <NUM>th percentile data, the number of positive foods seen in Depression vs. non-Depression 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 Depression in subjects. The test has discriminatory power to detect Depression with <NUM>% sensitivity and <NUM>% specificity. Additionally, the absolute number and percentage of subjects with <NUM> positive foods is also very different in Depression vs. non-Depression subjects, with a far lower percentage of Depression subjects (<NUM>%) having <NUM> positive foods than non- Depression subjects (<NUM>%). The data suggests a subset of Depression patients may have Depression 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 will be performed with <NUM> food items from Table <NUM>, which shows most positive responses to Depression patients. To attenuate the influence of any one subject on this analysis, each food-specific and gender-specific dataset will be bootstrap resampled <NUM> times. Then, for each food item in the bootstrap sample, sex-specific cutpoint will be determined using the 90th and 95th percentiles of the control population. Once the sex-specific cutpoints are determined, the sex-specific cutpoints will be compared with the observed ELISA signal scores for both control and Depression subjects In this comparison, if the observed signal is equal or more than the cutpoint value, then it will be determined "positive" food, and if the observed signal is less than the cutpoint value, then it will be determined "negative" food.

Once all food items were determined either positive or negative, the results of the <NUM> (<NUM> foods x <NUM> cutpoints) calls for each subject will be saved within each bootstrap replicate. Then, for each subject, <NUM> calls will be summed using <NUM>th percentile as cutpoint to get "Number of Positive Foods (<NUM>th)," and the rest of <NUM> calls will be summed using <NUM>th percentile to get "Number of Positive Foods (<NUM>th). " Then, within each replicate, "Number of Positive Foods (<NUM>th)" and "Number of Positive Foods (<NUM>th). " will be summarized across subjects to get descriptive statistics for each replicate as follows: <NUM>) overall means equals to the mean of means, <NUM>) overall standard deviation equals to the mean of standard deviations, <NUM>) overall medial equals to the mean of medians, <NUM>) overall minimum equals to the minimum of minimums, and <NUM>) overall maximum equals to maximum of maximum. In this analysis, to avoid non-integer "Number of Positive Foods" when computing frequency distribution and histogram, the authors will pretend that the <NUM> repetitions of the same original dataset were actually <NUM> sets of new subjects of the same size added to the original sample. Once the summarization of data is done, frequency distributions and histograms will be generated for both "Number of Positive Foods (<NUM>th)" and "Number of Positive Foods (<NUM>th). " for both genders and for both Depression subjects and control subjects using programs "a_pos_foods. sas, a_pos_foods_by_dx.

Method for measuring diagnostic performance: To measure diagnostic performance for each food items for each subject, we will use data of "Number of Positive Foods (<NUM>th)" and "Number of Positive Foods (<NUM>th). " for each subject within each bootstrap replicate described above. In this analysis, the cutpoint was set to <NUM>. Thus, if a subject has one or more "Number of Positive Foods (<NUM>th)", then the subject will be called "Has Depression. " If a subject has less than one "Number of Positive Foods (<NUM>th)", then the subject will be called "Does Not Have Depression. " When all calls were made, the calls were compared with actual diagnosis to determine whether a call was a True Positive (TP), True Negative (TN), False Positive (FP), or False Negative (FN). The comparisons will be summarized across subjects to get the performance metrics of sensitivity, specificity, positive predictive value, and negative predictive value for both "Number of Positive Foods (<NUM>th)" and "Number of Positive Foods(<NUM>th)" when the cutpoint is set to <NUM> for each method Each (sensitivity, <NUM>-specificity) pair becomes a point on the ROC curve for this replicate.

To increase the accuracy, the analysis above will be repeated by incrementing cutpoint from <NUM> up to <NUM>, and repeated for each of the <NUM> bootstrap replicates. Then the performance metrics across the <NUM> bootstrap replicates will be summarized by calculating averages using a program "t_pos_foods_by_dx. The results of diagnostic performance for female and male are shown in Tables 13A and 13B (90th percentile) and Tables <NUM> A and 14B (95th percentile).

Claim 1:
A test panel for testing food intolerance in patients diagnosed with or suspected to have Depression, comprising:
a plurality of distinct food preparations, wherein each distinct food preparation is independently coupled to an individually addressable solid carrier;
wherein the plurality of distinct food preparations consists of almond, tomato, tobacco, carrot, orange, cucumber, broccoli, lettuce, malt, cantaloupe, corn, wheat, honey, chocolate, oat, avocado, rye, strawberry, cauliflower, safflower, tea, banana, squashes, green pepper, butter, buck wheat, rice, soybean, grapefruit, oyster, brewer's yeast, peach, cane sugar, cow's milk, and spinach.