Abstract
Dietary determinants of colorectal mucosa proliferation were studied in 69 subjects previously operated for at least two sporadic colon adenomas. Information on recent dietary habits was collected by a validated food frequency questionnaire, and proliferation was measured by [3H]thymidine incorporation in colorectal biopsies by determining the labeling index (LI) and the percentage of LI in the upper part of the crypt, two parameters that are increased in subjects at high risk of colon cancer. The LI was significantly higher in women as compared with men (P = 0.01). Diet showed several associations with colorectal mucosa proliferation: (a) subjects in the highest tertile of fish consumption had a significantly lower LI (P = 0.0013) compared with those in the lower tertiles [5.20 ± 1.87 versus 6.80 ± 2.18 (mean ± SD)]; (b) subjects with a low red meat consumption had lower proliferation in the upper part of the crypt [2.38 ± 2.10, 5.30 ± 4.62, and 5.89 ± 4.82 in the low, middle, and high tertile of consumption, respectively (mean ± SD); P = 0.0093]; (c) according to estimated nutrient intakes, the LI was lower in subjects reporting a high intake of starch (P = 0.006) and higher in subjects with a low intake of β-carotene (P = 0.002). The results show that subjects reporting a diet rich in fish, starch, and β-carotene and low in red meat had lower colorectal mucosa proliferation and a normal pattern of proliferation along the crypt. Given the correlation between colorectal proliferative activity and colon cancer risk, such a dietary pattern might be beneficial for subjects at high risk of colon cancer.
Introduction
Different dietary habits affect the occurrence of colon cancer in humans and experimental animals (1, 2, 3, 4, 5, 6, 7, 8, 9). It is generally agreed that high dietary fibers are inversely associated with colon cancer risk, the reverse being true for high dietary lipids, calories, and sucrose. (1, 2, 4). The data on complex carbohydrates, such as starch, on colon cancer are more controversial; some authors are proposing that these nutrients are inversely associated with cancer risk (5, 6, 10, 11, 12, 13), and others are considering a high consumption of starchy foods (white bread, pasta, and rice) as a risk factor (1, 7, 8, 9).
One of the mechanisms proposed to explain the effect of different dietary components on colon carcinogenesis is the modification of the proliferative activity of colon mucosa by different nutrients (14, 15). Clinical studies have demonstrated, in fact, that subjects at high risk of colon cancer (such as familial adenomatous polyposis, ulcerative colitis, or sporadic colon cancer patients) have a high mucosal proliferative activity as compared with controls (16). Besides, in normal subjects, colon proliferative activity is confined to the lower part of the crypt and, in high-risk subjects, an upwards shift of the proliferative compartment from the basal to the upper part of the crypt occurs (16). Moreover, in experimental animals, substances known to promote or initiate colon carcinogenesis (14, 17, 18) increase mucosal proliferation. Therefore, although someone questions the association of high proliferation-colon cancer risk (19), others consider a low proliferation as a protective condition (20, 21, 22, 23).
Most epidemiological studies on diet and colorectal proliferation have looked at the effect of various supplements or have compared the proliferative activity of human populations with markedly different dietary habits (20, 21, 22, 23, 24, 25). On the contrary, only a few studies (26, 27) have evaluated within a given population whether individual dietary habits affect colorectal proliferation in subjects not following any specific diet or receiving dietary supplements.
Given these considerations, we wanted to study the correlation between dietary habits and colorectal mucosa proliferation in a group of subjects previously operated for at least two colonic adenomas, thus considered at high risk of colon cancer (28); these subjects had not received specific dietary recommendations by their clinicians due to the lack of accepted guidelines in this field.
Materials and Methods
Subjects.
The study was conducted on a group of patients operated for sporadic colon polyps in the Gastroenterology Unit of the Regional Careggi Hospital and in the Digestive Endoscopy Unit of the Camerata Hospital of Florence, Italy. Subjects were identified in the Endoscopy Registries of these hospitals using the following criteria: they had undergone a polypectomy 6–24 months before entering the study, with removal of at least two adenomatous or villous polyps, one of which was >1 cm. Subjects responding to these criteria were invited to participate in the study and, if they agreed, were asked to undergo an endoscopy, performed without bowel preparation (see below), and to be interviewed by a professional dietitian who compiled a validated food frequency questionnaire relative to the food consumption of the last month (see below). We analyzed 44 males and 25 females (mean age ± SE, 58.0 ± 0.8 years; range, 40–70). The study was approved by the Regional Ethical Committee in accordance with the Helsinki declaration of 1975, and written informed consent was obtained from all subjects.
Determination of Colorectal Mucosa Proliferative Activity.
Proliferative activity was assessed in vitro by [3H]thymidine incorporation in colorectal biopsy specimens. Because bowel preparations used before endoscopic examinations have been reported to affect colon proliferation (29), patients were instructed not to take any laxatives or enemas before endoscopy that was performed in the early afternoon (at about 3 p.m.). Biopsies were obtained from normal-appearing mucosa at about 15 cm from the anal verge. At least three biopsies were processed for autoradiography, according to standard methods (30). Only full longitudinal crypt sections (i.e., from the base to the bottom of the crypt) were scored. For each subject (n = 69), a mean of 25.9 ± 1.1 (SE) crypts was scored (range, 14–41). For each crypt, we recorded the number of cells/crypt column (i.e., number of cells from the bottom to the top of the half crypt appearing in the section) and the number and position of the labeled cells along the crypt, dividing each crypt into three equal compartments: lower, middle, and upper. For each subject, proliferative activity was expressed as the LI3 (number of labeled cells counted in all of the crypts of the same subject/number of cells in all of the crypt sections of the same subject × 100). The distribution of proliferative activity along the crypt was calculated for each patient by dividing the total number of labeled cells in each compartment (lower, middle, and upper) of all of the crypts scored by the total number of labeled cells scored in the crypts and multiplying this value by 100 (percentage of LI in each compartment of the crypt). The microscopic slides were coded and read independently by two observers. The Spearman correlation coefficient between these two observers on a set of 27 LI scores was 0.87 (P <0.001). When the continuous measurements of LI were categorized in binary variables, low or high, according to the median of the LI values distribution of each reader (6.06 and 5.58, respectively), κ coefficient also indicated a good agreement between the two observers (κ, 0.70; 95% confidence intervals,0.44–0.97).
