Personalized Food Elimination Diet: A Clinical Trial based on Food sensitivity assessment

doi:10.21203/rs.3.rs-4596789/v1

Food sensitivity, a common but overlooked condition, has been associated with a variety of gastrointestinal symptoms. Typically, patients suffering from food sensitivity are placed on elimination diets, although the success of this approach has not been sufficiently validated. We aimed to assess whether effective serological assessment for food sensitivity and the implementation of a subsequent elimination diet improved patients’ symptoms and overall well-being. In this study, 52 participants were tested for serum IgG and IgA levels against 262 food antigens. Based on the results, participants followed personalized elimination diets excluding foods they were reactive to, for a period of 4 weeks. Symptoms were assessed weekly using the ‘Food Sensitivity-Symptom Severity Scale’ (FS-SSS), which was seen to decrease in 88.46% of participants from baseline to week 4 (p < 0.05). The physician’s evaluation of participants' responses was analyzed using the ‘Food Sensitivity-Global Improvement Scale’ (FS-GIS), where 84.61% of participants saw notable improvements. Antibody titers post-intervention showed improved IgG levels in 96.15% of the participants while IgA levels showed improvement in 84.61% of the participants. Serological assessment followed by a personalized elimination diet effectively addressed food sensitivity, evidenced by reduced symptoms, improved antibody titers, and favorable physician assessments of patient response.

Personalized Medicine

Gastroenterology & Hepatology

Immunology

food sensitivity

IgG antibody

IgA antibody

elimination diet

gastrointestinal symptoms

Food sensitivity is a broad term used for symptoms perceived to be caused by the consumption of certain foods [1]. Due to the non-specificity, it is subject to a wide range of interpretations which warrants the need for improved healthcare modalities to address the condition [1]. Food sensitivity is seen to globally affect approximately 20% of the population with its prevalence increasing over the past decade [2, 3]. Despite the ambiguity, it is known to be a non-immunoglobulin (Ig) E-mediated response to food antigens that can give rise to gastrointestinal (GI) and extra-GI symptoms [4]. Broadly, food sensitivity is considered to be a result of IgA and IgG antibody reactions. In the gut, IgA antibodies in the mucosa trigger immune responses by reacting with food antigens that cross the epithelium, resulting in local GI symptoms [5]. Systemic IgG reacts with dietary antigens that have translocated through the intestinal wall. IgG reactions with food antigens located on the surface of tissues lead to the rapid local destruction of these tissues [6]. As a result, these antibodies react with food proteins to form complexes that accumulate in various tissues in the body, thereby affecting various body systems that could result in GI distress, skin issues, neurological disruptions, respiratory discomfort, and/or musculoskeletal manifestations [6, 3]. If these reactions persist on regular dietary intakes, they can give rise to severe conditions due to chronic inflammation. Food sensitivity may manifest as abdominal pain, bloating, bowel irregularity, brain fog headache, muscle and joint pain, fatigue, depression, extremity numbness, dermatitis, or even anemia [4]. These symptoms are known to dramatically affect the quality of life and the overall well-being of the individual.

The concept that dietary components can elicit non-IgE immune responses has gained substantial acceptance owing to the understanding of Celiac disease (CD). In this condition, the gliadin peptide in gluten leads to local inflammation damaging the villi of the small intestinal and decreasing intestinal function which manifests with GI and extra GI symptoms [7]. Previously, using our silicon-based microarray platform, IgG and IgA antibodies against gliadin epitopes were detected at 99% sensitivity and 100% specificity [8]. Our quest for understanding the role of food in different diseases led us to investigate the association of autoantibodies in wheat-related conditions wherein we found that a significant number of patients with wheat-related diseases had autoimmunity marked by the presence of anti-extractable nuclear antigens (ENA) autoantibody biomarkers [9]. Previous research indicated that rheumatoid arthritis (RA) patients who followed a ‘gluten-free diet’ experienced a significant reduction in disease activity [10]. We then sought to understand the association of wheat-related disorders with RA and cardiovascular diseases (CVDs) respectively [11, 12]. The results from these different studies indicated a significant correlation between the biomarkers of wheat-related disorders and the characteristic biomarkers of RA and CVDs, respectively [11, 12]. These results highlight the notion that food-related conditions can profoundly affect other body systems and can be intricately linked to various diseases, thereby affecting overall health.

Food-sensitive reactions are not immediate and they could manifest slowly, occurring a few hours to days after the ingestion of a particular food. However, they may persist for many years without any suspicion of the cause [13]. As a result, it is very difficult to associate a specific food with experienced symptoms [6]. It is also imperative to bear in mind that foods consumed are generally a mixture of various ingredients, which makes it challenging to identify the cause of the food-sensitive reaction. In the context of food sensitivity, ‘masking’ refers to the repetitive ingestion (usually, more often than once in 3 days) of the reaction-sensitive foods, enough to enable the patient’s adaptative defenses to mask the unpleasant symptom or reaction to each feeding for as long as the food is consumed regularly [14]. While it appears that this could temporarily make the patient feel better by avoiding a symptom, excessive exposure to that food can cause the symptom to become chronic, and/or other symptoms to appear [14]. Therefore, it becomes important to clinically diagnose food sensitivity. Food sensitivity is generally diagnosed by examining the patient’s symptoms, exclusion of other diseases, assessing the impacts of dietary modifications, and blood tests [4]. The lack of specific biomarkers for food sensitivity and overlapping clinical manifestations renders it difficult to detect the underlying food sensitivity. Currently, the clinical gold standard for confirming food sensitivity is adherence to a strict elimination diet followed by a challenge diet involving a gradual, one-by-one systematic reintroduction of individual foods [3]. However, this process is time-consuming, and patients fail to reintroduce foods often due to (atypical) symptoms during reintroduction, the fear of a food reaction, and not being convinced by the challenge test result [15]. This warrants the need for a more refined approach to addressing food sensitivity.

