1 Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL.
2 Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL.
Received for publication January 27, 2004; accepted for publication May 25, 2004.
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ABSTRACT |
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cardiovascular diseases; insulin-like growth factor I; insulin-like growth factor binding protein 3; men; risk factors
Abbreviations: Abbreviations: CARDIA, Coronary Artery Risk Development in Young Adults; HDL, high density lipoprotein; IGF, insulin-like growth factor; IGFBP, insulin-like growth factor binding protein; LDL, low density lipoprotein.
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INTRODUCTION |
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IGF-1 has been associated with blood pressure in some studies, but not all. Three clinical studies (68) found mean IGF-1 levels to be statistically significantly higher in patients with hypertension than in normotensive controls. Among other studies, one found no difference in mean IGF-1 levels between borderline-hypertensive patients and normotensive age-matched controls (9), and another found negative correlations of IGF-1 with systolic blood pressure in men and women and with diastolic blood pressure in women. These associations became nonsignificant after adjustment for age (10). None of the studies included lifestyle factors that have been linked to IGF-1. Thus, further investigation of the relation between IGF-1 and blood pressure in adults should account for the potentially confounding effects of other factors in analyses.
The Coronary Artery Risk Development in Young Adults (CARDIA) Male Hormone Study is an ancillary study that was designed to examine the determinants of serum sex hormone and growth factor levels among Black and White male participants in the CARDIA Study. The CARDIA Male Hormone Study provided us with an opportunity to examine the hypothesis that systolic and diastolic blood pressure and levels of total cholesterol, LDL cholesterol, and triglycerides are inversely associated with IGF-1 levels cross-sectionally and longitudinally. Because IGFBP-3 is the major circulating binding protein of IGF-1, we hypothesized that there are inverse associations between IGFBP-3 and cardiovascular disease risk factors as well. Because of the small volume of serum available, it was not possible for us to measure other components of the IGF system. However, the large size of this cohort permitted the evaluation of these hypotheses by race and with adjustment for other factors associated with IGF-1, including body mass index, cigarette smoking, alcohol intake, and physical activity.
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MATERIALS AND METHODS |
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Study sample
The numbers of Black men and White men who completed the baseline examination were 1,157 and 1,171, respectively. The CARDIA Male Hormone Study was designed to compare 8-year changes in serum hormone levels and growth factors between 624 Black male and 796 White male CARDIA participants who had serum samples available from both the year 2 and the year 10 examinations; year 7 serum samples were also used when available. Within each race group, there were no statistically significant differences in baseline body mass index or waist:hip ratio between men for whom hormone levels were measured and men for whom they were not measured. Black men included in the CARDIA Male Hormone Study were slightly older than those not included (24.4 years vs. 23.9 years; p = 0.01) and had a slightly greater number of years of education (13.1 vs. 12.7; p = 0.0004). For White men, baseline age and education did not differ. The institutional review board of Northwestern University approved the protocol of the CARDIA Male Hormone Study.
In the primary analysis, participants who had not fasted for 8 hours prior to examination and participants who were missing data on growth factors, cardiovascular disease risk factors, or covariates were excluded from analysis for that examination. In this cohort of 1,420 Black and White men, 129 men and 130 men were excluded at the year 2 and year 10 visits, respectively, because they had not fasted for at least 8 hours prior to the examination or because they were missing pertinent data. At the year 7 examination, 117 of the 1,211 men who had data were excluded for these reasons. Thus, the sample sizes were 1,291, 1,094, and 1,290 at the year 2, year 7, and year 10 examinations, respectively.
In a secondary analysis, final multivariate analyses were rerun with further exclusion at a given examination of participants who were using antihypertensive medication, angiotensin-converting enzyme inhibitors, or lipid-lowering medication or because they had triglyceride levels of 400 mg/dl.
Data collection
Data were collected by centrally trained and certified technicians according to the CARDIA manual of operations. Throughout the study, the quality of the data collection was monitored by the Coordinating Center and the CARDIA Quality Control Committee. Participants were asked to fast for 12 hours and to avoid smoking and heavy physical activity for 2 hours before each examination. Venous blood was drawn between 7:30 a.m. and noon from over 95 percent of the CARDIA Male Hormone Study participants, and there were no meaningful differences in average time of blood drawing between Black men and White men.
Total cholesterol and total triglyceride levels were determined enzymatically (12). High density lipoprotein (HDL) cholesterol level was determined by the dextran sulfate method of Warnick et al. (13). LDL cholesterol level was calculated using the Friedewald equation (14).
