1 Department of Epidemiology, Nutrition and Toxicology Research Institute Maastricht (NUTRIM), Maastricht University, Maastricht, the Netherlands.
2 Departments of Epidemiology and Urology, University Medical Center Nijmegen, Nijmegen, the Netherlands.
3 Department of Nutritional Epidemiology, TNO Nutrition and Food Research, Zeist, the Netherlands.
Received for publication April 8, 2004; accepted for publication July 21, 2004.
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ABSTRACT |
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anthropometry; body height; body mass index; carcinoma, renal cell; energy intake; exercise; kidney neoplasms; leisure activities
Abbreviations: Abbreviations: BMI, body mass index; CI, confidence interval; IGF-I, insulin-like growth factor I; RCC, renal cell carcinoma; RR, rate ratio.
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INTRODUCTION |
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BMI is a measure of body mass relative to height. In general, there is good correlation between BMI and percentage of body fat. Obesity or excess body fat is caused by excess energy intake relative to energy expenditure, which consists of resting metabolic rate, the thermic effect of food, posture and spontaneous activity, and voluntary physical activity (22).
Two different biologic mechanisms have been proposed to explain the observed relation between BMI and renal cancer risk. Physical activity and energy intake may also fit these mechanisms. Yu and Rohan (23) proposed that the insulin-like growth factor I (IGF-I) system may be the mechanistic link between obesity and the development of RCC. IGF-I has been shown to stimulate cell proliferation and inhibit apoptosis (23), both of which favor tumor growth. In humans, it has been shown that obese persons have increased serum levels of free IGF-I (22). Overnutrition has been reported to increase levels of IGF-I (23), while no conclusions can be drawn regarding the effect of physical activity on IGF-I levels (22, 2428).
Secondly, the process of lipid peroxidation may be involved (29). Byproducts of lipid peroxidation have been shown to react with renal cell DNA to form adducts, which may lead to mutations. Obese subjects are known to exhibit increased lipid peroxidation, while exercise programs result in reduced lipid peroxidation. Furthermore, it has been proposed that lipid peroxidation may also explain the roles of other risk factors, such as smoking, and protective factors, such as intake of foods high in antioxidants, in the development of RCC (29).
Hence, it would be logical to investigate BMI, energy intake, and physical activity simultaneously. To our knowledge, only one study has investigated the roles of BMI, physical activity, and energy intake together (21), but the emphasis of that study was on physical activity. In this study, a prospective cohort study with a relatively large number of cases, we estimated the effects of height, weight, BMI, BMI at age 20 years, BMI change since age 20 years, energy intake, and physical activity on risk of RCC. We also investigated the effects of BMI, energy intake, and physical activity simultaneously.
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MATERIALS AND METHODS |
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Follow-up
Incident cancer cases occurring in the total cohort were identified through record linkage to the Netherlands cancer registries and PALGA (the national automated pathology archive) (32). The completeness of cancer follow-up was estimated to be more than 96 percent (33). This gave us 275 incident cases (179 men and 96 women) with microscopically confirmed adenocarcinoma of the renal parenchyma and no prevalent cancer at baseline. Follow-up of the subcohort was almost complete; out of the 5,000 subcohort members (2,411 men and 2,589 women), only two men were lost to follow-up after 9.3 years (September 1986December 1995). Subcohort members with prevalent cancer (other than skin cancer) at baseline were excluded (76 men and 145 women) from the analyses, leaving 4,779 subcohort members (2,335 men and 2,444 women).
Questionnaire
At baseline, all cohort members completed a self-administered questionnaire, which has been described elsewhere (34). Questions were asked about current height, current weight, weight at age 20 years, family history of cancer, physical activity, job history, and usual consumption of foods and beverages during the year preceding the start of the study.
BMI was calculated by dividing weight (kg) by height squared (m2). Rate ratios are presented per 1-kg/m2 increment for BMI at baseline, BMI at age 20 years, and BMI gain between age 20 years and baseline. In addition, BMI at baseline was categorized into the following categories: BMI < 23, 23 BMI < 25, 25
BMI < 27, 27
BMI < 30, and BMI
30. Because of missing values, analyses for BMI were based on 264 incident cases and 4,592 subcohort members. For BMI at age 20 years, participants were categorized into four groups: BMI < 20, 20
BMI < 21, 21
BMI < 23, and BMI
23. Gain in BMI (kg/m2) was categorized as <0, 04, 48, or
8. Analyses for BMI at age 20 and BMI gain since age 20 were based on 227 cases and 3,905 subcohort members, since not all participants provided information on their weight at age 20.
Energy intake was calculated from the food frequency questionnaire (34, 35) using the computerized Dutch food composition table (36). Fifteen cases and 338 subcohort members with incomplete or inconsistent dietary data were excluded from the analyses. Details are given elsewhere (34). On the basis of the distribution in the subcohort, energy intake was divided into quintiles for men and women separately.
