Calcium and vitamin D and risk of colorectal cancer: results from a large population-based case-control study in Newfoundland and Labrador and Ontario.
Dairy products contain large amounts of calcium and vitamin D through fortification. It has been shown that calcium, especially in combinations as found in milk, effectively precipitates luminal cytotoxic surfactants and thus inhibits colonic cytotoxicity. (21,22) Jarvinen et al. (23) indicated that individuals with a high consumption of milk have a potentially reduced risk of colon cancer; however, the association did not appear to be due to intake of calcium, vitamin D, or to specific effects of fermented milk. Recent research indicates that calcium and vitamin D might act together, rather than separately, to reduce risk of CRC. (24) Results from a multicentre, placebo-controlled randomized clinical trial found that calcium supplementation was inversely associated with adenoma recurrence only when vitamin D levels were above the median (29.1 ng/ml). (25)
Despite the biological plausibility, epidemiological studies have been suggestive but inconclusive for a protective role of dietary calcium and vitamin D in CRC prevention. The World Cancer Research Fund/American Institute for Cancer Research expert report in 2007 (24) summarized 11 cohort studies regarding dietary and serum vitamin D intake and colorectal cancer risk. Six of these have reported nonsignificant decreased risk; (5,26-30) two have shown no impact on CRC; (27,31) and three other studies have indicated non-significant increased risk. (23,32,33) The expert report also indicated that increased milk and dietary calcium intake are associated with reduced CRC risk. (24)
We investigated these possible associations among persons in both NL and ON. NL is geographically isolated, culturally distinct, and relatively economically disadvantaged, thus fresh fruits and vegetables are less often available. Consequently, people may consume more preserved and salted traditional foods. Our interdisciplinary CRC research team tried to explore whether and how the high incidence of CRC in NL can be partly explained by the unique dietary habits of the NL population. Squire et al. recently found that in NL, higher intakes of red pickled meat were associated with increased risk of CRC. (34) In contrast, ON is a centrally located, culturally diverse, and economically advantaged province. It is hypothesized that consistent results of the protective effects of calcium and vitamin D in two diverse provinces would provide support to the argument that calcium and vitamin D have chemo-preventive effects on CRC. To our knowledge, little has been done in this area in Canada. Therefore, the purpose of this report is to assess the effects of dietary calcium, vitamin D and dairy products on the occurrence of CRC and to compare these possible associations between the two provinces.
SUBJECTS AND METHODS
Selection of cases and controls
Data for this case-control study were obtained from the Ontario Familial Colorectal Cancer Registries (OFCCR) and Newfoundland Familial Colorectal Cancer Registries (NFCCR). In ON, incident cases diagnosed during 1997-2000 were identified through the population-based Ontario Cancer Registry (Phase one). In NL, incident cases diagnosed during 1999-2003 were identified through the population tumour registry maintained by the Newfoundland Cancer Registry. Both registries were used to identify newly diagnosed cases of colon or rectal cancer (pathology confirmed ICD 9th revision codes: 153.0-153.9, 154.1-154.3, and 154.8 or ICD-10 codes: 18.0-18.7, 19.9, 20.9) among those aged 20-74 years. Phase two of the OFCCR enrolled cases diagnosed in ON during 2003-2006. (35,36)
Initial contact was with the surgeon/physician identified on the pathology report. Once physician consent was obtained, cases were then contacted to inform them of the study. Participants who indicated their willingness to participate in the study were sent, in sequence, a written consent form, family history questionnaire (FHQ), personal history questionnaire (PHQ), and food frequency questionnaire (FFQ). Non-responders were sent postcard reminders and wre phoned several weeks after initial contact to remind them of the mailing. Controls were a random sample of residents in each province aged 20-74 years. In ON, controls were identified through a list of residential phone numbers or from population-based property assessment rolls (owners and occupants). In NL, controls were identified through random digit dialing. (34) Both registries frequency matched controls to cases on sex and five-year age strata. Once verbal consent for participation was obtained from controls during the phone contact, the same survey package as sent to cases was forwarded to each potential participant.
Dietary and epidemiologic data collection
Information on dietary intake was collected using a self-administered FFQ. The FFQ administered in ON was the well-known Hawaii FFQ. (37,38) The FFQ administered in NL was modified based on the ON questionnaire and adapted to include regional foods in NL. (34) The FFQ was used to assess diet over the 1-2 years prior to diagnosis or interview in each province. Participants were questioned about their intake of almost 170 foods which were believed to be important to the contribution of most nutrients in the diet. For each food item, subjects were asked to estimate the frequency of food intake and their usual portion size: 'Regular', 'Small' or 'Large'. Food photographs were provided that showed regular, small and large portion sizes for vegetables, meat and chicken. Participants were also questioned on their use of any individual or multivitamin supplements, including the usual brand name, the amounts taken and the duration of consumption. Nutrient intakes were computed by multiplying the frequency of consumption of each food item by the nutrient content of the portion size.
Possible associations between CRC risk and the consumption of five groups of dairy foods (total dairy products, milk, non-milk dairy products (e.g., cream), yogurt, cheese) were also investigated. Total dairy food consumption was computed by adding the daily servings of all foods in the dairy categories. Total milk consumption was calculated by adding the daily servings of non-fat milk or skim milk, low-fat milk (2%), and whole milk. Non-milk dairy product consumption was calculated by adding the daily servings of yogurt, cheese and cream.
The self-administered personal history questionnaire included many close-ended questions about medical history, bowel screening history, medication use, diet, physical activity, alcohol and tobacco use and socio-demographic measures such as education and income. Identifying information such as sex, age, date of birth, and marital status was collected. For female participants, there were additional questions relating to reproductive factors.