After the participant had rested for 5 minutes in a quiet room, blood pressure was measured three times at 1-minute intervals using a random-zero cuff sphygmomanometer, and the average of the second and third readings was used in the analyses. Height and weight were measured with the participant wearing light clothing and no shoes. Height was recorded to the nearest 0.5 cm and weight to the nearest half pound (0.2 kg). Body mass index was computed as weight (kg) divided by height squared (m2). Waist circumference was measured in duplicate at the narrowest part of the waist. Age, race, years of education, and number of cigarettes smoked per day were self-reported. Alcohol intake (ml/day) was computed from self-reported weekly consumption of beer, wine, and liquor (15). A physical activity score was obtained from the CARDIA Physical Activity History, a modified version of the Minnesota Leisure Time Physical Activity Questionnaire (16).
IGF-1 and IGFBP-3
IGF-1 and IGFBP-3 were measured using immunoradiometric assay kits obtained from Diagnostic Systems Laboratories (Webster, Texas). We monitored assay variability by including approximately 10 percent blind quality control samples in each batch of samples. The quality control serum was obtained from a large pool that was aliquoted into storage vials, labeled identically to those for the CARDIA participant samples. The within- and between-batch coefficients of variation were 4.4 percent and 10.4 percent, respectively, for IGF-1 and 4.8 percent and 8.0 percent, respectively, for IGFBP-3.
Statistical analysis
Students t tests were used to compare racial differences in growth factor, cardiovascular disease risk factor, anthropometric, and lifestyle measures at the year 2 examination. To assess whether there might be nonlinear associations between IGF-1 and IGFBP-3 and cardiovascular disease risk factors, we determined quintiles of IGF-1 and IGFBP-3 and computed multivariable-adjusted mean systolic and diastolic blood pressure and total cholesterol, HDL cholesterol, LDL cholesterol, and triglyceride values for the quintiles. We compared mean levels of cardiovascular disease risk factors over quintiles of IGF-1 and IGFBP-3 using analysis of variance. When the result of the analysis-of-variance test was at least marginally significant (p < 0.10), the multivariable-adjusted mean values for the cardiovascular disease risk factors computed over the quintiles of IGF-1 or IGFBP-3 were examined for monotonic patterns. Since the results of this quintile analysis suggested that meaningful patterns were monotonic, race-specific age- and multivariable-adjusted Pearson partial correlation coefficients were computed for each examination and are reported.
We also compared mean IGF-1 and IGFBP-3 levels between hypertensive participants (systolic blood pressure of 135 mmHg or diastolic blood pressure of
85 mmHg or use of antihypertensive medication) and normotensive participants (systolic blood pressure of <135 mmHg, diastolic blood pressure of <85 mmHg, and no use of antihypertensive medication) at year 2, year 7, and year 10 using analysis of covariance.
To assess the associations between levels of cardiovascular disease risk factors (i.e., systolic blood pressure, diastolic blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, and triglycerides) over the 8-year follow-up period and change in IGF-1 or IGFBP-3 over the course of follow-up, we used statistical models employing generalized estimating equations (17). More specifically, we used the following model to evaluate change in cardiovascular disease risk factor levels in relation to change in IGF levels:
Yit = ß0 + ß1T7 + ß2T10 + ß3Uit + ß4Zi2 + ß5Zit + ß6Wi2 + ß7
Wit + eit,
where, for t = 2, 7, and 10, Yit is the cardiovascular disease risk factor measurement for person i at year t; T7 and T10 are indicator variables representing the year 7 and year 10 examinations, respectively; Uit is the age of person i at time t; Zi2 is the year 2 examination value of a covariate (i.e., body mass index, cigarette smoking, alcohol intake, or total physical activity); Zit = Zit Zi2 is the change in body mass index, cigarette smoking, alcohol intake, or physical activity between the year t and year 2 examinations for person i; Wi2 is the year 2 examination value of IGF-1 or IGFBP-3;
Wit = Wit Wi2 is the change in IGF-1 or IGFBP-3 between the year t and year 2 examinations; and eit is the error term. The coefficients ß1 and ß2 measure the secular change in cardiovascular disease risk factor level between the year 2 and year 7 examinations and the year 2 and year 10 examinations, respectively, that is not related to changes in age or other covariates. The coefficient ß3 measures the covariate-adjusted association between cardiovascular disease risk factor level and visit age. The coefficient ß4 measures the relation between mean cardiovascular disease risk factor level and year 2 body mass index (or cigarette smoking, etc.) with adjustment for covariates. On the other hand, ß5 measures the association of changes in body mass index (or cigarette smoking, etc.) and changes in cardiovascular disease risk factor levels over time. Similarly, ß6 and ß7 measure the relation between cardiovascular disease risk factor level and year 2 IGF-1 or IGFBP-3 and the association of changes in IGF-1 or IGFBP-3 with changes in cardiovascular disease risk factor levels.
