Serum IGF1, metabolic syndrome, and incident cardiovascular disease in older people: a population-based study

in European Journal of Endocrinology
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  • 1 Departments of Internal Medicine, Epidemiology and Biostatistics, section Endocrinology ZH 4A62

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Objective

High as well as low levels of IGF1 have been associated with cardiovascular diseases (CVD). The relationship of IGF1 with (components of) the metabolic syndrome could help to clarify this controversy. The aims of this study were: i) to investigate the association of IGF1 concentration with prevalent (components of) the metabolic syndrome; and ii) to examine the role of (components of) the metabolic syndrome in the relationship between IGF1 and incident CVD during 11 years of follow-up.

Methods

Data were used from the Longitudinal Aging Study Amsterdam, a cohort study in a representative sample of the Dutch older population (≥65 years). Data were available in 1258 subjects. Metabolic syndrome was determined using the definition of the US National Cholesterol Education Program Adult Treatment Panel III. CVD were ascertained by self-reports and mortality data.

Results

Levels of IGF1 in the fourth quintile were associated with prevalent metabolic syndrome compared with the lowest quintile (odds ratio: 1.59, 95% confidence interval (CI) 1.09–2.33). The middle up to the highest quintile of IGF1 was positively associated with high triglycerides in women. Metabolic syndrome was not a mediator in the U-shaped relationship of IGF1 with CVD. Both subjects without the metabolic syndrome and low IGF1 levels (hazard ratio (HR) 1.75, 95% CI 1.12–2.71) and subjects with the metabolic syndrome and high IGF1 levels (HR 2.28, 95% CI 1.21–4.28) demonstrated increased risks of CVD.

Conclusions

In older people, high-normal IGF1 levels are associated with prevalent metabolic syndrome and high triglycerides. Furthermore, this study suggests the presence of different pathomechanisms for both low and high IGF1 levels and incident CVD.

Abstract

Objective

High as well as low levels of IGF1 have been associated with cardiovascular diseases (CVD). The relationship of IGF1 with (components of) the metabolic syndrome could help to clarify this controversy. The aims of this study were: i) to investigate the association of IGF1 concentration with prevalent (components of) the metabolic syndrome; and ii) to examine the role of (components of) the metabolic syndrome in the relationship between IGF1 and incident CVD during 11 years of follow-up.

Methods

Data were used from the Longitudinal Aging Study Amsterdam, a cohort study in a representative sample of the Dutch older population (≥65 years). Data were available in 1258 subjects. Metabolic syndrome was determined using the definition of the US National Cholesterol Education Program Adult Treatment Panel III. CVD were ascertained by self-reports and mortality data.

Results

Levels of IGF1 in the fourth quintile were associated with prevalent metabolic syndrome compared with the lowest quintile (odds ratio: 1.59, 95% confidence interval (CI) 1.09–2.33). The middle up to the highest quintile of IGF1 was positively associated with high triglycerides in women. Metabolic syndrome was not a mediator in the U-shaped relationship of IGF1 with CVD. Both subjects without the metabolic syndrome and low IGF1 levels (hazard ratio (HR) 1.75, 95% CI 1.12–2.71) and subjects with the metabolic syndrome and high IGF1 levels (HR 2.28, 95% CI 1.21–4.28) demonstrated increased risks of CVD.

Conclusions

In older people, high-normal IGF1 levels are associated with prevalent metabolic syndrome and high triglycerides. Furthermore, this study suggests the presence of different pathomechanisms for both low and high IGF1 levels and incident CVD.

Introduction

IGFs and their associated binding proteins play important roles in normal development and growth. IGF1 is a key regulator of cell proliferation and an inhibitor of cell apoptosis and necrosis (1). In the cardiovascular system, IGF1 is postulated to protect against endothelial dysfunction, atherosclerotic plaque development, clinical instability, and ischemic myocardial damage (2). Aging is associated with a gradual decline of GH secretion and serum IGF1 concentration. Various studies have investigated the effect of serum IGF1 concentration on development of cardiovascular diseases (CVD) and mortality. Low-normal IGF1 levels have been associated with development of ischemic heart disease and stroke (3, 4, 5). The relationship of IGF1 with (cardiovascular) mortality is less clear. In a previous study in a Dutch cohort of older people, we have demonstrated a U-shaped relationship with mortality, with fatal CVD being the most critical outcome (6). One possible pathway of the relationship of IGF1 with cardiovascular outcomes is through the relationship of IGF1 with (components of) the metabolic syndrome. The metabolic syndrome is a cluster of cardiovascular risk factors, including hypertension, abdominal obesity, unfavorable lipid profile, and hyperglycemia. Older persons with metabolic syndrome are more likely to experience any CVD event than subjects without metabolic syndrome (7, 8). In two previous studies on the association of IGF1 with the metabolic syndrome in older populations, U-shaped relationships have been reported (9, 10). Others report low IGF1 values to be related to a greater metabolic burden (11, 12, 13, 14). These cohort studies only speculate about the role of this association in the development of CVD over time. Therefore, we investigated the relationship between serum total IGF1 concentration and (components of) metabolic syndrome and hypothesize to encounter a U-shaped relationship. Subsequently, we examine the role of (components of) the metabolic syndrome in the relationship between IGF1 and incident CVD during 11 years of follow-up in a Dutch cohort of older persons.

Materials and methods

Study sample

Data were collected in the context of the Longitudinal Aging Study Amsterdam (LASA). LASA is an ongoing multidisciplinary cohort study on predictors and consequences of changes in physical, cognitive, emotional, and social functioning in older people in The Netherlands. Data collection and sampling have been explained in more detail elsewhere (15). Briefly, a sample of older men and women aged 55–85 years, predominantly Caucasian (>99%), stratified by age, sex, urbanization grade, and expected 5-year mortality, was drawn from the population registers of 11 municipalities in three regions in The Netherlands, being a representative sample of the Dutch population. At baseline (1992/1993) and every 3 years thereafter, subjects participated in an interview performed by trained nurses at the subject's home. Informed consent was obtained from all respondents. The study was approved by the Medical Ethics Committee of the VU University Medical Center in Amsterdam.

This study was performed in a subgroup of the LASA population, selecting participants from the second cycle (1995/1996) aged 65 years or older on January 1st, 1996 (n=1509). Women using estrogens and subjects using recombinant GH were excluded from the analysis (n=14). Subjects with decreased renal function (creatinine >200 μmol/l) and clinical hypothyroidism were also excluded (n=17). Eventually, due to missing values, IGF1 values and metabolic syndrome could be determined in 1258 subjects. Subjects included in this study were significantly younger, smoked less, had higher alcohol use, and reported more physical activity per day (all P<0.05) than the 251 subjects not included in this study.

Measurements

Serum IGF1

Blood samples were drawn in the morning after tea and plain toast, processed, and centrifuged within 60 min. Samples were kept frozen until determination. IGF1 levels were measured using an immunoradiometric assay after extraction (DSL, Webster, TX, USA) with a detection limit of 1 nmol/l. Interassay coefficient of variation (CV) was <14%. The reference range (P5–P95) for IGF1 values for this method is 11–19 nmol/l for both men and women aged 60–70 years. There were some subjects with an IGF1 level below or above the reference range for people aged >60 years. No indications of incorrect sample treatment or storage problems were detected. As the reference range for age above 70 years was not defined, it was assumed that these levels were at the lower or upper part of the reference range. These analyses were carried out at the Endocrine Laboratory of the VU University Medical Center, Amsterdam.

Metabolic syndrome

Metabolic syndrome was defined as the presence of three or more of the following criteria: triglycerides ≥1.70 mmol/l (150 mg/dl), HDL-cholesterol <1.00 mmol/l (40 mg/dl) for men and <1.30 mmol/l (50 mg/dl) for women, blood pressure ≥160/90 mmHg or antihypertensive medication, waist circumference >102 cm for men and >88 cm for women, and fructosamine ≥0.247 mmol/l or antidiabetic medication. This is the definition established by the US National Cholesterol Education Program (NCEP) Adult Treatment Panel III (16), with an increased cutoff for blood pressure, adjusted for an older population (17). The cutoff of 0.247 mmol/l for fructosamine corresponds to 6.1 mmol/l for fasting plasma glucose (18).

Blood pressure was measured in sitting position using a standard mercury sphygmomanometer. Waist circumference was averaged over two readings measured midway between the lower rib margin and the iliac crest. Fructosamine was determined by a colorimetric test, and HDL-cholesterol and triglycerides were determined by an enzymatic colorimetric test (Roche Diagnostics). The interassay CV was <2.8% for fructosamine and triglycerides and <6.4% for HDL-cholesterol. All laboratory analyses were performed in EDTA-plasma samples stored at −80 °C at the Department of Clinical Chemistry of the VU University Medical Center in 2005. Prescription drugs taken in the previous 2 weeks were identified by container inspection.