For analyses, we excluded those who did not provide sufficient dietary information, those who failed to provide information on potential risk factors, those who reported energy intake in the upper or lower 2.5% of intake (lower and upper cutoff: in NL, 925 and 4700 kcal for men, 1100 and 4900 kcal for women, respectively; in ON, 1040 and 5200 kcal for men, 835 and 4100 kcal for women, respectively), and patients who had familial adenomatous polyposis (FAP) and/or an in-situ tumour. In ON, 896 subjects were excluded; in NL, 281 subjects were excluded. After these exclusions, based on those who completed both the PHQ and FFQ, 3,102 subjects (1,272 cases and 1,830 controls) from ON and 1,139 subjects (488 cases and 651 controls) from NL remained. Data collected from these subjects were used for the analysis. Since one of the main objectives of this study was an interprovincial comparison, a province-stratified rather than pooled analysis was performed.
Descriptive statistics stratified by case-control status were used to describe the demographic/ health-related characteristics and dietary intakes of subjects. Intakes of calcium and vitamin D were energy-adjusted by using residuals calculated from the linear regression of the log of nutrient intake versus the log of energy intake. (39) Intakes of total calcium and total vitamin D were calculated by adding energy-adjusted nutrients from food and unadjusted nutrients from supplements. Intakes of calcium, vitamin D and dairy products were categorized into quintiles based on the distribution among the study population without missing endpoints and were entered into models as indicator variables with the lowest quintile as the referent group.
Age and total energy intake-adjusted odds ratios (OR) and their corresponding 95% confidence intervals (95% CI) were calculated from maximum-likelihood estimates in unconditional logistic regression to assess the association of the outcome with dietary intakes. Multivariate unconditional logistic regression was used to evaluate the association of intakes of calcium, vitamin D and dairy products with CRC risk after adjusting for a set of potential confounders or covariates. Tests for trend were used to assess dose-response relationships based on the median of each category of dietary intake.
Potential confounding factors include age (18-49, 50-59, 60-69 and 70+ years); sex; body mass index (BMI<18.5, 18.5-24.9, 25-29.9, and [greater than or equal to]30 kg/[m.sup.2]); physical activity (<7.4, 7.4-22.4, 22.4-53.0, and >53.0 metabolic equivalent hours/week, METs/week); first-degree relatives with CRC (yes, no); polyps (yes, no); diabetes (yes, no); history of colon screening procedure (yes, no); cigarette smoking (ever smoke, never smoke); alcohol drinking (<14, [greater than or equal to]14 drinks/week); education attainment (high school graduate or less, technical school/some college/university, and bachelor's degree/graduate degree); household income (<$12,000; $12,000-29,999, $30,000-49,999, and >$50,000); marital status (married, single/never married, and separated/divorced/widowed); regular use of medication and supplements: non-steroid anti-inflammatory drug (NSAID)(yes, no), multivitamin supplements (yes, no), folate supplement (yes, no); reported hormone replacement therapy (HRT, females only)(yes, no); dietary intakes: total energy intake (quintiles), fruits (0-6, 6-7, 7-14, and >14 servings/week), vegetables (0-6, 6-7, 7-14, and >14 servings/week), red meat (0-2, 2-3, 3-5, and >5 servings/week); and province of residence (NL, ON). The basis for the assessment of confounding factors included: 1) literature and previous studies, 2) biological plausibility, 3) whether the regression coefficient of the primary dependent variable changed by 10% or more after addition of the potentially confounding variable, or 4) whether the covariate entered the model at p<0.05. The final list of potential confounding factors included in the model was based on both backwards-stepwise procedure and the literature. Statistical tests were two-sided, and p values less than 0.05 were considered statistically significant. Statistical analyses were performed using SAS statistical software. (40)
Demographic and lifestyle characteristics of the study participants, stratified by province and case-control status, are shown in Table 1. The study participants included 1,760 cases (488 from NL, 1,272 from ON) and 2,481 controls (651 from NL, 1,830 from ON) with average response rates of 65.0% and 53.5% in cases and controls, respectively. NL cases were slightly older than controls (mean, 62.7 for cases, 60.5 for controls), while ON cases were slightly younger than controls (mean, 58.4 for cases, 61.5 for controls). In both provinces, cases had higher BMI than controls; more often had first-degree relatives with CRC; were less likely to report any colon screening procedure, to report use of multivitamin supplements, and to have taken HRT over the previous 1-2 years (females only). Physical activity (METs/week) or heavy alcohol drinking history did not vary significantly between cases and controls in the two provinces. NL cases tended to be smokers and less likely to have acquired higher education or to obtain a high income during the previous year (all p<0.05). ON CRC cases less often used NSAID during the previous year (all p<0.05).
Table 2 gives the mean intakes of food, selected nutrients and dairy products by the cases and controls in both provinces. Both provinces' cases reported higher intakes of total energy than controls. There was higher red meat consumption among ON cases, but no marked differences in the fruit and vegetable consumption between cases and controls were found in either province. Controls generally reported higher levels of mean daily intake of calcium and vitamin D, however, the extent of the differences varied by province. Specifically, both provinces' controls reported significantly higher intakes of total calcium, calcium from food, calcium from supplements, total vitamin D and vitamin D from supplements compared to their respective cases (all p<0.05). In ON, controls also reported significantly higher consumption of vitamin D from food, total dairy products and milk than did cases (all p<0.05).
