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© 1999 American Society for Clinical Oncology Dietary Fat and Carbohydrates Are Independently Associated With Circulating Insulin-Like Growth Factor 1 and Insulin-Like Growth Factor–Binding Protein 3 Concentrations in Healthy AdultsFrom the Division of Endocrinology, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, and Department of Internal Medicine, Newton Wellesley Hospital, Newton, MA; and Department of Epidemiology, Athens University Medical School, Athens, Greece. Address reprint requests to Christos S. Mantzoros, MD, DSc, Endocrinology, RN 325, Beth Israel Deaconess Medical Center, 99 Brookline Ave, Boston, MA 02215; email cmantzor{at}bidmc.harvard.edu
PURPOSE: To evaluate and quantify the association between consumption of specific food groups/macronutrients and concentrations of serum insulin-like growth factor 1 (IGF-1) and insulin-like growth factor–binding protein 3 (IGFBP-3). SUBJECTS AND METHODS: Data from a comprehensive food-frequency questionnaire administered to 115 healthy subjects were used to study cross-sectionally the relationship between nutritional factors and circulating IGF-1 and IGFBP-3 concentrations. Adjustment for the effect of total energy intake and a series of epidemiologic parameters (age, sex, height, body mass index, smoking, alcohol consumption, and coffee drinking) was implemented through multivariate linear regression. RESULTS: We observed that serum levels of IGF-1 are positively associated with consumption of redmeats, fats, and oils. In addition, serum levels of IGF-1 are independently and positively associated with energy intake from lipids and negatively associated with energy intake from carbohydrates. Finally, serum levels of IGFBP-3 are independently and negatively associated with energy intake from saturated fat. CONCLUSION: Serum IGF-1 and/or IGFBP-3 concentrations are associated with red meat, carbohydrate intake, and fat intake and, thus, may mediate the effect of these dietary factors on the pathogenesis of several disease states. Additional studies are needed to further quantify these associations and elucidate the underlying mechanisms.
INSULIN-LIKE GROWTH factor 1 (IGF-1) acts in an autocrine, paracrine, or endocrine manner to promote normal growth, induce malignant cellular proliferation, and regulate metabolism in an insulin-like manner.1-3 IGF-1 has been implicated in the pathophysiology of diabetes mellitus, osteoporosis, and renal disease1-6 and has recently been associated with increased risk for the development of breast, colon, and prostate cancer.2,7-11 More specifically, two recent case-control studies7,9 and a case-control study nested in a cohort study8 have shown that circulating IGF-1 is positively and strongly associated with prostate cancer risk.7-9 Two recent studies, one case-control study nested in a cohort study10 and another case-control study focusing on ductal carcinoma-in-situ,11 have demonstrated that serum IGF-1 levels are associated with breast cancer.10,11 In addition, acromegaly, a disorder characterized by elevated serum concentrations of IGF-1, has been associated with elevated risk for developing colon cancer,12 whereas insulin-like growth factor–binding protein 3 (IGFBP-3), the major IGF-1–binding protein in plasma, has been inversely and independently associated with the risk of breast and prostate cancer.8,10,13 Nutrition is thought to have a central role in the development of cancer. Red meat and fat intake are positively associated with risk for malignancies, including prostate, colon, and, possibly, breast cancer.14-16 Interestingly, experimental data suggest that nutrition may also influence serum IGF-1 and IGFBP-3 levels.17 However, no previous epidemiologic studies have explored the role of dietary factors in predicting concentrations of serum IGF-1 and IGFBP-3 in humans. Because identification of modifiable determinants of serum concentrations of these hormones could potentially have preventive and therapeutic implications, we have undertaken a study among apparently healthy, adult Greek subjects to explore whether total energy intake or consumption of specific food groups or macronutrients may have an independent effect on serum IGF-1 and IGFBP-3 concentrations.
