AU2017293937A1 - Compositions, devices, and methods of depression sensitivity testing - Google Patents

Abstract

Contemplated test kits and methods for food sensitivity are based on rational-based selection of food preparations with established discriminatory p-value. Particularly preferred kits include those with a minimum number of food preparations that have an average discriminatory p-value of ≤ 0.07 as determined by their raw p-value or an average discriminatory p-value of ≤ 0.10 as determined by FDR multiplicity adjusted p-value. In further contemplated aspects, compositions and methods for food sensitivity are also stratified by gender to further enhance predictive value

Description

COMPOSITIONS, DEVICES, AND METHODS OF DEPRESSION SENSITIVITY

TESTING

Related Applications [0001] This application claims priority to our U.S. provisional patent application with the serial number, 62/359909, filed July 8, 2016, which is incorporated by reference herein in its entirety.

Field of the Invention [0002] The field of the invention is sensitivity testing for food intolerance, and especially as it relates to testing and possible elimination of selected food items as trigger foods for patients diagnosed with or suspected to have Depression.

Background [0003] 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.

[0004] 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.

[0005] 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

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PCT/US2017/041166 advocacy group (e.g., http://www.which.co.uk/news/2008/08/food-allergy-tests-could-risk-yourhealth-154711/). 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 (Alternative Medicine Review, Vol. 9, No. 2, 2004: pp 198-207), 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.

[0006] All publications identified herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0007] 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.

Summary [0008] The subject matter described herein provides systems and methods for testing food intolerance in patients diagnosed with or suspected to have Depression. One aspect of the disclosure is a test kit for testing food intolerance in patients diagnosed with or suspected to have Depression. 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

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PCT/US2017/041166 discriminatory p-value of < 0.07 as determined by raw p-value or an average discriminatory pvalue of < 0.10 as determined by FDR multiplicity adjusted 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.

[0009] Another aspect of the embodiments described herein includes a method of testing food intolerance in patients diagnosed with or suspected to have Depression. 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 Depression. The bodily fluid is associated with gender identification. In certain embodiments, 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.

[0010] 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 Depression. 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 Depression and bodily fluids of a control group not diagnosed with or not suspected to have Depression. 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.

[0011] 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 Depression. The plurality of distinct food preparations are selected based on their

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PCT/US2017/041166 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.

[0012] 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.

Brief Description of The Drawings [0013] Table 1 shows a list of food items from which food preparations can be prepared.

[0014] Table 2 shows statistical data of foods ranked according to 2-tailed FDR multiplicityadjusted p-values.

[0015] Table 3 shows statistical data of ELISA score by food and gender.

[0016] Table 4 shows cutoff values of foods for a predetermined percentile rank.

[0017] Figure 1A illustrates ELISA signal score of male Depression patients and control tested with almond.

[0018] Figure IB illustrates a distribution of percentage of male Depression subjects exceeding the 90^th and 95^th percentile tested with almond.

[0019] Figure 1C illustrates a signal distribution in women along with the 95^th percentile cutoff as determined from the female control population tested with almond.

[0020] Figure ID illustrates a distribution of percentage of female Depression subjects exceeding the 90^th and 95^th percentile tested with almond.

[0021] Figure 2A illustrates ELISA signal score of male Depression patients and control tested with tomato.

[0022] Figure 2B illustrates a distribution of percentage of male Depression subjects exceeding the 90^th and 95^th percentile tested with tomato.

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PCT/US2017/041166 [0023] 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.

[0024] Figure 2D illustrates a distribution of percentage of female Depression subjects exceeding the 90^th and 95^th percentile tested with tomato.

[0025] Figure 3A illustrates ELISA signal score of male Depression patients and control tested with tobacco.

[0026] Figure 3B illustrates a distribution of percentage of male Depression subjects exceeding the 90^th and 95^th percentile tested with tobacco.

[0027] Figure 3C illustrates a signal distribution in women along with the 95^th percentile cutoff as determined from the female control population tested with tobacco.

[0028] Figure 3D illustrates a distribution of percentage of female Depression subjects exceeding the 90^th and 95^th percentile tested with tobacco.

[0029] Figure 4A illustrates ELISA signal score of male Depression patients and control tested with carrot.

[0030] Figure 4B illustrates a distribution of percentage of male Depression subjects exceeding the 90^th and 95^th percentile tested with carrot.

[0031] Figure 4C illustrates a signal distribution in women along with the 95^th percentile cutoff as determined from the female control population tested with carrot.

[0032] Figure 4D illustrates a distribution of percentage of female Depression subjects exceeding the 90^th and 95^th percentile tested with carrot.

[0033] Figure 5A illustrates distributions of Depression subjects by number of foods that were identified as trigger foods at the 90^th percentile.

[0034] Figure 5B illustrates distributions of Depression subjects by number of foods that were identified as trigger foods at the 95^th percentile.

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PCT/US2017/041166 [0035] Table 5A shows raw data of Depression patients and control with number of positive results based on the 90^th percentile.

[0036] Table 5B shows raw data of Depression patients and control with number of positive results based on the 95^th percentile.

[0037] Table 6A shows statistical data summarizing the raw data of Depression patient populations shown in Table 5 A.

[0038] Table 6B shows statistical data summarizing the raw data of Depression patient populations shown in Table 5B.

[0039] Table 7A shows statistical data summarizing the raw data of control populations shown in Table 5A.

[0040] Table 7B shows statistical data summarizing the raw data of control populations shown in Table 5B.

[0041] Table 8A shows statistical data summarizing the raw data of Depression patient populations shown in Table 5 A transformed by logarithmic transformation.

[0042] Table 8B shows statistical data summarizing the raw data of Depression patient populations shown in Table 5B transformed by logarithmic transformation.

[0043] Table 9A shows statistical data summarizing the raw data of control populations shown in Table 5 A transformed by logarithmic transformation.

[0044] Table 9B shows statistical data summarizing the raw data of control populations shown in Table 5B transformed by logarithmic transformation.

[0045] Table 10A shows statistical data of an independent T-test to compare the geometric mean number of positive foods between the Depression and non-Depression samples based on the 90^th percentile.

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PCT/US2017/041166 [0046] Table 10B shows statistical data of an independent T-test to compare the geometric mean number of positive foods between the Depression and non-Depression samples based on the 95^th percentile.

[0047] Table 11A shows statistical data of a Mann-Whitney test to compare the geometric mean number of positive foods between the Depression and non-Depression samples based on the 90^th percentile.

[0048] Table 11B shows statistical data of a Mann-Whitney test to compare the geometric mean number of positive foods between the Depression and non-Depression samples based on the 95^th percentile.

[0049] Figure 6A illustrates a box and whisker plot of data shown in Table 5A.

[0050] Figure 6B illustrates a notched box and whisker plot of data shown in Table 5A.

[0051] Figure 6C illustrates a box and whisker plot of data shown in Table 5B.

[0052] Figure 6D illustrates a notched box and whisker plot of data shown in Table 5B.

[0053] Table 12A shows statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5A-11 A.

[0054] Table 12B shows statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5B-1 IB.

[0055] Figure 7A illustrates the ROC curve corresponding to the statistical data shown in Table 12 A.

[0056] Figure 7B illustrates the ROC curve corresponding to the statistical data shown in Table 12B.

[0057] Table 13A shows a statistical data of performance metrics in predicting Depression status among female patients from number of positive foods based on the 90^th percentile.

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PCT/US2017/041166 [0058] Table 13B shows a statistical data of performance metrics in predicting Depression status among male patients from number of positive foods based on the 90^th percentile.

[0059] Table 14A shows a statistical data of performance metrics in predicting Depression status among female patients from number of positive foods based on the 95^th percentile.

[0060] Table 14B shows a statistical data of performance metrics in predicting Depression status among male patients from number of positive foods based on the 95^th percentile.

Detailed Description [0061] 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.

[0062] 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.

[0063] 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.

[0064] 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

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PCT/US2017/041166 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.

[0065] 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.

[0066] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. 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.

[0067] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the

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PCT/US2017/041166 specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

[0068] In one aspect, the inventors therefore contemplate a test kit or test panel that is suitable for testing food intolerance in patients where the patient is diagnosed with or suspected to have Depression. Most preferably, such test kit or panel will include a plurality of distinct food preparations (e.g., raw or processed extract, preferably aqueous extract with optional co-solvent, which may or may not be filtered) that are coupled to individually addressable respective solid carriers (e.g, in a form of an array or a micro well plate), wherein the 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.

[0069] 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.

[0070] 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

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PCT/US2017/041166 below. Thus, it should be appreciated that the food items need not be limited to the items described herein, but that all items are contemplated that can be identified by the methods presented herein. Therefore, exemplary food preparations include at least two, at least four, at least eight, or at least 12 food preparations prepared from foods 1-26 of Table 2. Still further especially contemplated food items and food additives from which food preparations can be prepared are listed in Table 1.

[0071] Using bodily fluids from patients diagnosed with or suspected to have Depression and healthy control group individuals (i.e., those not diagnosed with or not suspected to have Depression), numerous additional food items may be identified. Preferably, such identified food items will have high discriminatory power and as such have a p-value of <0.15, more preferably < 0.10, and most preferably < 0.05 as determined by raw p-value, and/or a p-value of < 0.10, more preferably < 0.08, and most preferably < 0.07 as determined by False Discovery Rate (FDR) multiplicity adjusted p-value.

[0072] In certain embodiments, such identified food preparations will have high discriminatory power and, as such, will have a p-value of < 0.15, < 0.10, or even < 0.05 as determined by raw pvalue, 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.

[0073] Therefore, where a panel has multiple food preparations, it is contemplated that the plurality of distinct food preparations has 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 more preferably 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. In further preferred aspects, it should be appreciated that the FDR multiplicity adjusted p-value may be adjusted for at least one of age and gender, and most preferably adjusted for both age and gender. On the other hand, where a test kit or panel is stratified for use with a single gender, it is also contemplated that in a test kit or panel at least 50% (and more typically 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

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PCT/US2017/041166 by FDR multiplicity adjusted p-value. Furthermore, it should be appreciated that other stratifications (e.g., dietary preference, ethnicity, place of residence, genetic predisposition or family history, etc.) are also contemplated, and the person of ordinary skill in the art (PHOSITA) will be readily appraised of the appropriate choice of stratification.

[0074] 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. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. 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.

[0075] 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).

[0076] Consequently, the inventors also contemplate a method of testing food intolerance in patients that are diagnosed with or suspected to have Depression. 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) of a patient that is diagnosed with or suspected to have Depression, and wherein the bodily fluid is associated with a gender identification. As noted before, the step of contacting is preferably performed under conditions that allow 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. In some embodiments, the signal is then compared

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PCT/US2017/041166 against a gender-stratified reference value (e.g., at least a 90th percentile value) 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).

[0077] In certain embodiments, such methods will not be limited to a single food preparation, but will employ multiple different food preparations. As noted before, suitable food preparations can be identified using various methods as described below, however, especially preferred food preparations include foods 1-26, of Table 2, and/or items of Table 1. As also noted above, it is generally preferred that 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 an average discriminatory p-value of < 0.10 (or < 0.08, or < 0.07) as determined by FDR multiplicity adjusted p-value.

[0078] 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.

[0079] As it is generally preferred that the food preparation is immobilized on a solid surface (typically in an addressable manner), 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 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. In other embodiments, the food preparation(s) will be coupled to a molecular tag that allows for binding to human immunoglobulins (e.g, IgG) in solution.

[0080] Viewed from a different perspective, the inventors also contemplate a method of generating a test for food intolerance in patients diagnosed with or suspected to have Depression.

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Because the test is applied to patients already diagnosed with or suspected to have Depression, 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 Depression patients. Such test will typically include a step of obtaining one or more test results (e.g., ELISA) for various distinct food preparations, wherein the test results are based on bodily fluids (e.g, blood saliva, fecal suspension) of patients diagnosed with or suspected to have Depression and bodily fluids of a control group not diagnosed with or not suspected to have Depression. Most preferably, 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)) is assigned for a predetermined percentile rank (e.g, 90th or 95th percentile).

[0081] As noted earlier, and while not limiting to the inventive subject matter, it is contemplated that the distinct food preparations include at least two (or six, or ten, or 15) food preparations prepared from food items selected from the group consisting of foods 1-26 of Table 2, and/or items of Table 1. On the other hand, where new food items are tested, it should be appreciated that the distinct food preparations include a food preparation prepared from a food items other than foods 1-26 of Table 2. Regardless of the particular choice of food items, it is generally preferred however, that the distinct food preparations 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. Exemplary aspects and protocols, and considerations are provided in the experimental description below.

[0082] 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.

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Experiments [0083] General Protocol for food preparation generation: Commercially available food extracts (available from Biomerica Inc., 17571 Von Karman Ave, Irvine, CA 92614) prepared from the edible portion of the respective raw foods were used to prepare ELISA plates following the manufacturer’s instructions.

[0084] 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 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.

[0085] 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 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.

[0086] 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

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PCT/US2017/041166 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 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.

[0087] 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 20-50 mM of buffer from 4-9 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.

[0088] 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.

[0089] 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, 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. 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

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PCT/US2017/041166 preferred aspects, the final list foods will be shorter than 50 food items, and more preferably equal or less than of 40 food items.

[0090] 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: 38.6% female, Depression: 74.3% 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., >1,000, more preferably >10,000, even more preferably >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.).

[0091] 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 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 2. Here the ranking of foods is according to 2-tailed permutation T-test p-values with FDR adjustment.

[0092] Based on earlier experiments (data not shown here, see US 62/359909), 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 3. 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.

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PCT/US2017/041166 [0093] 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 1000 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 95% 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 1000 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 4.