The OR and 95% CI of CRC according to intakes of calcium and vitamin D from food and supplements, stratified by province, are shown in Table 3. Inverse associations with CRC risk were observed for high intakes of age-energy-adjusted total calcium, calcium from food and total vitamin D in both provinces; however, after other potential covariates were taken into account, the inverse associations were no longer significant in NL, while the protective effect of these nutrients remained significant in ON. The multivariate adjusted OR of CRC in ON for individuals in the highest quintile of intake compared with those in the lowest quintile was 0.57 for total calcium (95% CI 0.42-0.77, p-trend=0.03), 0.76 for dietary calcium (95% CI 0.60-0.97, p-trend=0.06), and 0.73 for total vitamin D (95% CI 0.54-1.00, p-trend=0.18). In addition, a higher intake of dietary vitamin D in ON subjects was also significantly and inversely associated with CRC risk (OR=0.77, 95% CI 0.61-0.99, p-trend=0.38). The observed reductions in risk among participants consuming calcium-containing supplements were 33% (NL) and 24% (ON). In NL, a 32% reduced risk emerged for consuming vitamin D-containing supplements.
In addition, we evaluated the consumption of total dairy foods and specific dairy foods in relation to the risk of CRC (Table 4). In ON, the risk of CRC was significantly reduced for those who consumed total dairy food >25.5 servings/week compared to those who consumed <3.1 servings/week (OR=0.78, 95% CI 0.60-1.00) in both age-energy-adjusted models and multivariate-adjusted models. In particular, those who consumed [greater than or equal to]14.9 cups/week of milk had a 22% lower risk of CRC compared to those who consumed <0.6 cups/week. A non-significant inverse association was found in yogurt intake. In NL, inverse associations were observed for age-energy-adjusted total dairy foods and milk; however, after adjusting for multi-variables, the inverse relationships were no longer significant.
When the combined effect of total calcium and total vitamin D was considered, the inverse association was most pronounced among subjects reporting high calcium and high vitamin D intakes compared to those reporting a low intake of both nutrients (Table 5).
In this large population case-control study, significant inverse associations were observed among the ON population for intakes of total calcium, dietary calcium, supplemental calcium, total vitamin D, dietary vitamin D, total dairy products and milk. In the NL population, inverse associations of supplemental calcium and supplemental vitamin D intakes with CRC risk were found. Our findings support a number of studies in other populations that have reported inverse associations between intakes of calcium and vitamin D and CRC risk. (4,41-45)
It is a little surprising that we did not observe meaningful associations of calcium and vitamin D intakes from food with CRC risk in NL after adjusting for multi-variables. We did observe these inverse associations in NL after adjusting for age and total energy intake only. One possibility is that intakes of these nutrients in NL were too low, even in the highest quintiles, for us to observe significant associations. This may be the case with calcium, for which intakes in ON subjects were found to be considerably higher than in NL subjects (Table 2). Other researchers have also found lower intakes of dietary calcium by NL adults as compared to those resident in ON. (46-48) It is also possible that the findings in this study may be due in part to collinearity between nutrients and foods of which they are constituents. For instance, dietary fat, phosphorous and dietary fibre may limit the intestinal absorption of calcium due to increased production of insoluble calcium complexes. (49-51) However, the inverse associations of calcium and vitamin D with CRC risk were most pronounced among NL subjects who use calcium- or vitamin D-containing supplements. NL controls were more likely than cases to consume calcium- or vitamin D-containing supplements (Table 2). Yet it is also likely that supplement users may be more conscious about health and therefore may have healthier dietary and physical activity habits as compared to non-supplement users. However, we did attempt to control for the effects of other physiologic, behavioural, and dietary factors in these analyses. Another possibility is that calcium- or vitamin D-containing supplements may have independent effects on cancer risk. Discussion of such potential biological mechanisms is beyond the scope of the present paper.
Results from this study, however, found that inverse associations did exist for total dairy products and milk in ON. It has been shown that calcium, especially in combinations as found in milk, effectively precipitates luminal cytolytic substances and reduces cytotoxicity of fecal water, an accepted risk marker for colon cancer. (21,22) Besides calcium and vitamin D through fortification, many other components of dairy foods have been shown experimentally to protect against CRC. Dairy foods contain conjugated linoleic acid and lactoferrin, which inhibit colonic carcinogenesis in animal models, (52,53) and it has been suggested that the milk protein casein has antimutagenic activity on the digestive tract. (54) Certain micro-organisms in fermented dairy foods have also been hypothesized to reduce the risk of CRC. (55) In this study, fermented dairy foods, such as cheese and yogurt, did not appear to be related to CRC risk. A possible reason is that cheese fats, particularly saturated fats, might increase risk. (56) However, the intakes of cheese and yogurt were too low to expect significant associations to emerge with analysis of the dietary intake data.
This study had a number of strengths. The large sample size allowed the identification of associations that might not be detectable in smaller studies. More importantly, previous findings by other researchers about the protective effects of these nutrients on CRC risk were confined to specific study populations, and this makes it difficult to generalize the results. In this study, we examined the effects of calcium, vitamin D and dairy product intake on the occurrence of CRC in two Canadian provinces with different rates of CRC incidence. We compared differences of these associations between the two provinces. Furthermore, nutrient intakes were adjusted for total energy intake. The use of calorie-adjusted values in multivariate models will often overcome the problem of high collinearity frequently observed between nutritional factors. (39) This adjustment also reduces between-person variation due to over- or under-reporting of food intakes. (39) The relationships of calcium, vitamin D, dairy products and CRC risk may differ appreciably by several factors, so we controlled for a wide range of potential confounding factors using multivariate logistic regression models. Although some random misclassification of dietary components is likely, non-differential misclassification generally tends to bias the risk estimates toward the null.
