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Cancer Epidemiology Biomarkers & Prevention Vol. 13, 2208-2216, December 2004
© 2004 American Association for Cancer Research

Longitudinal Associations of Age, Anthropometric and Lifestyle Factors with Serum Total Insulin-Like Growth Factor-I and IGF Binding Protein-3 Levels in Black and White Men: the CARDIA Male Hormone Study

Susan M. Gapstur1,3, Peter Kopp2,3, Brian C-H. Chiu1,3, Peter H. Gann1,3, Laura A. Colangelo1 and Kiang Liu1

Departments of 1 Preventive Medicine and 2 Medicine, Center for Endocrinology, Metabolism and Molecular Medicine, Feinberg School of Medicine and 3 Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois

Requests for reprints: Susan M. Gapstur, Department of Preventive Medicine, Northwestern University, 680 North Lake Shore Drive, Suite 1102, Chicago, IL. Phone: 312-908-0306; Fax: 312-908-9588. E-mail:sgapstur{at}northwestern.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Although several studies have assessed cross-sectional correlates of serum insulin-like growth factor-I (IGF-I) and IGF binding protein-3 (IGFBP-3), there are no longitudinal studies of the correlates of long-term changes in these measures. We examined the 8-year longitudinal associations of age, body mass index (BMI), waist circumference, physical activity, number of cigarettes smoked per day, and alcohol intake with serum total IGF-I and IGFBP-3 concentrations in 622 Black and 796 White male participants of the Coronary Artery Risk Development in Young Adults Study who were ages 20 to 34 years at the time of the first IGF measurement. In generalized estimating equation analyses, IGF-I decreased by 5.6 and 5.9 ng/mL per year increase in age for Black and White men, respectively (P< 0.0001), and there was an age-related decline in IGFBP-3 that was stronger in Whites (P < 0.0001) than Blacks (P = 0.21). Average IGF-I (ß = –17.51 ng/mL) and IGFBP-3 (ß = –355.4 ng/mL) levels across all three exams were lower in Blacks than Whites (P < 0.0001). Increased BMI was associated with decreased IGF-I (P < 0.0002), but was not associated with IGFBP-3. There were no meaningful associations with waist circumference. Increased physical activity was associated with a decrease in IGFBP-3 (P < 0.05), but was not associated with IGF-I. In White men, there were weak inverse associations between the number of cigarettes smoked per day with IGF-I (P=0.15) and with IGFBP-3 (P = 0.19), and in Black men, increased alcohol intake was associated with a decrease in IGF-I (P = 0.011). In conclusion, these results support an age-related decline and Black-White difference in serum IGF-I and IGFBP-3 levels. Importantly, they suggest that IGF-I and/or IGFBP-3 levels could be influenced by changes in BMI, and perhaps by physical activity, alcohol intake, and cigarette smoking.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Insulin-like growth factor-I (IGF-I) plays an essential role in regulating cell proliferation, differentiation, and apoptosis (1). Over 75% of IGF-I in circulation is bound to IGF binding protein-3 (IGFBP-3), and nearly all of the remaining fraction is bound to five other IGFBPs which regulate IGF-I tissue bioavailability (2). IGFBP-3 also has important tumor suppressor activity independent of its modulating effects on IGF-I (1). Serum levels of IGF-I and IGFBP-3 have been associated with risk of prostate (3), colon (4), and lung cancer (5).

Most IGF-I and IGFBP-3 measured in circulation is produced in the liver (1), but they are also produced in other tissues where they have both autocrine and paracrine effects (6). The expression of IGF-I and IGFBP-3 is controlled primarily by growth hormone, and both measures are low in patients with growth hormone deficiency and high in patients with acromegaly (7). Circulating IGF-I and IGFBP-3 concentrations peak during puberty and thereafter seem to decline with age (7, 8). Whereas it is well recognized that severe energy and protein restriction decrease serum IGF-I levels (9),other determinants of serum IGF-I and IGFBP-3 levels in normal healthy adults are not well understood. Data from one twin study showed that approximately 38% of the proportion of interindividual variation in serum IGF-I and 66% of the variation in IGFBP-3 could be attributed to genetic factors (10). In addition, higher IGF-I and IGFBP-3 concentrations were reported in White men compared with Black men (11). Most research on the associations of IGF-I and IGFBP-3 with modifiable lifestyle factors such as cigarette smoking,alcohol consumption and physical activity, and with anthropometric factors such as height, body mass index (BMI) and measures of central obesity have been conducted using data from cross-sectional studies or small experimental studies (12). Because of the interrelationships among these variables, data from cross-sectional studies make it difficult to disentangle their independent associations.