In the context of a large ongoing study, we collected, during a 1-year period, morning blood samples from 130 apparently healthy subjects who were between the ages of 30 and 84 years old and living in the greater Athens area, as previously described.18 Fewer than 5% of the individuals approached declined to participate and were replaced. Although detailed physical examinations and laboratory analyses were not performed, none of the participants had a history or symptoms of malnourishment, metabolic diseases, diabetes mellitus, thyroid pathology, or liver or kidney failure. Data on the 115 subjects, with complete data sets, are included in the models presented herein. Participants responded to an interviewer-administered questionnaire by giving information on their exact age, height, weight, cigarette-smoking habits (as a trichotomous variable: never a smoker, past smoker, or current smoker; for the last group, number of cigarettes and duration of smoking), and usual consumption of caffeinated coffee (in cups per month) and alcohol (in glasses per month), as previously described.19-22 All coffee (caffeinated and decaffeinated) was looked at and adjusted for in the analysis, although the majority of Greek subjects drink caffeinated coffee. In addition, subjects reported their average consumption of 115 food or beverage items (per month, per week, or per day) for the period immediately before the interview day, using an interviewer-administered, standardized food-frequency questionnaire similar to the ones described previously.23-25 Previously performed validation studies, using a series of 12 24-hour diet recalls as the standard, showed that correlation coefficients derived from similar questionnaires (around 40 for most nutrients and food groups) were comparable to the correlation coefficients reported by other nutritional epidemiology validation studies.23,24 For analysis, the consumption frequency of different food items and beverages was later quantified in terms of the number of times per month the specific food was consumed. Thus, daily consumption was multiplied by 30 and weekly consumption was multiplied by four; a value of 0.5 was assigned to food items rarely consumed and a value of zero was assigned to food items never consumed.24 Food items were considered in groups as recommended by Davidson and Passmore26 and presented in similar previous studies.25,27,28 The food groups formed were as follows: cereals; starchy roots; sugars and syrups; pulses, nuts, and seeds; vegetables; fruits; meats; fish; eggs; milk and milk products; oils and fats; and nonalcoholic beverages. Some cooked meals were allocated to two food groups (one half in each), as previously described.25 A number of items, important in the Greek diet because they are frequently consumed, such as olive oil, vegetable oil, cooking fat (usually of animal origin), butter, margarine, potatoes, and bread, were also considered individually. Macronutrient intakes for study subjects were estimated by multiplying the nutrient content of a selected typical portion of each food item by the frequency that this food item was consumed per month and adding these estimates for all items based on a nutrient database developed earlier in Greece.25 We first investigated macronutrients, ie, total protein, total fat, and total carbohydrate, and then estimated total energy intake, as previously described.23-25,29 Because macronutrients are positively correlated with total energy intake,24,30 energy adjustment was used in the analyses31 as indicated below. In addition, since body size may also contribute to the variation in specific nutrient intakes,31 we adjusted for its potential confounding effect in additional models. Blood samples were collected before 10:00 AM in the morning and were immediately centrifuged. The frozen serum samples were shipped from Athens, Greece, to Beth Israel Deaconess Medical Center in Boston, MA, packed in dry ice. The coded samples arrived unthawed and were analyzed blindly, in a single run. IGF-1 and IGFBP-3 levels were measured using a commercially available immunoradiometric kit (Diagnostic Systems Laboratories, Inc, Webster, TX), as previously described.9 IGFBP-3 was approximately normally distributed. Because of the slightly skewed nature of the IGF-1 distribution in our study sample, a logarithmic transformation of IGF-1 was performed to create a more normal distribution of this variable. Simple regression analysis was used to estimate correlations between IGF-1, IGFBP-3, and the study's independent variables. Simple linear regression was used to examine the relationship between dietary factors and serum levels of IGF-1 or IGFBP-3 (dependent variables), as indicated in the relevant tables. We then examined the potential role of either specific food items or macronutrients on serum IGF-1 and IGFBP-3 levels in univariate and multivariate models. Total energy intake is associated with nutrients that contribute directly to energy intake and may also be associated with hormone levels as well as other potentially confounding variables, such as body size, metabolic efficiency, physical activity, and so on.32 Thus, several multivariate linear regression analysis models were built with and without adjustment for (1) total energy intake, (2) other macronutrients, (3) age and sex, (4) age, sex, body mass index, cigarette smoking, and alcohol and coffee consumption, and (5) mutual adjustment for the