Assessment of Dietary Habits.
A food frequency questionnaire, designed to investigate dietary habits in the Italian population, has been recently developed and validated within the framework of the European Union EPIC program (31). The questionnaire was designed for optical scanning. Dietary habits reported at interview were also transformed, using a specific software (31), into estimates of intakes of specific nutrients using information on the average frequency of consumption and portion sizes for a list of 150 food items. Nutrient composition was based on updated Italian food tables (Ref. 32; Table 4).
Statistical Analysis.
To investigate the relationship between proliferative activity of the colon mucosa and dietary habits, we compared LI values and percentage of proliferation in the upper part of the crypt for different levels of food group consumption and nutrient intake. For each of the 69 subjects, these values were obtained from the observation of a mean of 25.9 ± 1.1 (SE) crypts/subject. To facilitate the analysis, we divided subjects into tertiles of intake of food groups and nutrients. The Wilcoxon’s rank-sum tests and Kruskal-Wallis tests were used for statistical evaluation of the differences in the LI value distributions of two or three comparison groups. Mean LI between tertiles of intake on food groups and nutrients were compared by analysis of covariance, introducing into the model age, gender, and adenoma’s histology. Post hoc Scheffé’s tests were conducted for three-way comparisons among nutrient tertiles. The Spearman rank-correlation coefficient was used to determine the correlation between LI values and nutrient intake. The relative importance of the various nutrients was assessed using a stepwise selection procedure based on a multiple regression model, with LI levels as dependent variables. Starting from a full model with all variables included, nonsignificant variables were progressively deleted with a step-down procedure based on a likelihood ratio test. Interaction terms between gender and selected nutritional variables were evaluated, in the final model, by means of the Wald χ2 test.
As forced variables in the regression model, we included age, as continuous variable, and gender and adenoma histology, as dummy variables. Because all patients had two or more adenomas, they were categorized according to the most severe histological type reported at polypectomy (villous type adenomas are considered more severe than tubulovillous, and tubulovillous are more severe than tubular adenomas (33).
Results
The different parameters describing the proliferative activity in the colorectal mucosa of the subjects under study are described in Table 1.
We first evaluated whether some individual characteristics such as age, sex, and polyp histology, affected the LI (Table 2). The results of this analysis showed a significant effect on gender, with women having a significantly higher (P = 0.001) proliferative activity (LI) than men. We also observed (Table 2) that patients who were previously operated for a villous adenoma, a histological type considered to have a higher malignant potential than tubulovillous or tubular adenomas (33), tended to have a higher proliferative activity than patients operated for tubular adenomas; however, this association was not statistically significant, probably because of the small number of subjects in the villous adenoma category.
The consumption of different food items in the previous month was evaluated with a validated dietary questionnaire and expressed as weekly frequency of consumption; the different food items were also transformed into specific nutrient intake as described in “Materials and Methods.” Table 3 presents the tertiles for the intake of the different food groups and nutrients used in the analysis of the correlation between proliferation and dietary habits.
We then determined the LI in the subjects falling within each tertile of weekly frequency of consumption of food groups (Table 4), and the differences in LI values of subjects belonging to different tertiles were analyzed with covariance analysis, Kruskal-Wallis test, and the Scheffé’s test. Moreover, to present the data in a more intuitive form, we also added a column with the percentage change in LI from the first to the third tertile of food consumption.
The results of these statistical evaluations (Table 4) indicated no association of any of the selected food groups, with the exception of a significant P for the differences among the tertiles of fish consumption. The results of the Scheffé’s test showed that the LI of subjects in the second tertile of consumption was significantly higher compared with the first and third tertiles. The combination of the first and second tertiles of fish consumption had a mean LI value of 6.80 ± 2.18 (mean ± SD; n = 46), significantly higher than the LI of the subjects in the third tertile of consumption [5.2 ± 1.87 (mean ± SD); n = 23; Wilcoxon’s rank-sum test for two samples, P = 0.0013; covariance analysis, F = 4.66 and P = 0.0347].
Because it has been suggested that a shift in the proliferative activity from the lower to the upper part of the crypt is a risk factor in the development of colon cancer (16, 22), we also studied the correlation between the consumption of different foods and the percentage of labeled cells in the upper compartment (percentage of LI in the upper compartment of the crypt). The results indicated (Table 5) that the only food item significantly associated with a variation in this parameter was red meat, with subjects falling within the different tertiles of red meat consumption having significantly higher proliferation in the upper part of the crypt.
We also evaluated whether the proliferative activity was associated with the intake of a particular nutrient. For each nutrient, we determined the mean LI of the subjects classified in each tertile of intake, as described above (Table 6). The results of the statistical evaluation by the Kruskal-Wallis test indicated significant differences among the tertiles of β-carotene and starch intake (Ps = 0.002 and 0.006, respectively). We also performed an analysis of covariance, taking into account the association with other variables such as sex, histology, and age. This analysis showed that the only nutrient significantly affecting LI was β-carotene. However, the Scheffé’s test for multiple comparisons among the different tertiles indicated that the LI in the high tertile of starch consumption was lower than that of the first and second tertiles and the LI in the low tertile of β-carotene intake was higher than those in the second and third tertiles. Moreover, when we calculated the percentage change of LI from the first to the third tertile of consumption, we observed that starch and β-carotene were the nutrients associated with the highest variation.