To date, there have been no longitudinal studies examining the long-term changes in serum IGF-I or IGFBP-3 concentrations and the correlates of these changes in a diverse sample of healthy adults. The Coronary Artery Risk Development in Young Adults (CARDIA) Study is a longitudinal study of young Black and White men and women with repeat measurements of anthropometric and lifestyle factors, along with blood sampling every 2 to 5 years. The CARDIA Male Hormone Study (CMHS) was designed to assess 8-year changes in serum growth factor and hormone concentrations in Black and White male CARDIA participants. In this longitudinal analysis, we examined the relationships of age, race, height, overall obesity (i.e., BMI), central obesity (i.e., waist circumference), total physical activity, cigarette smoking, and alcohol intake with serum total IGF-I and IGFBP-3 concentrations. An analysis of the correlates of changes in IGFs in Black and White men during young to middle adulthood is of particular interest because this is a period in life when many important anthropometric and lifestyle changes are occurring.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
The CARDIA Male Hormone Study
The CARDIA Study is a multicenter, longitudinal study designed to examine physiologic, psychological and lifestyle factors that might affect the development of cardiovascular disease risk factors in young Black and White men and women. Briefly, 5,115 participants who were ages 18 to 30 years completed a baseline examination from 1985 to 1986 at one of four clinical centers: Birmingham, AL, Chicago, IL, Minneapolis, MN, or Oakland, CA. Four follow-up examinations were completed from 1987 to 1988 (year 2), 1990 to 1991 (year 5), 1992 to 1993 (year 7), and 1995 to 1996 (year 10). A detailed description of the design, recruitment, and methods of this study was published previously (13).

The number of Black and White men who completed the baseline examination was 1,157 and 1,171, respectively. For the CMHS, growth factor concentrations were measured in serum collected at the year 2, year 7 (when available), and year 10 examinations. Because the primary goal of the CMHS was to evaluate 8-year longitudinal changes in growth factor levels, only men who had serum available from at least the year 2 and the year 10 examinations were included (i.e., 624 Black men and 796 White men). Two Black men were excluded because their baseline age was more than 31 years. Within each race group, there were no statistically significant differences in baseline BMI, or waist–hip ratio between men for whom hormones were and were not measured. For White men, baseline age and education did not differ. However, Black men included in the CMHS were slightly older than those not included (24.4 versus 23.9 years, respectively, P = 0.03), and had a slightly greater number of years of education (13.1 versus 12.7, P = 0.0002). The Institutional Review Board at Northwestern University approved the CMHS.

Data Collection
In the CARDIA Study, all data collection technicians were centrally trained and certified. The CARDIA Coordinating Center, and the CARDIA Quality Control Committee monitored data collection throughout the study. Informed consent was obtained from each participant at each examination.

Participants were asked to fast for 12 hours, and to avoid smoking and heavy physical activity for 2 hours before each examination. Weight and height were measured using a balance beam and a vertical ruler with participants wearing light clothing and no shoes. Height was recorded to the nearest 0.5 cm and weight to the nearest 0.5 lb. BMI was calculated as the weight (kg) divided by the height squared (m 2). Waist circumference was measured as twice the minimum abdominal girth. Age, race, and cigarette use were assessed by questionnaire. In addition, average daily alcohol intake (milliliters of ethanol per day) was determined from a self-reported amount of beer, wine, and liquor consumed per week (14). A physical activity score was derived from the CARDIA Physical Activity History questionnaire, which queried participants about 13 types of physical activity regarding the frequency and duration of each activity over the last year. Scores related to energy expenditure were computed from intensity levels and frequency of each activity and summed to achieve the total activity score (15).

