help button home button Endocrine Society JCEM JCEM Call for Nominations for EIC
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
Journal of Clinical Endocrinology & Metabolism, doi:10.1210/jc.2006-0745
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
91/9/3287    most recent
Author Manuscript (PDF)
Right arrow Submit a related Letter to the Editor
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Copyright Permission
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Martin, R. M.
Right arrow Articles by Gunnell, D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Martin, R. M.
Right arrow Articles by Gunnell, D.
Related Collections
Right arrow Diabetes and Insulin
Right arrow Metabolism
Right arrow Pediatric Endocrinology
The Journal of Clinical Endocrinology & Metabolism Vol. 91, No. 9 3287-3295
Copyright © 2006 by The Endocrine Society

Associations of Adiposity from Childhood into Adulthood with Insulin Resistance and the Insulin-Like Growth Factor System: 65-Year Follow-Up of the Boyd Orr Cohort

Richard M. Martin, Jeff M. P. Holly, George Davey Smith and David Gunnell

Department of Social Medicine (R.M.M., G.D.S., D.G.), University of Bristol, Bristol BS8 2PR, United Kingdom; and Clinical Sciences at North Bristol (J.M.P.H.), Southmead Hospital, Bristol BS10 5NS, United Kingdom

Address all correspondence and requests for reprints to: Dr. Richard M. Martin, Department of Social Medicine, University of Bristol, Bristol BS8 2PR, United Kingdom. E-mail: richard.martin{at}bristol.ac.uk.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: One metabolic pathway through which adiposity influences disease risk may be via alterations in insulin and IGF metabolism.

Objective: Our objective was to investigate associations of adiposity at different stages of life with insulin and the IGF system.

Design, Setting, and Participants: The study was a 65-yr follow-up of 728 Boyd Orr cohort participants (mean age, 71 yr) originally surveyed between 1937 and 1939.

Main Outcomes: Outcomes included homeostasis model assessment of insulin resistance, total IGF-I and IGF-II, IGF binding protein (IGFBP)-2, and IGFBP-3 in adulthood.

Results: Childhood body mass index (BMI) was weakly inversely related to adult IGF-I (coefficient per BMI SD, –3.4 ng/ml; 95% confidence interval, –7.3 to 0.5; P = 0.09). IGF-II (but not IGF-I) increased with higher current fat mass index (coefficient, 26.1 ng/ml; 95% confidence interval, 4.6 to 47.6; P = 0.02) and waist-hip ratio (30.0 ng/ml; 9.4 to 50.5; P = 0.004). IGFBP-2 decreased by 21.2% (17.2 to 24.9; P < 0.001), and homeostasis model assessment of insulin resistance increased by 38.8% (28.9 to 49.6; P < 0.001) per SD higher adult BMI. Among thin adults (BMI tertiles 1 and 2), IGFBP-2 was positively, and insulin resistance was inversely, associated with childhood BMI.

Conclusion: There was only weak evidence that associations of childhood BMI with chronic disease risk may be mediated by adult IGF-I levels. Circulating IGFBP-2 in adulthood, a marker for insulin sensitivity, was inversely associated with current adiposity, but overweight children who became relatively lean adults were more insulin sensitive than thinner children. The findings may indicate programming of later insulin sensitivity and consequently IGFBP-2 levels in response to childhood adiposity. The role of IGF-II in obesity-related chronic diseases warrants additional investigation.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
IN THE LAST 30 yr, the prevalence of overweight children has tripled to around 20% in the United States (1) and United Kingdom (2). The long-term metabolic consequences of childhood obesity are, therefore, of great interest. Although hyperinsulinemia and insulin resistance are known features of childhood adiposity (3, 4), the impact of adiposity on IGFs is unclear (5). In adulthood, positive (5, 6, 7), inverse (8, 9, 10, 11, 12, 13, 14, 15, 16), and null (17, 18, 19, 20, 21, 22, 23, 24, 25) IGF-I-adiposity associations have been described in cross-sectional studies, whereas this association is generally positive in children (26, 27, 28, 29, 30). We previously showed that IGF-binding protein (IGFBP)-2 was inversely associated with body mass index (BMI) in healthy middle-aged men (19), consistent with reports in disease-free women (13, 31, 32, 33) and healthy elderly men (25). Given its inverse relationship with insulin (13, 25, 34, 35, 36), this suggests that IGFBP-2 may index adiposity-related hyperinsulinemia across the population range of BMI (19, 35). Greater understanding of these associations is valuable to help determine the possible long-term consequences of rising levels of childhood obesity. This is particularly important in view of research suggesting that the insulin-IGF-I system may mediate the effect of energy balance on cancer risk (37). In contrast, high IGF-I levels may protect against metabolic syndrome (38) and cardiovascular disease (17).

