Gut barrier dysfunction promotes chronic inflammation, contributing to several gastrointestinal diseases, including colorectal cancer. Preliminary evidence suggests that vitamin D and calcium could prevent colorectal carcinogenesis, in part, by influencing gut barrier function. However, relevant human data are scarce. We tested the effects of supplemental calcium (1,200 mg/day) and/or vitamin D3 (1,000 IU/day) on circulating concentrations of biomarkers of gut permeability (anti-flagellin and anti-lipopolysaccharide IgA and IgG, measured via ELISA) from baseline to 1 and 3 or 5 years postbaseline among 175 patients with colorectal adenoma in a randomized, double-blinded, placebo-controlled clinical trial. We also assessed factors associated with baseline concentrations of these biomarkers. We found no appreciable effects of supplemental vitamin D3 and/or calcium on individual or aggregate biomarkers of gut permeability. At baseline, a combined permeability score (the summed concentrations of all four biomarkers) was 14% lower among women (P = 0.01) and 10% higher among those who consumed >1 serving per day of red or processed meats relative to those who consumed none (Ptrend = 0.03). The permeability score was estimated to be 49% higher among participants with a body mass index (BMI) > 35 kg/m2 relative to those with a BMI < 22.5 kg/m2 (Ptrend = 0.17). Our results suggest that daily supplemental vitamin D3 and/or calcium may not modify circulating concentrations of gut permeability biomarkers within 1 or 3–5 years, but support continued investigation of modifiable factors, such as diet and excess adiposity, that could affect gut permeability.

Prevention Relevance:

Calcium and vitamin D may be involved in regulating and maintaining the integrity of the intestinal mucosal barrier, the dysfunction of which results in exposure of the host to luminal bacteria, endotoxins, and antigens leading to potentially cancer-promoting endotoxemia and chronic colon inflammation. While our results suggest that daily supplementation with these chemopreventive agents does not modify circulating concentrations of gut permeability biomarkers, they support continued investigation of other potential modifiable factors, such as diet and excess adiposity, that could alter gut barrier function, to inform the development of treatable biomarkers of risk for colorectal neoplasms and effective colon cancer preventive strategies.

The intestinal epithelium plays an important role as a physical, chemical, and immune barrier between the host's body and the external environment (1, 2). Loss of gut barrier integrity exposes the host to luminal bacteria, endotoxins, and antigens, leading to potentially cancer-promoting endotoxemia and inflammation (3). Abnormal gut barrier function contributes to multiple gastrointestinal disorders, including inflammatory bowel disease and colorectal neoplasms (4–7). Factors associated with gut hyperpermeability have not been studied extensively, yet limited human and animal experimental evidence suggests that obesity and diet may influence gut permeability (8–10). Therefore, identifying chemopreventive agents or modifiable factors that could strengthen the gut barrier and reduce colorectal inflammation could lead to the development of new strategies for preventing colorectal neoplasms and other pathologic conditions of the gastrointestinal tract.

Strong biological plausibility, animal experimental evidence, and human observational data support that calcium and vitamin D protect against colorectal neoplasms (11). Recent data suggest that calcium, vitamin D, and the vitamin D receptor (VDR) are involved in maintaining the integrity of the intestinal mucosal barrier (12, 13). Calcium can bind bile and fatty acids in the colon lumen by forming insoluble soaps, thus preventing them from damaging the colonic mucosa and consequently promoting inflammation (14), which, in turn, may help in maintaining a healthy gut barrier. In animal and cell culture models, the VDR played an important role in mucosal barrier function by preserving the integrity of epithelial junction complexes and increasing the mucosal regeneration and healing capacity of the colonic epithelium (12). Furthermore, the active form of vitamin D, 1,25(OH)2D3, in animal and cell culture models, regulated key tight junction proteins (15–22), increased intestinal transepithelial electric resistance (an indicator of epithelial barrier integrity; refs. 15, 16, 18), and regulated the expression and release of human antimicrobial peptides (e.g., cathelicidin and defensin β2; ref. 23) that protect against bacterial and viral infections and accelerate wound healing (24–26). All these data support the hypothesis that vitamin D and calcium beneficially modulate gut barrier function. However, to our knowledge, no clinical trials reported testing the effects of vitamin D and calcium, alone and in combination, on gut permeability in humans.

To address these gaps in the literature, we measured circulating concentrations of flagellin- and lipopolysaccharide (LPS)-specific IgA and IgG among patients with previous colorectal adenomas from a large, randomized, double-blinded, placebo-controlled clinical trial of supplementation with calcium and vitamin D. LPS is a part of the outer membrane of the Gram-negative bacterial cell wall, and flagellin is the primary structural component of bacterial flagella. Both LPS and flagellin play a major role in acute and chronic inflammation, and are targets of immune surveillance (27, 28). Therefore, circulating concentrations of LPS- and flagellin-specific IgA and IgG serve as markers of long-term systemic exposure to these endotoxins, and may indicate altered adaptive immune responses related to gut barrier dysfunction (28–30). We tested whether vitamin D and/or calcium affected circulating concentrations of these biomarkers after 1 and 3–5 years of treatment, and assessed whether demographic, diet, and lifestyle factors were associated with the biomarkers at baseline.

### Clinical trial protocol

This study was an adjunct biomarker investigation using data and blood samples from a large, randomized, placebo-controlled, partial 2 × 2 factorial chemoprevention clinical trial (“parent study;” vitamin D and calcium polyp prevention study, NCT00153816) that tested the efficacy of supplemental calcium and vitamin D3, alone or in combination, over 3–5 years on colorectal adenoma recurrence in patients with colorectal adenoma in the United States. The parent study protocol, eligibility, and exclusion criteria were published previously (31).