other macronutrients and anthropometric and lifestyle variables, as indicated in the relevant tables. We used standard multivariate energy partition and nutrient density models, as previously described.32 Although these models may be considered different formulations of the same model, each one provides a different perspective of the data and can lead to a better insight of the relationship between diet and serum IGF-1 or IGFBP-3 levels. Examining the association of dietary fat with the above hormones in a standard multivariate model adjusting for total energy intake would address the independent effect of fat on serum levels of these hormones given that the diet is "isocaloric," ie, the total amount of energy intake remains stable. In contrast, examining the association of dietary fat with the above hormones in a multivariate model adjusting for energy from carbohydrates and protein (Table 1) would address the effect of fat intake on serum hormone levels in a case in which although the caloric intake from protein and carbohydrates is stable, the caloric intake from fat (and thus the total caloric intake) varies.31,32 Similarly, complementary information is also derived from the different models on the specific fat types. For example, information from a model in which saturated fat is expressed as a nutrient density (in a model adjusting for total energy + total fat/energy + saturated fat/energy, ie, Table 6) can be interpreted as substituting a certain percentage of energy from saturated fat for the same amounts of energy from other fat.31,32 Models including different types of fat but not adjusting for total fat intake (such as in Tables 7 and 9) address a somewhat different question: Is any type of fat, substituted for other sources of energy, independently associated with hormone levels?
All analyses were performed using the SPSS statistical package (SPSS for windows, release 5.0.1; SPSS Inc, Chicago, IL). P values presented in this article are two-tailed.
Table 1 presents descriptive statistics of the study variables, ie, the mean, median, SD, 25%, and 75% of IGF-1, IGFBP-3, age, height, body mass index, cigarette smoking (cigarettes per day), alcohol drinking (glasses per month), and coffee drinking (cups per month). Table 1 also presents descriptive statistics of the nutritional variables used in this study. Table 2 shows the frequency distribution of sex and the lifestyle predictors of IGF-1 and IGFBP-3 considered in the study. The distributions of these lifestyle predictors are fairly similar to those previously described in the Greek population.7,23
We then performed linear regression models, as outlined below. There were no serious colinearity problems in the analysis and/or the models presented herein. Results from the univariate and multivariate linear regression models are presented in Tables 3, 4, and 5. Table 3 shows simple and partial regression-derived coefficients of serum IGF-1 and IGFBP-3 levels regressed on specific food items. It seems that red meat, as well as oils and fats, are positively associated with serum IGF-1 levels, whereas consumption of bread is negatively associated with serum IGF-1 levels. Importantly, these associations persist even after adjustment for a variety of factors, including total energy intake. In contrast, serum IGF-1 levels are not significantly associated with any other food groups, and serum IGFBP-3 levels are not associated with any of the food groups evaluated in this study.
Simple regression of serum IGF-1 and IGFBP-3 levels on energy from macronutrients or total energy intake revealed nonsignificant results. We then analyzed the data and included a model that used caloric intake from fat, carbohydrates, and protein, either alone or after adjustment for potential confounders, study nutritional variables, and/or total energy intake (Table 4). It seems that fat intake is positively associated with serum IGF-1 levels, whereas carbohydrate intake has a negative association. Similar results were obtained using a multivariate nutrient density model (Table 5).31 In these models, simple and multiple regression indicates that the fraction of calories in the diet derived from fat is positively associated with serum IGF-1 levels, whereas that derived from carbohydrates has a negative association (Table 5). These data indicate that increasing the percentage of caloric intake derived from fat by 20% results in an increase of logIGF-1 levels by 84, whereas increasing the percentage of caloric intake derived from carbohydrates by 20% results in a decrease of logIGF-1 levels by 43. Finally, we evaluated the effect of saturated, monounsaturated, and polyunsaturated fatty acids on serum IGF-1 and IGFBP-3 levels using several multivariate energy partition and nutrient density models (Tables 6 through 9). Saturated fat is inversely, independently, and significantly associated with IGFBP-3 in all models (univariate r = -.20, P < .05; r = -.22, P < .05 after adjustment for age and sex). In addition, substituting saturated fat for either poly- or monounsaturated fat was significantly and inversely associated with IGFBP-3 levels (P = .02) in models with or without adjustment for potential confounders such as age, sex, smoking, and alcohol intake (data not shown). In contrast, monounsaturated and polyunsaturated fatty acids do not seem to play a role of comparable significance in regulating either IGF-1 or IGFBP-3 levels (data not shown).