To assess the relative importance of the various nutrients on proliferation, we also used a stepwise selection procedure based on a multiple regression model, with the LI as a dependent variable. Starting from a full model with all variables included, nonsignificant variables were progressively deleted with a step-down procedure based on a likelihood ratio test. The results of this analysis indicated that a regression model in which age, gender, and adenoma histology were included as independent variables accounted for 12% (r2 = 0.12) of the total variability of the data and that among all of the nutrients evaluated the only ones that accounted for a further effect were β-carotene (r2 = 0.05) and starch (r2 = 0.02). All of the other nutrients did not significantly affect proliferation.
The correlation between each nutrient intake and the distribution of the proliferative activity along the crypt, carried out with the same model, did not indicate any significant association (data not shown).
Discussion
This study describes some dietary determinants of colorectal proliferation in the normal mucosa of subjects previously operated for at least two colonic adenomas, a condition that has been associated with a high risk of later-developing colon cancer (28). It is important to stress the fact that the majority of the studies relating colorectal proliferation to dietary habits has been carried out with high-risk subjects (21, 22, 23, 26), and this may constitute a limitation of these studies. Therefore, caution must be applied in generalizing the results to the general population, with no apparent risk of cancer.
Another possible limitation of the present study is the small sample size, a fact that limits the statistical power.
Considering these limitations, the main results described in this study were: (a) the LI (i.e., proliferative activity of the colon mucosa) was significantly higher in women as compared with men; (b) the LI was significantly lower in subjects with a higher consumption of fish; (c) the LI was lower in subjects with a high intake of starch and was higher in subjects with a low intake of β-carotene; and (d) the proliferation in the upper compartment of the crypt was higher in subjects who eat more red meat.
The association with gender and colorectal proliferative activity has never been documented in detail before. A study by Morgan and Singh (27) found a borderline positive association between female gender and colorectal proliferation, but other studies showed no significant association (27, 34). Colon cancer incidence in the Florence area, is 24.5/100,000 and 20.2/100,000 in males and females, respectively (35). We do not have an explanation for the higher mucosal proliferation in females described in the present study. However, in the multivariate analyses evaluating the association between different nutrients and colon proliferation, sex was always included as a covariate, together with adenoma histology and age.
The other main findings of this study relate to the association between some foods or nutrients and colorectal proliferation. Among the different food items analyzed, we found that the LI of subjects in the second tertile of fish consumption was significantly higher than in the first and third tertiles. The combination of the first and second tertiles of fish consumption gave a mean LI value higher than that of the subjects in the third tertile of consumption, thus suggesting that a high intake of fish is correlated with lower proliferative activity in the colorectal mucosa. Given the lack of dose-response across the tertiles of fish consumption, it is possible that this result is a chance finding. However, another explanation for the lack of dose-response may be a threshold effect of fish intake. This second possibility would be supported by epidemiological and experimental data showing that a high consumption of fish or fish oil is associated with a low risk of colorectal cancer (36, 37, 38). This protective effect has been attributed to the presence of ω-3 fatty acids in fish, which protect carcinogen-treated rats from colon cancer (38). Bartram et al. (39) have also demonstrated that fish oil supplementation reduces colorectal proliferation in healthy subjects. Similarly, Anti et al. (22) showed that ω-3 fatty acid supplementation normalized proliferation in the upper compartment of the crypt in subjects at risk for colon cancer for previous adenomatous polyps. These last results are in agreement with our findings, showing that a relatively higher consumption of fish is correlated with lower proliferative activity.
In our study, we observed that a higher consumption of red meat was associated with an increased proliferative activity in the upper compartment of the crypt. Increased red meat intake has been associated with a high risk of colorectal cancer in many epidemiological studies (40). The association between red meat and colorectal proliferation had not been documented before, although in a recent study Bostick et al. (26) reported that subjects with a high consumption of meat (white and red) had a borderline significant increase of proliferative activity in the upper part of the crypt. Although in normal subjects proliferative activity in the colorectal mucosa is mostly confined in the lower part of the crypt (16, 41, 42), subjects at high risk for developing colon cancer (patients with familial polyposis, adenomatous polyps, or cancer of the large bowel) have a shift of proliferative activity toward the upper part of the crypt (16). It has also been reported that chemopreventive agents are able to decrease the proliferative activity in the upper part of the crypt (20, 22, 23). Therefore, our study and that of Bostick et al. (26) suggests that meat consumption may increase cancer risk by upsetting the regulation of the proliferative activity along the crypt.
Because nutrient analysis may give a more analytical evaluation of dietary habits than food frequency of consumption, we also assessed whether proliferative activity in colorectal mucosa was associated with the intake of particular nutrients. Our results showed that β-carotene significantly affected colorectal proliferation, subjects with a low intake having a significantly higher LI.
The supplementation with β-carotene (30 mg/day) had been previously shown to decrease rectal mucosal proliferation, as assessed by ornithine decarboxylase activity in colon cancer patients (43). On the contrary, Frommel et al. (44) found that dietary supplementation with β-carotene (30 mg) did not alter colorectal proliferation in patients with previous colonic polyps or cancer. However, in a study in which vitamins A, C, and E were administered to patients with colorectal adenomas (20), the authors found a significant decrease in proliferation in the upper compartment of the crypt after vitamin supplementation (20). The subjects in our study did not report the use of any vitamin supplements.
Various epidemiological studies have demonstrated that diets rich in fruits and vegetables protect against the development of different types of cancer, including colon cancer (1, 4). This effect has been attributed, at least in part, to the fact that fruits and vegetables are rich in β-carotene, a compound with a strong antioxidant activity, although some other components of fruit and vegetables, such as fibers or other vitamins, may play a role. Accordingly, the observed association between β-carotene and mucosal proliferation in our study may reflect a spurious correlation with the consumption of fruit or vegetables, because fibers also vary mucosal proliferation (45). However, in our study, the dietary fiber or fruit and vegetable intake was not correlated with proliferation.
We also found an association between starch consumption and proliferation. In a multiple regression model in which age, gender, and adenomas histology were included as independent variables, starch and β-carotene were the only nutrients negatively correlated with proliferative activity.