Briefly, participants were 45–75 years of age, had at least one colorectal adenoma removed within 120 days of enrollment with no remaining polyps after a complete colonoscopy, and anticipated to undergo a 3- or 5-year colonoscopic follow-up examination. Patients for this adjunct biomarker study were recruited at two of the 11 participating clinical centers (Georgia and South Carolina). Of 2,259 patients randomized in the parent study, 175 patients who agreed to provide blood samples at baseline, after 1 year of supplementation, and at the end-of-treatment (EOT) with the study agents between July 2004 and July 2008 were included in this adjunct biomarker study.

Peripheral venous blood samples were collected after the subject sat upright with his or her legs uncrossed for 5 minutes. Blood was drawn into prechilled Vacutainer tubes for plasma and serum, and then immediately placed on ice and shielded from light. Tubes were immediately processed, plasma and serum were aliquoted into cryopreservation tubes, and then the aliquots were immediately placed in a −80°C freezer until analysis.

Eligible patients were in good general health and did not have familial colorectal cancer syndromes, serious intestinal disease, conditions that indicated that the study agents would pose a health risk (e.g., a history of kidney stones), or conditions that would indicate a need for either agent (e.g., osteoporosis). We also did not include patients who had a serum calcium concentration outside the normal range, a creatinine concentration >20% above the upper limit of the normal range, or a 25-hydroxyvitamin D concentration <12 or >90 ng/mL.

We evaluated four regimens, all of which involved two identical tablets taken daily: 1,000 IU of vitamin D3, 1,200 mg of elemental calcium via its carbonate, both agents, or placebo. Women could elect to be randomly assigned to receive either calcium or calcium plus vitamin D (two-arm randomization); all other patients were randomly assigned to receive one of the four regimens (full factorial randomization). The doses of each study agent were chosen to increase the total intake substantially, with a margin of safety below the highest mean daily intake level believed unlikely to cause adverse effects in most people at the time that the trial began (2,000 IU of vitamin D and 2.5 g of calcium). Study treatment was to continue until the anticipated 3- or 5-year colonoscopic examination.

At enrollment, participants provided information regarding demographic data, medical history, medications, nutritional supplements, behavioral factors, and diet (using the NutritionQuest Block Brief 2000 food frequency questionnaire). Randomization by the coordinating center was performed with the use of computer-generated random numbers with permuted blocks and stratification according to clinical center, sex, anticipated colonoscopic examination at 3 or 5 years, and full factorial or two-group randomization. All study staff were unaware of the treatment assignments. Participants agreed to avoid taking study agents outside the trial. However, because of increasing publicity regarding the possible benefits of these supplements, daily personal use of up to 1,000 IU of vitamin D, 400 mg of elemental calcium, or both was permitted, although discouraged, from April 2008 onward.

Participants were contacted by telephone every 6 months and questioned regarding adherence to study agents, illnesses, medication and supplement use, dietary calcium intake, and colorectal procedures. Bottles of study tablets were mailed to participants every 4 months. Patients who wanted to take a multivitamin were offered a special preparation that did not include calcium or vitamin D. The study intervention ended on 31 August 2013; the treatment-phase follow-up continued until 30 November 2013, to accommodate the final 5-year participants. Serum concentrations of 25-hydroxyvitamin D were measured at baseline, year 1, and shortly before the EOT examinations.

All participants provided signed informed consent; the research was approved by the institutional review board at each study center. An independent data and safety monitoring committee oversaw the study.

### Laboratory measurements

Plasma concentrations of flagellin- and LPS-specific IgA and IgG were measured via a previously described custom-made ELISA at Georgia State University (Atlanta, GA; refs. 28–30). ELISA Plates (Costar) were coated overnight with laboratory made flagellin (100 ng/well; prepared from Salmonella typhimurium, strain SL 3201 fljB−/− as described previously; ref. 32) or purified Escherichia coli (E. coli) LPS (2 mg/well; from E. coli 0128: B12, Sigma, catalog no., 2887). Plasma samples diluted 1:200 were applied to wells coated with flagellin or LPS. After incubation and washing, the wells were incubated either with anti-IgG coupled to horseradish peroxidase (GE Healthcare Life Sciences, catalog no., 375112) or, in the case of IgA-specific antibodies, with horseradish peroxidase–conjugated anti-IgA (KPL, catalog no., 14-10-01). Using the established platform, specificity of flagellin/LPS was observed when the signal was extremely low, when using serum from germ-free mice (very low flagellin- or LPS-specific Igs), and completely abolished, using serum from RAG-1–knockout mice and germ-free mice on an elemental diet (no flagellin- or LPS-specific Igs). The specificity of the anti-human IgA and anti-human IgG was in accordance with the manufacturer's specifications, KPL and GE Healthcare Life Sciences, respectively.

Quantitation of total Igs was performed using the colorimetric peroxidase substrate, tetramethylbenzidine, and optical density (OD) was read at 450 and 540 nm (the difference was taken to compensate for optical interference from the plate), with an ELISA plate reader. Data are reported as OD corrected by subtracting background (determined by readings in blank samples) and are normalized to each plate's control sample, which was prepared in bulk, aliquoted, frozen, and thawed daily as used. Standardization was performed using preparations of known concentrations of IgA and IgG. The technician was blinded to treatment group and treated all samples identically.

Baseline, follow-up, and end-of-study samples from each participant were included in the same batch. The laboratory previously performed assays of these biomarkers in replicates with a very low coefficient of variation (CV < 5%); therefore, our samples were analyzed in singleton to minimize costs and time. The average within-batch CVs were 11%, 12%, 15%, and 11% for flagellin IgA, flagellin IgG, LPS IgA, and LPS IgG, respectively, on the basis of three quality control samples included in each batch; the corresponding between-batch CVs were 7%, 12%, 10%, and 5%, respectively.