IGF-1 is a peptide hormone secreted mainly by the liver but also produced by several other tissues in response to growth hormone.1-3,33 In addition to its role in metabolism, IGF-1 has important autocrine and paracrine actions to promote normal growth and induce malignant cellular proliferation.1-3,34 Nutritional epidemiology of cancer is a rapidly growing field. Several independent lines of investigation have suggested that a diet rich in red meat and fat may predispose to cancer of the prostate, breast, and, especially, colon cancer. However, these associations have not been proven conclusively. Ecologic studies also support a role for high fat intake in the etiology of prostate, breast, and colon cancer.14 Animal fat, especially fat from red meat, is associated with an increased risk of colon cancer,14 although dietary fat from sources other than red meat, such as vegetable fat, does not seem to increase risk appreciably.14,35 In addition, regular consumption of red meat and fat has been positively associated in most case-control and cohort studies with increased risk of prostate cancer,16,36,37 whereas some studies have shown a null association, but none has shown a negative association.36 Furthermore, both retrospective and prospective studies indicate that high fat intake promotes the emergence of aggressive, metastatic prostate cancer.15,16 Finally, experimental evidence suggests that specific fatty acids may enhance breast cancer invasion and metastatic potential.38 A pooled analysis of primary data derived from case-control studies revealed a weak positive association between risk of breast cancer in postmenopausal women and fat intake,39-41 whereas little or no association between total fat intake and breast cancer risk has been observed in prospective studies.39,42 Thus, it has been suggested that dietary fat is primarily related to postmenopausal breast cancer and that the inability to detect differences in cohort studies is due to measurement error and the relative homogeneity of diets measured.43 However, the mechanism linking nutrients to the development of cancer is still largely unknown.37 These converging lines of evidence provide the background biologic context for considering a role for diet in regulating IGF-1 and IGFBP-3 levels. Chronic starvation results in reduced serum IGF-1 levels, and refeeding of subjects with malnutrition normalizes bioactive IGF-1 levels.17,40 In addition, the growth hormone–IGF-1 axis may have important effects on lipid and carbohydrate metabolism by stimulating lipid mobilization and preventing visceral fat accumulation.40,44,45 Thus, IGF-1 is widely thought of as the hormone primarily responsible for nutrient control of growth in health and disease.36 However, no epidemiologic studies on the association between different macronutrients or food groups and serum levels of IGF-1 in humans have been published to date. Because IGF-1 is a potentially important determinant of disease, especially cancer, epidemiologic identification of modifiable determinants of serum IGF-1 could be of clinical importance. We and others have recently shown that age, sex, and smoking are independent predictors of serum IGF-1 and IGFBP-3 concentrations.18,46,47 In this study, we demonstrate that consumption of red meat, oils, and fats as well as dietary fat intake is positively associated with serum IGF-1 and independent of other variables, whereas dietary carbohydrates and consumption of bread (a significant source of carbohydrates in the Greek diet) are negatively associated with serum IGF-1 levels. Saturated fat seems to be negatively related to IGFBP-3 levels only. The relationship between these nutritional variables on the one hand and serum IGF-1 or IGFBP-3 levels on the other hand has not previously been reported, to our knowledge. The observed associations have a strong biologic basis. IGF-1 levels are positively regulated by growth hormone, which is, in turn, regulated by a number of factors, including postprandial hyper- or hypoglycemia.48 More specifically, hypoglycemia increases whereas hyperglycemia decreases growth hormone levels in humans,48 which is consistent with the inverse association between carbohydrate intake and serum IGF-1 levels observed in this study. Fatty acids may also act synergistically with glucose and amino acids in regulating growth hormone levels in humans, but their effect on the IGF-1 system has not been adequately studied.48 However, animal experiments have shown that increasing dietary fat