Cardiovascular disease

CVD include nonfatal and fatal cardiac, vascular, and cerebrovascular events. The development of nonfatal CVD was based on self-reported (symptoms of) CVD every 3 years in the preceding years. Time of event was defined as halfway the interval between the study cycles where CVD was first reported and the previous cycle. For respondents who died, the date of death was traced through death certificates from municipal registers through June 1st 2007. Using death certificates from the Dutch Central Bureau of Statistics (The Netherlands), all cardiovascular deaths during follow-up were identified. Cardiovascular deaths were defined as International Classification of Disease, 10th Revision (ICD-10) codes I20-I79.

Potential confounders

Data on age and sex were derived from the population registries at baseline. Self-reported lifestyle variables included smoking (never, former, and current), alcohol use (Garretsen alcohol index: none, light, moderate, and excessive) (19), and physical activity in the past 2 weeks using the LASA Physical Activity Questionnaire (LAPAQ) and were based on the following activities: walking outdoors, bicycling, light and heavy household activities, and a maximum of two sports activities. Total physical activity score was calculated as time spent on physical activity in minutes per day (min/day). This variable was divided into tertiles for analysis, with the first tertile representing the lowest activity. Health status variables included the presence of diabetes mellitus (based on information obtained from inspection of medicine bottles) and BMI (measured weight (kg)/measured height (m2)). Serum albumin (as indicator for nutritional status) was measured in blood samples in three different laboratories in The Netherlands. The results were converted by use of a validated formula to make the data comparable (20).

Statistical analysis

Categorical data were expressed as number (%); continuous data were expressed as mean (s.d.) for normally distributed variables or as median (interquartile range) for skewed variables. At baseline, continuous variables were compared by ANOVA or by Kruskal–Wallis test, whereas categorical variables were compared by χ2 test. Spearman and Pearson correlation coefficients were calculated to examine multicollinearity. The individual variables were checked for linearity. Because of nonlinearity, IGF1 levels were divided into quintiles (Q), with the first quintile representing the lowest IGF1 levels. Logistic regression analyses were performed to study the association between IGF1 concentrations and the metabolic syndrome and the individual components. Two models were applied to adjust for potential confounders. First, adjustments were made for sex and age. Potential confounders that, after inclusion in the first model, showed an important change (>10%) in the regression coefficient of the association between IGF1 and the outcome variable were included in the fully adjusted model. In addition, all analyses were repeated after exclusion of subjects with diabetes mellitus (n=100) and lipid-lowering medication (n=55). For the association of IGF1 with the components of the metabolic syndrome (except abdominal obesity), BMI was subsequently tested as a relevant confounder. Associations of IGF1 concentrations with components of the metabolic syndrome as continuous variables were analyzed with linear regression, both unadjusted and adjusted for relevant confounders. Subjects using lipid-lowering, antihypertensive, or antidiabetic medication were excluded for corresponding analyses. An interaction term with gender was tested in all models. For the longitudinal analyses, Cox proportional hazard model was used, both unadjusted and adjusted for age, gender, smoking habits, alcohol use, physical activity, and albumin. Subjects with a medical history of CVD or CVD present at baseline were excluded. To determine the role of metabolic syndrome in the association of IGF1 concentration with developing CVD, two options were explored. First, metabolic syndrome was studied as a potential mediator by testing the influence on the regression coefficient of entering the variable metabolic syndrome into the fully adjusted model of IGF1 and CVD. Secondly, an IGF1 concentration by metabolic syndrome interaction was tested in the regression model. Two-sided P values of 0.05 or less were considered significant, except for the interaction terms, for which 0.10 was tolerated (21). The statistical analyses were performed by the statistical software package SPSS version 15.0 (SPSS, Inc.).

Results

Baseline characteristics

Among the 1258 participants, 638 (50.7%) were female. The mean age at baseline was 75.5 (s.d. 6.5) years. The mean IGF1 concentration was 13.8 (s.d. 5.2) nmol/l. Age, sex, smoking habits, albumin and triglyceride concentration, and diastolic blood pressure differed significantly between the quintiles of IGF1 concentration at baseline in the total study population (Table 1).

Table 1

Baseline characteristics of the study population for metabolic syndrome stratified by quintiles (Q) of IGF1 concentration.

Q1Q2Q3Q4Q5P value
No. of subjects251253248257249
IGF1 (nmol/l)7.4 (1.7)11.0 (0.8)13.5 (0.6)16.0 (0.9)21.4 (4.2)
Age (years)77.8 (6.4)76.9 (6.7)75.2 (6.5)74.2 (6.0)73.3 (6.1)<0.001
No. of females149 (59.4)138 (54.5)130 (52.4)115 (44.7)106 (42.6)0.001
BMI (kg/m2)26.5 (4.6)26.8 (4.2)26.7 (4.4)26.9 (3.9)27.0 (3.6)0.74
Smoking0.01
 Never113 (45.0)92 (36.4)91 (36.7)74 (28.8)73 (29.3)
 Former94 (37.5)117 (46.2)112 (45.2)133 (51.8)130 (52.2)
 Current44 (17.5)44 (17.4)45 (18.1)50 (19.5)46 (18.5)
Alcohol use0.05
 None71 (28.3)55 (21.7)62 (25.1)58 (22.6)55 (22.1)
 Light128 (51.0)135 (53.4)120 (48.6)125 (48.6)129 (51.8)
 Moderate42 (16.7)52 (20.6)53 (21.5)45 (17.5)50 (20.1)
 Excessive10 (4.0)11 (4.3)12 (4.9)29 (11.3)15 (6.0)
Physical activity (min/day)0.62
 Tertile 180 (31.9)88 (34.9)91 (36.7)83 (32.3)80 (32.1)
 Tertile 278 (31.1)79 (31.3)84 (33.9)82 (31.9)92 (36.9)
 Tertile 393 (37.1)85 (33.7)73 (29.4)92 (35.8)77 (30.9)
Albumin (g/l)43.9 (2.6)44 (2.9)44.5 (2.8)44.5 (2.5)44.9 (2.5)<0.001
Fructosamine (μmol/l)a227 (37)226 (30)226 (32)230 (34)229 (33)0.35
Triglycerides (mmol/l)a1.20 (0.60)1.30 (0.80)1.30 (0.80)1.40 (1.00)1.50 (0.90)<0.001
HDL-cholesterol (mmol/l)1.36 (0.46)1.35 (0.42)1.36 (0.44)1.31 (0.42)1.31 (0.39)0.41
Waist circumference (cm)94 (12)96 (12)95 (11)96 (10)97 (11)0.07
Systolic blood pressure (mmHg)152 (29)154 (25)154 (25)152 (25)153 (26)0.90
Diastolic blood pressure (mmHg)81 (14)83 (13)83 (12)83 (13)85 (14)0.01
Prevalent CVD102 (40.6)93 (36.8)91 (36.7)106 (41.2)75 (30.1)0.08
 Cardiac disease66 (26.3)67 (26.6)71 (28.6)81 (31.5)55 (22.1)0.19
 Vascular disease44 (17.5)35 (13.9)30 (12.1)33 (12.8)19 (7.6)0.02
 Stroke26 (10.4)19 (7.5)15 (6.0)16 (6.2)20 (8.0)0.37
Diabetes mellitus24 (9.6)19 (7.5)18 (7.3)17 (6.6)22 (8.8)0.74

Continuous variables are expressed as mean (s.d.) for normally distributed variables or median (interquartile range) for skewed variablesa; categorical variables are given as numbers (%). χ2 test is used for categorical variables, one-way ANOVA for continuous variables, or Kruskal–Wallis test for skewed variablesa.

Metabolic syndrome

The metabolic syndrome was present in 37.1% of the subjects. The results of the logistic regression analysis are presented in Table 2. The outcome of the fully adjusted model is visualized in Fig. 1, demonstrating significantly higher odds ratios (ORs) for the presence of the metabolic syndrome for IGF1 values in the fourth quintile compared with the first quintile. No interaction was found with gender. Excluding subjects with prevalent diabetes mellitus (ORs for Q2 1.11, 95% confidence interval (CI) 0.74–1.66; Q3 1.20, 95% CI 0.80–1.81; Q4 1.60, 95% CI 1.07–2.40; Q5 1.32, 95% CI 0.87–2.01) or those using lipid-lowering medication (ORs for Q2 1.06, 95% CI 0.72–1.57; Q3 1.23, 95% CI 0.83–1.82; Q4 1.70, 95% CI 1.15–2.50; Q5 1.34, 95% CI 0.90–1.99) did not substantially influence the outcome in the fully adjusted model.