[0094] Typical examples for the gender difference in IgG response in blood with respect to almond 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 Depression subjects exceeding the 90^th and 95^th percentile, while 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 Depression subjects exceeding the 90^th and 95^th percentile. In the same fashion, Figures 2A-2D exemplarily depict the differential response to tomato, Figures 3A-3D exemplarily depict the differential response to tobacco, and Figures 4A-4D exemplarily depict the differential response to carrot. Figures 5A-5B show the distribution of Depression 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 will be notably distinct.

[0095] 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

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PCT/US2017/041166 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.

[0096] 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 0.1 and the normalized value of the patient’s IgG specific to malt can be 0.3. 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.

[0097] 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 seventimes 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.

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PCT/US2017/041166 [0098] 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 26 food samples.

[0099] Table 5A and Table 5B provide exemplary raw data. As should be readily appreciated, the data indicate number of positive results out of 26 sample foods based on 90^th percentile value (Table 5A) or 95^th percentile value (Table 5B). The first column is Depression (n=l 14); second column is non-Depression (n=132) by ICD-10 code. Average and median number of positive foods was computed for Depression and non-Depression patients. From the raw data shown in Table 5A 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 50% lower than the percentage of patients with zero positive foods in the nonDepression population (27.2% vs. 51.5%, respectively) based on 90^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 40% lower) than that seen in the non-Depression population (39.5 % vs. 66.7%, respectively) based on 95^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.

[00100] 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 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 95% confidence interval (CI) for the mean and median

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PCT/US2017/041166 representing number of positive foods in the Depression population and the non-Depression population.

[00101] 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.

[00102] 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 11 A) 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 26 food samples is significantly higher in the Depression population than in the non-Depression 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.

[00103] 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 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 26 food samples is significantly higher in the Depression population than in the non-Depression population with an average discriminatory p-value of < 0.0001. These statistical data is also illustrated as a box and whisker plot in Figure 6C, and a notched box and whisker plot in Figure 6D.

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PCT/US2017/041166 [00104] 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 5 at discriminating Depression from non- Depression subjects. When a cutoff criterion of more than 4 positive foods is used, the test yields a data with 36.8% sensitivity and 87.8% specificity, with an area under the curve (AUROC) of 0.665. The p-value for the ROC is significant at a p-value of <0.0001. Figure 7A 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 4, 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.

[00105] As shown in Tables 5A-12A, and Figure 7A, based on 90^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 36.8% sensitivity and 87.8% specificity. Additionally, the absolute number and percentage of subjects with 0 positive foods is also very different in Depression vs. non-Depression subjects, with a far lower percentage of Depression subjects (27.2%) having 0 positive foods than non-Depression subjects (51.5%). The data suggests a subset of Depression patients may have Depression due to other factors than diet, and may not benefit from dietary restriction.

[00106] Table 12B shows exemplary statistical data of a Receiver Operating Characteristic (ROC) curve analysis of data shown in Tables 5B-1 IB to determine the diagnostic power of the test used in Table 5 at discriminating Depression from non-Depression subjects. When a cutoff criterion of more than 0 positive foods is used, the test yields a data with 60.5% sensitivity and 66.7% specificity, with an area under the curve (AUROC) of 0.659. The p-value for the ROC is significant at a p-value of <0.0001. Figure 7B illustrates the ROC curve corresponding to the statistical data shown in Table 12B. Because the statistical difference between the Depression

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PCT/US2017/041166 population and the non-Depression population is significant when the test results are cut off to positive number of >0, 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.

[00107] As shown in Tables 5B-12B, and Figure 7B, based on 95^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 60.5% sensitivity and 66.7% specificity. Additionally, the absolute number and percentage of subjects with 0 positive foods is also very different in Depression vs. non-Depression subjects, with a far lower percentage of Depression subjects (39.5%) having 0 positive foods than non- Depression subjects (66.7%). The data suggests a subset of Depression patients may have Depression due to other factors than diet, and may not benefit from dietary restriction.

[00108] 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 26 food items from Table 2, 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 1000 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 sexspecific 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.

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PCT/US2017/041166 [00109] Once all food items were determined either positive or negative, the results of the 52 (26 foods x 2 cutpoints) calls for each subject will be saved within each bootstrap replicate. Then, for each subject, 26 calls will be summed using 90^th percentile as cutpoint to get “Number of Positive Foods (90^th),” and the rest of 26 calls will be summed using 95^th percentile to get “Number of Positive Foods (95^th).” Then, within each replicate, “Number of Positive Foods (90^th)” and “Number of Positive Foods (95^th)” will be summarized across subjects to get descriptive statistics for each replicate as follows: 1) overall means equals to the mean of means, 2) overall standard deviation equals to the mean of standard deviations, 3) overall medial equals to the mean of medians, 4) overall minimum equals to the minimum of minimums, and 5) 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 1000 repetitions of the same original dataset were actually 999 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 (90^th)” and “Number of Positive Foods (95^th)” for both genders and for both Depression subjects and control subjects using programs “a_pos_foods.sas, a_pos_foods_by_dx.sas”.

[00110] 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 (90^th)” and “Number of Positive Foods (95^th)” for each subject within each bootstrap replicate described above. In this analysis, the cutpoint was set to 1. Thus, if a subject has one or more “Number of Positive Foods (90^th)”, then the subject will be called “Has Depression.” If a subject has less than one “Number of Positive Foods (90^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 (90^th)” and “Number of Positive Foods(95^th)” when the cutpoint is set to 1 for each method. Each (sensitivity, 1-specificity) pair becomes a point on the ROC curve for this replicate.

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PCT/US2017/041166 [00111] To increase the accuracy, the analysis above will be repeated by incrementing cutpoint from 2 up to 26, and repeated for each of the 1000 bootstrap replicates. Then the performance metrics across the 1000 bootstrap replicates will be summarized by calculating averages using a program “t pos foods by dx.sas”. The results of diagnostic performance for female and male are shown in Tables 13A and 13B (90th percentile) and Tables 14 A and 14B (95th percentile).

[00112] Of course, it should be appreciated that certain variations in the food preparations may be made without altering the inventive subject matter presented herein. For example, where the food item was yellow onion, that item should be understood to also include other onion varieties that were demonstrated to have equivalent activity in the tests. Indeed, the inventors have noted that for each tested food preparation, certain other related food preparations also tested in the same or equivalent manner (data not shown). Thus, it should be appreciated that each tested and claimed food preparation will have equivalent related preparations with demonstrated equal or equivalent reactions in the test.

[00113] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C .... and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

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PCT/US2017/041166

Abalone	Cured Cheese	Onion	Walnut, black
Ad lay	Cuttlefish	Orange	Watermelon
Almond	Duck	Oyster	Welch Onion
American Cheese	Durian	Papaya	Wheat
Apple	Eel	Paprika	W’heat bran
Artichoke	Egg White (separate)	Parsley	Yeast (S. cerevisiae)
Asparagus	Egg Yolk (separate)	Peach	Yogurt
Avocado	Egg, white/yolk (comb.)	Peanut
Baby Bok Choy	Eggplant	Pear	FOOD ADDITIVES
Bamboo shoots	Garlic	Pepper, Black	Arabic Gum
Banana	Ginger	Pineapple	Carboxymethy l Cellulose
Barley, whole grain	Gluten - Gliadin	Pinto bean	Carrageneenan
Beef	Goat's milk	Plum	FD&C Blue #1
Beets	Grape, white/concord	Pork	FD&C Red #3
Beta-lactoglobulin	Grapefruit	Potato	FD&C Red #40
Blueberry	Grass Carp	Rabbit	FD&C Yellow #5
Broccoli	Green Onion	Rice	FD&C Yellow #6
Buckwheat	Green pea	Roquefort Cheese	Gelatin
Butter	Green pepper	Rye	Guar Gum
Cabbage	Guava	Saccharine	Maltodextrin
Cane sugar	Hair Tail	Safflower seed	Pectin
Cantaloupe	Hake	Salmon	Whey
Caraway	Halibut	Sardine	Xanthan Gum
Carrot	Hazelnut	Scallop
Casein	Honey	Sesame
Cashew	Kelp	Shark fin
Cauliflower	Kidney bean	Sheep’s milk
Celery	Kiwi Fruit	Shrimp
Chard	Lamb	Sole
Cheddar Cheese	Leek	Soybean
Chick Peas	Lemon	Spinach
Chicken	Lentils	Squashes
Chili pepper	Lettuce, Iceberg	Squid
Chocolate	Lima bean	Strawberry-
Cinnamon	Lobster	String bean
Clam	Longan	Sunflower seed
Cocoa Bean	Mackerel	Sweet potato
Coconut	Malt	Swiss cheese
Codfish	Mango	Taro
Coffee	Marjoram	Tea, black
Cola nut	Millet	Tobacco
Com	Mung bean	Tomato
Cottage cheese	Mushroom	Trout
Cow's milk	Mustard seed	Tuna
Crab	Oat	Turkey
Cucumber	Olive	Vanilla

Table 1

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Ranking of Foods according to 2-tailed Permutation T-test p-values with FDR adjustment

Rank	Food	Raw p-value	FDR Multiplicity-adj p-value
1	Almond	0.0001	0.0080
2	Tomato	0.0002	0.0080
3	Tobacco	0.0006	0.0166
4	Carrot	0.0010	0.0185
5	Orange	0.0010	0.0185
6	Cucumber	0.0017	0.0252
7	Broccoli	0.0028	0.0323
8	Lettuce	0.0029	0.0323
9	Malt	0.0034	0.0340
10	Cantaloupe	0.0044	0.0390
11	Corn	0.0048	0.0390
12	Wheat	0.0066	0.0449
13	Honey	0.0069	0.0449
14	Chocolate	0.0071	0.0449
15	Oat	0.0101	0.0567
16	Avocado	0.0102	0.0567
17	Rye	0.0119	0.0602
18	Strawberry	0.0122	0.0602
19	Cauliflower	0.0130	0.0606
20	Safflower	0.0136	0.0606
21	Tea	0.0151	0.0630
22	Banana	0.0156	0.0630
23	Squashes	0.0184	0.0710
24	Green_Pepper	0.0213	0.0790
25	Butter	0.0237	0.0842
26	Buck_Wheat	0.0258	0.0885
27	Rice	0.0307	0.1013
28	Soybean	0.0363	0.1118
29	Grapefruit	0.0364	0.1118
30	Oyster	0.0427	0.1266

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Rank	Food	Raw p-value	FDR Multiplicity-adj p-value
31	Yeast_Brewer	0.0473	0.1359
32	Peach	0.0569	0.1584
33	Cane-Sugar	0.0594	0.1603
34	Cow__Milk	0.0616	0.1613
35	Spinach	0.0667	0.1697
36	Mustard	0.0719	0.1779
37	Cinnamon	0.0800	0.1923
38	Eggplant	0.0854	0.2001
39	Cabbage	0.1034	0.2303
40	Pinto__Bean	0.1053	0.2303
41	Onion	0.1061	0.2303
42	Sunflower_Sd	0.1204	0.2552
43	Walnut_Blk	0.1233	0.2552
44	Blueberry	0.1312	0.2655
45	Cottage_Ch_	0.1398	0.2714
46	Cheddar__Ch__	0.1403	0.2714
47	Goat__Milk	0.1438	0.2715
48	Lemon	0.1464	0.2715
49	Apple	0.1891	0.3435
50	Olive	0.1965	0.3498
51	Garlic	0.2065	0.3604
52	Yeast-Baker	0.2187	0.3742
53	Parsley	0.2262	0.3798
54	Sweet_Pot_	0.2585	0.4231
55	Yogurt	0.2615	0.4231
56	Swiss_Ch_	0.2712	0.4311
57	Amer_Cheese	0.2839	0.4394
58	Beef	0.2873	0.4394
59	Barley	0.2934	0.4394
60	Clam	0.2962	0.4394
61	Green_Pea	0.3118	0.4550
62	Salmon	0.3239	0.4649
63	Scallop	0.3518	0.4969
64	Celery	0.3573	0.4969

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Rank	Food	Raw p-value	FDR Multiplicity-adj p-value
65	Chicken	0.4077	0.5582
66	Sardine	0.4479	0.5963
67	Lima_Bean	0.4489	0.5963
68	Codfish	0.4567	0.5977
69	Cashew	0.4784	0.6171
70	Peanut	0.5032	0.6384
71	Potato	0.5093	0.6384
72	Millet	0.5283	0.6530
73	Turkey	0.5557	0.6775
74	Pork	0.5674	0.6820
75	Mushroom	0.5748	0.6820
76	Coffee	0.6105	0.7150
77	Trout	0.6332	0.7319
78	Crab	0.6492	0.7407
79	Pineapple	0.6858	0.7726
80	Lobster	0.7421	0.8256
81	Egg	0.7893	0.8673
82	Sesame	0.8279	0.8886
83	Sole	0.8314	0.8886
84	Tuna	0.8387	0.8886
85	Halibut	0.8863	0.9280
86	String_Bean	0.9103	0.9421
87	Coia_Nut	0.9604	0.9739
88	Chili_Pepper	0.9629	0.9739
89	Grape	0.9849	0.9849

Table 2

SUBSTITUTE SHEET (RULE 26)

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Basic Descriptive Statistics of ELISA Score by Food and Gender