Despite many cross-sectional studies (5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30), few reports assess associations of adiposity over the life course with the IGF system (11, 16). The Boyd Orr cohort has records of measured adiposity over 65 yr of follow-up. We investigated relationships of adiposity in childhood and old age with insulin resistance, IGF-I, IGF-II, IGFBP-2, and IGFBP-3 in adulthood, in a cohort in which positive associations of childhood BMI with cancer (40) and ischemic heart disease (41) have been demonstrated.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
The study is an historical cohort based on the Carnegie (Boyd Orr) Survey of Diet and Health in Pre-War Britain, 1937–39 (42). Altogether, 4999 children from 1343 families were surveyed at 16 centers in England and Scotland. Physical examinations were carried out on 3762 of the children. A total of 4397 participants (88%) have been traced and flagged using the National Health Service Central Register.

Childhood anthropometry

Measurements of height and weight measured at one point in time on 2997 children aged 2 yr to 14 yr 9 months are available. Childhood standing height was measured to the nearest millimeter with a portable measuring stand, and weight was measured with a W&T Avery standard model calibrated level balance (now known as Avery Berkel, Smethick, UK) to the nearest ounce (28.4 g).

Adult follow-up

Between 2002 and 2003, a total of 1295 surviving participants were invited to participate in a follow-up study (42, 43). Blood samples and measured adult heights and weights were obtained from 728 subjects, either at a research clinic (n = 405), where measurements were conducted using a standardized protocol, or by their general practitioner (GP) who posted blood samples for analysis in approved packaging (n = 323) (Fig. 1Go). Of these, 725 had adult BMI (weight/height; kg/m2) and IGF measured, whereas 682 (53%) had complete data for multivariable analysis. Clinic-measured adult height and weight in study members with complete data for multivariable analysis are available for 385 (30%) of the subjects. As in previous publications of the adult anthropometric data (43), we based our analysis relating adult BMI with IGFs on the participants with clinic measures to reduce measurement error. In a sensitivity analysis, associations of IGF with adult BMI (measured by either the GP or in the research clinic) were repeated using all 682 respondents with complete data for multivariable analyses. In total, 456 participants with IGF measures in adulthood also had childhood BMI data, and 431 (33%) had complete data for multivariable analyses.


Figure 1
View larger version (21K):
[in this window]
[in a new window]
  		FIG. 1. Description of study numbers: target population, respondents with blood samples, numbers with measures of adiposity in childhood and adulthood, and numbers with complete data for multivariable analysis who were included in the final analyses.

 
Additionally, the following adulthood measures of fat and lean mass and central adiposity were measured on 405 participants in the research clinic: body composition (percent body fat, fat mass, and lean mass) measured by leg-to-leg bioimpedance (Tanita TBF 300; Tanita Corp., Tokyo, Japan) and waist (midway between lower ribs and iliac crests in relaxed exhalation) and hip (point of maximum circumference) circumferences. Circumference values were based on the mean of two measures. Fat mass and lean mass indices were defined as fat mass/height (kg/m2) and lean mass/height (kg/m2), respectively. Overall, 385 (30%) participants with these adiposity measures had complete data for multivariable analyses.

IGF and insulin resistance measurements

The IGF outcomes were IGF-I, IGF-II, IGFBP-2, IGFBP-3, and the molar ratio IGF-I:IGFBP-3 (a measure of the biological availability of IGF-I). Full details of how the blood samples were processed and stored have been provided in a previous publication (43). As reported in detail previously, there was evidence that storage time was associated with mean levels of IGF-II (43), but controlling for length of storage time or restricting to the clinic samples made no material difference to the results for IGF-II for this, or previous (43), analyses. A total of 370 participants in the clinic follow-up provided blood samples after fasting for 6 h or more, whereas the 323 participants whose blood was sent by post were not asked to fast.