Table 2

Odds ratios (ORs) for the presence of metabolic syndrome for IGF1 concentration, stratified by quintiles (Q). Data are expressed as ORs (95% CI). Adjusted for sex and age; fully adjusted for sex, age, smoking, alcohol use, physical activity, and albumin.

Q1Q2Q3Q4Q5
No. of subjects251253248257249
No. of events86889210794
Unadjusted modelReference1.02 (0.71–1.48)1.13 (0.78–1.63)1.37 (0.96–1.96)1.16 (0.81–1.68)
Adjusted modelReference1.07 (0.74–1.55)1.24 (0.85–1.80)1.60 (1.10–2.32)*1.39 (0.95–2.04)
Fully adjusted modelReference1.08 (0.74–1.57)1.16 (0.79–1.70)1.59 (1.09–2.33)*1.30 (0.88–1.92)

*P<0.05.

Figure 1
Figure 1

Association of IGF1, divided in to quintiles, with the presence of metabolic syndrome in older people. Results are shown as odds ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CIs.

Citation: European Journal of Endocrinology 168, 3; 10.1530/EJE-12-0784

Components of the metabolic syndrome

Blood pressure (66.1%) and abdominal obesity (51.4%) were the most prevalent components of the metabolic syndrome. The results of the logistic regression analyses are presented in Table 3, demonstrating only significantly increased ORs for the component of high triglycerides. An interaction with gender was demonstrated for the component of high triglycerides and this analysis was repeated stratified for gender (Fig. 2). Excluding subjects with prevalent diabetes mellitus or those using lipid-lowering medication did not substantially influence the outcomes. Subsequently, adding BMI as a confounder to the fully adjusted models did not change the outcomes. The different components of the metabolic syndrome were analyzed as continuous variables in a linear regression analysis. IGF1 was entered in the models as quintiles due to nonlinear associations with most of the components. To normalize distributions of the residues, both lipids and systolic blood pressure variables were log transformed. In the fully adjusted models (age, gender, smoking habits, alcohol use, physical activity, and albumin), an IGF1 concentration in the highest quintile was positively associated with waist circumference and diastolic blood pressure compared with the lowest quintile (β 2.12, s.e.m. 1.02, P=0.04, and β 4.24, s.e.m. 1.58, P=0.01 respectively). An interaction with gender was observed for triglycerides, HDL-cholesterol, and systolic blood pressure. For men, an IGF1 concentration in the middle quintile was negatively associated with triglycerides compared with the lowest quintile (ln β −0.13, s.e.m. 0.06, P=0.04), and for women, the middle up to the highest quintile was positively associated compared with the lowest quintile with a maximum ln β of 0.25 (s.e.m. 0.06, P<0.001). Again, including BMI as a confounder did not substantially alter the outcomes.

Table 3

Odds ratios (ORs) for the presence of components of the metabolic syndrome for IGF1 concentration, stratified by quintiles (Q). Data are expressed as ORs (95% CI). Adjusted for sex and age; fully adjusted for sex, age, smoking, alcohol use, physical activity, and albumin.

Q1Q2Q3Q4Q5
No. of subjects251253248257249
High fructosamine
 No. of events6951626666
 Unadjusted modelReference0.67 (0.44–1.01)0.88 (0.59–1.31)0.91 (0.61–1.35)0.95 (0.64–1.41)
 Adjusted modelReference0.70 (0.46–1.06)1.01 (0.67–1.52)1.12 (0.75–1.69)1.23 (0.81–1.85)
 Fully adjusted modelReference0.69 (0.44–1.06)0.87 (0.57–1.34)1.07 (0.70–1.65)1.05 (0.68–1.62)
High triglycerides
 No. of events5266799598
 Unadjusted modelReference1.35 (0.89–2.05)1.78 (1.19–2.67)*2.23 (1.50–3.32)2.47 (1.66–3.68)
 Adjusted modelReference1.35 (0.89–2.04)1.75 (1.17–2.64)*2.20 (1.47–3.30)2.42 (1.61–3.65)
 Fully adjusted modelReference1.39 (0.91–2.12)1.74 (1.15–2.63)*2.15 (1.43–3.24)2.35 (1.55–3.56)
Low HDL-cholesterol
 No. of events9992919578
 Unadjusted modelReference0.87 (0.61–1.25)0.88 (0.61–1.26)0.89 (0.62–1.27)0.69 (0.48–1.00)*
 Adjusted modelReference0.90 (0.62–1.29)0.92 (0.64–1.33)0.98 (0.67–1.41)0.77 (0.52–1.13)
 Fully adjusted modelReference0.92 (0.64–1.34)0.86 (0.59–1.26)0.99 (0.68–1.45)0.74 (0.50–1.10)
Abdominal obesity
 No. of events135129124128130
 Unadjusted modelReference0.89 (0.62–1.26)0.86 (0.60–1.22)0.84 (0.59–1.20)0.93 (0.65–1.32)
 Adjusted modelReference0.95 (0.66–1.37)0.97 (0.67–1.41)1.06 (0.73–1.53)1.23 (0.85–1.80)
 Fully adjusted modelReference0.94 (0.65–1.36)0.95 (0.66–1.39)1.05 (0.72–1.52)1.23 (0.84–1.80)
Hypertension
 No. of events162165155178167
 Unadjusted modelReference0.98 (0.68–1.42)0.88 (0.61–1.28)1.20 (0.82–1.74)1.08 (0.74–1.57)
 Adjusted modelReference1.04 (0.71–1.50)1.01 (0.69–1.47)1.46 (0.99–2.15)1.38 (0.94–2.04)
 Fully adjusted modelReference1.03 (0.71–1.50)1.02 (0.70–1.49)1.45 (0.98–2.14)1.36 (0.92–2.02)

*P<0.05, P<0.001.

Figure 2
Figure 2

Association of IGF1, divided in to quintiles, with the presence of the component high triglycerides in older people, stratified for gender. Results are shown as odds ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CIs.

Citation: European Journal of Endocrinology 168, 3; 10.1530/EJE-12-0784

Cardiovascular diseases

After exclusion of prevalent CVD, 790 subjects remained (55.6% female, mean age 74.6 (s.d. 6.3) years). Metabolic syndrome was present in 31.6% of this cohort. The maximum follow-up was 11.6 (median 10.8) years. IGF1 demonstrated a U-shaped relationship with CVD (Q1: hazard ratio (HR) 1.51, 95% CI 1.04–2.19, Q3: reference, Q5: HR 1.46, 95% CI 1.02–2.09). Adding metabolic syndrome to the fully adjusted model did not significantly influence the regression coefficients. The interaction term of IGF1 and metabolic syndrome was significant with a P value of 0.07. The analysis of the association of IGF1 with CVD was repeated after stratification for the prevalence of the metabolic syndrome. In subjects without the metabolic syndrome, CVD occurred in 36.9%, and in subjects with metabolic syndrome, this was 41.2% (P=0.24). In subjects without the metabolic syndrome, IGF1 concentrations in the lowest quintile demonstrated a significantly increased risk (HR 1.75, 95% CI 1.12–2.71) of developing CVD compared with the middle quintile (Fig. 3A). Subjects with the metabolic syndrome at baseline demonstrated an increased risk for developing CVD when having an IGF1 concentration in the highest quintile (HR 2.28, 95% CI 1.21–4.28) compared with the middle quintile (Fig. 3B).

Figure 3
Figure 3

Association of IGF1, divided into quintiles, with developing cardiovascular diseases in older people, stratified for the presence of metabolic syndrome. A) Subjects without metabolic syndrome; B) subjects with metabolic syndrome. Results are shown as hazard ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CI.

Citation: European Journal of Endocrinology 168, 3; 10.1530/EJE-12-0784

Discussion

This population-based study among older persons demonstrated that subjects with higher IGF1 concentrations have an increased probability of prevalent metabolic syndrome, especially concentrations within the fourth quintile. IGF1 levels showed the same positive association with high triglycerides in women but not in men. The relationship of IGF1 and the metabolic syndrome did not directly reflect into the U-shaped relationship of IGF1 with incident CVD. But the relationship of IGF1 with incident CVD did depend on the presence of the metabolic syndrome.