Comparing Depression to Control

EL/SA Score

Sex	Food	Diagnosis	N	Mean	SD	Min	Max
FEMALE	Almond	Control	51	4.057	1.630	1.275	8.090
		Depression	84	6.053	5.372	1.210	33.082
		Diff (1-2)		-1.996	4.360
	Amer_Cheese	Control	51	32.961	66.654	0.100	400.00
		Depression	84	31.594	42.040	1.002	244.77
		Diff (1-2)		1.367	52.660
	Apple	Control	51	4.793	6.600	0.100	44.163
		Depression	84	4.700	5.000	0.288	29.157
		Diff (1-2)		0.093	5.655
	Avocado	Control	51	2.144	1.129	0.100	5.561
		Depression	84	3.734	6.321	0.099	43.891
		Diff (1-2)		-1.589	5.041
	Banana	Control	51	3.042	2.909	0.100	17.212
		Depression	84	4.254	3.270	0.605	21.544
		Diff (1-2)		-1.212	3.139
	Barley	Control	51	3.867	3.919	0.100	25.110
		Depression	84	9.751	27.814	0.504	227.10
		Diff (1-2)		-5.883	22.103
	Beef	Control	51	9.007	12.047	1.029	81.664
		Depression	84	10.130	15.645	2.337	114.56
		Diff (1-2)		-1.124	14.398
	Blueberry	Control	51	3.533	4.712	0.100	26.459
		Depression	84	3.925	2.782	0.706	17.918
		Diff (1-2)		-0.392	3.630
	Broccoli	Control	51	5.211	4.424	0.107	29.602
		Depression	84	7.516	3.766	1.311	26.282
		Diff (1-2)		-2.305	4.026
	Buck__Wheat	Control	51	5.151	4.281	0.100	26.453
		Depression	84	6.040	6.662	1.543	51.255
		Diff (1-2)		-0.890	5.881
	Butter	Control	51	17.809	24.981	0.100	150.93
		Depression	84	19.850	26.939	1.776	169.07

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EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	□iff (1-2)		-2.041	26.220
Cabbage	Control	51	5.038	6.005	0.346	37.840
	Depression	84	5.930	5.330	0.706	28.026
	Diff (1-2)		-0.892	5.593
Cane_Sugar	Control	51	15.189	10.152	3.462	50.454
	Depression	84	25.216	32.661	4.615	273.09
	Diff (1-2)		-10.027	26.542
Cantaloupe	Control	51	4.707	2.368	1.153	12.761
	Depression	84	6.464	6.047	1.506	39.659
	Diff (1-2)		-1.757	4.993
Carrot	Control	51	2.702	1.549	0.100	6.945
	Depression	84	4.670	4.958	0.706	33.323
	Diff (1-2)		-1.968	4.030
Cashew	Control	51	8.621	13.756	0.100	81.886
	Depression	84	13.003	44.243	0.504	400.00
	Diff (1-2)		-4.382	35.954
Cauliflower	Control	51	4.203	2.424	0.427	11.768
	Depression	84	5.122	4.128	1.109	31.116
	Diff (1-2)		-0.920	3.583
Celery	Control	51	7.815	5.561	2.058	32.827
	Depression	84	7.848	6.159	1.923	37.247
	Diff (1-2)		-0.034	5.941
Cheddar_Ch_	Control	51	25.261	59.385	1.533	400.00
	Depression	84	25.785	46.652	0.992	271.88
	Diff (1-2)		-0.524	51.807
Chicken	Control	51	14.077	8.350	2.690	50.000
	Depression	84	16.733	25.109	2.054	164.81
	Diff (1-2)		-2.655	20.485
Chili_Pepper	Control	51	7.281	6.348	0.571	32.357
	Depression	84	6.294	5.445	1.326	43.789
	Diff (1-2)		0.987	5.801
Chocolate	Control	51	13.516	6.136	3.405	30.536
	Depression	84	17.719	9.932	4.135	47.953
	Diff (1-2)		-4.203	8.701

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
Cinnamon	Control	51	8.317	6.347	1.490	38.804
	Depression	84	10.753	11.058	1.169	89.952
	Diff (1-2)		-2.436	9.563
Clam	Control	51	36.890	57.603	7.452	400.00
	Depression	84	26.931	27.003	4.547	171.60
	Diff (1-2)		9.960	41.261
Codfish	Control	51	27.484	34.270	6.174	203.91
	Depression	84	24.346	32.188	3.328	232.15
	Diff (1-2)		3.137	32.986
Coffee	Control	51	34.003	55.076	6.732	400.00
	Depression	84	28.989	44.549	3.065	312.78
	Diff (1-2)		5.014	48.774
Cola__Ni.it	Control	51	11.928	5.390	2.629	27.263
	Depression	84	12.964	6.626	2.521	37.735
	Diff (1-2)		-1.035	6.190
Corn	Control	51	7.351	5.170	2.076	27.010
	Depression	84	10.825	11.201	1.614	71.435
	Diff (1-2)		-3.474	9.399
Cottage__Ch__	Control	51	83.139	107.442	2.115	400.00
	Depression	84	87.213	117.081	3.341	400.00
	Diff (1-2)		-4.074	113.554
Cow_Milk	Control	51	65.188	90.937	1.798	400.00
	Depression	84	69.385	88.404	2.220	400.00
	Diff (1-2)		-4.197	89.365
Crab	Control	51	30.366	21.673	3.770	114.37
	Depression	84	27.416	27.641	3.955	183.02
	Diff (1-2)		2.951	25.562
Cucumber	Control	51	6.559	5.110	1.269	26.496
	Depression	84	10.518	12.910	1.655	105.57
	Diff (1-2)		-3.959	10.669
Egg	Control	51	70.569	97.119	5.109	400.00
	Depression	84	63.169	102.626	2.103	400.00
	Diff (1-2)		7.399	100.591
Eggplant	Control	51	3.980	4.120	0.100	26.496

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/04U66

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	Depression	84	4.808	4.545	0.769	31.159
	Diff (1-2)		-0.828	4.390
Garlic	Control	51	8.615	4.650	0.100	23.410
	Depression	84	12.491	21.359	2.825	195.76
	Diff (1-2)		-3.876	17.112
Goat_Milk	Control	51	12.631	28.034	0.100	149.14
	Depression	84	10.452	14.140	0.769	75.952
	Diff (1-2)		2.179	20.499
Grape	Control	51	13.069	6.123	2.351	44.190
	Depression	84	11.984	6.244	5.166	48.166
	Diff (1-2)		1.085	6.199
Grapefruit	Control	51	3.129	1.808	0.100	8.039
	Depression	84	4.425	4.535	0.473	27.688
	Diff (1-2)		-1.297	3.750
Green_Pea	Control	51	5.735	5.596	0.100	27.006
	Depression	84	6.700	5.824	1.473	32.598
	Diff (1-2)		-0.966	5.739
Green_Pepper	Control	51	4.001	2.220	1.153	11.464
	Depression	84	4.961	3.740	0.568	28.231
	Diff (1-2)		-0.960	3.253
Halibut	Control	51	11.307	6.424	0.855	35.367
	Depression	84	10.593	9.057	3.026	77.575
	Diff (1-2)		0.713	8.167
Honey	Control	51	8.234	3.765	0.534	22.795
	Depression	84	11.119	9.294	3.158	69.779
	Diff (1-2)		-2.885	7.697
Lemon	Control	51	2.560	1.273	0.100	6.269
	Depression	84	2.960	1.809	0.100	10.352
	Diff (1-2)		-0.400	1.628
Lettuce	Control	51	9.676	5.180	3.726	27.085
	Depression	84	13.809	13.075	2.919	68.119
	Diff (1-2)		-4.133	10.807
Lima_Bean	Control	51	4.955	4.158	0.100	25.007
	Depression	84	5.594	3.681	0.100	22.803

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	□iff (1-2)		-0.640	3.867
Lobster	Control	51	7.008	4.348	1.175	23.980
	Depression	84	7.919	9.497	0.302	81.273
	Diff (1-2)		-0.910	7.962
Malt	Control	51	13.807	7.087	1.923	31.196
	Depression	84	17.205	10.029	3.138	64.864
	Diff (1-2)		-3.399	9.036
Millet	Control	51	3.883	6.158	0.100	45.888
	Depression	84	3.293	1.457	0.284	7.902
	Diff (1-2)		0.590	3.947
Mushroom	Control	51	22.698	22.937	1.486	106.22
	Depression	84	26.098	35.501	2.596	269.25
	Diff (1-2)		-3.399	31.374
Mustard	Control	51	5.398	2.689	0.914	11.657
	Depression	84	6.532	5.934	1.346	51.410
	Diff (1-2)		-1.133	4.969
Oat	Control	51	11.120	9.563	0.100	49.538
	Depression	84	21.731	30.234	1.224	172.55
	Diff (1-2)		-10.611	24.593
Olive	Control	51	15.480	19.299	3.048	111.23
	Depression	84	15.462	13.966	1.815	83.159
	Diff (1-2)		0.019	16.178
Onion	Control	51	9.740	10.258	1.169	70.461
	Depression	84	11.730	10.513	2.045	57.666
	Diff (1-2)		-1.990	10.418
Orange	Control	51	23.728	28.881	4.173	149.43
	Depression	84	40.043	61.930	3.273	400.00
	Diff (1-2)		-16.314	52.029
Oyster	Control	51	44.125	34.722	9.622	168.93
	Depression	84	47.302	55.126	2.746	365.92
	Diff (1-2)		-3.177	48.473
Parsley	Control	51	21.959	46.256	5.988	342.33
	Depression	84	23.172	46.201	4.615	395.02
	Diff (1-2)		-1.214	46.221

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

ELiSA Score

Food	Diagnosis	N	Mean	SD	Min	Max
Peach	Control	51	6.507	7.491	0.100	34.647
	Depression	84	10.153	13.064	0.501	86.767
	Diff (1-2)		-3.646	11.296
Peanut	Control	51	5.445	4.273	0.100	24.233
	Depression	84	4.574	3.767	0.401	23.669
	Diff (1-2)		0.871	3.965
Pineapple	Control	51	8.460	18.977	0.100	122.86
	Depression	84	9.496	10.815	0.301	68.963
	Diff (1-2)		-1.036	14.435
Pinto_Bean	Control	51	9.830	9.653	0.214	47.923
	Depression	84	9.690	8.769	1.519	49.208
	Diff (1-2)		0.140	9.111
Pork	Control	51	15.095	8.745	4.796	44.259
	Depression	84	14.848	16.951	2.105	136.08
	Diff (1-2)		0.247	14.424
Potato	Control	51	8.664	2.240	4.899	14.014
	Depression	84	9.829	6.551	3.530	50.433
	Diff (1-2)		-1.165	5.354
Rice	Control	51	18.985	14.969	4.896	73.099
	Depression	84	27.187	27.511	3.039	183.65
	Diff (1-2)		-8.203	23.591
Rye	Control	51	4.185	2.647	0.229	17.994
	Depression	84	5.528	6.331	0.568	50.541
	Diff (1-2)		-1.343	5.258
Safflower	Control	51	6.557	5.363	1.619	36.646
	Depression	84	10.104	18.078	1.513	158.79
	Diff (1-2)		-3.547	14.655
Salmon	Control	51	13.155	11.632	3.483	68.368
	Depression	84	11.244	7.823	2.005	38.104
	Diff (1-2)		1.911	9.437
Sardine	Control	51	29.733	14.098	12.950	76.726
	Depression	84	29.036	13.839	7.297	71.123
	Diff (1-2)		0.696	13.937
Scallop	Control	51	53.504	22.302	15.624	107.71

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/04U66

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	Depression	84	48.185	31.160	12.400	183.38
	Diff (1-2)		5.320	28.159
Sesame	Control	51	91.740	91.167	6.639	400.00
	Depression	84	74.492	86.939	3.652	400.00
	Diff (1-2)		17.248	88.552
Shrimp	Control	51	31.906	31.340	5.364	151.14
	Depression	84	19.220	28.356	0.908	174.30
	Diff (1-2)		12.685	29.513
Sole	Control	51	5.010	3.858	0.229	29.089
	Depression	84	5.160	3.607	0.568	32.149
	Diff (1-2)		-0.151	3.703
Soybean	Control	51	14.277	10.254	4.153	51.573
	Depression	84	20.340	45.681	1.412	330.35
	Diff (1-2)		-6.063	36.631
Spinach	Control	51	20.914	15.580	3.294	66.869
	Depression	84	19.305	19.975	3.530	112.21
	Diff (1-2)		1.609	18.446
Squashes	Control	51	5.697	2.997	2.054	13.836
	Depression	84	7.591	7.642	1.461	61.130
	Diff (1-2)		-1.895	6.311
Strawberry	Control	51	4.585	4.756	0.107	27.904
	Depression	84	5.668	8.119	0.288	71.144
	Diff (1-2)		-1.083	7.046
String_Bean	Control	51	34.495	21.114	12.544	94.207
	Depression	84	33.201	17.652	6.617	91.525
	Diff (1-2)		1.294	19.028
Sunflower_Sd	Control	51	7.402	4.308	1.487	21.171
	Depression	84	8.016	5.975	1.420	42.615
	Diff (1-2)		-0.614	5.409
Sweet_Pot_	Control	51	13.319	8.694	4.463	53.650
	Depression	84	17.751	42.527	3.013	387.79
	Diff (1-2)		-4.432	34.016
Swiss__Ch__	Control	51	37.893	78.801	1.486	400.00
	Depression	84	32.905	56.882	1.422	369.23