Serum IGF-I, IGF-II, and IGFBP-3 levels were measured using in-house double-antibody RIAs as previously described (44). IGFBP-2 was measured by one-step sandwich ELISA (DSL-10–7100; Diagnostic Systems Laboratories, Webster, TX). The average coefficients of variation for intraassay variability for IGF-I, IGF-II, IGFBP-3, and IGFBP-2 were 6.7, 10, 3.9, and 5% and for interassay variation were 9.7, 14, 8.1, and 7.1%. Based on the molecular weight of IGF-I (7500) and IGFBP-3 (40,000, mean of glycosylated variants), we calculated the molar ratio of IGF-I/IGFBP-3 by multiplying the ratio by 5.33 (40,000/7,500).

Among fasted clinic participants, insulin resistance was estimated according to the homeostasis model assessment (HOMA) as the product of fasting glucose (mmol/liter) and insulin (µU/ml) divided by the constant 22.5 (45). HOMA scores were not calculated for subjects with a fasting glucose of at least 7 mmol/liter or those with diagnosed diabetes because the results are inaccurate in these groups (45). Thus, 346 individuals were included in the adulthood BMI analysis and 226 in the childhood BMI analysis in relation to insulin resistance (327 and 214, respectively, in multivariable analyses) (Fig. 1Go).

Statistical analyses

Relationships of childhood or adulthood BMI with the insulin-IGF system in adulthood were assessed using multiple linear regression. We calculated robust SE to account for lack of independence between observations within families (46). Adiposity measures were expressed as z-scores, internally age and sex standardized for childhood BMI (40, 41) and sex standardized for our measures of adulthood adiposity (BMI, fat mass index, lean mass index, percent body fat, waist circumference, and waist-hip ratio). The regression coefficients show the change in IGF and IGFBP levels per SD increase in adiposity and are thus directly comparable. IGFBP-2 and HOMA insulin resistance values were highly positively skewed and were loge transformed; thus, the compound percent change in IGFBP-2/HOMA insulin resistance per SD increase in adiposity measure is given.

Basic models control for age at adult examination, sex, and clinic-obtained vs. posted blood sample. The fully adjusted models control for the following variables, categorized as fully described in a previous publication (43): per capita household food expenditure in childhood, social class in childhood determined from the occupation of the head of the household, social class in adulthood, smoking, alcohol consumption, and exercise.

The impact of growth trajectory since childhood on IGF levels was demonstrated by computing mean IGF levels in a 3 x 3 matrix of childhood BMI tertiles by tertiles of adulthood BMI. Tests for trend across tertiles of adult BMI for each childhood BMI tertile (and vice versa) were based on fully adjusted regression models with tertiles of BMI in adulthood and in childhood, respectively, entered as continuous variables. We carried out likelihood ratio tests for interactions with sex, age at BMI measurement in childhood [defined a priori as in previous analyses (40, 43) as <8 (prepubertal) or ≥8 yr (may have entered puberty)] and for the interaction of childhood BMI tertiles with tertiles of adulthood BMI. All analyses were conducted using Stata 9.2 (Stata Corp., College Station, TX).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
The mean ages of the study participants were 6 yr at childhood measurement and 71 yr at follow-up (Table 1Go). There was no evidence that the 728 participants who provided blood samples differed from those eligible participants who did not (n = 567) in terms of childhood or adulthood BMI (43). Overall, BMI in childhood was not correlated with indices of fat mass in adulthood (r < 0.05) and was weakly correlated with adult lean mass and BMI (r = 0.18 and 0.12, respectively) (Table 2Go), although correlations of BMI in children at least 8 yr of age with adulthood fat mass and waist circumference were stronger (r = 0.12). IGF-I was weakly positively correlated with HOMA insulin resistance scores (r = 0.16), waist circumference (r = 0.17), and waist-hip ratio (r = 0.22), whereas IGF-II was positively associated with percent body fat and fat mass index (r = 0.25 and 0.21, respectively). IGF-II was weakly positively associated with adult BMI (r = 0.13) but was weakly negatively associated with BMI measured in childhood (r = –0.13). IGFBP-2 was inversely related to adulthood measures of HOMA insulin resistance (r = –0.38), BMI (r = –0.43), percent body fat (r = –0.34), fat mass index (r = –0.41), lean mass index (r = –0.24), waist circumference (r = –0.42), and waist-hip ratio (r = –0.31). Adulthood IGFBP-2 was, however, weakly positively correlated with BMI measured in childhood (r = 0.10).


