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Cancer Control Research, British Columbia Cancer Agency, Vancouver, British Columbia, V5Z 4E6 Canada [M. J. B., R. P. G.]; Department of Health Care and Epidemiology, University of British Columbia, Vancouver, British Columbia, V6T 1Z3 Canada [M. J. B., S. B. S., R. P. G.]; Department of Health Research and Policy, Stanford University, Stanford, California 94305-5092 [A. S. W.]; Department of Preventative Medicine, University of Southern California, California 90033 [A. H. W.]; and Cancer Prevention Research, Fred Hutchinson Cancer Research Center and Department of Epidemiology, University of Washington, Washington 98109-1024 [J. D. P.]
| Abstract |
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| Introduction |
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The role of insulin in CRC is a fairly recent area of investigation. The idea is consistent with the normal role of insulin as a growth factor for human colonic mucosal cells, which have both insulin and IGF-I receptors (5 , 9 , 14 , 18) . A role for insulin is also supported by prospective studies of human subjects with non-insulin-dependent diabetes mellitus (an insulin-resistant state), in whom an increased risk of CRC has been found (11 , 12) . Thus insulin, a known growth factor, is a biologically plausible agent in colorectal carcinogenesis, particularly at chronically elevated levels.
The research hypothesis of this study is that "total carbohydrate minus carbohydrate fiber" (hereafter called "effective carbohydrate," i.e., the digestible nonfiber portion of carbohydrate intake that stimulates insulin release) is a significant risk factor for CRC via chronic insulin stimulation. Although some databases may already have the fiber component subtracted, those analyses that are based on total carbohydrate including fiber might be biased toward the null because of the possibly protective effect of the embedded fiber (19) , i.e., a negative confounder. Effective carbohydrate consumption may be a variable with a significant impact on health and heretofore not properly controlled for in analyses using a carbohydrate variable that includes fiber.
| Materials and Methods |
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Cases.
Of the 805 eligible North American patients, 235 died between selection and interview, 91 refused to participate, 58 were too ill or had moved, and 421 (52%) were interviewed. To assess potential bias from loss to interview by death, next of kin were contacted for those who died before being interviewed (n = 235), and 52 relatives (22%) knew enough about the subjects lifestyle and agreed to provide a surrogate interview. Data from these interviews showed no significant differences in mean values for CRC risk factors such as family history, physical activity, dietary, demographic, medical, reproductive, and migrant factors; therefore, information from patient and surrogate interviews was pooled, giving data on 473 of the 805 eligible cases (59%).
Controls.
Chinese-American population controls were frequency matched to cases by sex, age (at 5-year intervals), and residential location, in an approximately 3:1 ratio. In Vancouver, controls were selected from the Medical Services Plan list of subscribers, matched on school district of residence, whereas in San Francisco and Los Angeles, controls were recruited by house-to-house canvassing of the cases neighborhood. Of 2219 potentially eligible controls, 446 could not be contacted because they were deceased or had moved, 581 refused to participate, and 1192 (54%) were interviewed.
Interviews.
The study questionnaire (20)
was administered by a trained interviewer in the participants home, in the language of their choice. The questionnaire covered diet and physical activity, length of residence in North America, body weight and height, as well as demographic, medical, and reproductive factors. The subjects were asked about themselves, including diet and weight at age 21, age 40, and a reference year (the year before diagnosis for cases and the year before interview for controls). To ensure a common protocol, interviewers at all centers were trained by a core group of trainers, and taped interviews were monitored throughout the study. During the study period, investigators from all centers convened at least twice a year to maintain communication and a consistent approach to data collection.
Dietary History.
Food items were selected to include the most frequently consumed foods, both Western and Chinese. Participants reported their average frequency of consumption of 84 foods in six food groups: meat, fish, and eggs; dairy; starches and sweets; vegetables; fruits; and beverages. They also reported portion sizes for items consumed more than once a week. Food models were used to simulate Chinese and Western style foods.
Data Coding.
A common protocol was used for questionnaire coding and data entry at all study sites. All nutrient intake variables were calculated by combining food frequencies with portion sizes and nutrient values obtained from three sources (21, 22, 23)
. A new variable was created for the current analysis, effective carbohydrate consumption (eCarb), defined as "total carbohydrate grams per day" minus "total fiber grams per day." For this calculation, total fiber was defined as grams per day of both pectin (soluble carbohydrate fiber) and NDF (insoluble carbohydrate fiber). Other independent variables included: age (as a continuous variable); sex (0 = male, 1 = female); saturated, monounsaturated, and polyunsaturated fat consumption (g/day); years in North America (0 = 09, 1 = 1019, 2 = 20+); total kilocalories consumed/day; calcium consumption (mg/day); fiber consumption (g/day of pectin and NDF); QI; and education (0 = less than 12 years, 1 = 12+ years).