Age- and multivariable-adjusted analyses were conducted. Covariates included in the partial correlation analysis, the quintile analyses, the analysis of covariance, and the generalized estimating equations analyses were body mass index, cigarette smoking, alcohol intake, and total physical activity score. Analyses were carried out using SAS software, version 8.2 (SAS Institute, Inc., Cary, North Carolina). PROC GENMOD was used for the longitudinal generalized estimating equations analysis. An exchangeable structure was specified for the within-subject correlation. To reduce skewness and kurtosis, we logarithmically transformed the data on triglyceride level in the analyses.
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RESULTS |
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For the lipids, there were some associations that were significant in both age- and multivariable-adjusted analysis. Among Black men, IGFBP-3 was significantly and positively associated with total cholesterol and LDL cholesterol at all three visits in age-adjusted and multivariable-adjusted analyses. There were also a significant positive association between IGFBP-3 and log(triglycerides) at year 10 and marginally significant associations at the other two visits in Black men. Among White men, there were significant, age-adjusted, negative partial correlations that remained at least marginally significant after adjustment for multiple variables between IGF-1 and HDL cholesterol at year 2 and for total cholesterol and LDL cholesterol at years 7 and 10. There were also significant inverse correlations between IGF-1 and log(triglycerides) at years 7 and 10, but these did not remain significant after adjustment for multiple variables. Finally, there were significant positive associations between IGFBP-3 and log(triglycerides) at all three examinations in White men and in Black men at year 10.
In longitudinal (generalized estimating equations) analysis (table 3), regression coefficients for year 2 IGF-1 and IGFBP-3 were consistent with results seen in the cross-sectional partial correlation analysis. That is, in Black men, year 2 IGFBP-3 was positively significantly associated with total cholesterol, LDL cholesterol, and log(triglycerides) in both the age- and visit-adjusted models and the multivariable-adjusted models. In White men, year 2 IGFBP-3 was significantly associated with diastolic blood pressure and with log(triglycerides) in both age- and visit-adjusted models and in multivariable-adjusted models. For changes in IGF-1 and IGFBP-3, there were no significant associations with either changes in systolic blood pressure or changes in diastolic blood pressure in Black or White men in the multivariable-adjusted model. However, there were significant positive associations between change in IGFBP-3 and changes in total and LDL cholesterol and log(triglycerides) in both Black men and White men. The changes were small; for instance, in Black men and White men, a 1,000-ng/ml increase in IGFBP-3 was associated with 3.7-mg/dl and 2.6-mg/dl higher total cholesterol levels, respectively.
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DISCUSSION |
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Our findings for blood pressure are consistent with those of three other studies that also found no association between blood pressure and IGF-1 (9, 10, 18). Five other studies have found positive associations. In particular, these other studies showed: 1) a higher mean IGF-1 level in hypertensive persons than in normotensive controls (68); 2) a positive association between blood pressure and IGF-1 (19); and 3) no differences between hypertensive persons and controls (20), although the hypertensive patients with left ventricular hypertrophy had higher mean IGF-1 levels than the hypertensive patients without left ventricular hypertrophy and the controls.
It is unlikely that differences across studies could be explained by age. In the present study, mean ages were 28.0 years and 27.0 years among Whites and Blacks, respectively, at the year 2 examination. In five studies, mean ages ranged from 46 years to 50 years (69, 20). In two other studies, participants had a mean age of 66.7 years (18) or had a wide range in age, from 25 years to 64 years (10). One study examined the blood pressureIGF-1 relation in a cohort of adolescents (19). Across all studies, the blood pressureIGF-1 relation does not vary consistently with age. For example, a pattern of inverse associations at younger ages that becomes positive at older ages is not evident. In the study conducted in adolescents (19) and in three of the studies conducted among middle-aged persons (68), there were positive associations, whereas the age-adjusted inverse associations found in the present study occurred in an age group that falls between adolescence and middle age.
Whether there is a true causal relation between blood pressure and IGF is unclear. The authors of at least two studies with middle-aged participants (6, 8) suggested that blood pressure affects IGF-1 levels. Diez and Laviades (8) stated that essential hypertension may alter IGF-1 synthesis. Andronico et al. (6) suggested that, in hypertensive persons, higher circulating levels of IGF-1 could result from the pressure load stress exerted on the vascular system as a whole (6). The authors cited evidence from Wåhlander et al. (21) that pressure load affects IGF-1 production. Wåhlander et al. (21) induced two-kidney, one-clip Goldblatt hypertension in rats, which produced an increase in IGF-1 messenger RNA in the left ventricle. They concluded that wall stress was the most likely source stimulating IGF-1 synthesis, because more IGF-1 protein was found in the endocardial layers than in the epicardial layers of the heart wall. Because the liver is the major source of circulating IGF-1, it is not known how much IGF-1 synthesized in the heart potentially contributes to circulating IGF-1 levels.