View this table:
[in this window]
[in a new window]
  		TABLE 1. Selected characteristics of the studied population by sex

 

View this table:
[in this window]
[in a new window]
  		TABLE 2. Pearson’s correlations between the IGF system, HOMA insulin resistance, and measures of adiposity (n = 225 with all measures)

 
Age-, sex-, and sample-type-adjusted mean IGF measures across quartiles of BMI in childhood and in adulthood are given in Table 3Go. No associations of childhood BMI were seen with adulthood levels of IGF-I, IGF-II, IGFBP-3, the molar ratio IGF-I/IGFBP-3, or HOMA insulin resistance. IGFBP-2 was weakly positively associated with childhood BMI (P for linear trend = 0.12). In adulthood, there was no evidence of a linear association of BMI with IGF-I. IGFBP-2 was strongly and inversely associated with adult BMI (P for linear trend < 0.001), whereas HOMA insulin resistance was strongly and positively associated with adult BMI (P for linear trend < 0.001). IGF-II was positively associated with adult BMI (P for linear trend = 0.03). There was no evidence that adult BMI was associated with IGFBP-3 or the IGF-I/IGFBP-3 molar ratio.


View this table:
[in this window]
[in a new window]
  		TABLE 3. Levels of IGF-I, IGF-II, IGFBP-2, IGFBP-3, and HOMA insulin resistance in relation to quartiles of BMI in childhood and adulthood

 
In fully adjusted models, there was weak evidence that childhood BMI was inversely associated with adult levels of IGF-I (P for trend = 0.09) and IGFBP-3 (P for trend = 0.13) (Table 4Go). Inverse associations of childhood BMI with IGF-I (P for interaction = 0.07) and IGFBP-3 (P for interaction = 0.04) in adulthood were observed for BMI measured aged less than 8 yr but not aged 8 yr or more. In fully adjusted models, IGFBP-2 was weakly and positively associated with childhood BMI (P for trend = 0.16), but it was strongly and inversely associated with adulthood BMI (P < 0.001), even after additionally controlling for IGF-I or IGF-II. An inverse association of childhood BMI with HOMA insulin resistance was strengthened (P for trend = 0.045), whereas the strong positive association of BMI in adulthood with insulin resistance (P < 0.001) was not altered in the fully adjusted models.


View this table:
[in this window]
[in a new window]
  		TABLE 4. Change in measure of the IGF system and HOMA insulin resistance (95% CI) per SD change in BMI in childhood and adulthood

 
Among 327 participants with data on BMI in adulthood, and levels of both IGFBP-2 and HOMA insulin resistance, the association of BMI in adulthood with IGFBP-2 was attenuated by 33% (coefficients changed from –22.1 to –14.9%) but remained statistically significant (P < 0.001) when HOMA insulin resistance was added to the fully adjusted model. The association of BMI in adulthood with HOMA insulin resistance was attenuated by 31% (coefficients changed from –38.8 to 26.9%) but remained statistically significant (P < 0.001) in a model additionally controlling for IGFBP-2.

There was little evidence that childhood or adulthood BMI associations with any of the measured components of the IGF system differed by sex (P for interaction > 0.10). Effect estimates based on all 682 respondents with adult BMI, measured by either the GP or in the research clinic, were similar to those based only on those with clinic measures.