Consideration must be given to the potential limitations in the present study that may have influenced the observed associations. First, as in most case-control studies, potential recall and selection biases are possible. Since exposure information was collected after diagnosis, differential recall between cases and controls could bias results. In particular, cases may recall dietary exposures differently from controls because of the presence of illness or symptoms. Controls may have agreed to join this study because of an interest in health and may therefore have healthier dietary and physical activity habits, a pattern that may exaggerate differences with the cases beyond what might have been seen with truly comparable controls.
Second, by design, cases and controls had similar sex distribution; however, cases and controls were not well matched according to age group. Estimates of nutrient intakes from a FFQ are not precise and there is always the potential for measurement error. Although the original FFQ used in this study has been validated, (37,38) this questionnaire requires further evaluation. Third, these findings may reflect problems of collinearity between various nutrients, between selected foods, and between multivitamin supplements, thus this possibility cannot be completely eliminated.
Another potential limitation of this study may be the absence of information on sun exposure. As we know, it is difficult to accurately measure vitamin D exposure in humans. (43) We did not have information on sunshine exposure at baseline. Finally, it is also possible that the 1-2 year referent period on which dietary data were based is insufficient if more remote dietary practices (e.g., 5-10 yrs) have stronger influence on CRC risk.
In conclusion, in Canada the results of our case-control study add to the evidence that dietary calcium and vitamin D are associated with a lower risk of CRC. Furthermore, dairy products, milk, supplemental calcium and vitamin D are inversely related with CRC risk. More specifically, the present data support a joint action of calcium and vitamin D in the prevention of colorectal carcinogenesis.
Acknowledgements: This work was supported by the Canadian Institutes of Health Research Team Grant [CIHR-CPT79845] and Canadian Institutes of Health Research Team in Interdisciplinary Research on Colorectal Cancer Studentship . Zhuoyu Sun was awarded by the Newfoundland and Labrador Centre for Applied Health Research through a Master's fellowship. Jing Zhao was supported by a trainee award from the Beatrice Hunter Cancer Research Institute with funds provided by The Terry Fox Foundation Strategic Health Research Training Program in Cancer Research at CIHR.
Conflict of Interest: None to declare.
Received: November 28, 2010
Accepted: May 1, 2011
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Zhuoyu Sun, MSc,  Peizhong Peter Wang, PhD, [1,2] Barbara Roebothan, PhD,  Michelle Cotterchio, PhD,  Roger Green, PhD,  Sharon Buehler, PhD,  Jinhui Zhao, PhD,  Josh Squires, BSc,  Jing Zhao, BMed,  Yun Zhu, BMed,  Elizabeth Dicks, PhD,  Peter T. Campbell, PhD,  John R. Mclaughlin, PhD,  Patrick S. Parfrey, MD 
[1.] Division of Community Health and Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL
[2.] School of Public Health, Tianjin Medical University, Tianjin, China
[3.] Population Studies and Surveillance, Cancer Care Ontario, Toronto, ON
[4.] Clinical Epidemiology Unit, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL
[5.] Epidemiology Research Program, American Cancer Society, Atlanta, GA
[6.] Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON
Correspondence: Dr. Peizhong Peter Wang, Division of Community Health & Humanities, Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL A1B 3V6, Tel: 709-777-8571, Fax: 709-777-7382, E-mail: firstname.lastname@example.org