In cohorts with younger subjects investigating the relationship of IGF1 with the metabolic syndrome, the common finding is an association of low IGF1 concentrations and the metabolic syndrome (12, 22, 23). In cohorts with older subjects, the findings are more heterogeneous. A small study in older people showed an association of low IGF1 concentrations with the metabolic syndrome (24), although an Italian cohort demonstrated no significant associations in older women, and a trend toward higher IGF1 values in men with the metabolic syndrome (25, 26). Most recently, Yeap et al. (10) studied a cohort of men of 70 years and older and found a U-shaped relationship, with increased risks for metabolic syndrome at the lower and upper end of IGF1 concentrations. However, there appears to be a difference in the association of IGF1 and metabolic syndrome in young and older persons. Chosen cutoff values for components of the metabolic syndrome have been established in predominantly middle-aged populations. This limitation is notable when the different cardiovascular risk factors are subsequently analyzed as continuous variables. In addition to triglycerides, waist circumference and (diastolic) blood pressure showed associations with IGF1, which was not the case for the dichotomous variables based on the general applied cutoff values.

Individually, the different components of the metabolic syndrome have most often been shown to be associated with low IGF1 values (27, 28, 29, 30, 31, 32, 33). In this study, when investigated as dichotomous variables, only the component of high triglycerides was associated with IGF1 concentrations. Subjects with the highest IGF1 values had an increased risk of high triglycerides compared with the lowest IGF1 values. Yeap et al. (10) describe this same phenomenon in an Australian cohort, with older men who have high triglycerides demonstrating higher IGF1 values than men without high triglycerides. Maison et al. (34) also conclude that IGF1 has a strong positive correlation with the lipid factor of metabolic syndrome. In adults with GH deficiency (GHD), alterations in the lipoprotein metabolism are often demonstrated. High levels of triglycerides are measured compared with healthy controls (35), but in other studies, only total cholesterol or HDL-cholesterol seems to be abnormal (36). On the other hand, in acromegaly, the same metabolic state is described. Here, triglyceride levels are elevated due to increased levels of plasma free fatty acids (FFA) attributed to lipolytic activity of GH (37).

Gender was demonstrated to be an effect modifier in the association of IGF1 with triglycerides. In women, the association was demonstrated to be positive. In men, only the linear regression analysis showed a significant association, which was negative. A gender difference is often described in studies on GHD (38), effects of GH replacement therapy (39), or GH–IGF1 axis in healthy older people (40). Mostly, sex hormone levels or substitution are considered accountable for the gender differences (22). Nevertheless, when comparing men and postmenopausal women in, for example, responsiveness to GH replacement therapy in GHD, differences are also demonstrated (41). Munzer et al. (40) speculate about a sexually dimorphic response of triglycerides to GH in healthy older persons. Increased GH-binding proteins in older women may be a contributing factor (42).

The relationship of IGF1 concentration with CVD is controversial. Low levels of IGF1 are demonstrated to be associated with an increased risk for CVD (3, 5). High levels of IGF1 have demonstrated similar associations (43, 44). Subsequently, in a previous study within the LASA cohort, a U-shaped relationship of IGF1 with CVD mortality was demonstrated (6). As Yeap et al. (10) already suggested, the heterogeneous outcomes in the association of IGF1 levels and the metabolic syndrome could explain the variance demonstrated in the associations of IGF1 levels with the development of CVD. This study is the first to include the role of the metabolic syndrome in the relationship of IGF1 with CVD. A U-shaped relationship was demonstrated with all cardiovascular events. Metabolic syndrome was not found to be a mediator in this relationship. It is suggested that older persons with metabolic syndrome are more likely to experience any CVD event than subjects without metabolic syndrome (7, 8). Nevertheless, the Prospective Study of Pravastatin in the Elderly at Risk (PROSPER) and the British Regional Heart Study (BRHS) showed negligible clinical association with incident vascular events in older people (45). In this study, a profound association of neither the metabolic syndrome nor the individual components, with incident CVD could be demonstrated. Excluding subjects with prevalent CVD at baseline in an elderly cohort could have led to an underestimation of the association due to a ‘healthy survivor’ bias. Metabolic syndrome was demonstrated to be an effect modifier in the relationship of IGF1 with CVD in this study. Subjects without the metabolic syndrome at baseline have an increased risk of developing CVD when they have an IGF1 concentration within the lowest quintile. Subjects with the metabolic syndrome present at baseline have increased risk of developing CVD when they have an IGF1 concentration in the highest quintile. This underlines the hypothesis that the increased risks for CVD for both low and high IGF1 levels, even within the normal range, should be explained by different mechanisms. In GHD or low-normal IGF1 levels, this mechanism of developing CVD remains unclear. In high-normal IGF1 levels, an important interaction with insulin resistance could contribute to the mechanism. The cross-sectional aspect of the study on IGF1 and metabolic syndrome might be a limitation. Repeated measurements of (components of) the metabolic syndrome could help to better understand these relationships.

Strengths of this study include the nationally representative and large sample, the accurate assessment of cardiovascular events (46), over a maximum follow-up of almost 12 years, and the possibility to adjust for a range of potential confounders. Nonetheless, there are limitations that we have to address. We measured total serum IGF1 as an accepted measure of IGF1 status. A recently reported bioassay based on activation of the IGF1-specific kinase receptor may provide a means of assessing circulating bioactive IGF1 (47), but this method is not in general use at this moment. Furthermore, biological effects and bioavailability of IGF1 are modulated through IGFBPs, which control IGF1 access to cell surface receptors (1). Unfortunately, we did not have IGFBPs available and therefore our results do not fully represent biologically active IGF1. Because the instructions before blood sampling allowed subjects to take tea and plain toast, but no dairy products, we could not guarantee fasting blood samples. Fructosamine is little affected by eating, unlike plasma glucose level. The cutoff used for fructosamine was shown to have maximal effectiveness in discriminating subjects with impaired glucose tolerance from subjects with normal glucose tolerance (18). We used fructosamine as a proxy for plasma glucose as plasma glucose levels were not available. The possibility of a nonfasting state might also have affected our findings for triglycerides and to a lesser extent HDL-cholesterol. However, it has been demonstrated that lipid profiles change minimally in response to normal food intake (HDL-cholesterol −0.1 mmol/l; triglycerides 0.2 mmol/l), and nonfasting levels still predict cardiovascular events (48). For the longitudinal analysis, assigning halfway of the interval between follow-up for timing might reduce the accuracy for time-to-event in the Cox proportional hazard model.

In conclusion, in this sample of older people, high-normal levels of IGF1 are associated with a higher probability of prevalent metabolic syndrome and high triglycerides, the latter especially in women. Metabolic syndrome is not a mediator in the U-shaped relationship of IGF1 concentration with incident CVD. Subjects without the metabolic syndrome and low IGF1 levels and subjects with the metabolic syndrome and high IGF1 levels have an increased risk of developing CVD. This study suggests the presence of different pathomechanisms for both low and high IGF1 levels and developing CVD. Clarifications of these underlying mechanisms are essential for, in the end, optimal cardiovascular risk assessment for the older patients in clinical practice.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

The Longitudinal Aging Study Amsterdam is largely supported by a grant from The Netherlands Ministry of Health Welfare and Sports, Directorate of Long-term Care.

Acknowledgements

The authors would like to thank Jan Poppelaars for his assistance in providing the data.

References

  • 1

    Juul A. Serum levels of insulin-like growth factor I and its binding proteins in health and disease. Growth Hormone & IGF Research 2003 13 113170. (doi:10.1016/S1096-6374(03)00038-8).

    • Search Google Scholar
    • Export Citation
  • 2

    Conti E, Carrozza C, Capoluongo E, Volpe M, Crea F, Zuppi C, Andreotti F. Insulin-like growth factor-1 as a vascular protective factor. Circulation 2004 110 22602265. (doi:10.1161/01.CIR.0000144309.87183.FB).

    • Search Google Scholar
    • Export Citation
  • 3

    Janssen JA, Stolk RP, Pols HA, Grobbee DE, Lamberts SW. Serum total IGF-I, free IGF-I, and IGFB-1 levels in an elderly population: relation to cardiovascular risk factors and disease. Arteriosclerosis, Thrombosis, and Vascular Biology 1998 18 277282. (doi:10.1161/01.ATV.18.2.277).

    • Search Google Scholar
    • Export Citation
  • 4

    Johnsen SP, Hundborg HH, Sorensen HT, Orskov H, Tjonneland A, Overvad K, Jorgensen JOL. Insulin-like growth factor (IGF) I, -II, and IGF binding protein-3 and risk of ischemic stroke. Journal of Clinical Endocrinology and Metabolism 2005 90 59375941. (doi:10.1210/jc.2004-2088).

    • Search Google Scholar
    • Export Citation
  • 5

    Juul A, Scheike T, Davidsen M, Gyllenborg J, Jorgensen T. Low serum insulin-like growth factor I is associated with increased risk of ischemic heart disease: a population-based case–control study. Circulation 2002 106 939944. (doi:10.1161/01.CIR.0000027563.44593.CC).