Studies have hypothesized that high concentrations of IgG antibodies brought about by increased intestinal epithelial permeability to food antigens could be triggering immunological reactions leading to food sensitivity [3]. Given that IgA is the predominant form of antigen-specific immunity present in the gut lumen, it can also be used to understand the manifestation of food sensitivity [16]. Conducting serological analysis can help detect the presence of IgG and IgA antibodies on ingestion of specific foods, leading to accurate identification of the reaction-inducing foods [1, 3]. In this study, we used our silicon-based peptide microarray [8, 17] and protein microarray technology [18, 19] to identify food sensitivity against 262 food substances via the quantification of IgG and IgA antibodies bound to each food using enzyme- or fluorescence-linked immunosorbent assays. Based on the serological detection of the reactive foods, a personalized ‘elimination diet’ devoid of the reaction-inducing food was recommended for 4 weeks. Studies reveal that a ‘4-week long’ elimination diet under appropriate medical care is an effective and safe means of addressing food-induced conditions [20, 21].

This study sought to understand whether effective serological assessment and the subsequent implementation of an elimination diet was a reasonable approach to food sensitivity, that improved patients’ symptoms and overall well-being.

Ethics and Trial registration

The study was approved by WCG IRB (Puyallup, Washington, USA) under IRB # 1-1586736-1. The study was conducted in accordance with the ethical principles that have their origins in the Declaration of Helsinki. Serum samples were collected from participants from 8 healthcare practices that were registered as study sites. All the participating subjects provided written informed consent. The protocol was registered on clinicaltrials.gov prior to enrolment (NCT05389683).

Study design

This was a single-arm, interventional study. It aimed to assess the effectiveness of an ‘elimination diet’ on food sensitivity symptoms. Patients experiencing GI symptoms were screened and recruited at healthcare practices. Participants were tested for food antigens at the protein and peptide levels. Based on the test results, an elimination diet devoid of antigenic foods was employed. Participants followed the diet for 4 weeks after which their blood was tested again to check for improvements in their food sensitivity biomarker profile. Appropriate questionnaires were administered at different time points to assess symptoms, participant’s response to the intervention, and their quality of life.

Subjects

Adults aged between 18–75 years presenting with GI symptoms, and who were willing to provide informed consent and have their blood drawn were enrolled in the study. Concurrent medications and supplementation for other health conditions were allowed if dosing was stable for 2 weeks prior to enrolment. The exclusion criteria included chronic illnesses, pregnancy, use of antibiotics within 1 month of enrolment, and previous treatments for food allergies/sensitivity. The study also excluded participants who have been introduced to an elimination diet or participants following a restricted diet of any kind.

Microarray Architecture

Peptide array

The comprehensive synthesis of the peptide array has been described in our previous work [8]. Silicon-based wafers (200-mm diameter) with a 100-nm-tall thermal oxide–coated feature area and nonfeature area containing silicon, were created for solid-phase peptide synthesis. They were synthesized using photolithography and an inductively coupled plasma deep-etching technique. The silicon-based wafer's surface was coated with a single layer of aminosilane, facilitating the attachment of peptides. Peptide synthesis was then conducted on these attachment sites using standard fluorenylmethoxycarbonyl (Fmoc) chemistry. Once the Fmoc protection was eliminated, the exposed amine was joined with the desired Fmoc amino acid using a specialized reticle that selectively activates the precise site where the incoming amino acid should be linked [17]. The procedure was then repeated for every individual layer of amino acids to generate the intended peptide sequences within each designated area.

Protein array

For protein microarrays, silicon wafers of prime grade 300 mm with p-type boron, (1 0 0) orientation, resistivity of 1 to 5 Ω cm − 1, and 725 µm thickness were deposited with 100 nm thermal oxide. This was carried out by dry oxidation at 1,000°C in a furnace under pure oxygen atmosphere for two hours. Wafers underwent thorough washing with deionized water for five minutes and were then spin-coated with a solution comprising 1% (vol/vol) of 3-aminopropyltriethoxysilane (APTES) in N-methylpyrrolidone (NMP). Subsequently, they were left at room temperature for 15 minutes. The wafers were cured at 120°C for 60 min under N2 atmosphere and humidity-controlled environment. A solution comprising a copolymer of poly(L-lysine) and poly (lactic acid) was applied onto the wafer's surface and allowed to react for 24 hours. This process aimed to create a surface with strong binding capabilities, enabling the immobilization of proteins through passive adsorption via hydrophobic interactions with the lysine polymer and covalent coupling with the lactic acid polymer [18].