Physical activity was divided into occupational and nonoccupational activity. In this paper, we use the term "nonoccupational physical activity" to cover both recreational physical activity and the physical activity involved in getting to and from work (e.g., walking, cycling). For estimation of occupational activity, participants were asked to report job title(s) and job duration(s). Assessment of physical activity at work was based on the job held for the longest amount of time. Total energy expenditure was based on a rating system developed by Hettinger et al. (37). Participants were classified into three energy expenditure groups: <8 kJ/minute, 812 kJ/minute, and 12 kJ/minute. Occupational physical activity was not calculated for women, since most women of this generation had not held a job or had worked for only a short period of time, mostly in the distant past.
Baseline nonoccupational physical activity was calculated by adding up the number of minutes spent per day on cycling/walking to work, shopping, and walking the dog and the number of hours spent per week on gardening/odd jobs, recreational cycling/walking, and sports/exercise, as reported (38).
Statistical analysis
On the basis of the literature, age (continuous variable), sex, and cigarette smoking (current smoking (yes/no), number of years of smoking, and number of cigarettes smoked per day) were considered as confounders. We did not adjust for family history of RCC (present or not present in a first-degree relative), since only 49 participants (four cases and 45 subcohort members) reported having a first-degree relative with RCC. Incidence rate ratios for height and weight were obtained from models in which both variables were always entered simultaneously. Results for BMI were additionally adjusted for energy intake and physical activity. We also investigated whether BMI at age 20 years (as a proxy for young adulthood) or BMI gain between age 20 and baseline was an independent predictor of RCC risk. In all BMI gain regression models, we adjusted for BMI at age 20. Furthermore, we calculated RCC rate ratios for energy intake and physical activity variables (entered into a model together) with adjustment for age only and adjustment for age, smoking, and BMI. Results are shown for men and women together to enhance the precision of our statistical analyses, unless the p value for the interaction term between sex and the variable of interest was less than or equal to 0.05.
Rate ratios and 95 percent confidence intervals for RCC were estimated in Cox proportional hazards models using 2001 Stata statistical software (release 7; Stata Corporation, College Station, Texas), after testing of the proportional hazards assumption using scaled Schoenfeld residuals (39). Standard errors were estimated using the robust Huber-White sandwich estimator to account for additional variance introduced by sampling from the cohort (40). To obtain p values for dose-response trends, we fitted ordinal exposure variables as continuous terms. Two-sided p values are reported throughout this paper.
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RESULTS |
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Height was not associated with RCC risk in men (table 2). In women, however, an increased risk of 1.23 per 5-cm increment (95 percent CI: 1.03, 1.46) was observed (table 2). Weight increased RCC risk approximately 10 percent per 5 kg for men and women (table 2), while further adjustment for smoking did not materially change the risk estimates.
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Energy intake was not related to risk of RCC. Estimates did not change after adjustment for smoking or after further adjustment for BMI and physical activity (data not shown). In addition, we tested the highest quintile against the first quintile for energy intake. We did not observe an increased risk for persons in the highest quintile, either in the model with age, sex, and physical activity (RR = 0.80, 95 percent CI: 0.50, 1.27) or in the model additionally adjusted for smoking and BMI (RR = 0.83, 95 percent CI: 0.51, 1.36).
For men, estimates for occupational physical activity greater than or equal to 8 kJ/minute or nonoccupational physical activity greater than or equal to 30 minutes/day were all less than 1 (table 4). Moreover, the risk for men was significantly decreased for nonoccupational physical activity of 3060 minutes/day (RR = 0.52, 95 percent CI: 0.30, 0.91), but there was no significant trend (p = 0.63). Estimates for nonoccupational physical activity for women (table 4) were mostly larger than 1 but never statistically significant.
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DISCUSSION |
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Since RCC is relatively rare, most studies that have evaluated the relation between anthropometric measures and RCC risk have been case-control studies. Only four cohort studies have reported on the relation between anthropometric factors and RCC risk (10, 13, 15, 41). The prospective cohort setting makes selection bias in our study unlikely, while case-control studies, especially hospital-based case-control studies, may suffer from selection and information bias. In the current study, the estimated rate ratio for the association between BMI and RCC was 1.07, based on 264 incident cases. This is in line with the findings of a meta-analysis of 14 studies (1) and the results of other cohort studies reporting on BMI and RCC risk (10, 13, 15, 41). Only one hospital-based case-control study reported no association between BMI and RCC risk (42). Height and weight were self-reported in our study, as in most other studies, which means misclassification might be present. Systematic underestimation of weight and overestimation of height have been reported (43, 44). More specifically, the higher the measured BMI, the greater the underestimation of weight and the overestimation of height for men and women (44). This tendency could have led to underestimation of the effect of BMI on RCC risk in our study.