    • Search Google Scholar
    • Export Citation
  • 6

    van Bunderen CC, van Nieuwpoort IC, van Schoor NM, Deeg DJH, Lips P, Drent ML. The association of serum insulin-like growth factor-I with mortality, cardiovascular disease, and cancer in the elderly: a population-based study. Journal of Clinical Endocrinology and Metabolism 2010 95 46164624. (doi:10.1210/jc.2010-0940).

    • Search Google Scholar
    • Export Citation
  • 7

    McNeill AM, Katz R, Girman CJ, Rosamond WD, Wagenknecht LE, Barzilay JI, Tracy RP, Savage PJ, Jackson SA. Metabolic syndrome and cardiovascular disease in older people: the cardiovascular health study. Journal of the American Geriatrics Society 2006 54 13171324. (doi:10.1111/j.1532-5415.2006.00862.x).

    • Search Google Scholar
    • Export Citation
  • 8

    Butler J, Rodondi N, Zhu Y, Figaro K, Fazio S, Vaughan DE, Satterfield S, Newman AB, Goodpaster B, Bauer DC et al.. Metabolic syndrome and the risk of cardiovascular disease in older adults. Journal of the American College of Cardiology 2006 47 15951602. (doi:10.1016/j.jacc.2005.12.046).

    • Search Google Scholar
    • Export Citation
  • 9

    Brugts MP, van Duijn CM, Hofland LJ, Witteman JC, Lamberts SWJ, Janssen JAMJ. Igf-I bioactivity in an elderly population: relation to insulin sensitivity, insulin levels, and the metabolic syndrome. Diabetes 2010 59 505508. (doi:10.2337/db09-0583).

    • Search Google Scholar
    • Export Citation
  • 10

    Yeap BB, Chubb SAP, Ho KKY, Setoh JWS, McCaul KA, Norman PE, Jamrozik K, Flicker L. IGF1 and its binding proteins 3 and 1 are differentially associated with metabolic syndrome in older men. European Journal of Endocrinology 2010 162 249257. (doi:10.1530/EJE-09-0852).

    • Search Google Scholar
    • Export Citation
  • 11

    Lam CSP, Chen MH, Lacey SM, Yang Q, Sullivan LM, Xanthakis V, Safa R, Smith HM, Peng X, Sawyer DB et al.. Circulating insulin-like growth factor-1 and its binding protein-3: metabolic and genetic correlates in the community. Arteriosclerosis, Thrombosis, and Vascular Biology 2010 30 14791484. (doi:10.1161/ATVBAHA.110.203943).

    • Search Google Scholar
    • Export Citation
  • 12

    Saydah S, Ballard-Barbash R, Potischman N. Association of metabolic syndrome with insulin-like growth factors among adults in the US. Cancer Causes & Control 2009 20 13091316. (doi:10.1007/s10552-009-9351-x).

    • Search Google Scholar
    • Export Citation
  • 13

    Sierra-Johnson J, Romero-Corral A, Somers VK, Lopez-Jimenez F, Malarstig A, Brismar K, Hamsten A, Fisher RM, Hellenius ML. IGF-I/IGFBP-3 ratio: a mechanistic insight into the metabolic syndrome. Clinical Science 2009 116 507512. (doi:10.1042/CS20080382).

    • Search Google Scholar
    • Export Citation
  • 14

    Parekh N, Roberts CB, Vadiveloo M, Puvananayagam T, Albu JB, Lu-Yao GL. Lifestyle, anthropometric, and obesity-related physiologic determinants of insulin-like growth factor-1 in the Third National Health and Nutrition Examination Survey (1988–1994). Annals of Epidemiology 2010 20 182193. (doi:10.1016/j.annepidem.2009.11.008).

    • Search Google Scholar
    • Export Citation
  • 15

    Deeg DJH, van Tilburg T, Smit JH, de Leeuw ED. Attrition in the Longitudinal Aging Study Amsterdam. The effect of differential inclusion in side studies. Journal of Clinical Epidemiology 2002 55 319328. (doi:10.1016/S0895-4356(01)00475-9).

    • Search Google Scholar
    • Export Citation
  • 16

    Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) (Adult Treatment Panel III). Journal of the American Medical Association 2001 285 24862497. (doi:10.1001/jama.285.19.2486).

    • Search Google Scholar
    • Export Citation
  • 17

    Dahlof B, Lindholm LH, Hansson L, Schersten B, Ekbom T, Wester PO. Morbidity and mortality in the Swedish Trial in Old Patients with Hypertension (STOP-Hypertension). Lancet 1991 338 12811285. (doi:10.1016/0140-6736(91)92589-T).

    • Search Google Scholar
    • Export Citation
  • 18

    Herdzik E, Safranow K, Ciechanowski K. Diagnostic value of fasting capillary glucose, fructosamine and glycosylated haemoglobin in detecting diabetes and other glucose tolerance abnormalities compared to oral glucose tolerance test. Acta Diabetologia 2002 39 1522. (doi:10.1007/s005920200007).

    • Search Google Scholar
    • Export Citation
  • 19

    Garretsen HFL. Probleemdrinken: prevalentiebepaling, beinvloedende factoren en preventiemogelijkheden. Theoretische overwegingen en onderzoek in Rotterdam. Lisse, 1983. PhD thesis submitted to Radboud University Nijmegen

  • 20

    Clase CM, St Pierre MW, Churchill DN. Conversion between bromcresol green- and bromcresol purple-measured albumin in renal disease. Nephrology, Dialysis, Transplantation 2001 16 19251929. (doi:10.1093/ndt/16.9.1925).

    • Search Google Scholar
    • Export Citation
  • 21

    Aiken LS, West SG & Reno RR. Multiple Regression: Testing and Interpreting Interactions. Ed McElroy S. California: Sage Publications, 1991

  • 22

    Hypponen E, Boucher BJ, Berry DJ, Power C. 25-Hydroxyvitamin D, IGF-1, and metabolic syndrome at 45 years of age: a cross-sectional study in the 1958 British Birth Cohort. Diabetes 2008 57 298305. (doi:10.2337/db07-1122).

    • Search Google Scholar
    • Export Citation
  • 23

    Tong PCY, Ho CS, Yeung VTF, Ng MCY, So WY, Ozaki R, Ko GTC, Ma RCW, Poon E, Chan NN et al.. Association of testosterone, insulin-like growth factor-I, and C-reactive protein with metabolic syndrome in Chinese middle-aged men with a family history of type 2 diabetes. Journal of Clinical Endocrinology and Metabolism 2005 90 64186423. (doi:10.1210/jc.2005-0228).

    • Search Google Scholar
    • Export Citation
  • 24

    Efstratiadis G, Tsiaousis G, Athyros VG, Karagianni D, Pavlitou-Tsiontsi A, Giannakou-Darda A, Manes C. Total serum insulin-like growth factor-1 and C-reactive protein in metabolic syndrome with or without diabetes. Angiology 2006 57 303311. (doi:10.1177/000331970605700306).

    • Search Google Scholar
    • Export Citation
  • 25

    Maggio M, Lauretani F, Ceda GP, Bandinelli S, Basaria S, Ble A, Egan J, Paolisso G, Najjar S, Jeffrey Metter E et al.. Association between hormones and metabolic syndrome in older Italian men. Journal of the American Geriatrics Society 2006 54 18321838. (doi:10.1111/j.1532-5415.2006.00963.x).

    • Search Google Scholar
    • Export Citation
  • 26

    Maggio M, Lauretani F, Ceda GP, Bandinelli S, Basaria S, Paolisso G, Ble A, Egan JM, Metter EJ, Abbatecola AM et al.. Association of hormonal dysregulation with metabolic syndrome in older women: data from the InCHIANTI study. American Journal of Physiology. Endocrinology and Metabolism 2007 292 E353E358. (doi:10.1152/ajpendo.00339.2006).

    • Search Google Scholar
    • Export Citation
  • 27

    Ceda GP, Dall'Aglio E, Magnacavallo A, Vargas N, Fontana V, Maggio M, Valenti G, Lee PD, Hintz RL, Hoffman AR. The insulin-like growth factor axis and plasma lipid levels in the elderly. Journal of Clinical Endocrinology and Metabolism 1998 83 499502. (doi:10.1210/jc.83.2.499).

    • Search Google Scholar
    • Export Citation
  • 28

    Colao A, Di Somma C, Cascella T, Pivonello R, Vitale G, Grasso LFS, Lombardi G, Savastano S. Relationships between serum IGF1 levels, blood pressure, and glucose tolerance: an observational, exploratory study in 404 subjects. European Journal of Endocrinology 2008 159 389397. (doi:10.1530/EJE-08-0201).