At each of the three examinations, blood was collected by venipuncture between 7:30 a.m. and 12 noon from over 95% of the CMHS participants, and there were no meaningful differences in average time of blood drawn between Black and White men. Aliquots of serum were transferred to appropriately labeled tubes and immediately stored at –70°C. Samples were packed in dry ice and shipped to a long-term freezer storage facility at Solomon Park Research Laboratories, Kirkland, WA.

Hormone Measurements
To minimize intraindividual laboratory variation, all three samples from each individual were assayed in the same batch. Total IGF-I and IGFBP-3 were measured by use of immunoradiometric assay kits obtained from Diagnostic Systems Laboratories (Webster, TX). The intra-assay and interassay coefficients of variation were 4.4% and 10.4% for IGF-I, and 4.8% and 8.0% for IGFBP-3, respectively.

Statistical Analysis
Serum concentrations of total IGF-I and IGFBP-3 seemed normally distributed for both Black and White men. For each growth factor, Spearman correlation coefficients between each of the growth factor concentrations measured at years 2 and 7, and the years 2 and 10 exams, were computed.

Analysis of covariance was used to compare age-adjusted differences between races for IGF-I, IGFBP-3, and anthropometric and lifestyle factors at each examination, and for the 8-year change in these measures. Analysis of covariance was also used to compute race-specific, age-adjusted mean concentrations of IGF-I and IGFBP-3 within strata of BMI, height, waist circumference, total physical activity score, number of cigarettes smoked per day and alcohol intake using the year 2 data. Quintiles were defined for BMI, height, circumference, and total physical activity score. For cigarette smoking and alcohol intake, the lowest strata were nonsmokers and nondrinkers, respectively, and the other strata were divided into quartiles of number of cigarettes smoked per day or amount of alcohol consumed per day. Age-adjusted Pearson partial correlation coefficients (and P values) were computed to assess potential linear cross-sectional associations of IGF-I and IGFBP-3 with the anthropometric and lifestyle factors.

The primary analysis was based on the generalized estimating equation method developed by Liang and Zeger (16). This method allows for simultaneously examining the cross-sectional relationships between each of the independent variables and average IGF-I or IGFBP-3, and the relationships between changes in these variables and changes in growth factor levels. A typical model is

where for t = 2, 7, 10,Yit is the growth factor concentration for person i at year t; T7 and T10 are indicator variables where T7 indicates the year 7 exam, and T10 indicates the year 10 exam; Uit is the age of person i at time t; Vi2 = is the year 2 exam value for height; Xi2 is the year 2 exam value of other covariates (i.e., BMI, waist circumference, physical activity, number of cigarettes per day, or alcohol intake); {Delta}Xit = Xit – Xi2 is the change in these variables between the year t and year 2 exams for person i and eit is the error term. We did not model the change in height because there was no meaningful change in this variable over the 8-year follow-up. The coefficients ß1 and ß2 measure the secular change in growth factor level between the years 2, 7 or 10 exams, respectively, which are not related to changes in age or the other covariates. The coefficient ß3 measures the covariate-adjusted association between growth factor and visit age. The coefficient ß4 measures the relationship between average growth factor concentration and year 2 height. The coefficient ß5 measures the relationship between average growth factor concentration and year 2 BMI (or other factors), whereas ß6 measures the association of changes in BMI (or other factors) and changes in growth factor levels over time. In analyses including race, the term ß7Zi was added to the model, where Zi is the dummy variable to separate the two race groups, and the coefficient ß7 measures the average difference in growth factors level between Black and White men adjusted for other covariates. A cross-product term for race and visit age was also included to determine whether the relationship of age with IGF-I or IGFBP-3 differed between Black and White men. The inclusion of a time-dependent indicator variable for medications that were categorized as "likely" to interfere with growth factor metabolism did not change any of the associations. Therefore, results are presented without adjustment for medication use. All analyses were conducted using SAS version 8.02 (SAS Institute, Inc., Cary, NC). For the longitudinal analyses, PROC GENMOD was used and an exchangeable structure was specified for the within-subjects correlation.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Preliminary Analyses
The average ages of Black and White men at the year 2 examination were 26.9 and 27.9 years, respectively (P for difference < 0.0001). For total IGF-I, Spearman correlation coefficients between the years 2 and 7 concentrations were 0.47 for Black men and 0.46 for White men, and between the years 2 and 10 concentrations were 0.39 for Black men and 0.44 for White men. For IGFBP-3, the correlations between years 2 and 7 were 0.50 and 0.49 for Black and White men, and between years 2 and 10 were 0.50 and 0.52 for Black and White men, respectively. All correlations were statistically significant (P < 0.0001).