Summary Descriptive Statistics.
Descriptive statistics were generated using SPSS Version 9.0 (24)
. All analyses were performed separately by sex. Independent t tests for continuous variables and
2 tests for categorical variables were used to examine whether statistically significant differences exist between the cases and the controls in any of the factors being considered for the multivariate model.
Variable Selection.
Initially, variables of interest and potential confounders were selected from the data, based on prior information from the literature. The final group of variables was selected by testing for their effect on the estimate of the eCarb OR (25
, 26)
. Total kilocalories consumed, a possible risk factor, was included in the model to control for energy intake not covered by the nutrient variables, using the standard multivariate method (27)
. Study center, physical inactivity (hours spent sitting/day), and protein consumption were also analyzed for effect on the eCarb OR before being excluded. The variable for income level was missing in 67 cases for men and 78 cases for women; therefore, the variable for education level, which was complete, was selected as a reasonable surrogate for socioeconomic status (28
, 29)
.
Multivariate Logistic Regression Model.
The SPSS logistic regression model building procedure (24)
was used to build an initial model, using the Likelihood Ratio statistic. Each covariate was tested by removal from the model, and if the eCarb OR was unchanged, the variable was excluded from subsequent models.
Residual values were saved during the regression run to allow checking for outliers and influential points. These were plotted against predicted values, and specific cases were then selected for examination. Two cases were found to have large leverage values (relative influence of an observation on the models fit), indicating outliers that could affect the results. Additional regression models were done excluding the two outlier cases, but the differences observed did not change the conclusion; therefore, the two cases were included in the final model.
All covariates included in the final model exhibit interaction with the primary variable to some degree, as evidenced by a change in eCarb OR when the variables are removed from the model, but none of the interaction terms reached statistical significance for these data.
The independent variable of interest, eCarb, was categorized into tertiles, based on sex-specific control population cutpoints of "effective" carbohydrate consumption. Tertiles were used to categorize the primary variable to allow analysis by sex and cancer site, given the study sample size. Education and years in North America were also categorized, whereas remaining independent variables were analyzed as continuous variables to conserve degrees of freedom in the model. Analysis of the interaction terms used the same coding used for testing confounding.
| Results |
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| Discussion |
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The data indicate about a 2-fold increase in risk of CRC for those with high intake of effective carbohydrates (highest tertile) versus those with low intake (lowest tertile), after controlling for potential confounders. This observed result gives support to our a priori hypothesis that increased consumption of effective carbohydrate is associated with increased risk of CRC. Although the differences in risk between total and effective carbohydrate consumption are not large, they are compatible with expectations in terms of direction. When the cancer sites are considered together, men and women appear similar in their response to carbohydrate consumption. The observed site-specific differences between the sexes, however, suggest possible differences in etiology for right and left colon cancers that are consistent with womens higher incidence of right colon tumors (31 , 32) . Given the male-female difference in incidence by subsite, it is likely that one or more risk factors might also exhibit differences such as those observed in this study.
The study results must be interpreted with caution, however, in light of the selection of one ethnic group (Chinese) for the study population. Chinese were specifically targeted because of the relatively low incidence of CRC among Chinese living in China compared with Chinese living in North America. It is possible, however, that people of Chinese ancestry may be either more or less sensitive than other ethnic groups to the effects of dietary carbohydrates, potentially affecting generalizability of this analysis.
Although this study supports recent work on carbohydrates and insulin resistance, the current results are not consistent with all previous studies of carbohydrates. This may be partly explained by the novel treatment of carbohydrate in this study, i.e., to subtract the fiber portion before analysis, something that other studies may not have done. Fiber, as in whole grains for example, is thought to modulate the glycemic response (33, 34, 35) . The observed sex and cancer site differences in the OR of pectin suggest that some fiber components may further confuse carbohydrate analysis by being a risk factor in some circumstances and a protective factor in other circumstances. Also, some previous studies have focused on sucrose consumption (36) with risk ratios similar to those we observed. Because sucrose is only a fraction of digestible carbohydrate, however, studying it in isolation from starch consumption may increase the likelihood of a null result.
One inconsistency of the female right colon result with existing literature is that components of the Western lifestyle such as diet and activity have been associated more often with left colon and rectal cancer than with right colon cancer. This may be explained partly by the preponderance of male subjects used in studies and the increased likelihood that a male pattern might predominate in published study results. It could also be attributable to the multifactorial nature of "Western lifestyle" and the difficulties of studying any one component without some residual confounding. A second inconsistency relates to the nonsignificant interaction term for QI and also the lack of effect on eCarb OR for physical inactivity, both of which might be expected to interact strongly with carbohydrate consumption. This may be related to limited variability in the study population for these characteristics, as shown in Table 1
.