    • Search Google Scholar
    • Export Citation
  • 29

    Gram IT, Norat T, Rinaldi S, Dossus L, Lukanova A, Tehard B, Clavel-Chapelon F, van Gils CH, van Noord PAH, Peeters PHM et al.. Body mass index, waist circumference and waist–hip ratio and serum levels of IGF-I and IGFBP-3 in European women. International Journal of Obesity 2006 30 16231631. (doi:10.1038/sj.ijo.0803324).

    • Search Google Scholar
    • Export Citation
  • 30

    Rajpathak SN, Gunter MJ, Wylie-Rosett J, Ho GYF, Kaplan RC, Muzumdar R, Rohan TE, Strickler HD. The role of insulin-like growth factor-I and its binding proteins in glucose homeostasis and type 2 diabetes. Diabetes/Metabolism Research and Reviews 2009 25 312. (doi:10.1002/dmrr.919).

    • Search Google Scholar
    • Export Citation
  • 31

    Sesti G, Sciacqua A, Cardellini M, Marini MA, Maio R, Vatrano M, Succurro E, Lauro R, Federici M, Perticone F. Plasma concentration of IGF-I is independently associated with insulin sensitivity in subjects with different degrees of glucose tolerance. Diabetes Care 2005 28 120125. (doi:10.2337/diacare.28.1.120).

    • Search Google Scholar
    • Export Citation
  • 32

    Sandhu MS, Heald AH, Gibson JM, Cruickshank JK, Dunger DB, Wareham NJ. Circulating concentrations of insulin-like growth factor-I and development of glucose intolerance: a prospective observational study. Lancet 2002 359 17401745. (doi:10.1016/S0140-6736(02)08655-5).

    • Search Google Scholar
    • Export Citation
  • 33

    Succurro E, Arturi F, Grembiale A, Iorio F, Laino I, Andreozzi F, Sciacqua A, Hribal ML, Perticone F, Sesti G. Positive association between plasma IGF1 and high-density lipoprotein cholesterol levels in adult nondiabetic subjects. European Journal of Endocrinology 2010 163 7580. (doi:10.1530/EJE-10-0113).

    • Search Google Scholar
    • Export Citation
  • 34

    Maison P, Balkau B, Souberbielle JC, Cunin P, Vol S, Macquin-Mavier I, Eschwege E. Evidence for distinct effects of GH and IGF-I in the metabolic syndrome. Diabetic Medicine 2007 24 10121018. (doi:10.1111/j.1464-5491.2007.02195.x).

    • Search Google Scholar
    • Export Citation
  • 35

    Rosen T, Eden S, Larson G, Wilhelmsen L, Bengtsson BA. Cardiovascular risk factors in adult patients with growth hormone deficiency. Acta Endocrinologica 1993 129 195200. (doi:10.1530/acta.0.1290195).

    • Search Google Scholar
    • Export Citation
  • 36

    de Boer H, Blok GJ, Voerman HJ, Phillips M, Schouten JA. Serum lipid levels in growth hormone-deficient men. Metabolism 1994 43 199203. (doi:10.1016/0026-0495(94)90245-3).

    • Search Google Scholar
    • Export Citation
  • 37

    Nikkila EA, Pelkonen R. Serum lipids in acromegaly. Metabolism 1975 24 829838. (doi:10.1016/0026-0495(75)90129-8).

  • 38

    Bates AS, Van't Hoff W, Jones PJ, Clayton RN. The effect of hypopituitarism on life expectancy. Journal of Clinical Endocrinology and Metabolism 1996 81 11691172. (doi:10.1210/jc.81.3.1169).

    • Search Google Scholar
    • Export Citation
  • 39

    van Bunderen CC, van Nieuwpoort IC, Arwert LI, Heymans MW, Franken AAM, Koppeschaar HPF, van der Lely AJ, Drent ML. Does growth hormone replacement therapy reduce mortality in adults with growth hormone deficiency? Data from the dutch national registry of growth hormone treatment in adults.. Journal of Clinical Endocrinology and Metabolism 2011 96 31513159. (doi:10.1210/jc.2011-1215).

    • Search Google Scholar
    • Export Citation
  • 40

    Munzer T, Harman SM, Sorkin JD, Blackman MR. Growth hormone and sex steroid effects on serum glucose, insulin, and lipid concentrations in healthy older women and men. Journal of Clinical Endocrinology and Metabolism 2009 94 38333841. (doi:10.1210/jc.2009-1275).

    • Search Google Scholar
    • Export Citation
  • 41

    Franco C, Koranyi J, Brandberg J, Lonn L, Bengtsson BK, Svensson J, Johannsson G. The reduction in visceral fat mass in response to growth hormone is more marked in men than in oestrogen-deficient women. Growth Hormone & IGF Research 2009 19 112120. (doi:10.1016/j.ghir.2008.07.001).

    • Search Google Scholar
    • Export Citation
  • 42

    Bulow B, Ahren B, Fisker S, Dehlin O, Hagberg B, Jensen E, Svensson T, Samuelsson G, Erfurth EM. The gender differences in growth hormone-binding protein and leptin persist in 80-year-old men and women and is not caused by sex hormones. Clinical Endocrinology 2003 59 482486. (doi:10.1046/j.1365-2265.2003.01872.x).

    • Search Google Scholar
    • Export Citation
  • 43

    Andreassen M, Raymond I, Kistorp C, Hildebrandt P, Faber J, Kristensen LO. IGF1 as predictor of all cause mortality and cardiovascular disease in an elderly population. European Journal of Endocrinology 2009 160 2531. (doi:10.1530/EJE-08-0452).

    • Search Google Scholar
    • Export Citation
  • 44

    Schneider HJ, Klotsche J, Saller B, Bohler S, Sievers C, Pittrow D, Ruf G, Marz W, Erwa W, Zeiher AM et al.. Associations of age-dependent IGF-I SDS with cardiovascular diseases and risk conditions: cross-sectional study in 6773 primary care patients. European Journal of Endocrinology 2008 158 153161. (doi:10.1530/EJE-07-0600).

    • Search Google Scholar
    • Export Citation
  • 45

    Sattar N, McConnachie A, Shaper AG, Blauw GJ, Buckley BM, de Craen AJ, Ford I, Forouhi NG, Freeman DJ, Jukema JW et al.. Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies.. Lancet 2008 371 19271935. (doi:10.1016/S0140-6736(08)60602-9).

    • Search Google Scholar
    • Export Citation
  • 46

    Kriegsman DM, Penninx BW, van Eijk JT, Boeke AJ, Deeg DJ. Self-reports and general practitioner information on the presence of chronic diseases in community dwelling elderly. A study on the accuracy of patients' self-reports and on determinants of inaccuracy. Journal of Clinical Epidemiology 1996 49 14071417. (doi:10.1016/S0895-4356(96)00274-0).

    • Search Google Scholar
    • Export Citation
  • 47

    Brugts MP, van den Beld AW, Hofland LJ, van der Wansem K, van Koetsveld PM, Frystyk J, Lamberts SWJ, Janssen JAMJ. Low circulating insulin-like growth factor I bioactivity in elderly men is associated with increased mortality. Journal of Clinical Endocrinology and Metabolism 2008 93 25152522. (doi:10.1210/jc.2007-1633).

    • Search Google Scholar
    • Export Citation
  • 48

    Langsted A, Freiberg JJ, Nordestgaard BG. Fasting and nonfasting lipid levels: influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation 2008 118 20472056. (doi:10.1161/CIRCULATIONAHA.108.804146).

    • Search Google Scholar
    • Export Citation

 

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  • View in gallery

    Association of IGF1, divided in to quintiles, with the presence of metabolic syndrome in older people. Results are shown as odds ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CIs.

  • View in gallery

    Association of IGF1, divided in to quintiles, with the presence of the component high triglycerides in older people, stratified for gender. Results are shown as odds ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CIs.

  • View in gallery

    Association of IGF1, divided into quintiles, with developing cardiovascular diseases in older people, stratified for the presence of metabolic syndrome. A) Subjects without metabolic syndrome; B) subjects with metabolic syndrome. Results are shown as hazard ratio after adjustment for age, gender, smoking habits, alcohol use, physical activity, and albumin. Bars refer to 95% CI.

  • 1

    Juul A. Serum levels of insulin-like growth factor I and its binding proteins in health and disease. Growth Hormone & IGF Research 2003 13 113170. (doi:10.1016/S1096-6374(03)00038-8).

    • Search Google Scholar
    • Export Citation
  • 2

    Conti E, Carrozza C, Capoluongo E, Volpe M, Crea F, Zuppi C, Andreotti F. Insulin-like growth factor-1 as a vascular protective factor. Circulation 2004 110 22602265. (doi:10.1161/01.CIR.0000144309.87183.FB).