Table 1. Selected Characteristics of Cases and Controls, Stratified by Province, CRC Case-control Study in NL and ON NL Characteristics Cases (n=488) Controls (n=651) Age (years) ([dagger]) 62.7 * [+ or -] 9.0 60.5 [+ or -] 9.5 BMI ([section]) (kg/ 27.8 * [+ or -] 4.8 27.2 [+ or -] 4.4 [m.sup.2]) ([dagger]) Physical activity 58.0 [+ or -] 74.7 50.2 [+ or -] 73.2 (METs/week ([section])) ([dagger]) First-degree relatives 163 * (33.4) 114 (17.5) with CRC (%) ([double dagger]) Reported any colon 60 * (12.3) 145 (22.3) screening (%) ([double dagger]) Regular use of NSAID 164 (33.5) 252 (38.7) ([section]) (%) ([double dagger]) Regular use of 66 * (13.5) 145 (22.3) multivitamin supplements (%) ([double dagger]) Reported HRT ([section]) 132 * (27.1) 251 (38.6) (%) ([double dagger]) Smokers, current and/or 353 * (72.3) 401 (61.6) past (%) ([double dagger]) Heavy drinkers 54 (11.0) 68 (10.4) ([section]) (%) ([double dagger]) High level of education 181 * (37.0) 351 (53.9) ([section]) (%) ([double dagger]) High household income 102 * (20.9) 229 (35.1) ([section]) (%) ([double dagger]) ON Characteristics Cases (n=1272) Controls (n=1830) Age (years) ([dagger]) 58.4 * [+ or -] 10.9 61.5 [+ or -] 9.7 BMI ([section]) (kg/ 26.7 * [+ or -] 4.7 26.3 [+ or -] 4.5 [m.sup.2]) ([dagger]) Physical activity 43.8 [+ or -] 58.6 41.4 [+ or -] 59.2 (METs/week ([section])) ([dagger]) First-degree relatives 341 * (26.8) 223 (12.2) with CRC (%) ([double dagger]) Reported any colon 198 * (15.6) 476 (26.0) screening (%) ([double dagger]) Regular use of NSAID 433 * (34.0) 787 (43.0) ([section]) (%) ([double dagger]) Regular use of 436 * (34.3) 701 (38.3) multivitamin supplements (%) ([double dagger]) Reported HRT ([section]) 440 * (34.6) 827 (45.2) (%) ([double dagger]) Smokers, current and/or 733 (57.6) 1078 (58.9) past (%) ([double dagger]) Heavy drinkers 154 (12.1) 205 (11.2) ([section]) (%) ([double dagger]) High level of education 700 (55.0) 1087 (59.4) ([section]) (%) ([double dagger]) High household income 506 (39.8) 758 (41.4) ([section]) (%) ([double dagger]) * Significant difference between cases and controls (p<0.05). ([dagger]) Continuous variables presented as mean [+ or -] SD (standard deviation), differences between cases and controls based on t-test. ([double dagger]) Categorical variables presented as number (%), differences between cases and controls based on chi-square test. ([section]) BMI, body mass index; METs/week, metabolic equivalent hours per week; NSAID, nonsteroidal anti-inflammatory drugs; HRT, hormone replacement therapy, female only; heavy drinkers, average drinks >14 times/week; high level of education, included some college, university or post-secondary school; high household income, average household income >$50,000/year. Table 2. Mean Intakes of Foods and Nutrients Among Cases and Controls, Stratified by Province, CRC Case-control Study NL Subjects Intakes of Foods and Cases Nutrients ([dagger]) (n=488) Fruit (servings/week) 9.6 [+ or -] 8.1 Vegetables (servings/week) 11.1 [+ or -] 7.6 Red meat (servings/week) 3.5 [+ or -] 3.3 Total energy (kcal/day) 2441.5 * [+ or -] 838.2 Calcium (mg/day) Total calcium 989.6 * [+ or -] 402.6 Calcium from food 933.4 * [+ or -] 354.1 Calcium from supplements 56.2 * [+ or -] 160.2 Vitamin D (IU/day) Total vitamin D 332.2 * [+ or -] 242.5 Vitamin D from food 244.9 [+ or -] 124.1 Vitamin D from supplements 87.3 * [+ or -] 201.0 Dairy products (servings/week) Total dairy products 12.8 [+ or -] 10.2 Milk 8.2 [+ or -] 8.0 Non-milk products 5.0 [+ or -] 5.7 Yogurt 2.0 [+ or -] 3.7 Cheese 3.0 [+ or -] 3.7 NL Subjects Intakes of Foods and Controls Nutrients ([dagger]) (n=651) Fruit (servings/week) 10.5 [+ or -] 8.2 Vegetables (servings/week) 11.9 [+ or -] 8.3 Red meat (servings/week) 3.6 [+ or -] 3.4 Total energy (kcal/day) 2293.6 [+ or -] 744.9 Calcium (mg/day) Total calcium 1108.3 [+ or -] 500.9 Calcium from food 989.0 [+ or -] 394.9 Calcium from supplements 119.3 [+ or -] 249.1 Vitamin D (IU/day) Total vitamin D 393.5 [+ or -] 299.5 Vitamin D from food 251.0 [+ or -] 130.0 Vitamin D from supplements 142.5 [+ or -] 260.3 Dairy products (servings/week) Total dairy products 13.4 [+ or -] 10.4 Milk 8.3 [+ or -] 8.0 Non-milk products 5.2 [+ or -] 5.9 Yogurt 2.2 [+ or -] 3.9 Cheese 3.0 [+ or -] 4.1 ON Subjects Intakes of Foods and Cases Nutrients ([dagger]) (n=1272) Fruit (servings/week) 11.3 [+ or -] 8.1 Vegetables (servings/week) 13.8 [+ or -] 9.0 Red meat (servings/week) 4.6 * [+ or -] 4.4 Total energy (kcal/day) 2266.0 * [+ or -] 796.1 Calcium (mg/day) Total calcium 1137.1 * [+ or -] 509.2 Calcium from food 956.0 * [+ or -] 302.0 Calcium from supplements 181.1 * [+ or -] 404.7 Vitamin D (IU/day) Total vitamin D 319.8 * [+ or -] 218.4 Vitamin D from food 202.1 * [+ or -] 104.5 Vitamin D from supplements 117.7 * [+ or -] 186.8 Dairy products (servings/week) Total dairy products 12.2 * [+ or -] 8.6 Milk 7.2 * [+ or -] 6.6 Non-milk products 5.6 [+ or -] 5.1 Yogurt 1.3 [+ or -] 1.9 Cheese 3.8 [+ or -] 4.2 ON Subjects Intakes of Foods and Controls Nutrients ([dagger]) (n=1830) Fruit (servings/week) 11.0 [+ or -] 8.2 Vegetables (servings/week) 13.2 [+ or -] 8.5 Red meat (servings/week) 4.0 [+ or -] 3.8 Total energy (kcal/day) 2161.5 [+ or -] 757.7 Calcium (mg/day) Total calcium 1231.6 [+ or -] 544.1 Calcium from food 1009.5 [+ or -] 314.9 Calcium from supplements 222.1 [+ or -] 440.6 Vitamin D (IU/day) Total vitamin D 352.7 [+ or -] 236.4 Vitamin D from food 220.8 [+ or -] 111.0 Vitamin D from supplements 131.9 [+ or -] 203.6 Dairy products (servings/week) Total dairy products 13.0 [+ or -] 9.9 Milk 8.1 [+ or -] 8.1 Non-milk products 5.5 [+ or -] 5.2 Yogurt 1.3 [+ or -] 1.7 Cheese 3.8 [+ or -] 4.3 * Significant difference between cases and controls (p<0.05). ([dagger]) Continuous variables presented as mean [+ or -] SD (standard deviation), differences between cases and controls based on t-test. Table 3. Associations (Adjusted OR ([dagger]) 95% CI ([dagger])) of Calcium and Vitamin D Intakes With CRC Risk Among Cases and Controls, Stratified by Province, CRC Case-control Study NL Subjects (n=1139) No. of Median Intakes of Calcium Cases/ Intake and Vitamin D Controls ([parallel]) Total calcium Q1 109/119 580.0 Q2 107/121 798.1 Q3 106/121 963.5 Q4 93/135 1190.0 Q5 73/155 1653.4 P for trend ([paragraph]) Calcium from food Q1 105/123 573.3 Q2 102/126 764.3 Q3 96/131 902.4 Q4 103/125 1089.7 Q5 82/146 1405.7 P for trend ([paragraph]) Calcium from supplements Non-users 407/471 0 Users 81/180 >0 Total vitamin D Q1 102/126 124.2 Q2 106/122 199.4 Q3 115/112 261.4 Q4 87/141 407.5 Q5 78/150 754.3 P for trend ([paragraph]) Vitamin D from food Q1 97/131 110.7 Q2 101/127 179.8 Q3 94/133 228.7 Q4 108/120 285.3 Q5 88/140 404.0 P for trend ([paragraph]) Vitamin D from supplements Non-users 402/474 0 Users 86/177 >0 NL Subjects (n=1139) OR ([double Intakes of Calcium dagger]) OR ([section]) and Vitamin D (95% CI) (95% CI) Total calcium Q1 1.00 1.00 Q2 1.04 (0.71,1.51) 1.30 (0.85,1.98) Q3 1.01 (0.70,1.48) 1.17 (0.76,1.78) Q4 0.77 (0.53,1.13) 0.94 (0.61,1.44) Q5 0.50 * (0.34,0.74) 0.68 (0.44,1.07) P for trend ([paragraph]) 0.02 0.15 Calcium from food Q1 1.00 1.00 Q2 1.04 (0.71,1.52) 1.33 (0.87,2.03) Q3 0.92 (0.63,1.35) 1.11 (0.72,1.72) Q4 1.02 (0.70,1.49) 1.26 (0.82,1.94) Q5 0.66 * (0.45,0.96) 0.94 (0.61,1.45) P for trend ([paragraph]) 0.11 0.67 Calcium from supplements Non-users 1.00 1.00 Users 0.51 * (0.38,0.68) 0.67 * (0.47,0.94) Total vitamin D Q1 1.00 1.00 Q2 1.11 (0.76,1.62) 1.36 (0.85,2.15) Q3 1.28 (0.88,1.86) 1.40 (0.90,2.65) Q4 0.71 * (0.48,1.03) 0.78 (0.49,1.24) Q5 0.60 * (0.41,0.88) 0.85 (0.53,1.37) P for trend ([paragraph]) 0.12 0.39 Vitamin D from food Q1 1.00 1.00 Q2 1.17 (0.80,1.71) 1.30 (0.85,1.98) Q3 1.01 (0.69,1.48) 1.26 (0.82,1.92) Q4 1.24 (0.85,1.81) 1.51 (0.98,2.31) Q5 0.81 (0.55,1.18) 0.95 (0.62,1.47) P for trend ([paragraph]) 0.49 0.90 Vitamin D from supplements Non-users 1.00 1.00 Users 0.55 * (0.41,0.73) 0.68 * (0.48,0.97) ON Subjects (n=3102) No. of Median Intakes of Calcium Cases/ Intake and Vitamin D Controls ([parallel]) Total calcium Q1 301/320 708.5 Q2 265/356 898.1 Q3 264/356 1071.7 Q4 231/390 1308.4 Q5 211/408 1834.0 P for trend ([paragraph]) Calcium from food Q1 283/338 656.3 Q2 267/354 816.7 Q3 261/359 946.5 Q4 237/384 1094.5 Q5 224/395 1382.9 P for trend ([paragraph]) Calcium from supplements Non-users 761/1002 0 Users 511/828 >0 Total vitamin D Q1 285/336 107.7 Q2 247/374 179.9 Q3 275/345 265.8 Q4 236/385 464.0 Q5 229/390 645.4 P for trend ([paragraph]) Vitamin D from food Q1 284/337 95.5 Q2 251/370 148.5 Q3 262/358 198.9 Q4 263/358 253.9 Q5 212/407 359.0 P for trend ([paragraph]) Vitamin D from supplements Non-users 874/1212 0 Users 398/618 >0 ON Subjects (n=3102) OR ([double Intakes of Calcium dagger]) OR ([section]) and Vitamin D (95% CI) (95% CI) Total calcium Q1 1.00 1.00 Q2 0.84 (0.67,1.06) 0.89 (0.67,1.20) Q3 0.84 (0.67,1.06) 0.97 (0.72,1.31) Q4 0.68 * (0.54,0.86) 0.66 * (0.49,0.90) Q5 0.61 * (0.48,0.77) 0.57 * (0.42,0.77) P for trend ([paragraph]) 0.02 0.03 Calcium from food Q1 1.00 1.00 Q2 0.97 (0.77,1.22) 0.95 (0.75,1.21) Q3 0.98 (0.78,1.23) 1.03 (0.82,1.31) Q4 0.79 * (0.63,1.00) 0.83 (0.66,1.05) Q5 0.71 * (0.56,0.90) 0.76 * (0.60,0.97) P for trend ([paragraph]) 0.02 0.06 Calcium from supplements Non-users 1.00 1.00 Users 0.87 * (0.75,1.00) 0.76 * (0.63,0.93) Total vitamin D Q1 1.00 1.00 Q2 0.86 (0.68,1.08) 0.89 (0.66,1.20) Q3 1.06 (0.84,1.33) 1.15 (0.85,1.54) Q4 0.76 * (0.61,0.96) 0.79 (0.58,1.07) Q5 0.79 * (0.63,0.99) 0.73 * (0.54,1.00) P for trend ([paragraph]) 0.19 0.18 Vitamin D from food Q1 1.00 1.00 Q2 0.91 (0.72,1.14) 0.92 (0.72,1.16) Q3 1.02 (0.81,1.28) 1.09 (0.86,1.38) Q4 1.03 (0.82,1.30) 1.07 (0.84,1.36) Q5 0.71 * (0.56,0.89) 0.77 * (0.61,0.99) P for trend ([paragraph]) 0.22 0.38 Vitamin D from supplements Non-users 1.00 1.00 Users 0.91 (0.78,1.06) 1.11 (0.76,1.61) * Significant difference from reference category, p<0.05. ([dagger]) OR, Odds ratio; 95% CI, 95% confidence interval. ([double dagger]) Adjusted for age and total energy intake. ([section]) Adjusted for total energy intake, age, sex, BMI, physical activity (METs/week), first-degree relatives with CRC, polyps, diabetes, reported colon screening procedure, cigarette smoking, alcohol drinking, education attainment, household income, marital status, regular use of NSAID, regular use of multivitamin supplements, reported HRT (females only), and intakes of fruits, vegetables and red meat. Variables were included in the final model based on a >10% alternation in the parameter coefficient of interest. ([parallel]) Units of mg/day for calcium, and IU/day for vitamin D. ([paragraph]) Two-sided p-value for test of linear trend was calculated by using median values for each quintile of intake. Table 4. Associations (Adjusted OR ([dagger]), 95% CI ([dagger]) of Dairy Product Intakes With CRC Risk Among Cases and Controls, Stratified by Province, CRC Case-control Study NL Subjects (n=1139) No. of Median Cases/ Intake Dairy Products Controls ([parallel]) Total Dairy Products Q1 110/133 2.4 Q2 87/127 7.2 Q3 103/126 10.5 Q4 97/129 16.1 Q5 91/136 25.9 P for trend ([paragraph]) Milk Q1 115/138 0 Q2 101/145 3.5 Q3 93/122 6.9 Q4 112/140 8.9 Q5 67/106 17.0 P for trend ([paragraph]) Non-milk Q1 97/143 0.3 Q2 98/129 2.0 Q3 105/117 3.6 Q4 103/124 6.0 Q5 85/138 11.4 P for trend ([paragraph]) Yogurt ** Q1 157/229 0 Q2 165/214 1.1 Q3 112/171 5.0 Q4 -- -- Q5 -- -- P for trend ([paragraph]) Cheese Q1 104/158 0 Q2 103/108 1.0 Q3 101/144 2.0 Q4 87/110 5.3 Q5 93/131 7.0 P for trend ([paragraph]) NL Subjects (n=1139) OR ([double dagger]) OR ([section]) Dairy Products (95% CI) (95% CI) Total Dairy Products Q1 1.00 1.00 Q2 0.85 (0.59,1.23) 0.89 (0.57,1.41) Q3 0.79 (0.54,1.14) 1.07 (0.68,1.70) Q4 0.75 (0.51,1.09) 0.90 (0.56,1.45) Q5 0.69 * (0.46,1.01) 0.89 (0.55,1.45) P for trend 0.03 0.42 ([paragraph]) Milk Q1 1.00 1.00 Q2 0.85 (0.60,1.22) 1.06 (0.69,1.64) Q3 0.89 (0.62,1.28) 1.27 (0.81,1.98) Q4 0.85 (0.60,1.22) 1.14 (0.73,1.77) Q5 0.67 * (0.45,0.99) 0.96 (0.58,1.57) P for trend 0.02 0.81 ([paragraph]) Non-milk Q1 1.00 1.00 Q2 1.35 (0.94,1.95) 1.40 (0.89,2.20) Q3 0.94 (0.64,1.38) 0.98 (0.61,1.59) Q4 1.16 (0.80,1.69) 1.43 (0.89,2.29) Q5 0.91 (0.61,1.37) 1.12 (0.67,1.89) P for trend 0.51 0.88 ([paragraph]) Yogurt ** Q1 1.00 1.00 Q2 1.15 (0.86, 1.53) 1.27 (0.87,1.85) Q3 0.91 (0.66, 1.25) 1.02 (0.75,1.39) Q4 -- -- Q5 -- -- P for trend 0.56 0.85 ([paragraph]) Cheese Q1 1.00 1.00 Q2 1.26 (0.87,1.81) 1.53 (0.97,2.43) Q3 1.12 (0.78,1.61) 1.34 (0.85,2.12) Q4 1.00 (0.68,1.46) 1.26 (0.78,2.02) Q5 0.97 (0.66,1.44) 1.25 (0.76,2.05) P for trend 0.34 0.94 ([paragraph]) ON Subjects (n=3102) No. of Median Cases/ Intake Dairy Products Controls ([parallel]) Total Dairy Products Q1 271/385 3.1 Q2 239/353 6.9 Q3 267/354 10.4 Q4 263/352 15.6 Q5 232/386 25.5 P for trend ([paragraph]) Milk Q1 333/475 0.6 Q2 240/345 3.0 Q3 296/392 6.9 Q4 254/346 7.9 Q5 149/272 14.9 P for trend ([paragraph]) Non-milk Q1 255/402 1.1 Q2 243/375 2.5 Q3 270/356 4.1 Q4 255/341 6.5 Q5 249/356 11.5 P for trend ([paragraph]) Yogurt ** Q1 553/766 0 Q2 230/320 0.3 Q3 130/196 0.5 Q4 168/266 1.3 Q5 191/282 3.5 P for trend ([paragraph]) Cheese Q1 303/444 0.5 Q2 230/359 1.3 Q3 244/349 2.5 Q4 259/322 5.0 Q5 236/356 10.0 P for trend ([paragraph]) ON Subjects (n=3102) OR ([double dagger]) OR ([section]) Dairy Products (95% CI) (95% CI) Total Dairy Products Q1 1.00 1.00 Q2 0.97 (0.77,1.22) 1.03 (0.81,1.31) Q3 1.05 (0.84,1.33) 1.12 (0.88,1.42) Q4 1.01 (0.80,1.28) 1.07 (0.84,1.37) Q5 0.74 * (0.58,0.94) 0.78 * (0.60,1.00) P for trend 0.12 0.21 ([paragraph]) Milk Q1 1.00 1.00 Q2 1.03 (0.83,1.28) 1.09 (0.87,1.36) Q3 1.09 (0.88,1.34) 1.12 (0.90,1.39) Q4 1.06 (0.85,1.32) 1.09 (0.87,1.37) Q5 0.73 * (0.56,0.94) 0.78 * (0.60,1.00) P for trend 0.18 0.23 ([paragraph]) Non-milk Q1 1.00 1.00 Q2 1.02 (0.81,1.28) 1.04 (0.82,1.32) Q3 1.15 (0.92,1.45) 1.14 (0.90,1.45) Q4 1.11 (0.88,1.40) 1.13 (0.88,1.44) Q5 0.96 (0.75,1.22) 0.98 (0.76,1.26) P for trend 0.69 0.79 ([paragraph]) Yogurt ** Q1 1.00 1.00 Q2 0.95 (0.77,1.16) 0.98 (0.79,1.21) Q3 0.85 (0.66,1.09) 0.92 (0.71,1.20) Q4 0.81 (0.65,1.02) 0.88 (0.69,1.11) Q5 0.83 (0.66,1.03) 0.85 (0.68,1.07) P for trend 0.23 0.06 ([paragraph]) Cheese Q1 1.00 1.00 Q2 0.94 (0.75,1.18) 0.98 (0.78,1.23) Q3 0.99 (0.79,1.24) 1.00 (0.79,1.25) Q4 1.10 (0.87,1.37) 1.12 (0.89,1.42) Q5 0.87 (0.69,1.10) 0.90 (0.70,1.14) P for trend 0.51 0.56 ([paragraph]) * Significant difference from reference category, p<0.05. ([dagger]) OR, Odds ratio; 95% CI, 95% confidence interval. ([double dagger]) Adjusted for age and total energy intake. [section] Adjusted for total energy intake, age, sex, BMI, physical activity (METs/week), first-degree relatives with CRC, polyps, diabetes, reported colon screening procedure, cigarette smoking, alcohol drinking, education attainment, household income, marital status, regular use of NSAID, regular use of multivitamin supplements, reported HRT (females only), and intakes of fruits, vegetables and red meat. Variables were included in the final model based on a >10% alternation in the parameter coefficient of interest. ([parallel]) Units of servings/week for each dairy product. ([paragraph]) Two-sided p-value for test of linear trend was calculated by using median values for each quintile of intake. ** Due to small sample size in NL, yogurt intake was only divided into 3 groups. Table 5. Adjusted OR ([dagger]), 95% CI ([dagger]) of CRC Risk According to Level of Total Calcium and Total Vitamin D Intake in the ON Population Total Calcium Intake (mg/day) T1 ([section]) Total Vitamin D ([less than or T2 ([section]) Intake (IU/day) equal to] 835.2) (835.3-1064.2) T1 ([section]) (S157.3) No. of cases/controls 343/420 99/127 OR ([dagger]) (95% CI) 1.00 0.94 (0.69,1.29) T2 ([dagger]) (157.4-241.5) No. of cases/controls 115/120 230/322 OR ([dagger]) (95% CI) 1.10 (0.80,1.86) 1.06 (0.84,1.34) T3([section]) (>241.5) No. of cases/controls 15/20 94/162 OR ([dagger]) (95% CI) 1.00 (0.50,2.02) 0.86 (0.63,1.17) Total Calcium Intake (mg/day) Total Vitamin D T3 ([section]) Intake (IU/day) (>1064.2) T1 ([section]) (S157.3) No. of cases/controls 15/30 OR ([dagger]) (95% CI) 0.84 (0.65,1.14) T2 ([dagger]) (157.4-241.5) No. of cases/controls 86/141 OR ([dagger]) (95% CI) 0.81 (0.59,1.11) T3([section]) (>241.5) No. of cases/controls 275/488 OR ([dagger]) (95% CI) 0.75 * (0.49,0.99) * Significant difference from reference category, p<0.05. ([dagger]) OR, Odds ratio; 95% CI, 95% confidence interval. ([double dagger]) Adjusted for total energy intake, age, sex, BMI, physical activity (METs/week), first-degree relatives with CRC, polyps, diabetes, reported colon screening procedure, cigarette smoking, alcohol drinking, education attainment, household income, marital status, regular use of NSAID, regular use of multivitamin supplements, reported HRT (females only), and intakes of fruits, vegetables and red meat. Variables were included in the final model based on a [greater than or equal to] 10% alternation in the parameter coefficient of interest. ([section]) Intakes of total calcium and vitamin D were categorized into tertiles based on the distribution among subjects, T1 for tertile 1, T2 for tertile 2, and T3 for tertile 3.
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|Title Annotation:||QUANTITATIVE RESEARCH|
|Author:||Sun, Zhuoyu; Wang, Peizhong Peter; Roebothan, Barbara; Cotterchio, Michelle; Green, Roger; Buehler,|
|Publication:||Canadian Journal of Public Health|
|Date:||Sep 1, 2011|
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