Table 5Go shows associations of fat mass, lean mass, waist-hip ratio, and waist circumference in adulthood with the insulin-IGF system among 385 participants with available data for multivariable analysis. There was no evidence that IGF-I was associated with fat mass or central adiposity, although there was an inverse association with lean mass in fully adjusted models (P = 0.03). IGF-II was strongly and positively associated with fat mass and central adiposity measures (all P < 0.04) but not lean mass (P = 0.9). IGFBP-2 was strongly inversely associated with fat mass, lean mass, and central adiposity measures (P < 0.001), although the magnitude of the lean mass association was about 35% less than the magnitude of the fat mass and central adiposity associations. There was weak evidence that IGFBP-3 was positively associated with waist-hip ratio (P = 0.04) and percent body fat (P = 0.16).


View this table:
[in this window]
[in a new window]
  		TABLE 5. Association of measures of fat mass, lean mass, and central adiposity in adulthood with IGF-I, IGF-II, IGFBP-2, IGFBP-3 (n = 385), and HOMA (n = 327)

 
Table 6Go demonstrates that current levels of adiposity had the largest impact on IGFBP-2 levels when compared with childhood adiposity. Irrespective of BMI in childhood, the heaviest adults had the lowest IGFBP-2 levels (P for linear trend all < 0.001). In contrast, for participants who were relatively lean as adults (i.e. the low and mid tertiles of adult BMI), the highest IGFBP-2 levels were among the heavier children (P trend = 0.02 and 0.01, respectively). The effect of childhood BMI on IGFBP-2 was attenuated among those who became the heaviest adults (P = 0.2), although there was little statistical evidence that child BMI-IGFBP-2 effect estimates differed by tertile of adulthood BMI (P for interaction = 0.3). The lowest levels of IGFBP-2 in adulthood were observed in those who were thinnest in childhood and were heaviest in adulthood (geometric mean = 323.2 ng/ml), whereas the highest levels were in those who were heaviest in childhood and were thinnest in adulthood (geometric mean = 646.9 ng/ml). The opposite pattern was seen with respect to HOMA insulin resistance, and here there was borderline evidence (P = 0.09) that associations of childhood BMI with HOMA differed according to adult BMI.


View this table:
[in this window]
[in a new window]
  		TABLE 6. IGF-I, IGF-II, IGFBP-2, IGFBP-3, and HOMA insulin resistance according to BMI in childhood and adulthood

 

   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
Childhood BMI was weakly inversely associated with levels in adulthood of IGF-I, IGFBP-3, and insulin resistance. There was little evidence that BMI in childhood was related to IGF-II or the molar ratio IGF-I/IGFBP-3. Current levels of adiposity, particularly fat mass and central measures, were strongly inversely associated with IGFBP-2 and positively associated with HOMA insulin resistance. In contrast, the heaviest children had the highest levels of IGFBP-2 and lowest levels of insulin resistance in adulthood; these associations were particularly evident among lean adults, suggesting that the influence of becoming overweight or obese in adulthood overshadows the effects of childhood adiposity. As in previous reports, IGFBP-2 was inversely associated with insulin resistance (13, 25, 34, 35, 36, 47, 48). Mutual adjustment of adult BMI-IGFBP-2 associations for HOMA insulin resistance, and vice versa, attenuated effects estimates by around 30%, but they remained statistically significant. There were strong positive associations of IGF-II with measures of current fat mass and central adiposity, and again there were indications for opposite associations with BMI measured in childhood that were overshadowed by the dominant effect of concurrent adiposity in adulthood.

Previous analyses of the Boyd Orr cohort demonstrated positive associations of energy intake and obesity in childhood with cancer in adulthood (40, 50). Given that IGF-I levels are positively associated with overnutrition (5), childhood adiposity (26, 27, 28, 29, 30), and the later development of some cancers (51), we had speculated that raised IGF-I levels in adulthood could underlie associations of childhood adiposity and energy intake with cancer. In contrast, our results are in line with others indicating inverse associations of adiposity measured in childhood (11, 16) or early adulthood (8) with IGF-I or the molar ratio IGF-I/IGFBP-3 measured later in life. A decline in total IGF-I levels with increased adiposity is biologically plausible because GH levels decrease with increased BMI in healthy individuals (52) as well as being depressed in obesity states (5). In line with many others, however, we found no evidence that adiposity in adulthood was associated with current levels of IGF-I (17, 18, 19, 20, 21, 22, 23, 24, 25). Overall, our data do not support the hypothesis that positive associations of energy intake or obesity in childhood with cancer risk in adulthood are mediated by a long-term positive influence of childhood BMI on IGF-I levels.