The effect of starch consumption on colon cancer development is controversial. Although several case-control studies have shown a high consumption of starchy foods (white bread, pasta, and rice) to be a risk factor (1, 7, 8, 9), other epidemiological studies suggest the reverse (5, 10, 12). Experimental studies have demonstrated that starch-rich diets decrease the growth of preneoplastic lesions in rat colon (46, 47). Accordingly, in azoxymethane-induced colorectal carcinogenesis it was shown that rats fed corn starch or starch-rich foods, such as pasta, have fewer intestinal tumors compared with rats fed sucrose (11, 48).
Our group has previously reported that rodents fed corn starch as a source of carbohydrates had a lower colorectal proliferation when compared with animals fed a sucrose-rich diet (47, 49). The results of the present study are also consistent with what was reported in a clinical trial (24) in which resistant starch significantly decreased colorectal proliferation in human healthy volunteers.
The results of all of the available evidence suggest that starch consumption, especially when compared with the consumption of simple carbohydrates such as sucrose, may be associated with a lower risk of colon cancer.
In conclusion, the data of this study show that a diet rich in fish, starch, and β-carotene is associated with a low colorectal mucosa proliferation in the crypt; moreover, a diet low in red meat is associated with a low proliferation in the upper part of the crypt. These results were obtained in a group of subjects who had at least two colonic adenomas removed and, thus, were considered at high risk of developing colon cancer (28).
Until now, it is not clear what strategy to follow to reduce the frequency of polyp recurrence or transformation in patients with a previous history of colonic polyps or cancer. In fact, there is no general agreement about specific drug therapies or dietary recommendations potentially active in reducing cancer risk. Increased colorectal proliferation has been associated with cancer risk in both epidemiological and experimental studies (16), and colorectal proliferation has been widely used as a “surrogate biomarker” to measure colon cancer risk in chemoprevention trials (50). Given the correlation between colorectal proliferative activity and colon cancer, our results suggest that a diet rich in fish, starch, and β-carotene and low in red meat might be beneficial for patients with previous adenomas and, possibly, for the general population. Long-term intervention trials with such diets should be planned to confirm this hypothesis.
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.
Supported by grants from Progetto Finalizzato ACRO (Applicazioni Cliniche Ricerca Oncologicala), the European Community AIR (Agro Industrial Research) Program (Grant CT94/0933), the European Community FAIR program (Grant CT95/0653), the Ministero della Università e della Ricerca Scientifica e Tecnologica, and the University of Florence (Florence, Italy). The dietary questionnaire was developed within the framework of EPIC Italy, a prospective study supported by a generous grant from the Associazione Italiana per la Ricerca sul Cancro (AIRC) and the European Union (Grant 97 SOC 200302 05F02).
The abbreviation used is: LI, labeling index.
Colorectal mucosal proliferation in the subjects under study
Total number of crypts evaluated in all subjects | 1788 |
Number of cells/crypt columna | 67.09 ± 1.1 |
LI in the entire crypta | 6.3 ± 0.3 |
% LI in the lower compartment of the crypta | 62.1 ± 1.3 |
% LI in the middle compartment of the crypta | 33.4 ± 1.0 |
% LI in the upper compartment of the crypta | 4.6 ± 0.5 |
Total number of crypts evaluated in all subjects | 1788 |
Number of cells/crypt columna | 67.09 ± 1.1 |
LI in the entire crypta | 6.3 ± 0.3 |
% LI in the lower compartment of the crypta | 62.1 ± 1.3 |
% LI in the middle compartment of the crypta | 33.4 ± 1.0 |
% LI in the upper compartment of the crypta | 4.6 ± 0.5 |
Values are means ± SE; n = 69.
Descriptive analysis of LI values according to age, sex, and adenoma histology
Characteristics . | No. . | Mean ± SD . | P a . | F-valueb . |
---|---|---|---|---|
Age groups (years) | ||||
(≤55) | 21 | 6.58 ± 2.79 | ||
(56–64) | 26 | 5.76 ± 1.81 | ||
(≥65) | 22 | 6.58 ± 1.98 | 0.4 | 0.52 |
Sex | ||||
Men | 44 | 5.65 ± 1.89 | ||
Women | 25 | 7.36 ± 2.32 | 0.001 | 10.97 |
Adenoma histology | ||||
Tubular | 38 | 5.93 ± 1.84 | ||
Tubulovillous | 26 | 6.62 ± 2.54 | ||
Villous | 5 | 7.08 ± 2.90 | 0.6 | 0.90 |
Characteristics . | No. . | Mean ± SD . | P a . | F-valueb . |
---|---|---|---|---|
Age groups (years) | ||||
(≤55) | 21 | 6.58 ± 2.79 | ||
(56–64) | 26 | 5.76 ± 1.81 | ||
(≥65) | 22 | 6.58 ± 1.98 | 0.4 | 0.52 |
Sex | ||||
Men | 44 | 5.65 ± 1.89 | ||
Women | 25 | 7.36 ± 2.32 | 0.001 | 10.97 |
Adenoma histology | ||||
Tubular | 38 | 5.93 ± 1.84 | ||
Tubulovillous | 26 | 6.62 ± 2.54 | ||
Villous | 5 | 7.08 ± 2.90 | 0.6 | 0.90 |
Wilcoxon’s rank-sum test for two group comparisons; Kruskal-Wallis test for three group comparisons.
Covariance analysis models including age, sex, and adenoma histology as covariates.