Protein attachment

The food antigen extracts were commercially sourced (Stallergenes Greer), and coupled to the wafer at a concentration of 1.0 µg/ml and allowed to react for 24 hours at 4°C. Any surplus unbound antigens were eliminated through thorough washing with aqueous phosphate buffer, and the remaining unreacted substrate was neutralized using a blocking solution containing BSA and glycine. Each wafer was marked with a distinctive identifier to categorize the attached antigens.

Pillar plate assembly

The pillar plate assembly process has been illustrated in our previous work [18, 19]. The wafers dedicated to each antigen were subsequently cut into microchips measuring 0.7 × 0.7 mm² using a stealth dicing method, as detailed previously [18, 19]. These diced wafers were then selected and transferred onto separate carrier tapes employing a conventional die sorting system. The carrier tapes were loaded onto a high-throughput surface mount technology (SMT) component placement system. Next, the microchips were picked up and positioned onto 24-pillar plates, with each pillar accommodating a layout of 251 microchips.

Serological Analysis

4 mL of blood was collected during each blood draw, before and after the implementation of the elimination diet. The blood collected was tested for 11 antigenic foods at the peptide level including wheat, corn, dairy, eggs, lectins, nuts, peanuts, seafood, soy, grains, and mammalian milk using the comprehensive microarray tests called the Food Zoomer. The Food Sensitivity test assessed 251 whole food extracts at the protein level. Serum samples were probed using 1:50 dilution and DBS samples using a 1:20 dilution on the pillar plate and reacted for 15 minutes at room temperature. The plate was then washed with Tris-buffered saline with Tween 20 (TBST) (Amresco) buffer 3 x 5 minutes each. The plates were finally dried completely before adding chemiluminescent substrate (Clarity Max from Bio-Rad) and scanned for five minutes on a standard Chemiluminescence Imager. Assaying is performed on automated liquid handlers enabling quick turnaround.

An in-house reporter software was used to extract the raw chemiluminescent signals for all the probes from the images. The chemiluminescent signals were then converted into intensity plots after quantile normalization, background and spatial correction. The signal threshold was defined for each antigen by calculating the mean +/- SD of the signal intensity for the same antigen among the healthy controls. The raw data was converted into arbitrary chemiluminescent units (CU) based on each individual antigen cut-off for further analysis.

Intervention

Based on the results from serological analysis, a personalized ‘elimination diet’ devoid of reaction-inducing foods was suggested for a period of 4 weeks. Any deviation from the suggested diet or the accidental/mistaken consumption of the eliminated food was to be reported.

Questionnaires

The ‘Food Sensitivity - Symptom Severity Scale’ (FS-SSS) was a 16-item questionnaire that assessed the severity of participants’ symptoms under the 3 main categories, GI distress, skin reactions, and general discomfort. The scores for each symptom ranged from 0 (none) to 5 (severe). Participants were asked to fill out the FS-SSS questionnaire at baseline and then weekly until the diet was completed.

The ‘Food Sensitivity - Quality of Life Questionnaire’ (FS-QoL) was a 21-item measure assessing the degree to which food sensitivity symptoms were affecting the participant’s quality of life. The questionnaire aimed to gauge the distress caused due to food sensitivity, with each item rated on a 5-point Likert scale. Participants filled out the FS-QoL twice during the study, at baseline and after diet completion. Lower scores were indicative of an improved quality of life.

The ‘Food Sensitivity - Global Improvement Scale’ (FS-GIS) was a single-item assessment of the physician’s view of the participant’s global functioning prior to and post the intervention. This questionnaire allowed the physician to report the participant’s response to the given intervention. The scale was a 7-point Likert scale ranging from ‘substantially improved’ to ‘substantially worse.’

Outcome measures

The primary outcomes of the study included improvement in food sensitivity symptoms, understanding the physician’s opinions on the participant’s response to the intervention, and change in the biomarker profile. The improvement in symptoms was assessed by investigating the change in FS-SSS scores between baseline and weeks, 1,2,3, and 4, respectively (primary endpoint of the study). The physician’s opinion on the intervention was understood by the FS-GIS scores. Change in the biomarker profile was measured by testing serum before and after the intervention.

Secondary outcomes included exploring the effect of demographic parameters such as body mass index (BMI), sex, and age on symptoms. Additionally, the influence of the intervention on the participant’s quality of life was also investigated. This was carried out by examining the pre-and post-intervention FS-QoL scores.

Data analyses

Data was analyzed using the Excel add-in, MegaStat in Microsoft Excel 2021. Baseline characteristics for the study population were summarized as means ± SD. As the data was not normally distributed, descriptive statistics represented the variables as medians [quartile 1 (Q1), quartile 3 (Q3)]. The intraindividual change in symptoms from baseline to weeks 1,2,3,4, respectively, was assessed by conducting a Wilcoxon Signed Rank Test. Additionally, the Friedman test was used to examine the overall change in symptoms across baseline to week 4. Changes in antibody titers for individual participants were computed by calculating the percent decrease in the sum of their pre- and post-intervention concentrations.