    • Search Google Scholar
    • Export Citation
  • 3

    Janssen JA, Stolk RP, Pols HA, Grobbee DE, Lamberts SW. Serum total IGF-I, free IGF-I, and IGFB-1 levels in an elderly population: relation to cardiovascular risk factors and disease. Arteriosclerosis, Thrombosis, and Vascular Biology 1998 18 277282. (doi:10.1161/01.ATV.18.2.277).

    • Search Google Scholar
    • Export Citation
  • 4

    Johnsen SP, Hundborg HH, Sorensen HT, Orskov H, Tjonneland A, Overvad K, Jorgensen JOL. Insulin-like growth factor (IGF) I, -II, and IGF binding protein-3 and risk of ischemic stroke. Journal of Clinical Endocrinology and Metabolism 2005 90 59375941. (doi:10.1210/jc.2004-2088).

    • Search Google Scholar
    • Export Citation
  • 5

    Juul A, Scheike T, Davidsen M, Gyllenborg J, Jorgensen T. Low serum insulin-like growth factor I is associated with increased risk of ischemic heart disease: a population-based case–control study. Circulation 2002 106 939944. (doi:10.1161/01.CIR.0000027563.44593.CC).

    • Search Google Scholar
    • Export Citation
  • 6

    van Bunderen CC, van Nieuwpoort IC, van Schoor NM, Deeg DJH, Lips P, Drent ML. The association of serum insulin-like growth factor-I with mortality, cardiovascular disease, and cancer in the elderly: a population-based study. Journal of Clinical Endocrinology and Metabolism 2010 95 46164624. (doi:10.1210/jc.2010-0940).

    • Search Google Scholar
    • Export Citation
  • 7

    McNeill AM, Katz R, Girman CJ, Rosamond WD, Wagenknecht LE, Barzilay JI, Tracy RP, Savage PJ, Jackson SA. Metabolic syndrome and cardiovascular disease in older people: the cardiovascular health study. Journal of the American Geriatrics Society 2006 54 13171324. (doi:10.1111/j.1532-5415.2006.00862.x).

    • Search Google Scholar
    • Export Citation
  • 8

    Butler J, Rodondi N, Zhu Y, Figaro K, Fazio S, Vaughan DE, Satterfield S, Newman AB, Goodpaster B, Bauer DC et al.. Metabolic syndrome and the risk of cardiovascular disease in older adults. Journal of the American College of Cardiology 2006 47 15951602. (doi:10.1016/j.jacc.2005.12.046).

    • Search Google Scholar
    • Export Citation
  • 9

    Brugts MP, van Duijn CM, Hofland LJ, Witteman JC, Lamberts SWJ, Janssen JAMJ. Igf-I bioactivity in an elderly population: relation to insulin sensitivity, insulin levels, and the metabolic syndrome. Diabetes 2010 59 505508. (doi:10.2337/db09-0583).

    • Search Google Scholar
    • Export Citation
  • 10

    Yeap BB, Chubb SAP, Ho KKY, Setoh JWS, McCaul KA, Norman PE, Jamrozik K, Flicker L. IGF1 and its binding proteins 3 and 1 are differentially associated with metabolic syndrome in older men. European Journal of Endocrinology 2010 162 249257. (doi:10.1530/EJE-09-0852).

    • Search Google Scholar
    • Export Citation
  • 11

    Lam CSP, Chen MH, Lacey SM, Yang Q, Sullivan LM, Xanthakis V, Safa R, Smith HM, Peng X, Sawyer DB et al.. Circulating insulin-like growth factor-1 and its binding protein-3: metabolic and genetic correlates in the community. Arteriosclerosis, Thrombosis, and Vascular Biology 2010 30 14791484. (doi:10.1161/ATVBAHA.110.203943).

    • Search Google Scholar
    • Export Citation
  • 12

    Saydah S, Ballard-Barbash R, Potischman N. Association of metabolic syndrome with insulin-like growth factors among adults in the US. Cancer Causes & Control 2009 20 13091316. (doi:10.1007/s10552-009-9351-x).

    • Search Google Scholar
    • Export Citation
  • 13

    Sierra-Johnson J, Romero-Corral A, Somers VK, Lopez-Jimenez F, Malarstig A, Brismar K, Hamsten A, Fisher RM, Hellenius ML. IGF-I/IGFBP-3 ratio: a mechanistic insight into the metabolic syndrome. Clinical Science 2009 116 507512. (doi:10.1042/CS20080382).

    • Search Google Scholar
    • Export Citation
  • 14

    Parekh N, Roberts CB, Vadiveloo M, Puvananayagam T, Albu JB, Lu-Yao GL. Lifestyle, anthropometric, and obesity-related physiologic determinants of insulin-like growth factor-1 in the Third National Health and Nutrition Examination Survey (1988–1994). Annals of Epidemiology 2010 20 182193. (doi:10.1016/j.annepidem.2009.11.008).

    • Search Google Scholar
    • Export Citation
  • 15

    Deeg DJH, van Tilburg T, Smit JH, de Leeuw ED. Attrition in the Longitudinal Aging Study Amsterdam. The effect of differential inclusion in side studies. Journal of Clinical Epidemiology 2002 55 319328. (doi:10.1016/S0895-4356(01)00475-9).

    • Search Google Scholar
    • Export Citation
  • 16

    Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) (Adult Treatment Panel III). Journal of the American Medical Association 2001 285 24862497. (doi:10.1001/jama.285.19.2486).

    • Search Google Scholar
    • Export Citation
  • 17

    Dahlof B, Lindholm LH, Hansson L, Schersten B, Ekbom T, Wester PO. Morbidity and mortality in the Swedish Trial in Old Patients with Hypertension (STOP-Hypertension). Lancet 1991 338 12811285. (doi:10.1016/0140-6736(91)92589-T).

    • Search Google Scholar
    • Export Citation
  • 18

    Herdzik E, Safranow K, Ciechanowski K. Diagnostic value of fasting capillary glucose, fructosamine and glycosylated haemoglobin in detecting diabetes and other glucose tolerance abnormalities compared to oral glucose tolerance test. Acta Diabetologia 2002 39 1522. (doi:10.1007/s005920200007).

    • Search Google Scholar
    • Export Citation
  • 19

    Garretsen HFL. Probleemdrinken: prevalentiebepaling, beinvloedende factoren en preventiemogelijkheden. Theoretische overwegingen en onderzoek in Rotterdam. Lisse, 1983. PhD thesis submitted to Radboud University Nijmegen

  • 20

    Clase CM, St Pierre MW, Churchill DN. Conversion between bromcresol green- and bromcresol purple-measured albumin in renal disease. Nephrology, Dialysis, Transplantation 2001 16 19251929. (doi:10.1093/ndt/16.9.1925).

    • Search Google Scholar
    • Export Citation
  • 21

    Aiken LS, West SG & Reno RR. Multiple Regression: Testing and Interpreting Interactions. Ed McElroy S. California: Sage Publications, 1991

  • 22

    Hypponen E, Boucher BJ, Berry DJ, Power C. 25-Hydroxyvitamin D, IGF-1, and metabolic syndrome at 45 years of age: a cross-sectional study in the 1958 British Birth Cohort. Diabetes 2008 57 298305. (doi:10.2337/db07-1122).

    • Search Google Scholar
    • Export Citation
  • 23

    Tong PCY, Ho CS, Yeung VTF, Ng MCY, So WY, Ozaki R, Ko GTC, Ma RCW, Poon E, Chan NN et al.. Association of testosterone, insulin-like growth factor-I, and C-reactive protein with metabolic syndrome in Chinese middle-aged men with a family history of type 2 diabetes. Journal of Clinical Endocrinology and Metabolism 2005 90 64186423. (doi:10.1210/jc.2005-0228).

    • Search Google Scholar
    • Export Citation
  • 24

    Efstratiadis G, Tsiaousis G, Athyros VG, Karagianni D, Pavlitou-Tsiontsi A, Giannakou-Darda A, Manes C. Total serum insulin-like growth factor-1 and C-reactive protein in metabolic syndrome with or without diabetes. Angiology 2006 57 303311. (doi:10.1177/000331970605700306).

    • Search Google Scholar
    • Export Citation
  • 25

    Maggio M, Lauretani F, Ceda GP, Bandinelli S, Basaria S, Ble A, Egan J, Paolisso G, Najjar S, Jeffrey Metter E et al.. Association between hormones and metabolic syndrome in older Italian men. Journal of the American Geriatrics Society 2006 54 18321838. (doi:10.1111/j.1532-5415.2006.00963.x).