IGFBP-2 is the second most abundant binding protein in serum (53), and is regulated differently from IGFBP-3 as indicated by a correlation coefficient between these two binding proteins in Boyd Orr adults of –0.27. It is well known that chronic dietary restriction (long-term fasting, anorexia, or protein-calorie malnutrition) increases circulating IGFBP-2 but decreases serum IGFBP-3 (5). Our data are in line with reports indicating inverse associations of IGFBP-2 with current adiposity (13, 19, 25, 31, 32, 33) and insulin levels (25, 34, 35, 36). Additionally, we demonstrated that the current adiposity-IGFBP-2 association remained after controlling for HOMA insulin resistance, although effect estimates were attenuated by 33%. Given likely measurement error, this model must be interpreted cautiously. Nevertheless, if IGFBP-2 partly reflects unmeasured insulin resistance, it could act as a useful additional parameter in the assessment of metabolic syndrome that is relatively stable to day-to-day fluctuations and does not require pretest fasting. Alternatively, there could be other obesity-related regulators of IGFBP-2, including IGF-I and IGF-II (both of which were inversely correlated with IGFBP-2; r = –0.22 and –0.31, respectively). Controlling BMI-IGFBP-2 associations for IGF-I or IGF-II, however, did not make any difference to effect estimates. Prospective studies would determine whether IGFBP-2 is associated with later disease independent of obesity or insulin resistance.

The positive associations of IGFBP-2 and insulin sensitivity with childhood BMI are counterintuitive because childhood adiposity is positively associated with insulin levels in adolescence and young adulthood (3, 4) and insulin is thought to inhibit the hepatic synthesis of IGFBP-2 (5, 54). In animal models, the GH-neuroendocrine axis can be altered by transient events in early life (55), and previous population-based reports by our group (including one randomized trial of milk-supplemented diets) (56) and others have shown that nutritional exposures in childhood that would be expected to increase (or decrease) circulating IGF-I levels in cross-section are associated with an opposite effect in adulthood (56, 57, 58) (Martin, R. M., J. Holly, N. Middleton, G. Davey Smith, D. Gunnell, submitted for publication). These studies are compatible with a nutritionally stimulated increase in hepatic IGF-I production, which then suppresses pituitary GH output with a long-term resetting of the GH-neuroendocrine axis resulting in lower IGF-I levels in adulthood (56, 57, 58, 59).

The positive childhood BMI-IGFBP-2 and BMI-insulin sensitivity associations were seen only in lean adults. We speculatively propose that overweight children, during a sensitive window of development (60), may adapt to the metabolic effects of high levels of adiposity in anticipation of relative adiposity in adult life, such that they synthesize greater quantities of IGFBP-2 and are less insulin resistant in later life than adults who were leaner in childhood. Such an adaptation could theoretically occur by a long-term resetting of the pituitary threshold levels for secreting GH, because greater adiposity suppresses hepatic production of IGFBP-1 and IGFBP-2 (13, 19, 25, 31, 32, 33), increasing free, bioactive IGF-I (34), which feeds back to the hypothalamus to reduce GH secretion, in turn stimulating glucose uptake, increasing IGFBP-2 (54), and improving insulin sensitivity (24). Insulin resistance, however, eventually develops and then increases progressively over time if obesity persists for a prolonged period (24). Thus, this adaptation may not hold at very high levels of overweight persisting into adulthood because of the dominant effect of concurrent insulin resistance. Our hypothesis is analogous to a classic example of developmental plasticity in which rats fed high-fat, high-cholesterol postweaning diets had 60% lower serum cholesterol concentrations at 32 wk of age (61). This hypothesis might also explain the observations from this study, albeit of borderline statistical significance, and others of inverse relationships of childhood BMI with IGF-I and IGFBP-3 in adulthood (11, 16), which are a reversal of previously reported positive associations of childhood BMI with IGF-I and IGFBP-3 in cross-section (5, 26, 27, 28, 29, 30). It is possible that such programming effects may underlie inverse associations of childhood BMI with breast cancer risk observed in a Danish birth cohort (62) and with risk of proliferative benign breast disease in the Nurses Health Study II (63).