Frequency of consumption of selected food groups (servings/week) or intake of different nutrients (g/day) determined with a food frequency questionnairea
Food group/nutrient . | First tertile (mean ± SD; max-min) . | Second tertile (mean ± SD; max-min) . | Third tertile (mean ± SD; max-min) . |
---|---|---|---|
Pasta | 4.14 ± 1.95; 6.69–1.27 | 8.20 ± 0.83; 9.47–7.00 | 12.89 ± 1.51; 14.74–9.70 |
Bread | 12.06 ± 3.41; 15.00–3.74 | 25.30 ± 4.06; 30.34–16 | 44.55 ± 10.45; 70.00–35.00 |
Red meat | 1.28 ± 0.79; 2.25–0.00 | 3.22 ± 0.58; 4.00–2.33 | 10.71 ± 8.85; 36.23–4.00 |
Fish | 0.46 ± 0.37; 0.94–0.00 | 1.28 ± 0.22; 1.64–1.00 | 2.75 ± 0.72; 5.00–2.00 |
Cooked vegetables | 3.26 ± 1.24; 4.80–0.00 | 6.48 ± 0.81; 7.70–4.96 | 10.76 ± 3.56; 24.00–7.86 |
Fruits | 3.39 ± 2.06; 6.87–0.00 | 10.76 ± 2.66; 14.29–7.00 | 22.46 ± 8.35; 50.39–14.67 |
Energy (Kcal) | 1500.3 ± 295.5; 1013.2–1960.9 | 2303.7 ± 206.5; 1999.6–2591.6 | 3097.3 ± 423.9; 2602.0–3963.8 |
Protein (g) | 57.1 ± 9.9; 33.5–71.0 | 85.9 ± 8.7; 71.6–99.8 | 115.8 ± 13.8; 100.7–146.7 |
Carbohydrates (g) | 187.9 ± 41.1; 88.9–242.2 | 295.7 ± 28.3; 246.9–345.1 | 442.8 ± 81.1; 350.0–592.9 |
Fiber (g) | 10.5 ± 2.3; 5.7–13.7 | 17.0 ± 1.9; 14.1–20.4 | 25.6 ± 4.0; 20.5–33.8 |
Fat (g) | 48.3 ± 9.3; 27.0–59.7 | 70.3 ± 5.5; 63.4–79.9 | 102.2 ± 21.7; 83.6–163.0 |
Alcohol (g) | 0.0 ± 0.0; 0.0–0.0 | 8.82 ± 8.2; 0.21–25.0 | 45.1 ± 20.2; 25.8–111.8 |
Calcium (mg) | 491.5 ± 121.0; 204.7–665.5 | 781.3 ± 80.6; 668.7–956.2 | 1190.3 ± 237.5; 956.4–1818.4 |
Ascorbic acid (mg) | 43.4 ± 8.4; 19.0–56.4 | 68.8 ± 9.9; 56.5–89.5 | 127.3 ± 33.5; 91.1–230.8 |
Retinol (μg) | 164.6 ± 56.9; 43.0–252.1 | 423.5 ± 231.0; 267.6–1128.2 | 2472.4 ± 1284.2; 1146.2–860.1 |
β-carotene (μg) | 1464.1 ± 355.0; 641.8–2241.7 | 2738.5 ± 291.2; 2292.6–3292.3 | 5202.1 ± 1786.3; 3293.1–576.1 |
Sucrose (g) | 17.8 ± 6.5; 6.6–27.3 | 34.4 ± 4.6; 27.7–41.4 | 56.4 ± 17.9; 41.7–111.6 |
Starch (g) | 79.9 ± 23.1; 37.4–118.9 | 155.3 ± 19.4; 123.6–196.1 | 276.8 ± 64.5; 200.2–423.2 |
Food group/nutrient . | First tertile (mean ± SD; max-min) . | Second tertile (mean ± SD; max-min) . | Third tertile (mean ± SD; max-min) . |
---|---|---|---|
Pasta | 4.14 ± 1.95; 6.69–1.27 | 8.20 ± 0.83; 9.47–7.00 | 12.89 ± 1.51; 14.74–9.70 |
Bread | 12.06 ± 3.41; 15.00–3.74 | 25.30 ± 4.06; 30.34–16 | 44.55 ± 10.45; 70.00–35.00 |
Red meat | 1.28 ± 0.79; 2.25–0.00 | 3.22 ± 0.58; 4.00–2.33 | 10.71 ± 8.85; 36.23–4.00 |
Fish | 0.46 ± 0.37; 0.94–0.00 | 1.28 ± 0.22; 1.64–1.00 | 2.75 ± 0.72; 5.00–2.00 |
Cooked vegetables | 3.26 ± 1.24; 4.80–0.00 | 6.48 ± 0.81; 7.70–4.96 | 10.76 ± 3.56; 24.00–7.86 |
Fruits | 3.39 ± 2.06; 6.87–0.00 | 10.76 ± 2.66; 14.29–7.00 | 22.46 ± 8.35; 50.39–14.67 |
Energy (Kcal) | 1500.3 ± 295.5; 1013.2–1960.9 | 2303.7 ± 206.5; 1999.6–2591.6 | 3097.3 ± 423.9; 2602.0–3963.8 |
Protein (g) | 57.1 ± 9.9; 33.5–71.0 | 85.9 ± 8.7; 71.6–99.8 | 115.8 ± 13.8; 100.7–146.7 |
Carbohydrates (g) | 187.9 ± 41.1; 88.9–242.2 | 295.7 ± 28.3; 246.9–345.1 | 442.8 ± 81.1; 350.0–592.9 |
Fiber (g) | 10.5 ± 2.3; 5.7–13.7 | 17.0 ± 1.9; 14.1–20.4 | 25.6 ± 4.0; 20.5–33.8 |
Fat (g) | 48.3 ± 9.3; 27.0–59.7 | 70.3 ± 5.5; 63.4–79.9 | 102.2 ± 21.7; 83.6–163.0 |
Alcohol (g) | 0.0 ± 0.0; 0.0–0.0 | 8.82 ± 8.2; 0.21–25.0 | 45.1 ± 20.2; 25.8–111.8 |
Calcium (mg) | 491.5 ± 121.0; 204.7–665.5 | 781.3 ± 80.6; 668.7–956.2 | 1190.3 ± 237.5; 956.4–1818.4 |
Ascorbic acid (mg) | 43.4 ± 8.4; 19.0–56.4 | 68.8 ± 9.9; 56.5–89.5 | 127.3 ± 33.5; 91.1–230.8 |
Retinol (μg) | 164.6 ± 56.9; 43.0–252.1 | 423.5 ± 231.0; 267.6–1128.2 | 2472.4 ± 1284.2; 1146.2–860.1 |
β-carotene (μg) | 1464.1 ± 355.0; 641.8–2241.7 | 2738.5 ± 291.2; 2292.6–3292.3 | 5202.1 ± 1786.3; 3293.1–576.1 |
Sucrose (g) | 17.8 ± 6.5; 6.6–27.3 | 34.4 ± 4.6; 27.7–41.4 | 56.4 ± 17.9; 41.7–111.6 |
Starch (g) | 79.9 ± 23.1; 37.4–118.9 | 155.3 ± 19.4; 123.6–196.1 | 276.8 ± 64.5; 200.2–423.2 |
Some variables were not included for brevity. Complete data are available from the corresponding author upon request.