The secondary outcome that explored the influence of demographics on symptoms and interventional outcomes was also conducted using the Wilcoxon Signed Rank Test and Friedman test, respectively. p values for all analyses were two-tailed, with the level of significance being less than 0.05. Additionally, various graphical representations were created to visually depict the distribution of the change in the biomarkers, FS-SSS and FS-GIS scores in the study population. Significance of the change in FS-QoL scores pre- and post-intervention was analyzed using the Wilcoxon Signed Rank Test.

Baseline characteristics

Figure 1 summarizes the enrolment and analysis in the clinical trial. The study included 52 individuals recruited from healthcare practices. The study population comprised 23% males and 77% females with the overall average age of the participants being 45.44 ± 13.67 years. The BMI was computed from the weight (lbs) and height (inches) of the participants. The average BMI of the population was 25.32 ± 5.95 (((lbs/inches²) *703)). The baseline FS-SSS score was 21 ± 14.27. The baseline characteristics for the study population have been given in Table 1.

Table 1. Baseline characteristics

Baseline characteristics
	(N = 52)
Age	45.44 ± 13.67
Female, n (%)	40 (77)
Male, n (%)	12 (23)
Body mass index ((lbs/inches2) *703)	25.32 ± 5.95
FS–SSS	21 ± 14.27

Data is given as mean ± SD

Abbreviations: FS-SSS - Food Sensitivity - Symptom Severity Scale

Intervention outcomes

Food Sensitivity - Symptom Severity Scale (FS-SSS)

To assess the effectiveness of the elimination diet, we aimed to understand the weekly improvement in symptoms during the intervention. For this, the baseline FS-SSS scores were compared to those of weeks, 1,2,3, and 4, respectively. The difference in the FS-SSS scores medians of the baseline and week 1 was dramatic with their medians [(Q1), (Q3)] being, 19.5 [(9.75), (31.25)] and 13.5 [(4.75), (23.25)], respectively. On statistically assessing the baseline and week 1 FS-SSS scores, a significant difference was observed (z = 4.11, p < 0.05) (Table 2). After week 1, a gradual decrease in FS-SSS scores was seen in weeks, 2, 3, and 4. Their medians [(Q1), (Q3)] were 11 [(4), (19.25)], 7 [(2.75), (14.5)], and 6 [(2), (11)], respectively. Statistically significant differences in FS-SSS scores were noted between baseline and week 2 (z = 5.27, p < 0.05), week 3 (z = 5.76, p < 0.05), and week 4 (z = 5.61, p < 0.05), respectively (Table 2). The Friedman test enabled assessing the overall change in the FS-SSS scores across baseline to week 4 which was also found to be statistically significant (p < 0.05) (Table 3). Figure 2 is indicative of the reduction in mean FS-SSS scores during the intervention.

Table 2. Weekly improvement in symptoms assessed by the Wilcoxon Signed Rank Test

Weekly improvement in symptoms
	(N = 52)

	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	p Value
	Baseline	Week 1
FS - SSS	19.5 [(9.75), (31.25)]	13.5 [(4.75), (23.25)]	< 0.05*

	Baseline	Week 2
FS - SSS	19.5 [(9.75), (31.25)]	11 [(4), (19.25)]	< 0.05*

	Baseline	Week 3
FS - SSS	19.5 [(9.75), (31.25)]	7 [(2.75), (14.5)]	< 0.05*

	Baseline	Week 4
FS - SSS	19.5 [(9.75), (31.25)]	6 [(2), (11)]	< 0.05*

*Statistically significant at α=0.05.

Abbreviations: FS-SSS - Food Sensitivity - Symptom Severity Scale

Table 3. Friedman Test analysis for overall improvement and effect of demographics on intervention outcomes

Friedman Test analysis for overall improvement and effect of demographics on intervention outcomes
	(N =52)
	Baseline FS-SSS	Week 1 FS-SSS	Week 2 FS-SSS	Week 3 FS-SSS	Week 4 FS-SSS	p Value
	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]

Overall improvement	19.5 [(9.75), (31.25)]	13.5 [(4.75), (23.25)]	11 [(4), (19.25)]	7 [(2.75), (14.5)]	6 [(2), (11)]	< 0.05*


BMI
Normal weight	14 [(6.5), (26.5)]	12 [(4), (19.5)]	9 [(4), (16.5)]	5 [(1.5), (14.5)]	6 [(1.5), (9.5)]	< 0.05*
Overweight	27 [(14), (37)]	17 [(6), (25)]	15 [(9), (20)]	9 [(5), (14)]	6 [(4), (11)]	< 0.05*


Sex
Female	20.5 [(11.75), (35.25)]	16.5 [(5), (24.5)]	13 [(6), (20)]	9 [(4), (16.25)]	6 [(2.75), (11.5)]	< 0.05*
Male	10.5 [(4), (22.25)]	8.5 [(3.5), (13.25)]	6.5 [(1.75), (12.25)]	5 [(1), (9)]	5 [(1), (6.25)]	< 0.05*


Age
18 - 45 years	24 [(12.5), (37)]	17 [(10), (25)]	12 [(8), (19.5)]	9 [(4.5), (18)]	6 [(4), (13)]	< 0.05*
46 years and above	14 [(5), (27)]	9 [(4), (19)]	9 [(2), (18)]	6 [(2), (13)]	6 [(1), (9)]	< 0.05*

Assessment was carried out by testing the FS-SSS scores from baseline to week 4.