Age-Adjusted Comparisons Between Black and White Men
In the age-adjusted analyses, mean total IGF-I and IGFBP-3 concentrations were statistically significantly lower in Black men compared with White men at each examination (Table 1). However, there was no difference between Black and White men in the 8-year change for either growth factor measurement. At each examination, mean BMI was significantly higher in Black men than in White men, and the increase from year 2 to year 10 was nearly 0.5 kg/m 2 greater in Blacks than in Whites. White men were slightly taller than Black men at the year 2 examination but there were no differences in height at the years 7 or 10 examinations. However, the 8-year increase in height was greater in Black men than in White men. Waist circumference was lower in Blacks than Whites at the year 2 examination, but there were no Black-White differences in waist circumference at the years 7 or 10 examinations. The 8-year change in waist circumference was significantly greater for Blacks than for Whites. The total physical activity score was higher in Black men than White men at each examination. This difference was statistically significant at the year 7 examination, and it was marginally significant at the year 10 examination. Physical activity decreased more in White men than Black men over the 8-year follow-up, however, the Black-White difference in change in physical activity was not significant. There were no differences in cigarette smoking or alcohol intake, or change in these lifestyle factors between Black and White men.


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  		Table 1. Age-adjusted race-specific means (SE) for IGF-I and IGFBP-3 concentrations, and for anthropometric and lifestyle factors at the years 2, 7, and 10 examinations in Black (n = 622) and White (n = 796) men

 
Year 2 Age-Adjusted, Cross-sectional Associations of IGF Measurements with Anthropometric and Lifestyle Factors
Table 2 shows results of year 2 cross-sectional analyses for age-adjusted means of total IGF-I and IGFBP-3 across strata of anthropometric and lifestyle factors for Black and White men, and age-adjusted Pearson partial correlations of IGF-I and IGFBP-3 with each of the anthropometric and lifestyle characteristics. For both Black and White men, there were no relationships between IGF-I and BMI, waist circumference, or physical activity. IGF-I was positively associated with height for Black men and White men, but was only statistically significant for Whites. There were inverse relationships of IGF-I with cigarette smoking and with alcohol intake in both race groups. For alcohol intake, this seemed to be largely due to the lower levels of IGF-I for men in the highest quartile of intake.


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  		Table 2. Age-adjusted means of serum IGF levels across strata of anthropometric and lifestyle factors and Pearson partial correlations between IGFs and these factors at the year 2 CARDIA examination for Black and White men

 
There was a statistically significant positive association between the IGFBP-3 levels of BMI and waist circumference in Blacks and Whites, and with height in White men. In addition, physical activity and number of cigarettes smoked per day were inversely associated with IGFBP-3 in White men but not in Black men. Alcohol intake was not associated with IGFBP-3 in either group of men.

Multivariable Analyses
In the generalized estimating equation analyses (Table 3), visit age was inversely associated with serum total IGF-I; on average, IGF-I decreased by approximately 5.6 and 5.85 ng/mL per year in Black and White men, respectively. Similarly, there was an age-related decline in IGFBP-3 in Black men(–10.4 ng/mL, P = 0.21) and in White men (–41.6 ng/mL, P < 0.0001). In a combined analysis, there were no racial differences in the age-associated decreases in IGF-I (P = 0.65) or IGFBP-3 (P = 0.11), as shown in Fig. 1A and B. In addition, the average concentration of IGF-I across all three exams was lower in Blacks than Whites (ß = –17.51 ng/mL, P < 0.0001); similarly, the average concentration of IGFBP-3 was also lower (ß = –355.4 ng/mL, P < 0.0001).