### Statistical analysis

We compared the baseline characteristics of the participants across treatment groups using the χ2 test for categorical variables and ANOVA for continuous variables. We assessed differences in biomarker concentrations from baseline to 1-year follow-up and to the 3- to 5-year colonoscopic examinations. We analyzed treatment effects between participants in the treatment group of interest and those in the comparison group using multivariable general linear mixed models. The models were included as predictors the intercept, visit (baseline, year 1 follow-up, and years 3–5), treatment group, and a treatment-by-visit interaction term. Adjustments for age, sex, clinical center, number of baseline adenomas, follow-up period, and physical activity did not affect the estimated treatment effects; therefore, only unadjusted results are presented. We evaluated changes in biomarker concentrations over time for the treatment groups that received (i) calcium relative to those that did not (“calcium vs. no calcium,” excluding two-arm participants), (ii) vitamin D relative to those that did not (“vitamin D vs. no vitamin D”), and (iii) vitamin D plus calcium relative to those that received only calcium (“vitamin D + calcium vs. calcium”). In addition, we evaluated changes in biomarker concentrations according to the four-arm full factorial and two-arm randomizations.

We initially analyzed each gut permeability biomarker individually. Then, we created several biomarker combinations to better capture different aspects of gut barrier function and exposure to endotoxins. These combinations included all four biomarkers combined as a permeability score (flagellin IgA + flagellin IgG + LPS IgA + LPS IgG; overall gut barrier function), anti-LPS Igs (LPS IgA + LPS IgG; exposure to LPS), anti-flagellin Igs (flagellin IgA + flagellin IgG; exposure to flagellin), IgG (flagellin IgG + LPS IgG; mucosal immune response to endotoxins), and IgA (flagellin IgA + LPS IgA; systemic immune response to endotoxins). We directly summed these biomarkers because their measurements were on approximately the same scale. Because the biomarker values were normally distributed, we did not transform them before statistical testing.

In all analyses of randomized treatments, we retained participants in their originally assigned treatment group, regardless of adherence to the study treatment and procedures. Treatment effects were calculated on the ratio scale as follows: relative treatment effect [(treatment group follow-up)/(treatment group baseline)]/[(control group follow-up)/(control group baseline)]. A relative effect of 1.2 would indicate a 20% increase in biomarker expression in the treatment group relative to the control group. We also conducted secondary analyses to assess potential treatment effect modification by stratifying the above analyses by sex, regular aspirin use, and baseline median of body mass index (BMI), total calcium intake, and blood 25-hydroxyvitamin D concentrations.

Finally, we assessed whether the baseline biomarker concentrations differed by categories of a priori–selected biologically plausible factors, including age, sex, BMI, smoking status, alcohol intake, red/processed meat intake, regular aspirin use, regular NSAID use, number of adenomas and advanced adenomas, diabetes diagnosis, and blood concentrations of IL10, IL6, and TNFα. We calculated mean biomarker differences (and their 95% confidence intervals and P values) across categories of the selected exposures using general linear models adjusted for age, sex, BMI, and study center. Additional adjustment for smoking status, alcohol consumption, number of adenomas at baseline, diabetes, and regular aspirin/NSAID use, where appropriate, did not materially affect the results. We present the results as proportional differences, calculated as [(comparison mean – reference mean)/reference mean] × 100%. We calculated P values for trend for categorical variables with more than two levels by including the ordered categories as a continuous variable in the same general linear model.

We conducted all statistical analyses using SAS 9.4 statistical software, and considered two-sided P < 0.05 statistically significant.

### Patient characteristics

The mean age of the study participants was 58 years, 62% were men, 81% were White, and the mean BMI was 29 kg/m2. Physical activity levels differed (P = 0.01) across the four-arm treatment groups (Table 1) The average baseline serum 25-hydroxyvitamin D concentration was 23.61 ng/mL. The overall adherence to taking study tablets at the EOT was 89% in the calcium group, 91% in the vitamin D group, and 92% in the calcium plus vitamin D group.

### Calcium and/or vitamin D treatment effects on gut permeability markers

Mean serum 25-hydroxyvitamin D concentrations increased by 28% and 32% (all P <0.001) in the groups taking vitamin D and vitamin D plus calcium relative to their respective control groups at the EOT, respectively (Supplementary Table S1).

Among the 175 participants, measurements of the plasma gut permeability biomarkers were available for 175 at baseline, 174 at year 1, and 170 at the EOT. Changes in the gut barrier biomarkers for each treatment comparison are shown in Table 2 (EOT) and Supplementary Table S2 (year 1). There were no substantial or statistically significant changes in any individual or aggregate biomarkers of gut permeability for any treatment group. Similar null results were found when we analyzed biomarker changes within the four- and two-arm treatment randomizations separately (Supplementary Table S3). In our secondary analyses, we found no substantial differences in the estimated treatment effects by selected baseline patient characteristics [including by median BMI (< vs. ≥28.5 kg/m2), total calcium intake (< vs. ≥723.3 mg/day), baseline serum 25-hydroxyvitamin D concentration (< vs. ≥21.6 ng/mL), and regular use of aspirin (yes vs. no); Supplementary Table S4].

Table 1.

Selected baseline characteristics of the adjunct biomarker study participants (n = 175), by treatment group assignment.