* Statistically significant at α=0.05

Abbreviations: FS-SSS - Food Sensitivity - Symptom Severity Scale

Food Sensitivity - Global Improvement Scale (FS-GIS)

The physician’s observations were gauged using the FS-GIS score for each participant. The score allowed the physician to report the participant’s response to the intervention by comparing his/her health status before and after the intervention. Of the total study population, 17.31% were rated with the score ‘1’ indicating substantial improvement. 36.53% received a score of ‘2’ and 30.77% received a score of ‘3’ indicating moderate and slight improvement, respectively. However, 9.62% of the participants received a score of ‘4’ showing no change while 5.77% received a score of ‘5’ indicating slightly worsened outcomes. No participants reported with the scores, ‘6’ and ‘7’ which were indicative of moderately and substantially worsened outcomes. Based on the score, overall, 84.61% of the population reported to have improved scores while the rest of the 15.39% of participants reported scores corresponding to no improvement or worsened conditions, accordingly. The distribution of the FS-GIS in the study population is represented in Figure 3.

Correlation between the Food Sensitivity - Symptom Severity Scale (FS-SSS) and Food Sensitivity - Global Improvement Scale (FS-GIS) scores

On analyzing the FS-SSS scores for each FS-GIS category, it was evident that FS-GIS scores coincided with the change in symptoms (Figure 4). FS-SSS scores in the ‘substantially improved -1’ category were seen to dramatically decrease from baseline to week 4. On the other hand, there appeared to be an exacerbation in symptoms in week 4 for the ‘slightly worse - 5’ category which justified the FS-GIS score (Figure 4).

Serological analysis

Antibody titers for food sensitivity are quantified on a scale from 0 to 30 for each food antigen. Titers ranging from 0 to 10 are classified as baseline antibody levels, 10 to 20 as moderately elevated, and 20 to 30 as highly elevated. These thresholds were established based on data from healthy individuals without gut issues who maintained a balanced diet and regular physical activity. In this study, the change in biomarkers was assessed by calculating the percent decrease between pre- and post-intervention values. The improvements in IgG and IgA values are summarized in Figure 5. Among the study participants, 96.15% exhibited improved IgG values, indicated by a reduction in IgG titers post-intervention. For IgA, 84.61% of participants showed a decrease in IgA titers following the intervention. Furthermore, when assessing the percent decrease in antibody titers relative to symptom improvement, 88.46% of participants showed symptomatic improvement, as indicated by the reduction in their FS-SSS scores from baseline to week 4 (Figure 5). Overall, 71.15% of the participants showed reductions in IgG, IgA, and FS-SSS values (Figure 5).

The above-mentioned values indicating changes in individual assessment parameters are also represented in the Venn diagram (Figure 6). This figure collectively elucidates the patterns of change across all assessment parameters including, IgG, IgA, FS-SSS, and FS-GIS values. It displays the absolute number of participants who experienced changes within each parameter (category) and their overlaps (sub-category) alongside the respective percentages. Percentages for each category and sub-category were calculated based on the total study population (N = 52). Between IgG titers and FS-SSS scores, 44 (84.61%) participants exhibited improvements, while 42 (80.76%) participants had improvements in IgG and FS-GIS values. Concurrent improvements in IgA and FS-SSS values, as well as IgA and FS-GIS values, were noted in 39 (75%) participants for each pair. Finally, 33 (63.46%) participants demonstrated improvements across all four parameters (Figure 6). A summary of the changes in IgG and IgA titers, FS-SSS values, and FS-GIS scores for each participant is provided in Supplementary Material, Table 1.

The effect of demographics on intervention outcomes

In this study, the effect of BMI on symptoms was explored by categorizing the underweight (BMI<18.5) and normal weight (BMI=18.5–24.9) participants under the ‘Normal weight’ group while the overweight (BMI=25–29.9) and obese (BMI ≥ 30) participants were categorized under the ‘Overweight’ group. Statistical analysis revealed that changes in symptoms in the ‘Normal weight’ group between baseline and weeks, 1 (z = 3.04, p < 0.05), 2 (z = 3.95, p < 0.05), 3 (z = 4.33, p < 0.05), 4 (z = 4.05, p < 0.05), respectively were statistically significant. A similar observation was made for the ‘Overweight’ group when the change in symptoms was compared between baseline and weeks, 1 (z = 2.92, p < 0.05), 2 (z = 3.54, p < 0.05), 3 (z = 3.82, p < 0.05), 4 (z = 3.90, p < 0.05), respectively. Correspondingly, when assessed based on sex, the change in the FS-SSS scores between baseline and weeks, 1 (z = 3.72, p < 0.05), 2 (z = 4.79, p < 0.05), 3 (z = 5.13, p < 0.05), 4 (z = 5.00, p < 0.05), respectively, was statistically significant for females. However, for males, the change between baseline and week 1 was not significant (z = 1.43, p=0.15) while the change in symptoms between baseline and weeks 2 (z = 2.00, p < 0.05), 3 (z = 2.66, p < 0.05), 4 (z = 3.31, p < 0.05), respectively, was statistically significant. On conducting age-based analysis, the change in symptoms for the ‘18 - 45 years’ group between baseline and weeks, 1 (z = 2.81, p < 0.05), 2 (z = 3.80, p < 0.05), 3 (z = 4.18, p < 0.05), 4 (z = 4.11, p < 0.05), respectively was statistically significant. Similarly, statistically significant differences in symptoms between baseline and weeks, 1 (z = 3.27, p < 0.05), 2 (z = 3.61, p < 0.05), 3 (z = 3.96, p < 0.05), 4 (z = 3.90, p < 0.05), respectively were observed for the age group, ‘46 years and above.’ These results obtained from the Wilcoxon Signed Rank Test have been summarized in Table 4. The Friedman test assessed the effect of all the demographic parameters on the FS-SSS scores across the baseline to week 4. This statistical assessment revealed significant changes in symptoms for all parameters throughout the intervention (Table 3).