Possible Mechanisms.
When insulin is present in abnormally high concentrations, it can react with other receptors because receptor binding is a function of both affinity and concentration. In contrast to its blood glucose regulation role, insulin is believed to act through IGF-I receptors in its mitogenic effect on colonic carcinoma cells in vitro (10
, 14)
. The mitogenic signal transduction may be mediated by p21ras (13)
, an important proto-oncogene in colon carcinogenesis. The wide range of effects seen with hyperinsulinemia is consistent with a general pattern such as cross-reaction with other receptor types. Insulin also increases the availability of IGF-I, an independent risk factor (18
, 37) , by down-regulating IGF binding proteins (38
, 39)
.
Insulin may also act indirectly through its role in the production and regulation of sex steroid hormones (40 , 41) . In the last three decades, evidence for a hormonal role in colorectal carcinogenesis has accumulated. The bile acid mechanism developed by McMichael and Potter (42, 43, 44, 45, 46) suggests that sex differences in the bile acid metabolic profile are relevant to the preponderance of right colon tumors in women compared with men. One possible way that carbohydrate intake might interact with colonic bacteria and secondary bile acid production is via the intermediary of female reproductive hormones, either directly by potentiation of sex hormone production (both estrogen and testosterone), or indirectly by inhibition of sex hormone binding globulin production (40) . These actions of insulin would result in higher plasma concentrations of free estrogen available to act at the tissue level.
The eCarb Variable.
Regarding the use of eCarb as a variable in place of total carbohydrate, only a small difference in OR might be expected because of the small amount of fiber relative to total carbohydrate consumed and the heterogeneity of the fiber component (47)
, which this study does not address. In addition, this analysis used crude categorizations, both in terms of carbohydrate subcomponents and also in the use of tertiles of consumption. Although they are not large, the differences observed between eCarb and total carbohydrate analyses are potentially important in dietary research, where ORs are typically modest, and the effects of even small differences are magnified by the universal nature of diet as an exposure. Additional research is needed on the nature and effects of carbohydrate subcomponents. It is possible that simple subtraction of fiber from total carbohydrate may underestimate the potentially greater effect of 1 additional g of fiber/day, compared with 1 g of nonfiber carbohydrate; therefore, the difference between the results using eCarb and those using total carbohydrate may be greater than those observed in this study.
When energy intake is associated with disease, it is important to use a measure of nutrient intake that is independent of total energy. In this study, the effect of total energy consumption was controlled by inclusion of kilocalories consumed/day in the multivariate model. The eCarb variable, therefore, can be interpreted as the nutrient effect, independent of the effects of total energy and fat consumption covariates. One complexity of this approach is that the interpretation of the coefficient for the kilocalories consumed variable changes from total energy to total energy independent of the nutrients in the model, which in this case are carbohydrate and fat.
Glycemic index, if used as the primary variable in this study, might provide more information about the speed of carbohydrate digestion. Glycemic index, however, was not used here because it presents significant additional measurement challenges. Glycemic index may be influenced by a variety of environmental factors such as freshness, method of cooking, other foods eaten at the same time, and industrial processes such as gelatinization (34 , 48) , about which accurate data were not collected.
Four main conclusions arise from this study: (a) increased carbohydrate consumption is associated with increased risk of CRC in both men and women. The risk ratio associated with high levels of effective carbohydrate consumption compared with low levels is 1.7 for men and 2.7 for women when all cancer sites are considered together; (b) carbohydrate and especially effective carbohydrate should now be considered important potential risk factors in future research; (c) right colon, left colon, and rectal cancers should be considered separately where possible because it appears from these data that the dietary etiological factors may differ by site; and (d) excess carbohydrate consumption may pose a special risk of right colon cancer for women. The results of this analysis support the a priori hypothesis of increased risk of CRC attributable to increased effective carbohydrate consumption and also provide direction for new work on possible sex-specific, carbohydrate-associated cancer risks in the female right colon.
| Acknowledgments |
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| Footnotes |
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1 To whom requests for reprints should be addressed, at Cancer Control Research, BC Cancer Agency, 600 West 10th Avenue, Vancouver, British Columbia, V5Z 4E6 Canada. Phone: (604) 877-6098, extension 3174; Fax: (604) 877-1868; E-mail: mborugia{at}bccancer.bc.ca ![]()
2 The abbreviations used are: CRC, colorectal cancer; IGF, insulin-like growth factor; NDF, neutral detergent fiber; OR, odds ratio; CI, confidence interval; QI, Quetelets index. ![]()
Received 3/23/01; revised 11/ 2/01; accepted 11/16/01.
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