In contrast to the findings for blood pressure, some associations of IGF-1 and IGFBP-3 with total cholesterol and LDL cholesterol remained significant in the multivariable analyses. This was the case for a positive correlation for IGFBP-3 among Black men and an inverse association for IGF-1 among White men in cross-sectional analysis. In longitudinal analysis, we found a positive association between change in IGFBP-3 and changes in total cholesterol, LDL cholesterol, and log(triglyceride) levels in both Black men and White men. Whereas the studies examining blood pressure found either no associations or positive associations for IGF-1, the findings of studies examining total, LDL, and HDL cholesterol have varied from positive to null and to inverse associations.
In a dietary intervention study, Prewitt et al. (4) found significant inverse associations between IGF-1 and total and LDL cholesterol in mildly hypercholesterolemic women with a mean age of 32 years (range, 2048 years) both after a 4-week high-fat diet and after a 20-week low-fat diet. They did not find an association between IGF-1 and HDL cholesterol. On the other hand, in a clinical study of 132 healthy elderly men and women, Ceda et al. (22) found a significant positive correlation between IGFBP-3 and HDL cholesterol after adjustment for age and body mass index but no association between either IGF-1 and total or LDL cholesterol or IGFBP-3 and total or LDL cholesterol. More recently, in a large cross-sectional study of men and women in the Singapore Chinese Health Study (5), there were positive correlations of total cholesterol and LDL cholesterol with both IGF-1 and IGFBP-3, but there were no associations for HDL cholesterol. Ceda et al. (22) stated that their finding of a positive correlation between HDL cholesterol and IGFBP-3 suggests that growth hormone, which activates IGF formation and secretion, plays a role in lipid metabolism. In their review of the literature, they reported that growth hormone deficiency has been found to be associated with hypercholesterolemia in adults and that growth hormone treatment has been found to improve lipid levels (i.e., to reduce total and LDL cholesterol levels and in some cases to increase HDL cholesterol levels).
In addition to the conflicting results of various studies regarding cardiovascular disease risk factors and IGF-1, studies examining manifestations of cardiovascular disease and IGF-1 or IGFBP-3 have yielded conflicting findings. For instance, consistent with Ceda et al.s (22) findings, Schuler-Lüttmann et al. (23) found an inverse association between IGFBP-3 and severity of arteriosclerosis in 189 men who underwent coronary angiography or percutaneous transluminal coronary angiography. They examined the associations of IGF-1 and IGFBP-3 with three coronary scoresa vessel score, a stenosis score, and an extent scoreand found IGFBP-3 to be significantly inversely associated with all three scores. They also reported that IGF-1 was inversely associated with arteriosclerosis in unadjusted analysis, but in multivariate analysis the association between vessel score and IGF-1 became positive and marginally significant and the associations for stenosis score and extent score became nonsignificant. Schuler-Lüttmann et al. (23) speculated that IGFBP-3 may be part of the insulin resistance cluster, which would underlie the physiologic basis of the associations between IGFBP-3 and arteriosclerosis.
In contrast to the inverse association found between IGFBP-3 and arteriosclerosis (23), a nested case-control study (24) reported a positive association between IGFBP-3 and ischemic heart disease. In that study, there was also an inverse association between IGF-1 and ischemic heart disease, supporting a cardioprotective effect of IGF-1. In a population-based sample from the Rotterdam Study (25), it was found that genetically determined low levels of IGF-1 are associated with increased risk of type 2 diabetes and myocardial infarction, further supporting a cardioprotective effect of IGF-1. Conversely, at least three other studies do not support a cardioprotective effect of high IGF-1 levels (2628). In a recent review, Janssen and Lamberts (29) suggested that IGF-1 deficiency plays a role in increasing the risk of atherosclerosis and a poor outcome of cardiovascular disease.
This study had some strengths that were found to be lacking in other studies. First, it had a large sample of young Black men and White men, which allowed us to assess associations by race. In addition, the collection of high-quality data on potential confounders allowed us to control for other determinants of IGFs. Finally, the use of multiple measurements over a period of 8 years allowed us to assess longitudinal associations. A limitation of this study is that we were not able to measure other components of the IGF system, such as IGF-2, IGFBP-1, or IGFBP-2, because of the small volume of serum available.
Because IGF-1 and IGFBP-3 are indices of growth hormone secretion, studies showing inverse associations between IGF-1 and LDL or total cholesterol and positive associations between IGF-1 or IGFBP-3 and HDL cholesterol support the view that growth hormone has a protective role in lipid metabolism. In part, the cross-sectional findings for White men in the present study support this hypothesis. However, because we observed positive associations between change in IGFBP-3 and changes in total cholesterol, LDL cholesterol, and log(triglycerides) which would support a proatherogenic effect of IGFBP-3, these results should be viewed as equivocal. Further research examining the causal nature of the relations between the IGF system and cardiovascular disease risk factors is needed.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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