Descriptive analysis of LI values according to tertiles of frequency of food consumption
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal-Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Pasta | 6.93 ± 2.81 | 5.91 ± 1.41 | 5.97 ± 2.13 | −13.85 | 0.3 | 0.89 | 0.42 | ||||
Bread | 6.65 ± 2.53 | 6.96 ± 1.82 | 5.21 ± 1.87 | −21.65 | 0.8 | 2.12 | 0.15 | ||||
Cheeses | 6.24 ± 2.65 | 6.25 ± 1.67 | 6.32 ± 2.28 | 1.28 | 0.9 | 0.04 | 0.96 | ||||
Red meat | 6.52 ± 2.66 | 6.46 ± 1.78 | 5.82 ± 2.11 | −10.74 | 0.4 | 1.48 | 0.23 | ||||
Processed meat | 6.32 ± 2.70 | 6.37 ± 1.87 | 6.12 ± 2.05 | −3.16 | 0.7 | 0.10 | 0.91 | ||||
White meat | 6.91 ± 2.63 | 6.23 ± 2.04 | 5.67 ± 1.76 | −17.95 | 0.2 | 0.74 | 0.45 | ||||
Fish | 6.37 ± 2.55 | 7.23 ± 1.78 | 5.20 ± 1.87 | −18.37 | 0.001 | 4.61 | 0.01 | 2 vs 1, 3 | |||
Raw vegetables | 6.45 ± 2.42 | 6.47 ± 2.47 | 5.89 ± 1.69 | −8.68 | 0.6 | 0.87 | 0.42 | ||||
Cooked vegetables | 6.80 ± 2.76 | 6.62 ± 1.70 | 5.39 ± 1.81 | −20.74 | 0.03 | 1.45 | 0.24 | ||||
Fruits | 6.27 ± 2.53 | 6.35 ± 1.91 | 6.20 ± 2.22 | −1.12 | 0.7 | 0.37 | 0.69 |
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal-Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Pasta | 6.93 ± 2.81 | 5.91 ± 1.41 | 5.97 ± 2.13 | −13.85 | 0.3 | 0.89 | 0.42 | ||||
Bread | 6.65 ± 2.53 | 6.96 ± 1.82 | 5.21 ± 1.87 | −21.65 | 0.8 | 2.12 | 0.15 | ||||
Cheeses | 6.24 ± 2.65 | 6.25 ± 1.67 | 6.32 ± 2.28 | 1.28 | 0.9 | 0.04 | 0.96 | ||||
Red meat | 6.52 ± 2.66 | 6.46 ± 1.78 | 5.82 ± 2.11 | −10.74 | 0.4 | 1.48 | 0.23 | ||||
Processed meat | 6.32 ± 2.70 | 6.37 ± 1.87 | 6.12 ± 2.05 | −3.16 | 0.7 | 0.10 | 0.91 | ||||
White meat | 6.91 ± 2.63 | 6.23 ± 2.04 | 5.67 ± 1.76 | −17.95 | 0.2 | 0.74 | 0.45 | ||||
Fish | 6.37 ± 2.55 | 7.23 ± 1.78 | 5.20 ± 1.87 | −18.37 | 0.001 | 4.61 | 0.01 | 2 vs 1, 3 | |||
Raw vegetables | 6.45 ± 2.42 | 6.47 ± 2.47 | 5.89 ± 1.69 | −8.68 | 0.6 | 0.87 | 0.42 | ||||
Cooked vegetables | 6.80 ± 2.76 | 6.62 ± 1.70 | 5.39 ± 1.81 | −20.74 | 0.03 | 1.45 | 0.24 | ||||
Fruits | 6.27 ± 2.53 | 6.35 ± 1.91 | 6.20 ± 2.22 | −1.12 | 0.7 | 0.37 | 0.69 |
Mean ± SD.
Percentage change from I to III tertile.
Covariance analysis models including age, sex, and adenoma histology as covariates.
Test for all multiple comparisons 1, 2, and 3 indicate the first, second, and third tertile.