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	□iff (1-2)		4.988	65.982
Tea	Control	51	19.459	7.609	8.932	38.009
	Depression	84	23.868	11.404	7.392	60.568
	Diff (1-2)		-4.409	10.145
Tobacco	Control	51	28.550	13.486	7.878	65.658
	Depression	84	40.993	33.443	6.961	266.42
	Diff (1-2)		-12.443	27.683
Tomato	Control	51	7.412	5.926	1.915	30.764
	Depression	84	11.842	14.852	1.052	121.09
	Diff (1-2)		-4.430	12.283
Trout	Control	51	15.254	16.016	3.000	93.127
	Depression	84	15.204	32.133	2.467	297.84
	Diff (1-2)		0.050	27.217
Tuna	Control	51	8.129	6.362	3.048	33.878
	Depression	84	7.920	7.040	1.504	42.894
	Diff (1-2)		0.209	6.793
Turkey	Control	51	11.859	5.301	4.489	28.920
	Depression	84	14.418	17.255	1.403	130.49
	Diff (1-2)		-2.559	14.013
Walnut_Blk	Control	51	19.796	13.830	5.668	79.531
	Depression	84	22.704	21.647	4.188	147.49
	Diff (1-2)		-2.908	19.088
Wheat	Control	51	14.031	16.566	3.201	116.33
	Depression	84	20.865	46.543	1.987	400.00
	Diff (1-2)		-6.834	38.145
Yeast_Baker	Control	51	6.905	4.321	2.226	24.959
	Depression	84	11.196	14.140	1.002	90.740
	Diff (1-2)		-4.292	11.480
Yeast_Brewer	Control	51	9.946	8.059	1.486	37.536
	Depression	84	16.898	21.682	2.220	133.32
	Diff (1-2)		-6.952	17.827
Yogurt	Control	51	19.256	34.792	0.100	223.20
	Depression	84	14.529	14.602	1.285	58.971
	Diff (1-2)		4.727	24.252

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

ELiSA Score

Sex	Food	Diagnosis	N	Mean	SD	Min	Max
MALE	Almond	Control	81	4.956	2.457	1.604	14.845
		Depression	30	19.240	48.521	2.209	261.78
		Diff (1-2)		-14.284	25.116
	Amer_Cheese	Control	81	33.623	47.729	1.711	234.20
		Depression	30	70.293	104.273	0.100	388.88
		Diff (1-2)		-36.670	67.563
	Apple	Control	81	4.768	4.226	0.994	30.113
		Depression	30	10.226	17.862	1.473	91.492
		Diff (1-2)		-5.457	9.899
	Avocado	Control	81	2.949	2.085	0.201	15.507
		Depression	30	4.977	6.902	0.670	29.430
		Diff (1-2)		-2.028	3.983
	Banana	Control	81	4.016	5.530	0.805	48.427
		Depression	30	7.836	9.483	1.531	40.890
		Diff (1-2)		-3.820	6.810
	Barley	Control	81	9.009	35.683	1.081	324.19
		Depression	30	15.659	45.746	1.435	254.23
		Diff (1-2)		-6.650	38.617
	Beef	Control	81	10.821	19.739	2.369	162.33
		Depression	30	19.620	24.830	3.199	123.25
		Diff (1-2)		-8.799	21.213
	Blueberry	Control	81	3.790	2.257	0.883	12.559
		Depression	30	5.614	5.070	1.031	26.070
		Diff (1-2)		-1.824	3.253
	Broccoli	Control	81	7.175	5.132	2.098	30.727
		Depression	30	18.338	44.970	2.251	250.15
		Diff (1-2)		-11.163	23.609
	Buck__Wheat	Control	81	5.548	3.014	1.667	23.702
		Depression	30	9.870	9.819	2.762	54.212
		Diff (1-2)		-4.323	5.685
	Butter	Control	81	16.652	19.179	1.546	93.145
		Depression	30	39.899	44.616	3.213	189.16
		Diff (1-2)		-23.246	28.277
	Cabbage	Control	81	5.952	10.811	0.985	94.740

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	Depression	30	20.647	62.792	1.244	347.26
	Diff (1-2)		-14.695	33.687
Cane_Sugar	Control	81	23.047	28.025	3.898	170.78
	Depression	30	30.241	26.558	5.441	131.80
	Diff (1-2)		-7.194	27.642
Cantaloupe	Control	81	5.879	4.368	1.965	29.569
	Depression	30	19.244	52.306	2.967	288.31
	Diff (1-2)		-13.366	27.238
Carrot	Control	81	4.016	3.787	1.177	27.684
	Depression	30	8.228	10.884	1.148	46.973
	Diff (1-2)		-4.212	6.484
Cashew	Control	81	9.724	11.603	1.020	59.196
	Depression	30	11.345	20.757	1.148	114.69
	Diff (1-2)		-1.621	14.609
Cauliflower	Control	81	4.865	3.698	1.514	24.163
	Depression	30	17.389	53.615	1.531	296.98
	Diff (1-2)		-12.524	27.836
Celery	Control	81	8.967	5.476	2.947	34.787
	Depression	30	20.042	50.509	3.677	284.26
	Diff (1-2)		-11.075	26.472
Cheddar__Ch__	Control	81	26.696	45.931	1.690	283.73
	Depression	30	73.052	117.039	3.478	400.00
	Diff (1-2)		-46.355	72.061
Chicken	Control	81	16.054	12.550	2.942	76.881
	Depression	30	18.502	12.193	4.671	47.618
	Diff (1-2)		-2.449	12.456
Chili__Pepper	Control	81	7.835	5.613	1.569	38.045
	Depression	30	11.129	16.881	1.856	96.246
	Diff (1-2)		-3.295	9.947
Chocolate	Control	81	16.623	11.019	3.007	59.473
	Depression	30	22.913	15.578	4.307	70.958
	Diff (1-2)		-6.289	12.397
Cinnamon	Control	81	9.850	7.037	1.640	40.477
	Depression	30	12.445	8.317	1.133	30.988

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	Diff (1-2)		-2.595	7.399
Clam	Control	81	33.566	20.277	3.189	98.482
	Depression	30	36.898	54.757	9.750	318.14
	Diff (1-2)		-3.332	33.159
Codfish	Control	81	25.075	33.650	6.487	277.17
	Depression	30	45.890	73.290	7.959	400.00
	Diff (1-2)		-20.815	47.541
Coffee	Control	81	30.318	43.408	4.323	356.95
	Depression	30	53.598	98.346	5.268	400.00
	Diff (1-2)		-23.280	62.898
Cola_Nut	Control	81	15.243	8.049	4.084	38.816
	Depression	30	16.580	9.872	4.987	50.994
	Diff (1-2)		-1.337	8.572
Corn	Control	81	9.923	12.544	2.358	95.512
	Depression	30	29.487	48.938	2.297	185.58
	Diff (1-2)		-19.564	27.435
Cottage_Ch_	Control	81	76.631	102.973	1.207	400.00
	Depression	30	140.923	154.222	5.851	400.00
	Diff (1-2)		-64.292	118.787
Cow_Milk	Control	81	60.822	83.166	1.767	400.00
	Depression	30	131.551	144.182	4.282	400.00
	Diff (1-2)		-70.728	102.991
Crab	Control	81	32.448	37.288	4.765	299.11
	Depression	30	36.378	35.136	7.680	194.02
	Diff (1-2)		-3.930	36.728
Cucumber	Control	81	8.752	8.584	1.877	61.859
	Depression	30	28.024	60.943	2.830	320.56
	Diff (1-2)		-19.272	32.284
Egg	Control	81	62.505	92.408	3.785	400.00
	Depression	30	85.498	116.862	3.215	400.00
	Diff (1-2)		-22.994	99.503
Eggplant	Control	81	5.045	5.910	1.367	48.789
	Depression	30	10.459	16.348	1.603	70.249
	Diff (1-2)		-5.414	9.836

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
Garlic	Control	81	11.918	9.606	3.041	52.161
	Depression	30	14.955	16.035	2.834	88.234
	Diff (1-2)		-3.037	11.668
Goat_Miik	Control	81	11.176	16.325	0.503	96.689
	Depression	30	35.670	59.210	1.879	210.41
	Diff (1-2)		-24.493	33.591
Grape	Control	81	15.645	5.750	8.058	47.251
	Depression	30	21.674	38.577	4.906	221.13
	Diff (1-2)		-6.029	20.499
Grapefruit	Control	81	4.255	3.962	0.807	32.913
	Depression	30	11.231	30.159	0.957	163.43
	Diff (1-2)		-6.976	15.922
Green__Pea	Control	81	7.021	6.334	1.020	35.195
	Depression	30	9.031	6.837	1.818	30.562
	Diff (1-2)		-2.010	6.471
Green_Pepper	Control	81	4.715	3.713	1.656	32.327
	Depression	30	14.672	46.012	0.957	256.35
	Diff (1-2)		-9.956	23.945
Halibut	Control	81	14.289	15.877	4.414	135.74
	Depression	30	19.259	22.743	4.874	96.737
	Diff (1-2)		-4.970	17.962
Honey	Control	81	10.351	5.111	2.733	29.823
	Depression	30	14.751	10.807	3.490	51.631
	Diff (1-2)		-4.400	7.089
Lemon	Control	81	3.050	2.460	0.201	20.655
	Depression	30	4.648	6.262	0.377	26.648
	Diff (1-2)		-1.598	3.857
Lettuce	Control	81	12.814	7.663	3.734	39.966
	Depression	30	27.973	42.368	4.151	211.92
	Diff (1-2)		-15.159	22.818
Lima_Bean	Control	81	6.294	5.248	1.546	35.107
	Depression	30	8.145	7.785	0.928	36.538
	Diff (1-2)		-1.851	6.028
Lobster	Control	81	9.455	6.640	1.311	41.983

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

ELISA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	Depression	30	8.911	6.106	2.808	26.300
	Diff (1-2)		0.544	6.502
Malt	Control	81	15.173	8.267	2.551	51.285
	Depression	30	20.386	11.297	6.461	53.111
	Diff (1-2)		-5.213	9.171
Millet	Control	81	4.065	4.304	1.435	40.360
	Depression	30	4.712	2.347	1.340	9.527
	Diff (1-2)		-0.647	3.881
Mushroom	Control	81	27.235	27.375	2.824	118.76
	Depression	30	32.179	33.673	4.434	131.33
	Diff (1-2)		-4.944	29.184
Mustard	Control	81	6.992	4.301	1.947	30.771
	Depression	30	16.454	41.250	2.547	233.06
	Diff (1-2)		-9.462	21.593
Oat	Control	81	18.201	20.144	1.176	88.428
	Depression	30	32.643	54.940	0.567	294.01
	Diff (1-2)		-14.442	33.180
Olive	Control	81	17.589	31.696	3.554	281.30
	Depression	30	41.626	68.210	4.194	274.07
	Diff (1-2)		-24.037	44.443
Onion	Control	81	13.450	23.822	2.271	210.93
	Depression	30	39.203	83.172	1.698	400.00
	Diff (1-2)		-25.753	47.508
Orange	Control	81	26.423	37.325	2.824	314.77
	Depression	30	59.267	67.356	5.328	279.36
	Diff (1-2)		-32.843	47.218
Oyster	Control	81	49.594	42.026	7.658	250.39
	Depression	30	101.583	81.794	9.637	278.65
	Diff (1-2)		-51.989	55.464
Parsley	Control	81	17.745	7.652	5.298	59.623
	Depression	30	32.472	53.175	9.491	303.98
	Diff (1-2)		-14.727	28.201
Peach	Control	81	10.414	10.155	1.913	53.125
	Depression	30	17.293	21.222	1.415	80.424

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

ELISA Score

Food	Diagnosis	N	Mean	SD	Min	Max
	□iff (1-2)		-6.880	13.983
Peanut	Control	81	5.730	9.913	1.223	89.273
	Depression	30	12.104	22.293	1.340	102.94
	Diff (1-2)		-6.374	14.295
Pineapple	Control	81	12.433	44.326	1.660	400.00
	Depression	30	22.446	42.174	0.943	211.16
	Diff (1-2)		-10.013	43.764
Pinto__Bean	Control	81	9.370	6.088	1.998	33.952
	Depression	30	26.024	65.400	3.067	359.76
	Diff (1-2)		-16.653	34.135
Pork	Control	81	16.675	14.641	4.198	89.423
	Depression	30	15.489	8.675	4.774	38.269
	Diff (1-2)		1.186	13.317
Potato	Control	81	12.243	8.339	4.922	75.768
	Depression	30	25.142	68.240	2.830	383.04
	Diff (1-2)		-12.899	35.916
Rice	Control	81	24.230	16.518	4.815	79.625
	Depression	30	39.755	72.482	5.189	400.00
	Diff (1-2)		-15.525	39.975
Rye	Control	81	5.122	3.376	1.569	23.489
	Depression	30	9.176	9.594	1.341	46.668
	Diff (1-2)		-4.055	5.732
Safflower	Control	81	7.553	4.020	2.452	27.492
	Depression	30	10.295	8.332	2.169	35.466
	Diff (1-2)		-2.742	5.507
Salmon	Control	81	16.307	15.972	0.100	136.52
	Depression	30	17.538	32.725	4.563	188.91
	Diff (1-2)		-1.231	21.730
Sardine	Control	81	33.099	14.613	7.838	87.492
	Depression	30	34.114	16.377	10.600	91.647
	Diff (1-2)		-1.015	15.103
Scallop	Control	81	50.308	23.097	11.061	116.33
	Depression	30	49.327	18.669	26.003	105.18
	Diff (1-2)		0.980	22.006