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  		Table 3. Multivariate 8-year longitudinal associations of IGF-I, and IGFBP-3 with age, anthropometric and lifestyle factors for Black and White men

 


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  		Figure 1. Age-associated change in IGF-I (A) and IGFBP-3 (B) over an 8-year period for Black men ({blacktriangleup}) and for White men ({circ}) after adjustment for year 2 height, BMI, total physical activity score, number of cigarettes smoked per day, alcohol intake, and for 8-year change in BMI, physical activity score, number of cigarettes smoked per day, and alcohol intake.

 
For the anthropometric factors, both the year 2 cross-sectional differences and the 8-year longitudinal changes in BMI were significantly inversely associated with total IGF-I in both Black and White men. However, there were no associations between BMI and IGFBP-3. Year 2 height was marginally positively associated with IGF-I in Blacks (P = 0.058) and Whites (P = 0.11), and with IGFBP-3 in Blacks (P = 0.10) and Whites (P = 0.094). To assess the relationships of central obesity with IGF-I and IGFBP-3 beyond its contribution to overall obesity (i.e., BMI), we included year 2 waist circumference and change in waist circumference in the models in addition to the other variables listed in Table 3. In general, there were no associations of waist circumference with IGF-I or IGFBP-3 in either Black or White men (P > 0.22), except increasing waist circumference over the 8-year follow-up was associated with a marginally significant decrease in IGF-I in Black men only (ß = –1.14 ng/mL, P = 0.06) independent of changes in BMI.

Among the lifestyle factors, there were no cross-sectional or longitudinal associations between physical activity and total IGF-I. There was evidence of an inverse relationship between year 2 physical activity and IGFBP-3 concentration, which was stronger in White men (P = 0.0011) than in Black men (P = 0.36). Over the 8-year follow-up period, an increase in physical activity of 10 units was associated with a significant decrease in IGFBP-3 that ranged from 1.67 ng/mL among White men to 2.09 ng/mL among Black men. A higher number of cigarettes smoked per day at year 2 was associated with lower IGF-I concentration in both Black men (ß = –6.0 ng/mL per 10 cigarettes) and in White men (ß = –5.22 ng/mL per 10 cigarettes), although these associations were not statistically significant. Whereas there was no relationship between cigarette smoking and IGFBP-3 levels in Black men, a higher number of cigarettes smoked per day at year 2 was associated with a lower concentration of IGFBP-3 among White men. Higher daily alcohol intake at year 2 was consistently associated with lower IGF-I but was not related to IGFBP-3 in Black and White men. Similarly, an increase in alcohol intake over the 8-year follow-up was associated with a decrease in IGF-I for Black men but not for White men.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
To date, epidemiologic research on serum IGF concentrations in men has been limited to cross-sectional or small experimental studies. The CMHS is the first longitudinal study of long-term changes in IGF-I and IGFBP-3 and the factors associated with these changes in young adult Black and White men. Consistent with most cross-sectional studies (8, 17-24), findings from our longitudinal analyses showed a decrease in total IGF-I and IGFBP-3 with increasing age. However, for IGF-I, we observed a greater rate of decrease (i.e., 5.6-5.9 ng/mL per year) than was reported in other studies. For example, in one study of older men whose mean age was 74 years, the average difference in IGF-I was –0.11 ng/mL per year of older age (21). In two other studies of men where the study subjects ranged in age from 25 to 64 (18) and 21 to 80 years (19), the average annual differences in IGF-I were –2.1 and –2.36 ng/mL per year, respectively. In our study, the average decrease in IGFBP-3 with increasing age was –10.4 ng/mL per year in Black men and –41.6 ng/mL per year in White men; this apparent racial difference was not statistically significant. The rates of decrease in IGFBP-3 are approximately similar to that of a study which showed a difference of –16.0 ng/mL per older year of age among men whose average age was 63 years (23). Whereas it is possible that variation in the strength of the association between age and IGF-I across studies is due to age differences across study samples. Yamamoto et al. (19) reported that the slope of the regression line was similar for younger and older men. Alternatively, differences in study design (i.e., longitudinal versus cross-sectional), or in the covariates included the analyses could also account for some of the variation across studies.