intake results inF increased serum IGF-1 levels in rats49 and that an isoenergetic, isonitrogenous, and isofibrous high-fat diet increases follicular fluid IGF-1 levels in cows.50 Moreover, increased dietary fat intake significantly increased IGF-1 and IGF-2 receptor mRNA and IGF-1 receptor protein levels in colon mucosas of rats,51 suggesting an IGF-1–mediated effect of dietary fat on mitogenesis.51 Thus, in addition to other possible links between diet and malignancy, such as heterocyclic amines, and similar to the effect of diet on serum androgens and estrogens,52,53 IGF-1 and IGFBP-3 levels may also be directly regulated by diet and may mediate diet's effect on normal growth and malignant cellular proliferation. Indeed, our findings are consistent with available epidemiologic evidence suggesting that the predictors of IGF-1 in this study, ie, red meat, animal fat, and energy from lipids, are also related to risk for malignancy. Interestingly, vegetable fat, which is not related to IGF-1 or IGFBP-3 levels in this study, has not been associated with increased risk for cancer either. In contrast, no association between IGF-1 or IGFBP-3 levels and total energy intake was observed in this study. Although IGF-1 levels are decreased in response to starvation, previous studies have reported a relatively low threshold of energy requirements (approximately 900 kcal/24 hours) for IGF-1 stabilization after refeeding.40 Although the distribution of the study variables in this sample does not differ from the corresponding distributions among subjects in previously published studies performed in Greece, the study subjects do not represent a strictly representative sample of any defined population. However, representativeness is not a prerequisite for validity in this epidemiologic context.54 This study is of moderate size, but we have considered a wide range of lifestyle factors and have controlled for mutual confounding. The focus of this study on healthy controls offers the advantage of minimizing the potential effect of disease and/or related treatment on hormone levels. Additional strengths of the study are the use of state-of-the art laboratory techniques and its cross-sectional design. On the basis of the biology of IGF-1, it is expected that diet may have a rather short-lived effect on hormone levels; therefore, IGF-1 levels should be studied in relation to current, and not past, diet. Reporting bias was also minimized because the interviewers were blinded to the hypothesis of the study and the food-frequency questionnaire responses were precoded, requiring little, if any, probing by the interviewer. Given the high response rate, selection bias could not have affected these data. Finally, it is expected that adjustment for total energy intake serves not only as control for confounding but also to reduce extraneous variation and predict the effect of dietary interventions, as previously suggested.32 This study has several limitations, however, including the fact that cross-sectional studies cannot elucidate pathophysiologic mechanisms or ascertain the temporal relationship between changes in diet and hormone levels. Although differential misclassification is unlikely given the blinded laboratory analysis, nondifferential misclassification due to random laboratory error is possible, but this would be expected to bias the results toward the null and should not have influenced the significant results of this study. In addition, although confounding was controlled for by standard statistical procedures, residual confounding by other unmeasured factors remains a possibility. In conclusion, dietary carbohydrates and fat seem to regulate serum IGF-1 and IGFBP-3 levels, a finding that may have important preventive and therapeutic implications because it provides a link between diet and several disease states, including common malignancies. Future, large studies are needed to confirm these findings and quantify more precisely the effect of nutrients on the IGF-1 system.
Supported by the Clinical Associate Physician Award (NIH and Beth Israel Deaconess Medical Center), the Junior Investigator award, and the Hershey family award (Beth Israel Deaconess Medical Center and Harvard Medical School) to C.S.M. and the "Maroudas" Scholarship of the University of Athens to V.G.K.
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Copyright © 1999 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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ABSTRACT
INTRODUCTION