    • Search Google Scholar
    • Export Citation
  • 26

    Maggio M, Lauretani F, Ceda GP, Bandinelli S, Basaria S, Paolisso G, Ble A, Egan JM, Metter EJ, Abbatecola AM et al.. Association of hormonal dysregulation with metabolic syndrome in older women: data from the InCHIANTI study. American Journal of Physiology. Endocrinology and Metabolism 2007 292 E353E358. (doi:10.1152/ajpendo.00339.2006).

    • Search Google Scholar
    • Export Citation
  • 27

    Ceda GP, Dall'Aglio E, Magnacavallo A, Vargas N, Fontana V, Maggio M, Valenti G, Lee PD, Hintz RL, Hoffman AR. The insulin-like growth factor axis and plasma lipid levels in the elderly. Journal of Clinical Endocrinology and Metabolism 1998 83 499502. (doi:10.1210/jc.83.2.499).

    • Search Google Scholar
    • Export Citation
  • 28

    Colao A, Di Somma C, Cascella T, Pivonello R, Vitale G, Grasso LFS, Lombardi G, Savastano S. Relationships between serum IGF1 levels, blood pressure, and glucose tolerance: an observational, exploratory study in 404 subjects. European Journal of Endocrinology 2008 159 389397. (doi:10.1530/EJE-08-0201).

    • Search Google Scholar
    • Export Citation
  • 29

    Gram IT, Norat T, Rinaldi S, Dossus L, Lukanova A, Tehard B, Clavel-Chapelon F, van Gils CH, van Noord PAH, Peeters PHM et al.. Body mass index, waist circumference and waist–hip ratio and serum levels of IGF-I and IGFBP-3 in European women. International Journal of Obesity 2006 30 16231631. (doi:10.1038/sj.ijo.0803324).

    • Search Google Scholar
    • Export Citation
  • 30

    Rajpathak SN, Gunter MJ, Wylie-Rosett J, Ho GYF, Kaplan RC, Muzumdar R, Rohan TE, Strickler HD. The role of insulin-like growth factor-I and its binding proteins in glucose homeostasis and type 2 diabetes. Diabetes/Metabolism Research and Reviews 2009 25 312. (doi:10.1002/dmrr.919).

    • Search Google Scholar
    • Export Citation
  • 31

    Sesti G, Sciacqua A, Cardellini M, Marini MA, Maio R, Vatrano M, Succurro E, Lauro R, Federici M, Perticone F. Plasma concentration of IGF-I is independently associated with insulin sensitivity in subjects with different degrees of glucose tolerance. Diabetes Care 2005 28 120125. (doi:10.2337/diacare.28.1.120).

    • Search Google Scholar
    • Export Citation
  • 32

    Sandhu MS, Heald AH, Gibson JM, Cruickshank JK, Dunger DB, Wareham NJ. Circulating concentrations of insulin-like growth factor-I and development of glucose intolerance: a prospective observational study. Lancet 2002 359 17401745. (doi:10.1016/S0140-6736(02)08655-5).

    • Search Google Scholar
    • Export Citation
  • 33

    Succurro E, Arturi F, Grembiale A, Iorio F, Laino I, Andreozzi F, Sciacqua A, Hribal ML, Perticone F, Sesti G. Positive association between plasma IGF1 and high-density lipoprotein cholesterol levels in adult nondiabetic subjects. European Journal of Endocrinology 2010 163 7580. (doi:10.1530/EJE-10-0113).

    • Search Google Scholar
    • Export Citation
  • 34

    Maison P, Balkau B, Souberbielle JC, Cunin P, Vol S, Macquin-Mavier I, Eschwege E. Evidence for distinct effects of GH and IGF-I in the metabolic syndrome. Diabetic Medicine 2007 24 10121018. (doi:10.1111/j.1464-5491.2007.02195.x).

    • Search Google Scholar
    • Export Citation
  • 35

    Rosen T, Eden S, Larson G, Wilhelmsen L, Bengtsson BA. Cardiovascular risk factors in adult patients with growth hormone deficiency. Acta Endocrinologica 1993 129 195200. (doi:10.1530/acta.0.1290195).

    • Search Google Scholar
    • Export Citation
  • 36

    de Boer H, Blok GJ, Voerman HJ, Phillips M, Schouten JA. Serum lipid levels in growth hormone-deficient men. Metabolism 1994 43 199203. (doi:10.1016/0026-0495(94)90245-3).

    • Search Google Scholar
    • Export Citation
  • 37

    Nikkila EA, Pelkonen R. Serum lipids in acromegaly. Metabolism 1975 24 829838. (doi:10.1016/0026-0495(75)90129-8).

  • 38

    Bates AS, Van't Hoff W, Jones PJ, Clayton RN. The effect of hypopituitarism on life expectancy. Journal of Clinical Endocrinology and Metabolism 1996 81 11691172. (doi:10.1210/jc.81.3.1169).

    • Search Google Scholar
    • Export Citation
  • 39

    van Bunderen CC, van Nieuwpoort IC, Arwert LI, Heymans MW, Franken AAM, Koppeschaar HPF, van der Lely AJ, Drent ML. Does growth hormone replacement therapy reduce mortality in adults with growth hormone deficiency? Data from the dutch national registry of growth hormone treatment in adults.. Journal of Clinical Endocrinology and Metabolism 2011 96 31513159. (doi:10.1210/jc.2011-1215).

    • Search Google Scholar
    • Export Citation
  • 40

    Munzer T, Harman SM, Sorkin JD, Blackman MR. Growth hormone and sex steroid effects on serum glucose, insulin, and lipid concentrations in healthy older women and men. Journal of Clinical Endocrinology and Metabolism 2009 94 38333841. (doi:10.1210/jc.2009-1275).

    • Search Google Scholar
    • Export Citation
  • 41

    Franco C, Koranyi J, Brandberg J, Lonn L, Bengtsson BK, Svensson J, Johannsson G. The reduction in visceral fat mass in response to growth hormone is more marked in men than in oestrogen-deficient women. Growth Hormone & IGF Research 2009 19 112120. (doi:10.1016/j.ghir.2008.07.001).

    • Search Google Scholar
    • Export Citation
  • 42

    Bulow B, Ahren B, Fisker S, Dehlin O, Hagberg B, Jensen E, Svensson T, Samuelsson G, Erfurth EM. The gender differences in growth hormone-binding protein and leptin persist in 80-year-old men and women and is not caused by sex hormones. Clinical Endocrinology 2003 59 482486. (doi:10.1046/j.1365-2265.2003.01872.x).

    • Search Google Scholar
    • Export Citation
  • 43

    Andreassen M, Raymond I, Kistorp C, Hildebrandt P, Faber J, Kristensen LO. IGF1 as predictor of all cause mortality and cardiovascular disease in an elderly population. European Journal of Endocrinology 2009 160 2531. (doi:10.1530/EJE-08-0452).

    • Search Google Scholar
    • Export Citation
  • 44

    Schneider HJ, Klotsche J, Saller B, Bohler S, Sievers C, Pittrow D, Ruf G, Marz W, Erwa W, Zeiher AM et al.. Associations of age-dependent IGF-I SDS with cardiovascular diseases and risk conditions: cross-sectional study in 6773 primary care patients. European Journal of Endocrinology 2008 158 153161. (doi:10.1530/EJE-07-0600).

    • Search Google Scholar
    • Export Citation
  • 45

    Sattar N, McConnachie A, Shaper AG, Blauw GJ, Buckley BM, de Craen AJ, Ford I, Forouhi NG, Freeman DJ, Jukema JW et al.. Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies.. Lancet 2008 371 19271935. (doi:10.1016/S0140-6736(08)60602-9).

    • Search Google Scholar
    • Export Citation
  • 46

    Kriegsman DM, Penninx BW, van Eijk JT, Boeke AJ, Deeg DJ. Self-reports and general practitioner information on the presence of chronic diseases in community dwelling elderly. A study on the accuracy of patients' self-reports and on determinants of inaccuracy. Journal of Clinical Epidemiology 1996 49 14071417. (doi:10.1016/S0895-4356(96)00274-0).

    • Search Google Scholar
    • Export Citation
  • 47

    Brugts MP, van den Beld AW, Hofland LJ, van der Wansem K, van Koetsveld PM, Frystyk J, Lamberts SWJ, Janssen JAMJ. Low circulating insulin-like growth factor I bioactivity in elderly men is associated with increased mortality. Journal of Clinical Endocrinology and Metabolism 2008 93 25152522. (doi:10.1210/jc.2007-1633).

    • Search Google Scholar
    • Export Citation
  • 48

    Langsted A, Freiberg JJ, Nordestgaard BG. Fasting and nonfasting lipid levels: influence of normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk prediction. Circulation 2008 118 20472056. (doi:10.1161/CIRCULATIONAHA.108.804146).

    • Search Google Scholar
    • Export Citation