Randomization to calcium and vitamin D (four-arm)Randomization to vitamin D only (two-arm)
CalciumVitamin DVitamin D + calciumPlaceboVitamin DPlacebo
Characteristicsc(n = 33)(n = 33)(n = 36)(n = 33)Pa(n = 19)(n = 21)Pb
Age, years 58.5 (6.9) 58.2 (7.5) 57.9 (6.3) 59.6 (6.0) 0.74 58.8 (6.6) 58.0 (5.3) 0.64
Women, % 27.3 21.2 11.1 18.2 0.39 100 100 —
Regular aspirin use, %d 57.6 30.3 30.6 45.5 0.07 26.3 23.8 0.86
Regular NSAID use, %e 78.8 63.6 72.2 66.7 0.56 57.9 71.4 0.38
Diabetes, % 18.2 9.1 11.1 9.1 0.63 15.8 14.3 0.90
Current smoker, % 6.1 11.1 9.1 0.29 15.8 0.06
Former smoker, % 24.2 42.4 38.9 42.4 0.37 31.6 33.3 0.91
White, % 72.7 81.8 97.1 75.8 0.22 84.2 71.4 0.49
BMI, kg/m2 31.5 (6.2) 29.0 (5.0) 29.4 (4.0) 28.6 (4.2) 0.09 27.4 (4.8) 29.3 (4.7) 0.23
IPAQ scoref 1.8 (0.7) 2.3 (0.8) 2.5 (0.7) 2.1 (0.8) 0.01 2.1 (0.9) 1.9 (0.8) 0.46
Take a multivitamin, % 51.5 48.5 52.8 45.5 0.94 89.5 71.4 0.16
Study center, %
GA 63.6 57.6 52.8 57.6 0.84 52.6 71.4 0.22
SC 36.4 42.4 47.2 42.4  47.4 28.6
Highest education level, %
Some college or less 45.5 18.2 33.3 42.4 0.09 42.1 42.9 0.96
Associate's or other degrees 54.6 81.8 66.7 57.6  57.9 57.1
Any adenomas 1.5 (0.9) 1.4 (0.7) 1.9 (0.6) 1.4 (0.6) 0.65 1.6 (1.0) 1.2 (0.7) 0.15
Advanced adenomasg 0.2 (0.5) 0.4 (0.6) 0.3 (0.6) 0.5 (0.7) 0.56 0.2 (0.4) 0.1 (0.3) 0.56
Serrated polyps 0.4 (0.8) 0.2 (0.4) 0.4 (0.7) 0.3 (0.6) 0.28 0.2 (0.7) 0.3 (0.5) 0.87
Daily dietary intakes
Total energy, kcalh 1,696 (560) 1,494 (510) 1,559 (562) 1,463 (428) 0.31 1,434 (590) 1,351 (555) 0.65
Total calcium, mgi 807 (320) 710 (249) 678 (324) 678 (319) 0.33 1,227 (547) 1,076 (479) 0.40
Red and processed meats, servings 0.9 (0.7) 0.9 (0.8) 1.0 (0.7) 1.1 (0.7) 0.46 0.7 (0.6) 0.7 (0.7) 0.85
Total vitamin D, IUi 172 (195) 200 (237) 225 (241) 175 (202) 0.74 500 (260) 416 (329) 0.45
Alcohol intake, drinks 0.6 (1.0) 0.8 (0.9) 0.8 (0.8) 0.8 (0.7) 0.71 0.4 (0.5) 0.6 (1.0) 0.46
Serum 25-(OH)-vitamin D, ng/mL 22.0 (10.1) 24.2 (9.6) 24.4 (6.9) 21.3 (7.2) 0.34 26.2 (9.5) 24.6 (8.8) 0.58
Randomization to calcium and vitamin D (four-arm)Randomization to vitamin D only (two-arm)
CalciumVitamin DVitamin D + calciumPlaceboVitamin DPlacebo
Characteristicsc(n = 33)(n = 33)(n = 36)(n = 33)Pa(n = 19)(n = 21)Pb
Age, years 58.5 (6.9) 58.2 (7.5) 57.9 (6.3) 59.6 (6.0) 0.74 58.8 (6.6) 58.0 (5.3) 0.64
Women, % 27.3 21.2 11.1 18.2 0.39 100 100 —
Regular aspirin use, %d 57.6 30.3 30.6 45.5 0.07 26.3 23.8 0.86
Regular NSAID use, %e 78.8 63.6 72.2 66.7 0.56 57.9 71.4 0.38
Diabetes, % 18.2 9.1 11.1 9.1 0.63 15.8 14.3 0.90
Current smoker, % 6.1 11.1 9.1 0.29 15.8 0.06
Former smoker, % 24.2 42.4 38.9 42.4 0.37 31.6 33.3 0.91
White, % 72.7 81.8 97.1 75.8 0.22 84.2 71.4 0.49
BMI, kg/m2 31.5 (6.2) 29.0 (5.0) 29.4 (4.0) 28.6 (4.2) 0.09 27.4 (4.8) 29.3 (4.7) 0.23
IPAQ scoref 1.8 (0.7) 2.3 (0.8) 2.5 (0.7) 2.1 (0.8) 0.01 2.1 (0.9) 1.9 (0.8) 0.46
Take a multivitamin, % 51.5 48.5 52.8 45.5 0.94 89.5 71.4 0.16
Study center, %
GA 63.6 57.6 52.8 57.6 0.84 52.6 71.4 0.22
SC 36.4 42.4 47.2 42.4  47.4 28.6
Highest education level, %
Some college or less 45.5 18.2 33.3 42.4 0.09 42.1 42.9 0.96
Associate's or other degrees 54.6 81.8 66.7 57.6  57.9 57.1
Any adenomas 1.5 (0.9) 1.4 (0.7) 1.9 (0.6) 1.4 (0.6) 0.65 1.6 (1.0) 1.2 (0.7) 0.15
Advanced adenomasg 0.2 (0.5) 0.4 (0.6) 0.3 (0.6) 0.5 (0.7) 0.56 0.2 (0.4) 0.1 (0.3) 0.56
Serrated polyps 0.4 (0.8) 0.2 (0.4) 0.4 (0.7) 0.3 (0.6) 0.28 0.2 (0.7) 0.3 (0.5) 0.87
Daily dietary intakes
Total energy, kcalh 1,696 (560) 1,494 (510) 1,559 (562) 1,463 (428) 0.31 1,434 (590) 1,351 (555) 0.65
Total calcium, mgi 807 (320) 710 (249) 678 (324) 678 (319) 0.33 1,227 (547) 1,076 (479) 0.40
Red and processed meats, servings 0.9 (0.7) 0.9 (0.8) 1.0 (0.7) 1.1 (0.7) 0.46 0.7 (0.6) 0.7 (0.7) 0.85
Total vitamin D, IUi 172 (195) 200 (237) 225 (241) 175 (202) 0.74 500 (260) 416 (329) 0.45
Alcohol intake, drinks 0.6 (1.0) 0.8 (0.9) 0.8 (0.8) 0.8 (0.7) 0.71 0.4 (0.5) 0.6 (1.0) 0.46
Serum 25-(OH)-vitamin D, ng/mL 22.0 (10.1) 24.2 (9.6) 24.4 (6.9) 21.3 (7.2) 0.34 26.2 (9.5) 24.6 (8.8) 0.58