Table 4. Effect of demographics on intervention outcomes based on the Wilcoxon Signed Rank Test

Effect of demographics on intervention outcomes based on the Wilcoxon Signed Rank Test
	(N = 52)
		Baseline FS-SSS	Week 1 FS-SSS	Week 2 FS-SSS	Week 3 FS-SSS	Week 4 FS-SSS
		Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]	Median [(Q1), (Q3)]

BMI
	Normal weight	14 [(6.5), (26.5)]	12 [(4), (19.5)]	9 [(4), (16.5)]	5 [(1.5), (14.5)]	6 [(1.5), (9.5)]
	p value†		< 0.05*	< 0.05*	< 0.05*	< 0.05*

	Overweight	27 [(14), (37)]	17 [(6), (25)]	15 [(9), (20)]	9 [(5), (14)]	6 [(4), (11)]
	p value†		< 0.05*	< 0.05*	< 0.05*	< 0.05*

Sex
	Female	20.5 [(11.75), (35.25)]	16.5 [(5), (24.5)]	13 [(6), (20)]	9 [(4), (16.25)]	6 [(2.75), (11.5)]
	p value†		< 0.05*	< 0.05*	< 0.05*	< 0.05*

	Male	10.5 [(4), (22.25)]	8.5 [(3.5), (13.25)]	6.5 [(1.75), (12.25)]	5 [(1), (9)]	5 [(1), (6.25)]
	p value†		0.15	< 0.05*	< 0.05*	< 0.05*

Age
	18 - 45 years	24 [(12.5), (37)]	17 [(10), (25)]	12 [(8), (19.5)]	9 [(4.5), (18)]	6 [(4), (13)]
	p value†		< 0.05*	< 0.05*	< 0.05*	< 0.05*

	46 years and above	14 [(5), (27)]	9 [(4), (19)]	9 [(2), (18)]	6 [(2), (13)]	6 [(1), (9)]
	p value†		< 0.05*	< 0.05*	< 0.05*	< 0.05*

† p values are from the Wilcoxon Signed Rank Test conducted by assessing the baseline FS-SSS scores with the scores from week 1,2,3,4, respectively.

* Statistically significant at α=0.05.

Abbreviations: FS-SSS - Food Sensitivity - Symptom Severity Scale

Quality of life

Due to low compliance with filling out the FS-QoL form, the analysis could only be conducted for a subset of the population. 47 participants from the study population completed the pre- and post-FS-QoL forms. Of the subset, 57% of participants experienced an improvement in their quality of life, as evidenced by a reduction in their FS-QoL scores after the intervention. However, 43% of participants did not experience an improvement in their quality of life indicated by their unchanged or increased FS-QoL scores at week 4. On conducting statistical analysis, it was observed that the difference in the pre-and post-test FS-QoL scores was not statistically significant (z = 1.82, p = 0.06).

The study sought to understand the effectiveness of comprehensive identification of food sensitivity using peptide and protein microarrays, and the subsequent implementation of a personalized elimination diet in improving symptoms and overall well-being. Although not well-established, the consensus on the recommended length of an elimination diet is thought to be between 2 and 12 weeks [20]. The European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) [22], the European Academy of Allergy and Clinical Immunology (EAACI) [23], the Diagnosis and Rationale for Action against Cow’s Milk Allergy (DRACMA) Guidelines [24] as well as Vandenplas et al. [25] suggest that 4 weeks of an elimination diet is suitable to judge the response and improvement in patients [20]. In our study, statistical assessment revealed that on following the elimination diet there was a significant reduction in symptoms between baseline and week 4 (p < 0.05) in 88.46% of participants, which was the primary outcome of the study. Moreover, when tested for weekly improvement by assessing the difference in FS-SSS scores between baseline and weeks, 1,2, and 3, respectively, statistical analysis revealed significance for all weeks (p < 0.05). This suggests that improvement in symptoms on adhering to an elimination diet can begin as early as week 1.