Descriptive analysis of the values of the percentage of LI in the upper compartment of the crypt according to tertiles of frequency of food consumption
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal- Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Pasta | 4.61 ± 3.78 | 4.74 ± 5.53 | 4.22 ± 3.37 | −8.46 | 0.7 | 0.06 | 0.9388 | — | |||
Bread | 5.13 ± 4.23 | 3.97 ± 4.53 | 4.48 ± 4.16 | −12.67 | 0.4 | 0.14 | 0.8723 | — | |||
Cheese | 4.18 ± 3.35 | 3.32 ± 3.28 | 6.07 ± 5.51 | 45.22 | 0.2 | 3.96 | 0.0241 | — | |||
Red meat | 2.38 ± 2.10 | 5.30 ± 4.62 | 5.89 ± 4.82 | 147.48 | 0.009 | 4.09 | 0.0215 | 1 vs 3 | |||
Processed meat | 4.39 ± 3.93 | 4.39 ± 3.46 | 4.80 ± 5.37 | 9.34 | 0.8 | 0.00 | 0.9980 | — | |||
White meat | 6.05 ± 5.17 | 2.95 ± 2.55 | 4.44 ± 4.15 | −26.61 | 0.1 | 2.93 | 0.0608 | — | |||
Fish | 5.47 ± 5.28 | 4.00 ± 3.37 | 4.10 ± 3.96 | −25.05 | 0.5 | 0.58 | 0.5619 | — | |||
Raw vegetables | 5.30 ± 4.16 | 4.04 ± 4.23 | 4.29 ± 4.50 | −19.06 | 0.3 | 0.40 | 0.6692 | — | |||
Cooked vegetables | 3.96 ± 3.81 | 5.29 ± 5.20 | 4.32 ± 3.72 | 9.09 | 0.7 | 0.86 | 0.4271 | — | |||
Fruits | 4.86 ± 4.61 | 4.04 ± 2.66 | 4.67 ± 5.26 | −3.91 | 0.7 | 0.35 | 0.7053 | — |
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal- Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Pasta | 4.61 ± 3.78 | 4.74 ± 5.53 | 4.22 ± 3.37 | −8.46 | 0.7 | 0.06 | 0.9388 | — | |||
Bread | 5.13 ± 4.23 | 3.97 ± 4.53 | 4.48 ± 4.16 | −12.67 | 0.4 | 0.14 | 0.8723 | — | |||
Cheese | 4.18 ± 3.35 | 3.32 ± 3.28 | 6.07 ± 5.51 | 45.22 | 0.2 | 3.96 | 0.0241 | — | |||
Red meat | 2.38 ± 2.10 | 5.30 ± 4.62 | 5.89 ± 4.82 | 147.48 | 0.009 | 4.09 | 0.0215 | 1 vs 3 | |||
Processed meat | 4.39 ± 3.93 | 4.39 ± 3.46 | 4.80 ± 5.37 | 9.34 | 0.8 | 0.00 | 0.9980 | — | |||
White meat | 6.05 ± 5.17 | 2.95 ± 2.55 | 4.44 ± 4.15 | −26.61 | 0.1 | 2.93 | 0.0608 | — | |||
Fish | 5.47 ± 5.28 | 4.00 ± 3.37 | 4.10 ± 3.96 | −25.05 | 0.5 | 0.58 | 0.5619 | — | |||
Raw vegetables | 5.30 ± 4.16 | 4.04 ± 4.23 | 4.29 ± 4.50 | −19.06 | 0.3 | 0.40 | 0.6692 | — | |||
Cooked vegetables | 3.96 ± 3.81 | 5.29 ± 5.20 | 4.32 ± 3.72 | 9.09 | 0.7 | 0.86 | 0.4271 | — | |||
Fruits | 4.86 ± 4.61 | 4.04 ± 2.66 | 4.67 ± 5.26 | −3.91 | 0.7 | 0.35 | 0.7053 | — |
Mean ± SD.
Percentage change from I to III tertile.
Covariance analysis models including age, sex, and adenoma histology as covariates.
Test for all multiple comparisons 1, 2, and 3 indicate the first, second, and third tertile.
Descriptive analysis of LI values according to tertiles of nutrient intakes
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal- Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Total calories | 6.84 ± 2.54 | 6.27 ± 2.01 | 5.71 ± 1.97 | −16.52 | 0.2 | 0.98 | 0.38 | ||||
Total protein | 6.83 ± 2.54 | 6.16 ± 2.22 | 5.82 ± 1.75 | −14.79 | 0.3 | 0.00 | 0.99 | ||||
Fiber | 6.75 ± 2.49 | 6.29 ± 2.11 | 5.77 ± 1.96 | −14.52 | 0.3 | 0.39 | 0.68 | ||||
Total lipid | 6.95 ± 2.55 | 5.95 ± 1.84 | 5.91 ± 2.10 | −14.96 | 0.2 | 0.45 | 0.64 | ||||
SFAe | 6.15 ± 1.71 | 6.89 ± 2.70 | 5.77 ± 2.03 | −6.18 | 0.2 | 0.87 | 0.42 | ||||
MUFA | 6.97 ± 2.54 | 5.86 ± 1.91 | 5.98 ± 2.04 | −14.20 | 0.2 | 0.86 | 0.43 | ||||
PUFA | 6.96 ± 2.52 | 5.57 ± 1.94 | 6.28 ± 1.98 | −9.77 | 0.09 | 1.83 | 0.17 | ||||
Cholesterol | 6.50 ± 2.63 | 6.20 ± 1.99 | 6.11 ± 2.01 | −6.00 | 0.9 | 0.34 | 0.71 | ||||
Alcohol | 6.96 ± 2.61 | 5.48 ± 1.74 | 6.41 ± 2.01 | −7.90 | 0.06 | 2.50 | 0.09 | 1 vs. 2 | |||
Calcium | 6.22 ± 1.92 | 6.64 ± 1.92 | 5.95 ± 2.72 | −4.34 | 0.2 | 0.87 | 0.42 | ||||