Abbreviations: IPAQ, International Physical Activity Questionnaire; IU, international units; kcal, kilocalories; No., number.

a{\chi ^2}$for categorical variables; general linear model for continuous variables. b{\chi ^2}$ for categorical variables; Student t test for continuous variables.

cData presented as means (SD), unless otherwise specified.

dRegular aspirin use = no. ≥4/week.

eRegular NSAID use = no. ≥4/week.

fMissing data on 1 patient.

gDefined as those with high-grade dysplasia, more than 25% villous features, or an estimated diameter of at least 1 cm; missing data on 3 patients.

hMissing data on 10 patients.

iTotal intake = dietary + supplemental intakes.

Table 2.

Effects of calcium and/or vitamin D supplementation on plasma concentration of gut barrier biomarkers in the adjunct biomarker study participants (n = 175)a.

BaselineEOTAbs. tx EOTb
nMean (SE)PnMean (SE)PMean (SE)PΔ tx EOTc
Permeability scored
Calcium 69 6.10 (0.20) 0.25 68 5.19 (0.20) 0.18 −0.06 (0.40) 0.88 0.98
No calcium 66 6.42 (0.20)  63 5.58 (0.21)
Vitamin D 88 6.73 (0.17) 0.11 85 5.13 (0.18) 0.33 0.15 (0.35) 0.66 0.87
No vitamin D 87 6.13 (0.17)  87 5.37 (0.17)
Vit. D + calcium 55 5.52 (0.21) 0.46 54 4.98 (0.21) 0.58 0.05 (0.42) 0.90 1.01
Calcium only 54 5.74 (0.21)  55 5.15 (0.21)
LPSe
Calcium 69 2.65 (0.11) 0.35 68 2.22 (0.11) 0.23 −0.04 (0.224) 0.85 0.97
No calcium 66 2.80 (0.11)  63 2.41 (0.12)
Vitamin D 88 2.45 (0.09) 0.07 85 2.20 (0.10) 0.35 0.11 (0.19) 0.55 1.04
No vitamin D 87 2.69 (0.09)  87 2.33 (0.09)
Vit. D + calcium 55 2.30 (0.11) 0.10 54 2.08 (0.11) 0.19 0.06 (0.23) 0.79 1.01
Calcium only 54 2.57 (0.11)  55 2.29 (0.11)
FLICe
Calcium 69 3.45 (0.10) 0.23 68 2.98 (0.10) 0.19 −0.02 (0.21) 0.93 0.99
No calcium 66 3.63 (0.11)  63 3.17 (0.11)
Vitamin D 88 3.28 (0.09) 0.25 85 2.93 (0.10) 0.40 0.04 (0.19) 0.84 1.00
No vitamin D 87 3.43 (0.09)  87 3.05 (0.09)
Vit. D + calcium 55 3.22 (0.11) 0.76 54 2.91 (0.11) 0.78 −0.01 (0.23) 0.98 1.00
Calcium only 54 3.17 (0.11)  55 2.86 (0.11)
IgGe
Calcium 69 2.86 (0.09) 0.28 68 2.42 (0.09) 0.06 −0.10 (0.18) 0.57 0.96
No calcium 66 3.00 (0.09)  63 2.65 (0.09)
Vitamin D 88 2.70 (0.08) 0.15 85 2.43 (0.08) 0.30 0.04 (0.15) 0.78 1.01
No vitamin D 87 2.86 (0.08)  87 2.54 (0.08)
Vit. D + calcium 55 2.59 (0.09) 0.38 54 2.36 (0.09) 0.62 0.05 (0.18) 0.78 1.02
Calcium only 54 2.71 (0.09)  55 2.43 (0.09)
IgAe
Calcium 69 3.24 (0.15) 0.36 68 2.78 (0.15) 0.47 0.04 (0.30) 0.90 1.00
No calcium 66 3.43 (0.15)  63 2.93 (0.15)
Vitamin D 88 3.03 (0.13) 0.19 85 2.70 (0.13) 0.48 0.11 (0.26) 0.67 1.03
No vitamin D 87 3.27 (0.13)  87 2.83 (0.13)
Vit. D + calcium 55 2.93 (0.16) 0.65 54 2.62 (0.16) 0.65 0.003 (0.31) 0.99 1.00
Calcium only 54 3.03 (0.16)  55 2.72 (0.16)
BaselineEOTAbs. tx EOTb
nMean (SE)PnMean (SE)PMean (SE)PΔ tx EOTc
Permeability scored
Calcium 69 6.10 (0.20) 0.25 68 5.19 (0.20) 0.18 −0.06 (0.40) 0.88 0.98
No calcium 66 6.42 (0.20)  63 5.58 (0.21)
Vitamin D 88 6.73 (0.17) 0.11 85 5.13 (0.18) 0.33 0.15 (0.35) 0.66 0.87
No vitamin D 87 6.13 (0.17)  87 5.37 (0.17)
Vit. D + calcium 55 5.52 (0.21) 0.46 54 4.98 (0.21) 0.58 0.05 (0.42) 0.90 1.01
Calcium only 54 5.74 (0.21)  55 5.15 (0.21)
LPSe
Calcium 69 2.65 (0.11) 0.35 68 2.22 (0.11) 0.23 −0.04 (0.224) 0.85 0.97
No calcium 66 2.80 (0.11)  63 2.41 (0.12)
Vitamin D 88 2.45 (0.09) 0.07 85 2.20 (0.10) 0.35 0.11 (0.19) 0.55 1.04
No vitamin D 87 2.69 (0.09)  87 2.33 (0.09)
Vit. D + calcium 55 2.30 (0.11) 0.10 54 2.08 (0.11) 0.19 0.06 (0.23) 0.79 1.01
Calcium only 54 2.57 (0.11)  55 2.29 (0.11)
FLICe
Calcium 69 3.45 (0.10) 0.23 68 2.98 (0.10) 0.19 −0.02 (0.21) 0.93 0.99
No calcium 66 3.63 (0.11)  63 3.17 (0.11)
Vitamin D 88 3.28 (0.09) 0.25 85 2.93 (0.10) 0.40 0.04 (0.19) 0.84 1.00
No vitamin D 87 3.43 (0.09)  87 3.05 (0.09)
Vit. D + calcium 55 3.22 (0.11) 0.76 54 2.91 (0.11) 0.78 −0.01 (0.23) 0.98 1.00
Calcium only 54 3.17 (0.11)  55 2.86 (0.11)
IgGe
Calcium 69 2.86 (0.09) 0.28 68 2.42 (0.09) 0.06 −0.10 (0.18) 0.57 0.96
No calcium 66 3.00 (0.09)  63 2.65 (0.09)
Vitamin D 88 2.70 (0.08) 0.15 85 2.43 (0.08) 0.30 0.04 (0.15) 0.78 1.01
No vitamin D 87 2.86 (0.08)  87 2.54 (0.08)
Vit. D + calcium 55 2.59 (0.09) 0.38 54 2.36 (0.09) 0.62 0.05 (0.18) 0.78 1.02
Calcium only 54 2.71 (0.09)  55 2.43 (0.09)
IgAe
Calcium 69 3.24 (0.15) 0.36 68 2.78 (0.15) 0.47 0.04 (0.30) 0.90 1.00
No calcium 66 3.43 (0.15)  63 2.93 (0.15)
Vitamin D 88 3.03 (0.13) 0.19 85 2.70 (0.13) 0.48 0.11 (0.26) 0.67 1.03
No vitamin D 87 3.27 (0.13)  87 2.83 (0.13)
Vit. D + calcium 55 2.93 (0.16) 0.65 54 2.62 (0.16) 0.65 0.003 (0.31) 0.99 1.00
Calcium only 54 3.03 (0.16)  55 2.72 (0.16)