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

EL/SA Score

Food	Diagnosis	N	Mean	SD	Min	Max
Sesame	Control	81	73.449	87.622	3.433	400.00
	Depression	30	87.565	119.362	6.227	400.00
	Diff (1-2)		-14.116	97.085
Shrimp	Control	81	34.185	40.052	2.925	272.28
	Depression	30	23.914	41.579	4.681	236.84
	Diff (1-2)		10.271	40.464
Sole	Control	81	5.290	2.521	2.243	20.373
	Depression	30	5.586	2.457	2.166	10.613
	Diff (1-2)		-0.296	2.504
Soybean	Control	81	16.814	12.312	3.479	81.383
	Depression	30	43.489	82.869	5.580	400.00
	Diff (1-2)		-26.676	44.026
Spinach	Control	81	14.620	6.503	5.378	40.130
	Depression	30	33.807	70.886	6.133	400.00
	Diff (1-2)		-19.188	36.985
Squashes	Control	81	7.200	4.790	2.259	24.675
	Depression	30	21.468	64.320	1.981	357.62
	Diff (1-2)		-14.268	33.430
Strawberry	Control	81	5.073	4.417	1.002	29.163
	Depression	30	25.236	73.898	1.341	400.00
	Diff (1-2)		-20.163	38.305
String_Bean	Control	81	37.257	22.322	7.894	146.17
	Depression	30	43.417	25.642	12.241	129.91
	Diff (1-2)		-6.160	23.252
Sunflower_Sd	Control	81	8.566	5.303	2.451	31.256
	Depression	30	18.573	40.806	2.888	231.71
	Diff (1-2)		-10.007	21.533
Sweet__Pot__	Control	81	17.536	13.698	4.101	74.660
	Depression	30	28.901	43.655	2.735	230.69
	Diff (1-2)		-11.365	25.392
Swiss_Ch_	Control	81	35.608	58.963	2.010	299.50
	Depression	30	88.439	135.669	3.237	400.00
	Diff (1-2)		-52.831	86.306
Tea	Control	81	23.966	9.868	7.617	46.395

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Sex Food	Diagnosis	N	ELISA Score Mean SD Min	Max
	Depression	30	31.665	19.407	8.589	101.02
	Diff (1-2)		-7.699	13.102
Tobacco	Control	81	36.231	21.642	8.831	125.93
	Depression	30	61.161	58.497	11.649	312.01
	Diff (1-2)		-24.930	35.414
Tomato	Control	81	9.199	6.995	2.319	40.933
	Depression	30	42.152	95.827	1.509	400.00
	Diff (1-2)		-32.953	49.790
Trout	Control	81	14.686	9.992	3.220	83.963
	Depression	30	19.945	42.469	2.207	242.68
	Diff (1-2)		-5.259	23.519
Tuna	Control	81	8.305	6.513	2.110	39.025
	Depression	30	8.443	7.910	1.428	36.792
	Diff (1-2)		-0.138	6.912
Turkey	Control	81	14.012	11.116	4.079	65.177
	Depression	30	13.494	8.545	5.336	39.414
	Diff (1-2)		0.518	10.494
Walnut_Blk	Control	81	20.821	10.402	5.682	58.466
	Depression	30	35.105	71.023	7.028	400.00
	Diff (1-2)		-14.284	37.702
Wheat	Control	81	13.359	10.034	3.237	71.930
	Depression	30	38.088	73.346	4.815	352.91
	Diff (1-2)		-24.729	38.796
Yeast_Baker	Control	81	12.471	20.370	2.073	123.35
	Depression	30	18.870	30.129	1.132	127.47
	Diff (1-2)		-6.399	23.368
Yeast__Brewer	Control	81	15.903	21.143	2.642	130.89
	Depression	30	29.020	52.876	1.603	246.75
	Diff (1-2)		-13.117	32.741
Yogurt	Control	81	15.651	16.295	3.004	73.200
	Depression	30	42.777	72.527	4.245	327.88
	Diff (1-2)	_	-27.126	39.930	_	_

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Upper Quantiles of ELISA Signal Scores among Control Subjects as Candidates for Test Cutpoints in Determining Positive or Negative

Top 26 Foods Ranked by Descending order of Discriminatory Ability using Permutation Test

Depression Subjects vs. Controls

Cutpoint

Food Ranking	Food	Sex	90th percentile	95th percentile
1	Almond	FEMALE	6.387	7.229
		MALE	8.201	10.364
2	Tomato	FEMALE	12.707	20.229
		MALE	16.458	23.901
3	Tobacco	FEMALE	46.676	52.120
		MALE	66.108	81.270
4	Carrot	FEMALE	5.028	6.030
		MALE	6.547	9.472
5	Orange	FEMALE	58.970	90.702
		MALE	47.782	67.581
6	Cucumber	FEMALE	12.530	18.094
		MALE	16.117	22.938
7	Broccoli	FEMALE	9.078	12.242
		MALE	13.227	17.923
8	Lettuce	FEMALE	17.214	21.301
		MALE	23.159	30.500
9	Malt	FEMALE	24.428	26.838
		MALE	26.135	30.793
10	Cantaloupe	FEMALE	7.816	9.816
		MALE	10.244	14.900
11	Corn	FEMALE	13.133	19.396
		MALE	16.893	27.942
12	Wheat	FEMALE	22.473	39.657
		MALE	23.851	30.952
13	Honey	FEMALE	12.418	14.289
		MALE	17.319	20.797
14	Chocolate	FEMALE	22.428	24.874
		MALE	31.758	37.652
15	Oat	FEMALE	22.737	27.403

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Food Ranking	Food	Sex	Cutpoint
90th percentile	95th percentile
		MALE	52.310	63.866
16	Avocado	FEMALE	3.604	4.378
		MALE	4.620	6.230
17	Rye	FEMALE	6.138	8.899
		MALE	8.345	11.849
18	Strawberry	FEMALE	8.038	14.219
		MALE	9.266	13.944
19	Cauliflower	FEMALE	7.651	9.029
		MALE	7.718	12.080
20	Safflower	FEMALE	10.386	16.047
		MALE	11.748	14.739
21	Tea	FEMALE	29.842	33.214
		MALE	37.508	42.162
22	Banana	FEMALE	5.442	9.272
		MALE	6.610	10.019
23	Squashes	FEMALE	10.270	11.945
		MALE	13.571	18.431
24	Green_Pepper	FEMALE	6.908	8.215
		MALE	7.040	9.133
25	Butter	FEMALE	40.015	66.921
		MALE	44.018	61.716
26	Buck_Wheat	FEMALE	9.667	13.975
		MALE	8.502	10.282

Table 4

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
171081AAB0001	0
171081AAB0002	0
171081AAB0003	4
171081AAB0004	0
171081AAB0005	5
171081AAB0006	1
171081AAB0008	3
171081AAB0009	5
171081AAB0010	0
171081AAB0011	3
171081AAB0012	5
171081AAB0014	18
171081AAB0015	1
171081AAB0016	5
171081AAB0017	11
171081AAB0018	7
171081AAB0019	1
171081AAB0020	1
171081AAB0022	7
171081AAB0023	1
171081AAB0025	0
171081AAB0027	18
171081AAB0028	8
171081AAB0029	11
171081AAB0030	2
171081AAB0032	7
171081AAB0033	19
171081AAB0037	4
171081AAB0039	1
171081AAB0040	8
171081AAB0043	0
171081AAB0044	13
171081AAB0045	0
171081AAB0046	0
171081AAB0047	7
171081AAB0049	18
171146AAB0002	0
171146AAB0003	4

NON-DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
BRH1244994	2
BRH1244995	0
BRH1244996	1
BRH1244997	0
BRH1244998	3
BRH1244999	0
BRH1245000	1
BRH1245001	0
BRH1245002	1
BRH1245004	0
BRH1245007	0
BRH1245008	0
BRH1245009	3
BRH1245010	4
BRH1245011	4
BRH1245014	0
BRH1245015	0
BRH1245018	1
BRH1245019	0
BRH1245022	11
BRH1245023	1
BRH1245024	2
BRH1245026	4
BRH1245029	0
BRH1245030	0
BRH1245031	3
BRH1245032	1
BRH1245033	0
BRH1245035	0
BRH1245037	0
BRH1245038	0
BRH1245039	11
BRH1245040	2
BRH1245041	2
BRH1267328	12
BRH1267329	1
BRH1267330	0
BRH1267332	0

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
171146AAB0004	2
171146AAB0005	1
171146AAB0006	1
171146AAB0007	0
171146AAB0008	6
171146AAB0009	7
171146AAB0010	1
171146AAB0011	14
171146AAB0013	1
171146AAB0014	3
171146AAB0015	4
171146AAB0016	0
171146AAB0017	3
171146AAB0018	5
171146AAB0019	11
171146AAB0020	2
171146AAB0021	7
171146AAB0022	11
171146AAB0023	7
171146AAB0025	3
171146AAB0026	1
171146AAB0027	2
171146AAB0028	12
171146AAB0029	0
171146AAB0030	0
171146AAB0031	1
171146AAB0032	4
171146AAB0033	0
171146AAB0036	7
BRH1339646	0
BRH1339647	2
BRH1339648	1
BRH1339649	0
BRH1339650	0
BRH1339655	6
BRH1339657	1
BRH1339658	2
BRH1339659	0
BRH1339660	0

NON-DEPRESSIQKJ POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
BRH1267333	0
BRH1267334	12
BRH1267335	3
BRH1267337	0
BRH1267338	0
BRH1267339	1
BRH1267340	6
BRH1267341	0
BRH1267343	3
BRH1267345	0
BRH1267346	0
BRH1267347	0
BRH1267349	0
BRH1244900	0
BRH1244901	6
BRH1244902	0
BRH1244903	0
BRH1244904	1
BRH1244905	0
BRH1244906	11
BRH1244907	0
BRH1244908	1
BRH1244909	0
BRH1244910	3
BRH1244911	2
BRH1244912	1
BRH1244913	0
BRH1244914	7
BRH1244915	1
BRH1244916	14
BRH1244917	11
BRH1244918	0
BRH1244920	2
BRH1244921	0
BRH1244922	14
BRH1244923	1
BRH1244924	0
BRH1244925	1
BRH1244926	10

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
BRH1339662	0
BRH1339664	6
BRH1339666	0
BRH1339667	0
BRH1339668	4
BRH1339669	20
BRH1339670	1
171081AAB0007	17
171081AAB0013	2
171081AAB0021	9
171081AAB0024	7
171081AAB0026	11
171081AAB0031	18
171081AAB0034	14
171081AAB0035	2
171081AAB0036	0
171081AAB0038	7
171081AAB0041	0
171081AAB0042	2
171081AAB0048	0
171081AAB0050	1
171146AAB0012	0
171146AAB0024	0
171146AAB0034	16
171146AAB0035	7
171146AAB0037	6
171146AAB0038	1
171146AAB0039	0
171146AAB0040	2
BRH1339651	7
BRH1339652	0
BRH1339653	0
BRH1339654	14
BRH1339656	1
BRH1339661	2
BRH1339663	1
BRH1339665	0

No of

114

NON-DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
BRH1244928	1
BRH1244929	3
BRH1244931	0
BRH1244932	2
BRH1244933	4
BRH1244934	2
BRH1244938	4
BRH1244939	0
BRH1244940	0
BRH1244941	0
BRH1244942	4
BRH1244943	1
BRH1244944	10
BRH1244945	1
BRH1244946	5
BRH1244947	0
BRH1244948	0
BRH1244949	1
BRH1244950	0
BRH1244951	0
BRH1244952	0
BRH1244953	1
BRH1244954	0
BRH1244956	18
BRH1244959	1
BRH1244960	0
BRH1244961	0
BRH1244962	0
BRH1244963	0
BRH1244964	5
BRH1244965	0
BRH1244967	0
BRH1244969	0
BRH1244970	1
BRH1244971	1
BRH1244972	0
BRH1244973	2
BRH1244974	0
BRH1244975	0

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 90th Percentile
Observations
Average Number	4.4
Median Number	2

# of Patients w/ 0 Pos Results	31
% Subjects w/ 0 pos results	27.2

NON-DEPRESSION POPUEATiON
Sample ID	# of Positive Results Based on 90th Percentile
BRH1244976	0
BRH1244977	0
BRH1244979	0
BRH1244980	0
BRH1244981	0
BRH1244982	0
BRH1244983	0
BRH1244985	0
BRH1244987	0
BRH1244988	2
BRH1244991	0
BRH1244992	2
BRH1267320	0
BRH1267322	4
BRH1267323	0
BRH1267325	3

No of Observations	132
Average Number	2.0
Median Number	0

# of Patients w/ 0 Pos Results	68
% Subjects w/ 0 pos results	51.5

Table 5A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
171081AAB0001	0
171081AAB0002	0
171081AAB0003	1
171081AAB0004	0
171081AAB0005	1
171081AAB0006	1
171081AAB0008	2
171081AAB0009	4
171081AAB0010	0
171081AAB0011	3
171081AAB0012	1
171081AAB0014	15
171081AAB0015	1
171081AAB0016	4
171081AAB0017	5
171081AAB0018	3
171081AAB0019	1
171081AAB0020	1
171081AAB0022	3
171081AAB0023	0
171081AAB0025	0
171081AAB0027	12
171081AAB0028	6
171081AAB0029	6
171081AAB0030	0
171081AAB0032	5
171081AAB0033	17
171081AAB0037	1
171081AAB0039	0
171081AAB0040	7
171081AAB0043	0
171081AAB0044	11
171081AAB0045	0
171081AAB0046	0
171081AAB0047	6
171081AAB0049	9
171146AAB0002	0
171146AAB0003	1

NON-DEPRESSiON POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
BRH1244994	0
BRH1244995	0
BRH1244996	0
BRH1244997	0
BRH1244998	2
BRH1244999	0
BRH1245000	1
BRH1245001	0
BRH1245002	0
BRH1245004	0
BRH1245007	0
BRH1245008	0
BRH1245009	1
BRH1245010	2
BRH1245011	2
BRH1245014	0
BRH1245015	0
BRH1245018	1
BRH1245019	0
BRH1245022	5
BRH1245023	1
BRH1245024	1
BRH1245026	3
BRH1245029	0
BRH1245030	0
BRH1245031	0
BRH1245032	0
BRH1245033	0
BRH1245035	0
BRH1245037	0
BRH1245038	0
BRH1245039	7
BRH1245040	1
BRH1245041	0
BRH1267328	8
BRH1267329	0
BRH1267330	0
BRH1267332	0