Ascorbic acid | 7.16 ± 2.59 | 5.63 ± 1.66 | 6.02 ± 2.05 | −15.92 | 0.06 | 1.36 | 0.26 | 1 vs. 2, 2 vs. 3 | |||
Retinol | 6.08 ± 1.83 | 6.51 ± 2.85 | 6.21 ± 1.86 | 2.14 | 1.0 | 0.59 | 0.56 | ||||
β-carotene | 7.54 ± 2.53 | 5.44 ± 1.40 | 5.83 ± 2.00 | −22.68 | 0.002 | 4.05 | 0.02 | 1 vs. 2, 3 | |||
α-tocoferol | 7.00 ± 2.49 | 5.69 ± 1.70 | 6.13 ± 2.23 | −12.43 | 0.1 | 1.42 | 0.25 | ||||
Total carbohydrates | 6.75 ± 2.57 | 6.74 ± 1.85 | 5.33 ± 1.90 | −21.04 | 0.02 | 2.08 | 0.13 | ||||
Starch | 6.80 ± 2.56 | 6.80 ± 1.80 | 5.21 ± 1.86 | −23.38 | 0.006 | 1.33 | 0.27 | 1, 2 vs. 3 | |||
Sugarsf | 6.47 ± 2.51 | 6.19 ± 1.74 | 6.15 ± 2.38 | −4.95 | 0.3 | 0.22 | 0.81 | ||||
Glucose | 6.20 ± 2.51 | 6.37 ± 2.00 | 6.24 ± 2.16 | 0.65 | 0.8 | 0.82 | 0.44 | ||||
Fructose | 6.46 ± 2.41 | 6.08 ± 2.22 | 6.28 ± 2.06 | −2.79 | 1.0 | 0.50 | 0.61 | ||||
Sucrose | 6.55 ± 2.54 | 6.50 ± 1.96 | 5.76 ± 2.08 | −12.06 | 0.3 | 0.13 | 0.88 | ||||
Others | 6.53 ± 1.78 | 6.00 ± 1.98 | 6.28 ± 2.79 | −3.83 | 0.2 | 0.38 | 0.68 |
. | Tertilea . | . | . | Percentage change of LIb . | Kruskal- Wallis test (P) . | Analysis of covariance . | . | Scheffe’s testd . | |||
---|---|---|---|---|---|---|---|---|---|---|---|
. | I (n = 23) . | II (n = 23) . | III (n = 23) . | . | . | F-valuec . | P . | . | |||
Total calories | 6.84 ± 2.54 | 6.27 ± 2.01 | 5.71 ± 1.97 | −16.52 | 0.2 | 0.98 | 0.38 | ||||
Total protein | 6.83 ± 2.54 | 6.16 ± 2.22 | 5.82 ± 1.75 | −14.79 | 0.3 | 0.00 | 0.99 | ||||
Fiber | 6.75 ± 2.49 | 6.29 ± 2.11 | 5.77 ± 1.96 | −14.52 | 0.3 | 0.39 | 0.68 | ||||
Total lipid | 6.95 ± 2.55 | 5.95 ± 1.84 | 5.91 ± 2.10 | −14.96 | 0.2 | 0.45 | 0.64 | ||||
SFAe | 6.15 ± 1.71 | 6.89 ± 2.70 | 5.77 ± 2.03 | −6.18 | 0.2 | 0.87 | 0.42 | ||||
MUFA | 6.97 ± 2.54 | 5.86 ± 1.91 | 5.98 ± 2.04 | −14.20 | 0.2 | 0.86 | 0.43 | ||||
PUFA | 6.96 ± 2.52 | 5.57 ± 1.94 | 6.28 ± 1.98 | −9.77 | 0.09 | 1.83 | 0.17 | ||||
Cholesterol | 6.50 ± 2.63 | 6.20 ± 1.99 | 6.11 ± 2.01 | −6.00 | 0.9 | 0.34 | 0.71 | ||||
Alcohol | 6.96 ± 2.61 | 5.48 ± 1.74 | 6.41 ± 2.01 | −7.90 | 0.06 | 2.50 | 0.09 | 1 vs. 2 | |||
Calcium | 6.22 ± 1.92 | 6.64 ± 1.92 | 5.95 ± 2.72 | −4.34 | 0.2 | 0.87 | 0.42 | ||||
Ascorbic acid | 7.16 ± 2.59 | 5.63 ± 1.66 | 6.02 ± 2.05 | −15.92 | 0.06 | 1.36 | 0.26 | 1 vs. 2, 2 vs. 3 | |||
Retinol | 6.08 ± 1.83 | 6.51 ± 2.85 | 6.21 ± 1.86 | 2.14 | 1.0 | 0.59 | 0.56 | ||||
β-carotene | 7.54 ± 2.53 | 5.44 ± 1.40 | 5.83 ± 2.00 | −22.68 | 0.002 | 4.05 | 0.02 | 1 vs. 2, 3 | |||
α-tocoferol | 7.00 ± 2.49 | 5.69 ± 1.70 | 6.13 ± 2.23 | −12.43 | 0.1 | 1.42 | 0.25 | ||||
Total carbohydrates | 6.75 ± 2.57 | 6.74 ± 1.85 | 5.33 ± 1.90 | −21.04 | 0.02 | 2.08 | 0.13 | ||||
Starch | 6.80 ± 2.56 | 6.80 ± 1.80 | 5.21 ± 1.86 | −23.38 | 0.006 | 1.33 | 0.27 | 1, 2 vs. 3 | |||
Sugarsf | 6.47 ± 2.51 | 6.19 ± 1.74 | 6.15 ± 2.38 | −4.95 | 0.3 | 0.22 | 0.81 | ||||
Glucose | 6.20 ± 2.51 | 6.37 ± 2.00 | 6.24 ± 2.16 | 0.65 | 0.8 | 0.82 | 0.44 | ||||
Fructose | 6.46 ± 2.41 | 6.08 ± 2.22 | 6.28 ± 2.06 | −2.79 | 1.0 | 0.50 | 0.61 | ||||
Sucrose | 6.55 ± 2.54 | 6.50 ± 1.96 | 5.76 ± 2.08 | −12.06 | 0.3 | 0.13 | 0.88 | ||||
Others | 6.53 ± 1.78 | 6.00 ± 1.98 | 6.28 ± 2.79 | −3.83 | 0.2 | 0.38 | 0.68 |
Mean ± SD.
Percentage change from I to III tertile.
Covariance analysis models including age, sex, and adenoma histology as covariates.
Test for all multiple comparisons 1, 2, and 3 indicate the first, second, and third tertile.
SFA, saturated fatty acids; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids.
Sum of glucose, fructose, and sucrose.