An increased risk of RCC with increasing BMI has been reported consistently. This observation may fit both suggested biologic mechanisms, since it has been reported that obese persons have higher levels of free IGF-I (22) and exhibit increased lipid peroxidation (29). Both factors have been linked to tumor development. In view of the strong relation between BMI and RCC, it is remarkable that no convincing mechanism has yet been proposed for this relation. Further research is needed.
In this study, we found RCC risk to increase with height (contradictory to most other studies on height and RCC) in women. An increased cancer risk with height has been observed before for women in the Netherlands Cohort Study on Diet and Cancer for breast cancer (45) and ovarian cancer (46). However, for men, an increased risk with height was not observed in the current study or in a study of prostate cancer carried out within the Netherlands Cohort Study on Diet and Cancer (47). Biologic mechanisms through which height and cancer risk are linked are not yet clear, but IGF-I is known to play a fundamental role in somatic growth (48). A real albeit relatively weak association between height and risk of several cancers possibly exists (48), but the relation has been unclear for RCC because research on this topic is scarce (48). It is unclear to us why sex would modify the association between height and RCC. As far as we know, no other studies have reported differences between men and women.
Another cohort study found weight at age 18 years and weight gain since age 18 to be independent risk factors for women (15). Our results on BMI at age 20 and BMI change between young adulthood and baseline do not support the hypothesis that BMI in young adulthood is an independent risk factor of RCC, but BMI gain might be. However, BMI at age 20 and BMI gain between age 20 and baseline were retrospectively reported, which means recall bias might have been present. However, the effect of any recall bias would have been nondifferential, since only incident cases were included in the analyses, and this would have resulted in attenuated rate ratios for these factors.
Only the International Renal Cell Cancer Study (19), a large case-control study, reported a positive association between energy intake and RCC risk, but this association might have been due to recall bias. In the current study, no association was observed between energy intake and RCC. This is in line with the findings of another cohort study (15). This is remarkable, since a high BMI is associated with increased risk of RCC and a high BMI results from excess energy intake in relation to energy expenditure (22). This might be explained by the fact that energy intake tends to be underreported by overweight persons (49). It might also be possible that measurement error concealed a possible effect of energy intake on RCC. The validity of reported energy intake in our study was checked by comparing the results of the food frequency questionnaire with 3-day diaries completed at three time points during a calendar year. Reported intakes were, on average, 300 kcal lower than those reported by means of the diaries. However, the questionnaire was able to rank subjects according to their energy intake (34). Energy intake does not seem to be an independent risk factor for RCC in the current study or in the other cohort study that reported on energy intake, but replication, preferably by other cohort studies, is desirable.
We did not find a clear association between physical activity and risk of RCC. Results of other studies are also inconsistent (16, 2022, 50). In general, there has been little standardization of the methods used for assessing physical activity in epidemiologic studies, and few methods have been appropriately tested for reliability and validity. The use of crude measures of physical activity is likely to result in measurement error and difficulty in determining the true nature of the relation between physical activity and cancer risk (51). Our measures of nonoccupational physical activity might have been affected by nondifferential misclassification, since it is socially desirable to be active. Thus, persons engaging in little or no activity may have overestimated their nonoccupational physical activity. The result of nondifferential misclassification is hard to predict. Our findings might be an underestimation of the real effect, but it is also possible that the effect in one of the categories was overestimated. An inverse association of leisure-time physical activity with obesity and mean BMI in men and women has been reported (52), which would suggest that the risk of RCC should be decreased for higher levels of (leisure-time) physical activity. Our results point in the direction of a possible protective effect of physical activity on RCC risk for men, though not as clearly as the results reported by Mahabir et al. (21). The study by Mahabir et al. was restricted to male smokers; thus, its results may not be generalizable. Furthermore, there were only five cases in the highest leisure-time physical activity category, and men with a higher level of recreational physical activity showed a healthier lifestyle (i.e., smoked less) (21). A more standardized manner of investigating the role of physical activity in large study groups might contribute to an unraveling of the role of physical activity.
In summary, our results confirm an increased risk of RCC with BMI, while BMI gain between young adulthood and baseline may also increase RCC risk. An effort should be undertaken to elucidate possible underlying mechanisms between factors such as BMI, BMI gain in adulthood, physical activity, and energy intake and cancer risk, specifically RCC risk.
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ACKNOWLEDGMENTS |
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The authors thank the staffs of the Dutch regional cancer registries and the Netherlands national database for pathology (PALGA) for providing incidence data. They also thank Dr. E. Dorant and C. A. de Brouwer for their preparatory work for this study; Dr. A. Volovics and Dr. A. Kester for statistical advice; S. van de Crommert, H. Brants, J. Nelissen, C. de Zwart, M. Moll, W. van Dijk, M. Jansen, and A. Pisters for data entry and processing; and H. van Montfort, T. van Moergastel, L. van den Bosch, and R. Schmeitz for programming assistance.
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NOTES |
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REFERENCES |
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