Abbreviations: Abs., absolute; FLIC, flagellin; tx, treatment; Vit. D, vitamin D.

aThe effect of treatment agent on biomarker concentration was assessed using mixed linear models.

bAbs. tx EOT = absolute treatment effect at the end of treatment = [(treatment group EOT) – (treatment group baseline)] – [(placebo group EOT) – (placebo group baseline)].

cΔ tx EOT = relative treatment effect at the end of treatment = [(treatment group EOT)/(treatment group baseline)]/[(placebo group EOT)/(placebo group baseline)].

dPermeability score = (flagellin IgA + flagellin IgG + LPS IgA + LPS IgG).

eLPS = (LPS IgA + LPS IgG); FLIC = (flagellin IgA + flagellin IgG); IgG = (flagellin IgG + LPS IgG); and IgA = (flagellin IgA + LPS IgA).

### Associations of baseline demographic and lifestyle factors with baseline gut permeability markers

Proportional mean gut barrier biomarker concentration differences across categories of a priori–selected participant characteristics at baseline are presented in Table 3 (a full version of the table is included as Supplementary Table S5). Women, on average, relative to men, had a lower permeability score (−14%; P = 0.01) and lower concentrations of LPS (−11%; P = 0.03), flagellin (−11%; P = 0.001), IgG (−13%; P = 0.001), and IgA (−9%; P = 0.001). Participants who had a BMI > 35 kg/m2, relative to those who had a BMI < 22.5 kg/m2, were estimated to have a 49% higher mean permeability score and higher mean concentrations of LPS (+72%), flagellin (+34%), IgG (+35%), and IgA (+62%). There was a suggestive pattern of increasing mean LPS concentrations with an increasing BMI (Ptrend = 0.06). Participants who consumed >1 serving per day of red and processed meats, relative to those who consumed none, had a 10% higher mean permeability score and higher concentrations of LPS (+12%), flagellin (+9%), IgG (+38%), and IgA (+12%). This pattern of increasing gut permeability with higher intake of red and processed meats was statistically significant for the permeability score (Ptrend = 0.03) and flagellin (Ptrend = 0.01) and IgA (Ptrend = 0.01) concentrations.

Table 3.

Mean baseline plasma concentrations of gut permeability biomarkers, by selected participant characteristicsa.