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
171146AAB0004	2
171146AAB0005	0
171146AAB0006	0
171146AAB0007	0
171146AAB0008	3
171146AAB0009	5
171146AAB0010	0
171146AAB0011	9
171146AAB0013	0
171146AAB0014	1
171146AAB0015	0
171146AAB0016	0
171146AAB0017	1
171146AAB0018	2
171146AAB0019	8
171146AAB0020	1
171146AAB0021	4
171146AAB0022	8
171146AAB0023	6
171146AAB0025	2
171146AAB0026	1
171146AAB0027	1
171146AAB0028	9
171146AAB0029	0
171146AAB0030	0
171146AAB0031	0
171146AAB0032	4
171146AAB0033	0
171146AAB0036	2
BRH1339646	0
BRH1339647	1
BRH1339648	0
BRH1339649	0
BRH1339650	0
BRH1339655	4
BRH1339657	1
BRH1339658	1
BRH1339659	0
BRH1339660	0

NON-OEPRESSOSI POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
BRH1267333	0
BRH1267334	8
BRH1267335	2
BRH1267337	0
BRH1267338	0
BRH1267339	0
BRH1267340	4
BRH1267341	0
BRH1267343	2
BRH1267345	0
BRH1267346	0
BRH1267347	0
BRH1267349	0
BRH1244900	0
BRH1244901	3
BRH1244902	0
BRH1244903	0
BRH1244904	0
BRH1244905	0
BRH1244906	7
BRH1244907	0
BRH1244908	1
BRH1244909	0
BRH1244910	1
BRH1244911	1
BRH1244912	0
BRH1244913	0
BRH1244914	3
BRH1244915	0
BRH1244916	9
BRH1244917	4
BRH1244918	0
BRH1244920	1
BRH1244921	0
BRH1244922	11
BRH1244923	0
BRH1244924	0
BRH1244925	1
BRH1244926	9

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
BRH1339662	0
BRH1339664	6
BRH1339666	0
BRH1339667	0
BRH1339668	2
BRH1339669	20
BRH1339670	0
171081AAB0007	17
171081AAB0013	2
171081AAB0021	4
171081AAB0024	6
171081AAB0026	10
171081AAB0031	17
171081AAB0034	13
171081AAB0035	0
171081AAB0036	0
171081AAB0038	2
171081AAB0041	0
171081AAB0042	2
171081AAB0048	0
171081AAB0050	1
171146AAB0012	0
171146AAB0024	0
171146AAB0034	12
171146AAB0035	2
171146AAB0037	2
171146AAB0038	1
171146AAB0039	0
171146AAB0040	2
BRH1339651	5
BRH1339652	0
BRH1339653	0
BRH1339654	12
BRH1339656	0
BRH1339661	1
BRH1339663	0
BRH1339665	0

No of

114

NON-DEPRESSiON POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
BRH1244928	0
BRH1244929	1
BRH1244931	0
BRH1244932	0
BRH1244933	3
BRH1244934	1
BRH1244938	1
BRH1244939	0
BRH1244940	0
BRH1244941	0
BRH1244942	3
BRH1244943	1
BRH1244944	3
BRH1244945	1
BRH1244946	3
BRH1244947	0
BRH1244948	0
BRH1244949	0
BRH1244950	0
BRH1244951	0
BRH1244952	0
BRH1244953	1
BRH1244954	0
BRH1244956	13
BRH1244959	0
BRH1244960	0
BRH1244961	0
BRH1244962	0
BRH1244963	0
BRH1244964	3
BRH1244965	0
BRH1244967	0
BRH1244969	0
BRH1244970	0
BRH1244971	0
BRH1244972	0
BRH1244973	0
BRH1244974	0
BRH1244975	0

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

DEPRESSION POPULATION
Sample ID	# of Positive Results Based on 95th Percentile
Observations
Average Number	3.0
Median Number	1

# of Patients w/ 0 Pos Results	45
% Subjects w/ 0 pos results	39.5

NON-DEPRESSiON POPUEATiON
Sample ID	# of Positive Results Based on 95th Percentile
BRH1244976	0
BRH1244977	0
BRH1244979	0
BRH1244980	0
BRH1244981	0
BRH1244982	0
BRH1244983	0
BRH1244985	0
BRH1244987	0
BRH1244988	1
BRH1244991	0
BRH1244992	1
BRH1267320	0
BRH1267322	1
BRH1267323	0
BRH1267325	0

No of Observations	132
Average Number	1.1
Median Number	0

# of Patients w/ 0 Pos Results	88
% Subjects w/ 0 pos results	66.7

Table 5B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summ ar y s tati sites

Variatsfe · ; Depressionpercentile ; ; DepressionOOtf percentile i
			1«
Lw st value			2.»
Billii oBillllllllllllllll			20000 :
Arifisnretss? mA-.sr·.			3 4?% ;
95% €1 for tfts m-san			3.4414 is 5.4007 :
Lfedfsn			Ζδϋϋϋ ;
			1 ΟΜβ tn 4 MOO i
Vanasc®			270742 '
Standard a			>2.'O3 ;
PeisiivA s%ndsrd <fev»rinn			1 %4? [11 δ49%) i
Standard erroref the snean			0.4345 :
Csefocfent of S isewtms			IllllllllllllllliliiOiii
			1 im? (P%1 057%
IliilliiiiO:® ΙΐΙΐίΙοίιιιιιιι			jgjgd: Msrmairty (fM .3001)
	liiiiill		65%. Confidence interval
2.5	0.0003 ;
5	ο.δδ@3 i		0.0000% 0..0000 ;
io (	0.0003 :		OOCOOtOOOOOO .
25 (	0.0003 i		O.OOOOfo i 0003 ;
7S (	7.0009		5.00013 ts 0003 i
90 (	131003 ;		S.3SS0· tr. 17.04 S3: (
9S (	τζ.δδδδ ;		13.8580 % 15.8568 i
97.5	ia0003 i

Table 6A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics

ιΙίι^Ιίιιιιιιβ	Depres s ion _95th_ perce ntil e Depression 35th percentiie
Sam pie size					114
illiest i iiiii					0800
					201S22.
lOililliilllllllll				3.0000
				2 1788 to 3.8232
ββ··ι					1.0000
			1.0000 to 2.0000
ilOeoiiiiiii					19.0014
Starwlatd deviation				4.4364
Reiafcve standard deviation			1.4788(147.88%)
Standard er rar of the rears			8.4155
			1 9281iPrflOSOI)
Coefficient of Kurtosis
D’Ag<JStifto-Pesisen test
for htormsf distnfeattor	lllllll	______________________________
i»®iiiiiiiiiiiiii
as			@.0000 :
S			0 0000 i		0.0000 to 8.0000
io			0.0000 i		0.0000 to 8.0080
2¾			0.0000 i		0 0000 to 0.0000
?s			4 0000		2.0000 to 6.9000
»a			9.1:000 i		50000 to 12.9482
:9:5			12.8000 ;		9.8880 to 17.0000
S7.-5			17.0000 :

Table 6B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics tton _De press i on _9Ws_perce n tile

Nofs-Depres-siofi 30th percentile

Variable

Sample size			132
Lowest vaiae			OM0
lOliOiliBilllllllli			IS 0000
Arithmetic mssn			I 902
95% Cl for the mean			13δδ2 to 2.5640
Median			0.0030
		0.0000 to 1. &000
			12.2199
			3 4957
Relates standard davialran		1.7815(178.16%}
		0.3843
			lllllllllliiiiiiiiiiiiiBlilliiiO
		ΙΙΙΙΙΙΙΙΙΙΙ|:όόόόόόόόό^^^^Β^Β^ίί(ί|§^^Β^Βί^ΟΙΟΟ^όΒΒόό
for Norrnai digit i button
Percentiles
25	δ.ΰδδδ
5	0.0000		0.0083 to 0.0030
40	q.qoot		0.0003 to 0.0000
25	0 CiGOO		0.0008 to 0.0030
	2.0000		13003 to 3.0000
so	S.QOOQ		43003 is 11.0000
S5	11QOOQ		6.61781s 14.0000
97.5	12.4000

Table 7A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics
Va r iah le N on	3 e pr e s sio n_9 5 th _pe rcentile i
Non-Depression S5th percentile ;
Sample size		132 ;
		δ.δδδδ ί
i Highest value		Ιδδδδδ I
Arithmetic rnes«		10530 ί
i 95% Cl for the meat?		Q.&&46 :ο 1.4512 ί
		δ.δδδδ ί
i 95% Cl for the median		δ.δδδδ to δ.δδδδ ί
Variance		5.3483 ί
		2 3126 ;
	2.1962 (219.62%) ί
Standard -errtir of the mean	δ.2δ13 ί
C o&ff:ci en t of Sik ew η e s s	!!!!!!!!!β
Coefficient of tor· to sis		ΙΙΙΙΙΙΙΙΙΙΙ®^^
	ί eject Ν ο? mality (P<0.0001)
for Normal distfbotion
Percentiles	<(<«<«<«<«<«<«(<»
25	8.Οδδδ ;
s	δ.δδδδ ;	δ.δδδδ ta δ.δδδδ ·
	δ.δδδδ ι	δ.δδδδ ί© δ.δδδδ ί
2&	δ.δδδδ	δ.δδδδ to δ.δδδδ ;
75	ι.δδδδ :	Ι.δδδδ ta 1 9068 ·
sa	3.δδδδ ί	δ.δδδδ la Τ.Οδδδ '
as	Τ.δδδδ ί	3.δδδδ to 3.6200 ί
i Si? 5	δ.δδδδ ;	:

Table 7B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics

	D e press ton _S δ th_ pe f c ea t ile _ 1 )
	Depression 90th percentile 1 i
Back-tfansfemed after logarithmic transfofmaiiss. I
Sample size		114- i
Lowest value		LJooo i
Highest value		28 .0000 ΐ
	1.4587 i
	1.0367 to 2.0544 i
fefe^		2.0000 ;
	1.0000 to 4.0000 i
Coefficient of Skewness	-0.4070 (P~@.0728) (
Coefficient of Kurtssis	lililiie
	lllllllllie

Percentiles	iiiiiiiiiiiiiiiiiiiiliiiiiiiB
25	0.10000 i
6	0.10000 i	Oz 10000 ts-0.10000
10	0.10000 i	0 10000 to- 0.10000
26	0.10000 :	0.10000 to 1.0000
Y	7.0000 i	5.0000 to 8.0000
SS	1.3.0987 i	9.8160 to 17.9458
96	17.7954 ;	13.0637 to 18.8535
57..5	18.0000 ΐ

Table 8A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary s tatistics

	Depress:i:on._95th__percentite__1 ; Depressisn §5th percentile 1 :
Back-transformed after icganthmic transformation. i
				114 :
				0.1000: :
				200000 i
Geometric mean				0.0117 :
9-5% Ci for the mean				0.5753 to 1.1453: :
					1.9900 j
95% Ci for the median			1.0000 Is 2.9000 :
			0.0450? (P=0.8348$ ?
Coeftfofont st Kurts sts				1111^^
for Noma®istrihutton				Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:Ϋ:$
Percentiles.
2.5			0.10900
5			0.10000		0 10000 to 0 WOOD i
w			0.10 WO	i	0 10030 to 0.10900 ;
25			0.10000		0.10990 to 0 10300 Ϊ
75			4.0000	i	2.0000 to 3.9900 ί
59			9.0553	:	5 0030 to 12.9462 ;
			12.7935 :	5.8519 to 17.0399 :
975			17.0900	:	:

Table 8B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics

Mon_D e pre s sisa _S 0 th __p ercen Q e_ t

Mcn-Depressiae 90th percentile 1

Variable

Back-transformed after legarithmic transformation. ]
Sample size	132 :
L s&vest O||:e	CLUQO
Highest value	13.3300 i
	3.4333.
	3.3S14 to 0.6622 ί
	0.10660 :
35% Ct for the mediae	0.16000 to 1.3300 ί
	Ι!!!!!!!!!!!!όόόόόόόόόόόόόόόό®
farfttormal distribution

Percentiles	iiiiiiiiiiiiiiiiiiiiliiiiiiiiiiiiiiiiiB
25	0.18000 ;
	0.16000 i	0.10800 to 0.13300
13	0.10080 1	0.10800 to 0.10300
26	0.18800 :	0.10000 to 0.10060
?&	2.63301 i	1.0000 to 2 0800
S3	€.8000 1	4.3300 to 11.0660
33.	11.0080 ;	6 .5895 to 14.3300
97.5	12.3787 j

Table 9A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Summary statistics

N on _ lllllllllllllllllll Non-	De pr e ss ton_95ft __ p e r c e π fte_ 1 : Depressis® 95 th percernt.iie 1 i
Back-transformed after logarithmic transfcmaticn !
Sample size Lowest vaite Highest vafcs Geometric mean Coefficient cf Skewness fo r N s rma I d istr ibutios	132 ί δ. W00 | 13.0800 ΐ 0.2788 ί 0.2141 to 0 3S31 . 0.10000 i .........................................................VJOOOO leO 10000 : ΙΐΐΐΙ|ΙΙΙΙΙΙ1ΐ|ΙΐΐΙΒΙΐΙΐΐΒθ®Λ;%·Ρ:5υ<ι* ..................................................................-044S4(P^a(21S2))
Percentiles	lllllllllllliiiiil	ΐίόΒΒΒΒΒΒΒΒΒΒΒΒΒΒό^^^όΟί^^ΟΪ^Β^όό^^^ΟΪίόό]
2-5	δ.ΐδδοο
5 Iit> 11|f | Iliii JJ 25 lilliiiiiiiiii lllll W	0.10000 ....................0 10000 δ.ΐδδδδ .......................ϊ'οδδδ ......................3 ϋδδύ ........................7 0000 .................δ,όόόο'	: 0.1 δ&ΟΟ Ιο 0.10000 I :.........................................................0 10000 to 0 17000 : ...............................................0.10000 taOJOOii :................................................................1.0000 to 1.9513 ; •..............................................................2 ΟΟΟδ to 7 ΟΟΟϋ' : .............. 3 OLOOto 3 577? | . ;

Table 9B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166 independent s am pies t--test

Sarnie 1
|0^O|||||||||||	D e pres s to n_ S Otfe... pe r can tite... 1 Depresston 5®th perce tdtle i
iiioi2
VatosFte	N an_D s pr e s si® n_30ft_ perse π tSe_ 1 Nan-Depressfan 33tn pereentiie J
Back-transformed after tegariihmic transform a tan.
	333333333333333β^^^^	Sample 2
iBeiiiiiiiiiiii		114 (	132
GeomaVic mean		1458? i	0.4852
	1.0357 to 2:.(1.544 i	0 3614 io 0.6522
Variance st Lors		3.842:3 i	0.5832
	.........................................................................................	P 3 652

T-test (assuming equal variances) uifto rence on Log-traBsforrred sci'to

Different

Stsnrfar-8 Error

35¾ Ci cf difference

Test stafes&cϊ

Llegreas of Fre«d»rn rDF;

T'Ms-tsifea prpfeafei% '

5a ck- Ira n star m ed res fits

Rabe- o^{ gponiPljif means

95U Cl o‘ rat>D ..........