In the CMHS, we also assessed the intraindividual variability in serum total IGF-I and IGFBP-3 concentrations over the 8-year follow-up period. Spearman partial correlations for IGF-I measured 5 or 8 years apart ranged from 0.39 to 0.47, and the comparable correlations for IGFBP-3 ranged from 0.49 to 0.52 for Black and White men. Two other cohort studies of older aged men measured the intraindividual variability in circulating IGF measurements in men (11, 21). In one study (21), there was a strong correlation (r = 0.94, P = 0.001) between IGF-I concentrations measured in blood samples collected 8 to 54 days apart. In the second study (11), blood samples were collected on average 3 years apart, and the Spearman partial correlation between samples was 0.70 for total IGF-I, and 0.68 for IGFBP-3. Overall, it seems that for circulating IGFs, the relative ranking of individuals is similar over a short period of time, but over a longer period, this ranking is attenuated. One explanation for this attenuation includes intraindividual changes in modifiable anthropometric and/or lifestyle factors, and the magnitude of these changes might be greater among younger men than among older men.

In children, serum total IGF-I and IGFBP-3 concentrations are strongly correlated with height (25), but most previous studies showed no meaningful associations in adults (18, 21-23, 26). However, in general, these studies did not consider the potential confounding effects of other anthropometric factors. One study of middle-aged Japanese men found marginally significant positive associations of adult height with IGF-I (P = 0.06) and with IGFBP-3 (P = 0.09) after adjustment for BMI, waist–hip ratio, cigarette smoking, alcohol intake and level of physical activity (27). These findings are consistent with our observations that also document weak, marginally significant positive associations of IGF-I and IGFBP-3 with height in White men, and to a lesser extent in Black men.

The relationships of IGFs with measures of obesity and physical activity are complex, and most studies did not consider their potential confounding effects. A higher BMI has been associated with lower serum total IGF-I levels in some (17, 23, 26, 28) but not all (18, 21, 22, 27, 29, 30) cross-sectional epidemiologic studies. In the multivariate analysis of the CMHS, increases in BMI were consistently associated with declines in serum IGF-I, but there was no relation between BMI and IGFBP-3. Additionally, whereas one study reported inverse association of visceral adiposity with total IGF-I (31), five other studies showed no associations among waist circumference, waist–hip ratio (18, 21, 27, 30), visceral, or subcutaneous adipose tissue (29). In the CMHS, we also observed no meaningful relationship between waist circumference and serum IGF-I or IGFBP-3 levels independent of BMI. The biological mechanism underlying the decline in serum total IGF-I level with increased obesity could be a result of reduced growth hormone secretion from the pituitary gland. In a study of 94 apparently healthy 21- to 86-year-old adults, growth hormone concentrations were measured in frequently collected blood samples collected during sleep and during the day (32). Across the age range, serum growth hormone secretion decreased with increased BMI, and there was a concomitant decrease in total IGF-I. It is interesting to note that whereas increased BMI might lead to a decrease in serum total IGF-I levels, some studies have shown a positive association between BMI and serum-free IGF-I levels (33). This apparent paradox could be explained by the interactions between insulin, which is positively related to BMI, and the synthesis of IGFBPs. Indeed, serum insulin levels are inversely associated with IGFBP-1 and IGFBP-2, and positively associated with free IGF-I (34-36). Unfortunately, the small volume of serum available (~0.5 mL) for the CMHS precluded the measurement of free IGF-I or other IGFBPs.

The age-related decline in IGF-I could be a result of decreasing activity with age (30). Consistent with our findings, most other epidemiologic studies have shown no association between the serum IGF-I concentration and the level of physical activity even after adjustment for age (18, 21-23, 27). Conversely, Poehlman and Copeland (30) found that both higher levels of maximal aerobic capacity (i.e., VO2 max) and leisure time activity over the past year were associated with higher serum IGF-I concentrations in younger and older men. Few studies have examined the associations of physical activity with serum IGFBP-3 concentrations. Chang et al. (23) found a positive association between high weekly physical activity level and IGFBP-3, whereas two other studies found no relationship (22, 27). We found that a higher level of activity at the year 2 exam was associated with a slightly lower level of IGFBP-3, and increased physical activity over the 8-year follow-up was associated with a slight decrease in IGFBP-3. Reasons for the inconsistencies among studies are unclear but they could be due to chance observations, differences in the method of measuring physical activity, or well-recognized limitations of self-reported data on physical activity that could result in attenuated associations.