Permeability scoreLPSFLICIgGIgA
CharacteristicsN% diffbPc% diffbPc% diffbPc% diffbPc% diffbPc
Age, years
≤55 57 Ref.  Ref.  Ref.  Ref.  Ref.
55.1–60 44 −0.7  0.8  −1.2  −0.4  −1.0
60.1–65 38 −5.4  −5.8  −5.0  −6.5  −4.4
>65 36 0.5 0.84 2.3 0.74 -0.3 0.97 −2.2 0.64 3.2 0.59
Sex
Men 109 Ref.  Ref.  Ref.  Ref.  Ref.
Women 66 −14.4 0.01 −11.4 0.03 −10.8 0.001 −13.3 0.001 −8.5 0.13
Study center
Georgia 103 Ref.  Ref.  Ref.  Ref.  Ref.
South Carolina 72 8.3 0.07 10.3 0.07 −6.8 0.11 1.9 0.63 14.3 0.03
BMI, kg/m2
<22.5 Ref.  Ref.  Ref.  Ref.  Ref.
22.6–25 24 27.0  34.3  22.2  25.9  28.5
25.1–27.5 36 36.3  51.9  26.2  33.2  39.8
27.6–30 45 28.0  44.8  17.2  25.5  31.0
30.1–35 38 13.9  21.6  9.0  12.7  15.5
>35 24 48.7 0.17 71.8 0.06 33.7 0.54 34.6 0.72 61.9 0.11
Red and processed meats intakes, servings/day
0 14 Ref.  Ref.  Ref.  Ref.  Ref.
0.1–0.5 50 8.5  13.2  5.0  4.7  12.2
0.6–1 48 18.5  16.7  19.8  7.0  29.9
>1 63 10.0 0.03 11.9 0.18 8.6 0.01 8.1 0.31 12.2 0.01
Permeability scoreLPSFLICIgGIgA
CharacteristicsN% diffbPc% diffbPc% diffbPc% diffbPc% diffbPc
Age, years
≤55 57 Ref.  Ref.  Ref.  Ref.  Ref.
55.1–60 44 −0.7  0.8  −1.2  −0.4  −1.0
60.1–65 38 −5.4  −5.8  −5.0  −6.5  −4.4
>65 36 0.5 0.84 2.3 0.74 -0.3 0.97 −2.2 0.64 3.2 0.59
Sex
Men 109 Ref.  Ref.  Ref.  Ref.  Ref.
Women 66 −14.4 0.01 −11.4 0.03 −10.8 0.001 −13.3 0.001 −8.5 0.13
Study center
Georgia 103 Ref.  Ref.  Ref.  Ref.  Ref.
South Carolina 72 8.3 0.07 10.3 0.07 −6.8 0.11 1.9 0.63 14.3 0.03
BMI, kg/m2
<22.5 Ref.  Ref.  Ref.  Ref.  Ref.
22.6–25 24 27.0  34.3  22.2  25.9  28.5
25.1–27.5 36 36.3  51.9  26.2  33.2  39.8
27.6–30 45 28.0  44.8  17.2  25.5  31.0
30.1–35 38 13.9  21.6  9.0  12.7  15.5
>35 24 48.7 0.17 71.8 0.06 33.7 0.54 34.6 0.72 61.9 0.11
Red and processed meats intakes, servings/day
0 14 Ref.  Ref.  Ref.  Ref.  Ref.
0.1–0.5 50 8.5  13.2  5.0  4.7  12.2
0.6–1 48 18.5  16.7  19.8  7.0  29.9
>1 63 10.0 0.03 11.9 0.18 8.6 0.01 8.1 0.31 12.2 0.01

Abbreviations: Diff, difference; FLIC, flagellin; N, number.

aAll means, SEs, and P values were calculated using general linear models, with adjustment for age, sex, center, and BMI, where appropriate.

b% difference = [(comparison mean – reference mean)/reference mean] × 100%.

cP value is for trend if the participant characteristic variable has more than two categories.

Our results suggest that (i) supplemental calcium and vitamin D, alone or in combination, have no substantial effect over 3–5 years on circulating concentrations of the biomarkers of gut barrier function, as measured among individuals with previously diagnosed colorectal adenoma and (ii) men and study participants with higher overall adiposity or high red and processed meat intakes may have higher circulating anti-flagellin and anti-LPS Igs concentrations, suggesting a history of greater gut permeability.

Despite strong biologic plausibility and promising experimental data, mostly for vitamin D and the VDR (12, 13, 15–26), the effects of calcium and vitamin D supplementation on gut permeability biomarkers have not been extensively studied in humans. Similar to our previously published report from another randomized, controlled trial of supplemental calcium (n = 193; ref. 33), we found no effects of calcium supplementation on circulating Igs against LPS and flagellin. However, in our other biomarker study, within the same parent study for this biomarker study (n = 105), we found that the expression of tight junction proteins (claudin-1 and occludin) and mucin-12 in the normal-appearing colorectal mucosa increased in the calcium group relative to the no calcium group after 1 year of treatment (34), suggesting that calcium could affect physical, but perhaps not immune gut barrier function. Contrary to our initial hypothesis, we did not observe changes in the circulating gut barrier biomarkers in the vitamin D–alone group or in the combined calcium plus vitamin D group. No previous clinical trials that tested the effects of vitamin D on these biomarkers have been reported. However, a small, double-blind, randomized, placebo-controlled trial (n = 27) reported that supplementation with 2,000 IU/day of vitamin D over 3 months increased plasma antimicrobial peptide cathelicidin concentrations and prolonged remission among patients with Crohn disease (35). Another study among individuals with liver cirrhosis (n = 338), found that participants who had severe vitamin D deficiency, relative to those with no deficiency, had statistically significantly higher serum concentrations of soluble CD14, a biomarker of intestinal permeability, and that soluble CD14 serum concentrations decreased in participants with de novo vitamin D supplementation (P = 0.002; ref. 36). Finally, a prospective study of 144 critically ill patients found higher plasma concentrations of endotoxin and zonulin, a commonly studied biomarker of gut permeability, to be associated with lower serum 25-hydroxyvitamin D concentrations (37). There are several possible reasons as to why calcium and/or vitamin D had no effects on gut permeability markers in our study. First, calcium and/or vitamin D may have no important effects on circulating biomarkers of intestinal permeability in humans at the administered doses or in this population at higher risk for colorectal neoplasms. Second, the biomarkers investigated in this study may not be the best and most direct measures of colon permeability. However, previous studies reported positive correlations between circulating anti-LPS and anti-flagellin Igs concentrations and a direct measure of intestinal barrier function (38) and circulating concentrations of LPS (39). Finally, concentrations of these Igs may reflect changes in the gut microbiota, in addition to changes in gut barrier function.