Physician’s opinions on patient’s response to interventions are critical in understanding the effectiveness of the given intervention. The GIS is a well-established rating tool applicable in clinical research that enables the physician to take into account the overall state of the patient at each visit (illness severity, distress, and other aspects of impairment). Keeping these aspects in mind, once the intervention has been initiated, the physician can judge the patient's overall health status by comparing his/her current state to that of his/her condition before the intervention [26]. In our study, physicians judged the participants’ state on completion of the elimination diet (after week 4). On assessing the overall study population, 84.61% of participants responded positively to the intervention as marked by the FS-GIS scores corresponding to substantially (17.31%), moderately (36.53%), and slightly (30.77%) improved scores, respectively. However, 9.62% of participants experienced no changes while the intervention was seen to slightly worsen the condition for 5.77% of them, in accordance with their FS-GIS scores. An interventional study indicated that patients suffering from irritable bowel syndrome (IBS) had greater improvements in their GIS scores after 4 and 8 weeks of following the elimination diet, as opposed to the healthy controls [27]. These findings strengthen the notion that an elimination diet is a good approach to alleviate GI symptoms and conditions, with patients showing overall improvements in response to the intervention.

Previous research has established the association of IgG antibodies with food sensitivity [3]. Under normal conditions, the gut mucosa is impermeable to antigens. However, damage to the intestinal epithelium allows antigens to permeate through it, leading to immunization and the production of IgG antibodies. The subsequent binding of IgG antibodies to these antigens results in immune reactions, including the formation of antigen-antibody complexes and activation of complement proteins and effector immune cells, characteristic of Type III hypersensitivity reactions. IgG antibodies against food antigens may indicate mucosal damage from antigen permeability and contribute to further intestinal damage and increased permeability. IgG not only protects against infections but also mediates inflammation [28]. Previously IgG and IgA antibodies specific to gliadin peptides have been identified in CD [17], prompting the inclusion of serological assessment of IgA levels against food antigens in this study. Diagnostic biomarker assessment reliably evaluates clinical conditions. We evaluated changes in IgG and IgA titers against 262 food antigens by computing the percent decrease in their pre- and post-intervention values. 96.15% of participants showed a reduction in IgG titers, while 84.61% showed a reduction in IgA titers following the personalized elimination diet. In a randomized controlled trial by Neuendorf et al. with overweight/obese adults, 83% of targeted foods showed decreased IgG antibody values post-IgG-based elimination diets [3].

Eliminating IgG-food antigens may prevent inflammation and immune reactions from IgG-mediated food hypersensitivity. In an interventional trial with IBS patients, those on an IgG-based elimination-rotation diet showed significant symptom improvement compared to those on a low FODMAP or control diet [29]. Additionally, IgA, which predominantly contributes to immunity in the gut, monitors commensal microbiota and responds to enteric pathogens [30]. Assessing serum IgA levels can help identify the food antigens that have translocated into the bloodstream and are capable of eliciting immune responses [31]. Given its crucial role in diagnosing conditions like CD and its ability to identify food antigens, evaluating IgA antibodies is essential for assessing food sensitivity. Our study findings indicate that decreased IgG and IgA titers correlated with symptom reduction in 71.15% of participants, highlighting the critical role of these antibodies in managing food sensitivity.

We sought to understand whether demographics impacted the improvement in symptoms during the intervention. On assessing the effect of BMI and age on the FS-SSS scores, statistical significance was achieved for both groups from weeks, 1 to 4. This indicated that these parameters did not affect the change in symptoms during the intervention as all the groups experienced a significant reduction in FS-SSS scores (p < 0.05). However, when assessed for sex, significance was achieved for females from weeks, 1 to 4. For males, significance was observed only in weeks 2 to 4 (p < 0.05). The difference in the FS-SSS scores between baseline and week 1 for males was not statistically significant (p = 0.15), indicating that males took longer to show symptom improvements on following the personalized elimination diet. Recent studies have shown that women are more likely to suffer from functional GI disorders associated with pronounced symptoms of diarrhea, constipation, and bloating [32]. In this study, the median baseline FS-SSS score for females was 20.5 while that for males was lower which was 10.5. Males may have taken a longer period to show symptom improvement due to potentially less severe symptoms, contrasting with females who, with more severe inherent symptoms, showed immediate improvement upon adhering to the elimination diet.

Quality of life (QoL) is a concept that aims to capture the well-being of an individual at a specific point in time [33]. Understanding the QoL is of paramount importance in fields of healthcare where the aggressive pursuit is to maximize the overall well-being of patients [33]. In this study, the FS-QoL score that gauged the distress caused by food sensitivity symptoms was seen to improve in 57% of the population. It is plausible that participants were more cautious once they were aware of the reaction-inducing foods. Knowledge of these foods and constantly having to avoid them must have contributed to the distress leading to poor FS-QoL scores in the rest of the study population. Given the complexity of the concept of QoL, its assessment is challenging and may require additional measures to capture subjectivity.