The effects of cigarette smoking and alcohol intake on the IGF system are also of considerable interest. Many previous studies showed no associations of cigarette smoking with either IGF-I (21-23, 26) or with IGFBP-3 (22 23 26 28). However, at least three other studies reported significant inverse associations between smoking and IGF-I (18, 27, 28), and one study also found an inverse relationship with IGFBP-3 (27). There has been little research on the relationship of alcohol intake with serum IGF-I and IGFBP-3 concentrations, and among these studies, results are inconsistent (21, 22, 27). Yu and Rohan ( 12) noted that the associations of alcohol and cigarette smoking with IGF-I and IGFBP-3 could be mutually confounding. In the multivariate analysis of the CMHS data, we also found that greater alcohol intake, and to a lesser extent, greater number of cigarettes smoked per day, were associated with lower serum IGF-I concentrations in both Black and White men. Smoking was also inversely associated with IGFBP-3, but only in White men. For alcohol intake, our results are consistent with those of experimental studies showing a reduction in serum IGF-I and IGFBP-3 concentrations after alcohol intake in animal models (37), and with studies showing that chronic alcoholics seem to have lower concentration of IGFs than nonalcoholics (38). It has been hypothesized that reduced levels of IGF-I and IGFBP-3 could result from an inhibitory effect of alcohol on either pituitary growth hormone secretion or hepatic response to growth hormone stimulation of IGF-I and IGFBP-3 production (37). Although clinical studies show that high nicotine cigarettes increase serum growth hormone levels (39, 40), it is conceivable that smoking, like alcohol intake, could induce hepatic growth hormone resistance leading to a systemic decrease in IGF-I and IGFBP-3.

In CMHS, the age-adjusted mean concentrations of serum total IGF-I and IGFBP-3 were consistently lower in Black men compared with White men. However, there were no meaningful racial differences in the 8-year longitudinal decrease in IGF-I or IGFBP-3. Importantly, none of the other covariates were confounders of the associations of IGF-I and IGFBP-3 with race. Only two other studies compared serum IGF-I and IGFBP-3 levels between Black and White men (11, 28). Although both studies showed statistically significantly lower serum IGFBP-3 concentrations in Blacks than in Whites, only one study (11) found a lower concentration of IGF-I in Black men. Based on these findings, it is unlikely that the higher rates of prostate, colon, and lung cancer incidence in Black men (41) could be attributed to circulating IGF-I concentrations. However, there may be important differences in genetic factors between Blacks and Whites that could affect intracellular IGF-I levels and/or the activation of signal transduction pathways. Additionally, IGFBP-3 seems to inhibit cell proliferation (42) and induce apoptosis (43) through IGF-independent mechanisms. Thus, a lower concentration of IGFBP-3 could contribute to higher rates of prostate, colon, and lung cancer in Black men than in White men.

In conclusion, the results of this longitudinal study showed an age-related decline in serum IGF-I and IGFBP-3 levels in both Black and White men. They also support previous studies showing that the mean concentrations of serum IGF-I and IGFBP-3 are consistently lower in Black men compared with White men. However, there were no meaningful differences in the 8-year longitudinal decrease in IGF-I or IGFBP-3 between Blacks and Whites. The results also provide further evidence that modifiable factors such as BMI, and perhaps physical activity, cigarette smoking, and alcohol intake could influence circulating levels of IGF-I and/or IGFBP-3. Based on our results, it is not clear whether the associations of these modifiable factors with cancer risk are mediated through IGF synthesis, action, and/or metabolism.


   Acknowledgments
 
We thank Charlene Franz for her technical assistance.


   Footnotes
 
Grant support: PHS grant R01-CA770403 from the National Cancer Institute, and PHS contracts N01-HC-48047, N01-HC-48048, N01-HC-48049, N01-HC-48050, and N01-HC-95095 from the National Heart, Lung and Blood Institute.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received 3/20/04; revised 6/ 1/04; accepted 6/ 1/04.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

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