-0.4745

...................t .03951

5A713 to -0.2777

......................-4 749

244

0.3354

02Ί 32 to 0.5276

Table 10A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Independent samples t-test

Sairtiste 1
Vansble	Dept esstan __S5th_,pereentiie_ 1 Depression 95ft percentile J
lliiBlllllllll	N on __ De pre sston_9-5th_pe cen-t iie_ 1 M on-De press -ion S5te percentile 1
Back-transformed after logarithmic transformation.
		333β^^
Sarnie stee i 114 ; 0.8117 i ...................................0.5753 to 14453 ’ i....... 0. ¢4 3 ϋ i	132 0.2738 ................8.2:141 fog' 8531 0.4434

F tastin' ugisa vattarrcss	ΙΙΙΙΙΙΙΙΙΙβ
T-test(asstiming equal variances)
Difference on Log-transformed scale
Difference	-e.404-1
Sisfidarii Error	0.0S384
95% Ci of difference	-8.6488 ti -8.2732
T est statists t	-4 846
Degrees »f Freedom (DP)	244
Tw-tateti prsFahWy	™ p i oer
Back-tran stormed resit its
Ratio of g 63 m sine means	0.3435
35% Cl af rsbc·	3.22:44 to 0.5257

Table 10B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Mann-Whitney test (Independent samples)

fiampfe 1
Variable	D epr es sio n_S Oth _ percen tie
	Depression 99th percentile
Semple 2
Variable	Non Depression 90ih percents
	Non-Depression 9Sth percentile
	ilililililililililB
		114 ·	132
liooioiiiiiiii		NC A	9.9990
		2Nl± j	13 ϋϋοο
		2.9990 i	9.9900
	0099 to 4..9090 i	0.0090 to 1.9990
InterrgisrSfe fasge		9.0900 to 7.0000 ;	0.9000 to 2.0000

yann-Whitney test (Independent samples)

		145..2149
		104.7462
		5046.50
iiBiiiiiiiiBlliiiBiiiiBiOillllli		4.699
Two-taife'i probability		p< 0.0091

Table 11A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Mann-Whitney test (independent samples)

|||®||)®||||||||||||||||||||||β^^^^
lililiilllllllllll	D epres sion_9 5th _ perce nh le Depression 3*3Βί percgntife
|B|(i|O|||||||||||i	N oti _D e pr s ssicn_95th_ pe r centime Nos-Deprsssisn 95th percentile

ewe st value value

Median

35¾ Cl foi the mefen interpua rttie rarge ||||||||||β

ZZZZZ'' W ......................C.&Q80 ....................2¾¾¾¾¾ .....................ϊοοοδ ΰϋΟΟΟ 'to 2.CO3C

AQjCCio 4 C030 :

llllllllillilll .............................Tv ..................Uewe ¹

1.3 JJ® : ®.Q{W ] € ..eow tooow Ϊ C.COOC to ’ Οί+ΰ ^ann-Wiitney test (independent samples)

Ave rags cf second group

Mann-Whto »y U

Test statistic Z (sofresfod for ties) I wa-tai led preha bility

144.407

1053&64 s 0

4.&Θ4 p <O'asi

Table 11B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

ROC cuw

VariaMe	i Depression f » ’- Η i : i Depression 1 > ί 9 th :
	i Diagnosis— 1 D»p> i n_0_Non_D8p?ession_ ; i Diagnosis/ 1 IHp > u · ό__Μαί?· Depression) :
	245 i
Kssrfive Nepa&ve grasp 4 ;	114 (45.34%) : 132(53.35%) ;

^A Diagnosis__1_D'©pressi:an_3_i4sn__Daprs ssion_ = 1 * Diagnosis__1JDepressianJM}<}n_D8pression_ = &

Disease prevai erace (¾} I anknawn

Area under the ROV. curve (AUC)

9.S65

.......0.0336 δ.δδί id ί 723

...........................4(893 <0 300·

Standard Error * £ slabs tic

Etgmficstwe fevei P(Ares=S.5| '*' DeLong et aL' 1S08............................

^a Snorris· ex ad

Yuutien ii-dex

Yaiidsn iilsfex)	0.2472
3E% C-onSdence interval*	0.09 744 io 0.3185
As sedated criterion
98% C-snWance ioter^si5	>2.24131557® to >S
Sensifeiy	36. 64
Specifics^	37.03

EC» bootstrap confidence intent si/iesd iterations: random number seed: §78).

Table 12A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

R OC curve

i Ysriable Clsesificalwn variabie	D ο pr ession.. _Te st. 3 St h : Degression Test.95th i Diagnosis._____1_Depressi8n_9_NonJDepressien_. ; DiagnosisC 1 DftproRsirtn 0 Non-PepirBssion} ;
i Sample sise-			246 i
ίιί-Νι rsjlivSrpnap illllll 1 Negam igte'up2 i/		.......................................................................................................	114 ,46.34%} : 132 j53.6&%<} :

* Diagnosis__1:_-Depression__0__Non__Depressiar_ = 1 * Diagnosis__1_Depras.s:ion_0_N:on_Depresswr_ = δ

Disease prays fence: (%.} iinfatown

Area under the ROC curve {AUC)

95% C-unhdence ntervaP z statistic _v

S^mfeance level P (Are8®8 5} ' ^x DeESn^ el'afl S33.............

^s Binormal exact

0.689

...............C.0322 Ί cTs's'to 0 7 W

914 *088 i

You den index

95% Confidence ntervaP

8.2719

Ο56?ίαθ743 >6 is >1

- - ------------......................................................................................60^53 liiiii isiisiiisiiiiiiiii :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: nz:

~ SC? bootstrap confidence intenai (108¾ iterations: random narnter seed: 978}.

Table 12B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Performance Metrics in Predicting Depression Status from Number of Positive Foods

Using 90th Percentile of EUSA Signal to determine Positive

Sex	No. of Positive Foods as Cutoff	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Overall Percent Agreement
FEMALE	1	0.83	0.39	0.69	0.59	0.66
	2	0.70	0.56	0.72	0.53	0.65
	3	0.58	0.67	0.74	0.50	0.62
	4	0.51	0.72	0.75	0.47	0.59
	5	0.45	0.76	0.76	0.46	0.57
	6	0.41	0.81	0.78	0.45	0.56
	7	0.35	0.85	0.79	0.44	0.54
	8	0.30	0.87	0.79	0.43	0.52
	9	0.25	0.89	0.79	0.42	0.49
	10	0.22	0.90	0.79	0.42	0.48
	11	0.20	0.91	0.79	0.41	0.47
	12	0.18	0.91	0.78	0.41	0.46
	13	0.15	0.93	0.77	0.40	0.45
	14	0.12	0.94	0.77	0.40	0.43
	15	0.10	0.95	0.80	0.39	0.43
	16	0.09	0.97	0.80	0.39	0.42
	17	0.08	0.97	0.83	0.39	0.42
	18	0.07	0.97	0.88	0.39	0.42
	19	0.07	1.00	1.00	0.39	0.41
	20	0.06	1.00	1.00	0.39	0.41
	21	0.06	1.00	1.00	0.39	0.41
	22	0.05	1.00	1.00	0.39	0.41
	23	0.04	1.00	1.00	0.39	0.40
	24	0.02	1.00	1.00	0.39	0.40
	25	0.02	1.00	1.00	0.38	0.39
	26	0.00	1.00	1.00	0.38	0.38

Table 13 A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Performance Metrics in Predicting Depression Status from Number of Positive Foods

Using 90th Percentile of ELISA Signal to determine Positive

Sex	No. of Positive Foods as Cutoff	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Overall Percent Agreement
MALE	1	0.86	0.38	0.34	0.88	0.51
	2	0.74	0.57	0.39	0.86	0.62
	3	0.60	0.69	0.42	0.83	0.67
	4	0.48	0.76	0.42	0.80	0.68
	5	0.42	0.81	0.46	0.79	0.71
	6	0.40	0.86	0.50	0.79	0.73
	7	0.38	0.88	0.54	0.79	0.74
	8	0.35	0.89	0.55	0.79	0.75
	9	0.33	0.90	0.55	0.78	0.75
	10	0.30	0.91	0.56	0.78	0.75
	11	0.27	0.92	0.57	0.78	0.75
	12	0.25	0.93	0.57	0.77	0.75
	13	0.24	0.94	0.57	0.77	0.75
	14	0.21	0.94	0.57	0.76	0.74
	15	0.19	0.94	0.57	0.76	0.74
	16	0.17	0.95	0.57	0.76	0.74
	17	0.14	0.96	0.50	0.75	0.74
	18	0.13	0.96	0.50	0.75	0.74
	19	0.11	0.96	0.60	0.75	0.74
	20	0.11	0.98	0.67	0.75	0.74
	21	0.10	0.98	0.67	0.75	0.74
	22	0.10	0.98	0.75	0.75	0.75
	23	0.06	1.00	1.00	0.74	0.75
	24	0.05	1.00	1.00	0.74	0.74
	25	0.00	1.00	1.00	0.73	0.73
	26	0.00	1.00	1.00	0.73	0.73

Table 13B

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Performance Metrics in Predicting Depression Status from Number of Positive Foods

Using 95th Percentile of ELISA Signal to determine Positive

Sex	No. of Positive Foods as Cutoff	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Overall Percent Agreement
FEMALE	1	0.71	0.55	0.72	0.53	0.65
	2	0.54	0.70	0.74	0.48	0.60
	3	0.44	0.77	0.76	0.46	0.57
	4	0.36	0.82	0.77	0.44	0.54
	5	0.30	0.86	0.78	0.43	0.51
	6	0.25	0.90	0.81	0.43	0.50
	7	0.21	0.91	0.80	0.42	0.48
	8	0.18	0.93	0.80	0.41	0.47
	9	0.15	0.94	0.80	0.40	0.45
	10	0.12	0.95	0.80	0.40	0.44
	11	0.09	0.97	0.80	0.39	0.43
	12	0.08	0.97	0.83	0.39	0.42
	13	0.06	0.97	0.86	0.39	0.41
	14	0.06	1.00	1.00	0.39	0.41
	15	0.05	1.00	1.00	0.39	0.41
	16	0.05	1.00	1.00	0.39	0.41
	17	0.04	1.00	1.00	0.39	0.41
	18	0.04	1.00	1.00	0.39	0.40
	19	0.04	1.00	1.00	0.39	0.40
	20	0.02	1.00	1.00	0.39	0.40
	21	0.02	1.00	1.00	0.38	0.39
	22	0.02	1.00	1.00	0.38	0.39
	23	0.02	1.00	1.00	0.38	0.39
	24	0.00	1.00	1.00	0.38	0.38
	25	0.00	1.00	1.00	0.38	0.38
	26	0.00	1.00		0.38	0.38

Table 14A

SUBSTITUTE SHEET (RULE 26)

WO 2018/009845

PCT/US2017/041166

Performance Metrics in Predicting Depression Status from Number of Positive Foods

Using 95th Percentile of EUSA Signal to determine Positive

Sex	No. of Positive Foods as Cutoff	Sensitivity	Specificity	Positive Predictive Value	Negative Predictive Value	Overall Percent Agreement
MALE	1	0.75	0.54	0.38	0.85	0.59
	2	0.59	0.75	0.46	0.83	0.70
	3	0.48	0.83	0.50	0.81	0.74
	4	0.36	0.88	0.53	0.79	0.74
	5	0.33	0.90	0.55	0.78	0.75
	6	0.30	0.92	0.56	0.78	0.75
	7	0.27	0.92	0.57	0.77	0.75
	8	0.24	0.94	0.57	0.77	0.75
	9	0.22	0.94	0.60	0.77	0.75
	10	0.20	0.96	0.60	0.76	0.75
	11	0.18	0.96	0.60	0.76	0.75
	12	0.17	0.96	0.67	0.76	0.75
	13	0.16	0.98	0.67	0.76	0.75
	14	0.14	0.98	0.67	0.75	0.75
	15	0.12	0.98	0.67	0.75	0.75
	16	0.10	0.98	0.75	0.75	0.75
	17	0.09	1.00	1.00	0.75	0.75
	18	0.09	1.00	1.00	0.75	0.75
	19	0.07	1.00	1.00	0.75	0.75
	20	0.06	1.00	1.00	0.74	0.75
	21	0.06	1.00	1.00	0.74	0.75
	22	0.06	1.00	1.00	0.74	0.75
	23	0.05	1.00	1.00	0.74	0.74
	24	0.00	1.00	1.00	0.73	0.73
	25	0.00	1.00	1.00	0.73	0.73
	26	0.00	1.00		0.73	0.73