From a clinical viewpoint, this study provides valuable insights into effectively addressing food sensitivity. The condition can manifest as a wide range of symptoms occurring hours to days post-intake of the provocative food. This makes it challenging to identify the root cause of the symptoms. The current study underscores the importance of accurate detection of antigenic foods for diagnosing food sensitivity and implementing personalized elimination diets. Healthcare professionals can utilize reliable diagnostic tests to assess food sensitivity and recommend personalized interventions. Moreover, serological assessment effectively detects the antigenic foods eliminating any ambiguity and the need to reintroduce foods following an elimination diet, which is oftentimes not preferred by patients [15]. The broad utilization of diagnostic tests for food sensitivity may enhance detection and management of sporadic GI and extra-GI symptoms, potentially improving public health. It is important to recognize that food is typically consumed as a complex mixture of ingredients. The average number of ingredients in various food combinations was found to be approximately 12 [34], which complicates the identification of specific causative ingredients responsible for food sensitivity. However, diagnostic assessment of food sensitivity can accurately identify the reactive food ingredient and help educate patients about their sensitivities across diverse food sources. This empowers patients to make informed food choices and mitigate risks of unpredictable food-sensitive reactions. Patients may often choose to simply monitor their diet and avoid foods that may trigger reactions, which seems like a practical approach to managing food sensitivity. However, this method may overlook the underlying causes that can only be identified through diagnostic testing. By choosing this method over diagnostic testing, patients may also overlook other potentially sensitive foods that have not yet caused symptoms. Moreover, unnecessary dietary restrictions can result in nutritional deficiencies [15]. Therefore, conducting a diagnostic test for food sensitivity provides clarity on identifying reaction-inducing foods.

The study exhibited both notable strengths and limitations. To our knowledge, this is the first study to test for serum IgA antibodies in addressing food sensitivity. Moreover, the study adopted a robust and holistic approach to addressing the condition by conducting serological assessments, capturing symptom improvement, understanding patient response based on the physician’s judgment, and gauging the patient’s quality of life. Nevertheless, it is crucial to acknowledge the limitations that were associated with the study. These include a small sample size, self-report instruments for patient health outcomes, and a relatively short intervention period of 4 weeks. Additionally, the study lacked a control group to assess the placebo effect of the intervention. However, designing a suitable control arm for behavioral interventions presents inherent challenges, underscoring the necessity for alternative methodologies to comprehensively grasp the concept. Lastly, the study did not account for the alteration in the gut microbiome, psychological stress, exercise, or travel, all of which could have an impact on the results.

Given the high prevalence of food sensitivity and the difficulty in identifying reactive foods from a mixture of food ingredients, there is a constant risk of individuals facing food reactions which can affect their overall health and wellbeing. The study successfully demonstrated that serological assessment of antibodies, followed by a personalized elimination diet, effectively addressed food sensitivity. The majority of participants had significant improvements in symptom severity and antibody levels, and were seen to positively respond to the intervention, indicating that this approach is a valid strategy for managing food sensitivity.

Ig – immunoglobulin

GI – Gastrointestinal

CD - Celiac disease

ENA - Extractable nuclear antigens

RA - Rheumatoid arthritis

CVDs - Cardiovascular diseases

Fmoc - Fluorenylmethoxycarbonyl

APTES - Aminopropyltriethoxysilane

NMP - N-methylpyrrolidone

CU - Chemiluminescent units

FS-SSS - Food Sensitivity - Symptom Severity Scale

FS-QoL - Food Sensitivity - Quality of Life Questionnaire

FS-GIS - Food Sensitivity - Global Improvement Scale

BMI - Body mass index

Q1 - Quartile 1

Q3 - Quartile 3

IBS - Irritable bowel syndrome

QoL - Quality of life

Disclosure

The data and materials in this manuscript have not been published elsewhere and are not under consideration by another journal.

Authors' contributions

Conception – HKK, VJ, JJR; Study design – HKK, MP; Data acquisition – KK, KB, TW, SB; Data analysis – HKK, MP; Critical review – CT, CW, RM, EL, TM, JR, EE, AML, MK; Manuscript preparation - HKK, MP.

Acknowledgement

We acknowledge Vibrant America LLC for supporting this research.

Competing interests

The authors have read the journal’s policy and the authors of this manuscript have the following competing interests: MP and SB are paid employees of Vibrant America LLC. HKK, VJ, KK, TW, KB, and JJR are paid employees of Vibrant Sciences LLC. Vibrant Sciences or Vibrant America is a commercial lab which performs serological assessment for food sensitivity at the peptide and protein level. Vibrant Sciences or Vibrant America could benefit from increased testing based on the observations. There are no patents, products in development, or marketed products to declare. This does not alter our adherence to Nutrients policies on sharing data and materials.

Availability of data and material

The data used to support the findings of this study can be acquired from Vibrant America LLC.

Ethics approval

Approval for this study was granted by the Institutional Review Board at WCG IRB (Puyallup, Washington, USA) under IRB# 1-1586736-1.

Consent to participate

Informed consent was obtained

Consent for publication

Written informed consent has been obtained from the patients to publish this paper.

Funding

Vibrant America provided funding for this study in the form of salaries for authors [HKK, MP, VJ, KK, TW, KB, SB, and JJR]. The specific roles of these authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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The authors declare potential competing interests as follows: The authors of this manuscript have the following competing interests: MP and SB are paid employees of Vibrant America LLC. HKK, VJ, KK, TW, KB, and JJR are paid employees of Vibrant Sciences LLC.

FSSupplementaryMaterialFSGIS.docx
FSSupplementaryMaterialFSQoL.docx
FSSupplementaryMaterialFSSSS.docx
FSSupplementaryMaterialTable1.docx
Food Sensitivity Supplementary Material Table 1

Personalized Food Elimination Diet: A Clinical Trial based on Food sensitivity assessment

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Ethics and Trial registration

Study design

Subjects

Microarray Architecture

Peptide array

Protein array

Serological Analysis

Intervention

Questionnaires

Outcome measures

Data analyses

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1