When we investigated associations of a priori–selected participant characteristics with circulating gut barrier biomarker concentrations at baseline, our results suggested that women, relative to men, had less gut permeability, as indicated by lower mean concentrations of our gut barrier biomarkers. This finding was consistent with our previous finding of women having a 7% lower permeability score (P = 0.05; ref. 33). Although the exact biologic mechanisms for these findings are unknown, it is possible that men have lower innate and adaptive immune responses than women (40), and are exposed to higher levels of endotoxins as a result of higher colonic permeability or differences in the gut microbiome composition, due, in part, to diet, lifestyle, or hormonal factors (41, 42).

Consistent with the results from our previous clinical trial (33) and several cross-sectional studies (43–47), these results suggest that higher adiposity may be associated with higher gut permeability, as indicated by the estimated positive association of BMI with our biomarkers of gut barrier function. Obesity can contribute to altered intestinal immunity, and was found to be associated with changes in the gut microbiota, intestinal barrier function, gut-residing innate and adaptive immune cells, and oral tolerance to luminal antigens (48). Obese individuals have altered gut microbiota, characterized by a higher abundance of Gram-negative bacteria (49), which may have a greater ability to translocate across the gut mucosa into the circulation than do Gram-positive bacteria (50). Furthermore, LPS is a major component of the outer membrane of Gram-negative bacteria; thus, obese individuals may have higher exposure to LPS, resulting in higher circulating anti-LPS Ig concentrations.

Finally, we found that intake of red and processed meats was positively associated with some biomarkers of gut barrier function, suggesting that they may increase gut permeability. Interactions between diet and the gut microbiota may affect gut epithelial integrity and intestinal homeostasis (48). In a cross-sectional study of 201 healthy men, high-energy diets and fat intake were associated with higher circulating LPS concentrations (51). Another cross-sectional study reported that a Western dietary pattern, characterized by higher intakes of red and processed meats, desserts, and refined grains, was positively associated with plasma soluble CD14 concentrations (52). In mice, meat proteins in a high-fat diet impaired the gut barrier through inhibiting mucus expression, gut inflammation, and downregulation of tight junction proteins, including occludin, claudin-1, zonula occludens-1, and E-cadherin (53). A red meat–derived glycan, N-glycolylneuraminic acid (Neu5Gc), promoted inflammation and cancer progression in animal models (54), and exposure to Neu5Gc could be modified by the gut microbiome (55).

Major strengths of our study include that it is the first clinical trial to test vitamin D effects on gut permeability markers, adherence to study treatment was high, participants largely avoided taking vitamin D and calcium in substantial amounts outside the study, and our novel gut permeability biomarkers. We also collected detailed questionnaire information and evaluated associations of baseline demographic, diet, and lifestyle factors with gut permeability biomarkers, which may provide insights for future epidemiologic studies.

Limitations of the study include that the trial was conducted among patients with a recent history of colorectal adenomas who were participating in a chemoprevention trial, and, thus, our results might have limited external generalizability. The vitamin D dose used in the study (1,000 IU/day) might have been insufficiently high to modify gut barrier function. Furthermore, the gut permeability biomarkers were measured in singleton; however, based on the previous assays on these same biomarkers, we expect that our biomarker measurement reliability was high. While it is possible that vitamin D has no effect on the biomarkers investigated in this study among individuals at higher risk for colorectal cancer, it remains probable that vitamin D, possibly in higher doses, may affect gut permeability (perhaps assessed with other gut permeability markers) in other populations with a high prevalence of vitamin D deficiency. Finally, our exploratory analyses of the associations of participants' characteristics with gut permeability markers were observational and cross-sectional in nature, and some of the associations could be partially explained by residual confounding. However, adjustment for additional factors, including total energy intake, number of baseline adenomas, diabetes, smoking status, and alcohol consumption, did not substantially change the reported results, and our findings support assessment in other studies.

In summary, contrary to our hypothesis, supplementation with vitamin D and/or calcium did not modify our selected circulating concentrations of gut barrier function biomarkers over 3–5 years in patients with sporadic colorectal adenoma. However, our results suggested that sex, BMI, and red and processed meats intakes may be associated with gut permeability. These findings support continued investigation of potential modifiable factors, such as diet and obesity, that could alter gut barrier function, to inform the development of treatable biomarkers of risk for colorectal neoplasms.

R.M. Bostick reports grants from NCI, NIH during the conduct of the study. E.L. Barry reports grants from NIH/NCI during the conduct of the study. V. Fedirko reports grants from NCI during the conduct of the study. No disclosures were reported by the other authors.

The NCI, the Georgia Cancer Coalition https://doi.org/10.13039/100001129, the Franklin Foundation, and Pfizer Consumer Healthcare https://doi.org/10.13039/100004319 had no influence on the design of this study; the collection, analysis, and interpretation of the data; the decision to submit the article for publication; or the writing of the article.

K. Vermandere: Formal analysis, writing–original draft, writing–review and editing. R.M. Bostick: Data curation, funding acquisition, writing–review and editing, study recruitment and sample collection. H.Q. Tran: Writing–review and editing, measurement of biomarkers. A.T. Gewirtz: Writing–review and editing, biomarker data acquisition. E.L. Barry: Resources, project administration, writing–review and editing. R.E. Rutherford: Writing–review and editing. M.E. Seabrook: Study recruitment and sample collection. V. Fedirko: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, methodology, writing–original draft, project administration, writing–review and editing.

This research was supported by the NCI of the NIH under award nos., R21 CA182752 (to V. Fedirko), R03 CA184578 (to V. Fedirko), R01 CA098286 (to John A. Baron), and R01 CA114456 (to R.M. Bostick); the Wilson P. and Anne W. Franklin Foundation (to R.M. Bostick); and a Georgia Cancer Coalition Distinguished Scholar award (to R.M. Bostick). Pfizer Consumer Healthcare provided the study agents. We thank all study participants for their time and dedication to the study.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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