Table 14B

SUBSTITUTE SHEET (RULE 26)

Claims (100)

1. What is claimed is:

   1. A test kit for testing food intolerance in patients diagnosed with or suspected to have Depression, comprising:

   one or more distinct food preparations, wherein each distinct food preparation of the one or more distinct food preparations is independently coupled to an individually addressable solid carrier;

   wherein each distinct food preparation of the one or more distinct food preparations 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, wherein the average discriminatory p-value is determined by a process comprising 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.
2. 2. The test kit of claim 1 wherein the one or more distinct food preparations includes at least two food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
3. 3. The test kit of claim 1 wherein the one or more distinct food preparations includes at least four food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
4. 4. The test kit of claim 1 wherein the one or more distinct food preparations includes at least eight food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
5. 5. The test kit of claim 1 wherein the one or more distinct food preparations includes at least 12 food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table

   SUBSTITUTE SHEET (RULE 26)

   WO 2018/009845

   PCT/US2017/041166
6. 6. The test kit of claim 1 wherein the one or more distinct food preparations has 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.
7. 7. The test kit of any one of claims 1-5 wherein the one or more distinct food preparations has 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.
8. 8. The test kit of claim 1 wherein the one or more distinct food preparations has 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.
9. 9. The test kit of any one of claims 1-5 wherein the one or more distinct food preparations has 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.
10. 10. The test kit of claim 1 wherein FDR multiplicity adjusted p-value is adjusted for at least one of age and gender.
11. 11. The test kit of any one of claims 1-8 wherein FDR multiplicity adjusted p-value is adjusted for at least one of age and gender.
12. 12. The test kit of claim 1 wherein FDR multiplicity adjusted p-value is adjusted for age and gender.
13. 13. The test kit of any one of claims 1-8 wherein FDR multiplicity adjusted p-value is adjusted for age and gender.
14. 14. The test kit of claim 1 wherein at least 50% of the one or more distinct food preparations, when adjusted for a single gender, 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.
15. 15. The test kit of any one of claims 1-13 wherein at least 50% of the one or more distinct food preparations, when adjusted for a single gender, has an average discriminatory p-value of <

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166

    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.
16. 16. The test kit of claim 1 wherein at least 70% of the one or more distinct food preparations, when adjusted for a single gender, 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.
17. 17. The test kit of any one of the claims 1-13 wherein at least 70% of the one or more distinct food preparations, when adjusted for a single gender, 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.
18. 18. The test kit of claim 1 wherein all of the one or more distinct food preparations, when adjusted for a single gender, 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.
19. 19. The test kit of any one of the claims 1-17 wherein all of the one or more distinct food preparations, when adjusted for a single gender, 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.
20. 20. The test kit of claim 1 wherein the one or more distinct food preparations is crude filtered aqueous extracts.
21. 21. The test kit of any one of the claims 1-19 wherein the one or more distinct food preparations is crude filtered aqueous extracts.
22. 22. The test kit of claim 1 wherein the one or more distinct food preparations is processed aqueous extracts.
23. 23. The test kit of any one of the claims 1-21 wherein the one or more distinct food preparations is processed aqueous extracts.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
24. 24. The test kit of claim 1 wherein the solid carrier is a well of a multiwall plate, a bead, an electrical, a chemical sensor, a microchip or an adsorptive film.
25. 25. The test kit of any one of the claims 1-23 wherein the solid carrier is a well of a multiwall plate, a bead, an electrical, a chemical sensor, a microchip or an adsorptive film.
26. 26. A 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: contacting the food preparation with a bodily fluid of a patient that is diagnosed with or suspected to have Depression, and wherein the bodily fluid is associated with a gender identification;

    wherein 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;

    measuring IgG bound to the at least one component of the food preparation to obtain a signal;

    comparing the signal to a gender-stratified reference value for the food preparation using the gender identification to obtain a result; and updating or generating a report using the result.
27. 27. The method of claim 26 wherein the bodily fluid of the patient is whole blood, plasma, serum, saliva, or a fecal suspension.
28. 28. The method of claim 26 wherein the step of contacting a food preparation is performed with a plurality of distinct food preparations.
29. 29. The method of claim 26 or claim 27 wherein the step of contacting a food preparation is performed with a plurality of distinct food preparations.
30. 30. The method of claim 28 wherein the plurality of distinct food preparations is prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
31. 31. The method of any of the claims 28-29, wherein the plurality of distinct food preparations is prepared from food items of Table 1 or selected from foods 1-26 of Table 2.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
32. 32. The method of claim 28, wherein the plurality of distinct food preparations 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.
33. 33. The method of any of the claims 28-29, wherein the plurality of distinct food preparations 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.
34. 34. The method of claim 28, wherein the plurality of distinct food preparations has 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.
35. 35. The method of any of the claims 28-29, wherein the plurality of distinct food preparations has 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.
36. 36. The method of claim 28, wherein the plurality of distinct food preparations has 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.
37. 37. The method of any of the claims 28-29, wherein the plurality of distinct food preparations has 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.
38. 38. The method of claim 28, wherein all of the plurality of distinct food preparations 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.
39. 39. The method of any of the claims 28-29, wherein all of the plurality of distinct food preparations 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 pvalue.
40. 40. The method of claim 26, wherein the food preparation is immobilized on a solid surface, optionally in an addressable manner.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
41. 41. The method of any of the claims 26-39, wherein the food preparation is immobilized on a solid surface, optionally in an addressable manner.
42. 42. The method of claim 26, wherein the step of measuring IgG bound to the at least one component of the food preparation is performed via an immunoassay test.
43. 43. The method of any of the claims 26-41, wherein the step of measuring IgG bound to the at least one component of the food preparation is performed via immunoassay test.
44. 44. The method of claim 26, wherein the gender-stratified reference value for the food preparation is an at least a 90 percentile value.
45. 45. The method of any of the claims 26-43, wherein the gender-stratified reference value for the food preparation is an at least a 90^ta percentile value.
46. 46. A method of generating a test for food intolerance in patients diagnosed with or suspected to have Depression, comprising:

    obtaining test results for a plurality of distinct food preparations, wherein the test results are based on bodily fluids of patients diagnosed with or suspected to have Depression and bodily fluids of a control group not diagnosed with or not suspected to have Depression;

    stratifying the test results by gender for each of the distinct food preparations; and assigning for a predetermined percentile rank a different cutoff value for male and female patients for each of the distinct food preparations.
47. 47. The method of claim 46, wherein the test result is an ELISA result.
48. 48. The method of claim 46, wherein the plurality of distinct food preparations includes at least two food preparations prepared from food items of Table 1 or selected foods 1-26 of Table 2.
49. 49. The method of claim 46 or claim 47, wherein the plurality of distinct food preparations includes at least two food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
50. 50. The method of claim 46, wherein the plurality of distinct food preparations includes at least six food preparations prepared from food items of Table 1 or selected from a group consisting of foods 1-26 of Table 2.
51. 51. The method of any of claim 46 or claim 47, wherein the plurality of distinct food preparations includes at least six food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
52. 52. The method of claim 46, wherein the plurality' of distinct food preparations includes a food preparation prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
53. 53. The method of any of claim 46 or 47, wherein the plurality of distinct food preparations includes a food preparation prepared from food items of Table 1 or selected from foods 1 -26 of Table 2.
54. 54. The method of claim 46, wherein the plurality of distinct food preparations 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.
55. 55. The method of any of claims 46-53, wherein the plurality of distinct food preparations 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.
56. 56. The method of claim 46, wherein the plurality of different food preparations has 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.
57. 57. The method of any of claims 46-53, wherein the plurality of different food preparations has 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.
58. 58. The method of claim 46, wherein the plurality of different food preparations has 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.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
59. 59. The method of any of claims 46-53, wherein the plurality of different food preparations has 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.
60. 60. The method of claim 46, wherein the bodily fluid of the patient is whole blood, plasma, serum, saliva, or a fecal suspension.
61. 61. The method of any of claims 46-59, wherein the bodily fluid of the patient is whole blood, plasma, serum, saliva, or a fecal suspension.
62. 62. The method of claim 46, wherein the predetermined percentile rank is an at least 90^th percentile rank.
63. 63. The method of any of claims 46-61, wherein the predetermined percentile rank is an at least 90^ta percentile rank.
64. 64. The method of claim 46, wherein the cutoff value for male and female patients has a difference of at least 10% (abs).
65. 65. The method of any of claims 46-63, wherein the cutoff value for male and female patients has a difference of at least 10% (abs).
66. 66. The method of claim 26 or 46, further comprising a step of normalizing the result to the patient’s total IgG.
67. 67. The method of any of claims 26-65, farther comprising a step of normalizing the result to the patient’s total IgG.
68. 68. The method of claim 26 or 46, farther comprising a step of normalizing the result to the global mean of the patient’s food specific IgG results.
69. 69. The method of any of claims 26-65, farther comprising a step of normalizing the result to the global mean of the patient’s food specific IgG results.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
70. 70. The method of claim 26 or 46, further comprising a step of identifying a subset of patients, wherein the subset of patients’ sensitivities to the distinct food preparations underlies Depression by raw p-value or an average discriminatory p-value of < 0.01.
71. 71. The method of any of claims 26-65, further comprising a step of identifying a subset of patients, wherein the subset of patients’ sensitivities to the distinct food preparations underlies Depression by raw p-value or an average discriminatoiy p-value of < 0.01.
72. 72. The method of claim 26 or 46, further comprising a step of determining numbers of the distinct food preparations, wherein the numbers of the distinct food preparations can be used to confirm Depression by raw p-value or an average discriminatoiy p-value of < 0.01.
73. 73. The method of any of claims 26-65, further comprising a step of determining numbers of the distinct food preparations, wherein the numbers of the distinct food preparations can be used to confirm Depression by raw p-value or an average discriminatory p-value of < 0.01.
74. 74. Use of a plurality of distinct food preparations coupled to individually addressable respective solid carriers in a diagnosis of Depression, wherein 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.
75. 75. Use of claim 74, wherein the plurality of food preparations includes at least two food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
76. 76. Use of claim 74, wherein the plurality of food preparations includes at least four food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
77. 77. Use of claim 74, wherein the plurality of food preparations includes at least eight food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.
78. 78. Use of claim 74, wherein the plurality of food preparations includes at least 12 food preparations prepared from food items of Table 1 or selected from foods 1-26 of Table 2.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
79. 79. Use of claim 74, wherein the plurality of distinct food preparations has 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.
80. 80. Use of any one of claims 74-78, wherein the plurality of distinct food preparations has 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.
81. 81. Use of claim of claim 74, wherein the plurality’ of distinct food preparations has 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.
82. 82. Use of any one of claims 74-78, wherein the plurality of distinct food preparations has 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.
83. 83. Use of claim 74, wherein FDR multiplicity adjusted p-value is adjusted for at least one of age and gender.
84. 84. Use of any one of claims 74-82, wherein FDR multiplicity adjusted p-value is adjusted for at least one of age and gender.
85. 85. Use of claim 74, wherein FDR multiplicity adjusted p-value is adjusted for age and gender.
86. 86. Use of any one of claims 74-82, wherein FDR multiplicity adjusted p-value is adjusted for age and gender.
87. 87. Use of claim 74, wherein at least 50% of the plurality of distinct food preparations, when adjusted for a single gender, 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.
88. 88. Use of any one of claims 74-86, wherein at least 50% of the plurality of distinct food preparations, when adjusted for a single gender, has an average discriminatory p-value of <

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166

    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.
89. 89. Use of claim 74, wherein at least 70% of the plurality of distinct food preparations, when adjusted for a single gender, 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.
90. 90. Use of any one of the claims 74-86, wherein at least 70% of the plurality of distinct food preparations, when adjusted for a single gender, 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.
91. 91. Use of claim 74, wherein all of the plurality of distinct food preparations, when adjusted for a single gender, 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 pvalue.
92. 92. Use of any one of the claims 74-86, wherein all of the plurality of distinct food preparations, w’hen adjusted for a single gender, 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.
93. 93. Use of claim 74, wherein the plurality of distinct food preparations is crude filtered aqueous extracts.
94. 94. Use of any one of the claims 74-92, wherein the plurality of distinct food preparations is crude filtered aqueous extracts.
95. 95. Use of claim 74, wherein the plurality of distinct food preparations is processed aqueous extracts.
96. 96. Use of any one of the claims 74-94, wherein the plurality of distinct food preparations is processed aqueous extracts.

    SUBSTITUTE SHEET (RULE 26)

    WO 2018/009845

    PCT/US2017/041166
97. 97. Use of claim 74, wherein the solid carrier is a well of a multiwall plate, a bead, an electrical sensor, a chemical sensor, a microchip, or an adsorptive film.
98. 98. Use of any one of the claims 74-96, wherein the solid carrier is a well of a multiwall plate, a bead, an electrical sensor, a chemical sensor, a microchip, or an adsorptive film.
99. 99. Use of any one of claims 74-96, wherein the average discriminatory p-value is determined by a process comprising comparing assay values of a first patient test cohort that is diagnosed with or suspected of having Depression headaches with assay values of a second patient test cohort that is not diagnosed with or suspected of having Depression headaches.
100. 100. The method of claim 46, wherein the test result is an ELISA result derived from a process that includes separately contacting each distinct food preparation with the bodily fluid of each patient.