Our finding that IGF-II is positively associated with current fat mass and central adiposity is in line with previous cross-sectional studies (31, 64) and a recent report of positive associations of IGF-II with BMI and with the amount of fat in breast tissue, as assessed by the translucent area on mammography (65). In a cross-sectional study of normal children, although IGF-I levels correlated with height and fat-free mass, levels of IGF-II were more closely correlated with fat mass (28). However, in prospective studies, IGF-II has been inversely associated with obesity and weight gain (66, 67). Again, there was an indication that the association of adult IGF-II with measured BMI in childhood was in the opposite direction to its association with concurrent BMI measured in adulthood, although this association was overshadowed by a dominant effect of the latter. This may indicate that excess weight gain early in life (in people who are lean as adults) is associated with low IGF-II levels, but adiposity itself increases IGF-II levels, and sustained weight gain is therefore associated with high IGF-II levels. In contrast to IGF-I, however, very little is known regarding the regulation of IGF-II throughout adult life.

The findings could have implications for health. IGFBP-2 has been inversely associated with postmenopausal breast cancer (13) and colon cancer (68). Insulin resistance and IGF-II are associated with increased risk of some cancers (69, 70). Future studies should elucidate the relative roles of IGFBP-2, insulin resistance, and IGF-II in obesity-related chronic disease.

Strengths and limitations

The major strength of this study is the measurement of BMI in childhood that could be related prospectively to IGFs and IGFBPs 65 yr later. There are five main limitations. First, childhood BMI is only a proxy for adiposity, and in our study, childhood BMI was correlated more strongly with lean mass than fat mass in adulthood. Second, when the participants were children in the 1930s, their dietary patterns and activity levels would not have been comparable to those of children today and may have been further influenced by rationing during and after the Second World War. In Boyd Orr, only 4% of participants were categorized as overweight and 0.2% as obese in childhood, compared with more recent figures of around 10% overweight and 1% obese in British growth surveys using identical thresholds (49). Thus, the results for the childhood measures may not be generalizable to current childhood adiposity, although the adult data are contemporary. Third, it is possible that in older populations, adiposity-BMI associations are disrupted by the known age-related decline in IGF-I levels or are masked by other age-related confounding factors. Fourth, free IGF-I has been shown to be increased in obesity in the presence of low-normal total IGF-I (36, 64), but we did not directly measure free IGF-I. The interpretation of free IGF-I is limited because of the known ability of IGFBPs to enhance IGF actions in some situations. The molar ratio IGF-I/IGFBP-3 used here is considered a marker of the biological availability of IGF-I. Finally, the studied sample represented one third of the eligible population. However, those studied were representative of nonresponders in terms of BMI in both childhood and adulthood, effect estimates were the same in our sensitivity analyses using GP- as well as clinic-based adult measures of BMI, and it seems unlikely that adiposity-IGF associations would differ between those who did and did not participate.

Conclusion

There was only weak evidence that associations of childhood BMI with chronic disease risk may be mediated by adult IGF-I levels. Circulating IGFBP-2 in adulthood, a marker for insulin sensitivity, was inversely associated with current adiposity, but overweight children who became relatively lean adults were more insulin sensitive than thinner children. The findings may indicate predictive programming of later IGFBP-2 levels in response to the metabolic effects of childhood adiposity. This suggests that childhood adiposity may not impact on adult levels of insulin resistance to the extent predicted from cross-sectional associations with obesity in adulthood. Because IGFBP-2 is inversely associated with both adiposity and insulin resistance, this protein could offer a nonfasting assessment of the metabolic syndrome and hyperinsulinemia-related cardiovascular risk. Furthermore, IGFBP-2 may have an important role in obesity and insulin-related carcinogenesis. Studies are required on the prospective associations of IGFBP-2 with the metabolic syndrome, ischemic heart disease, and obesity-related cancer risk. The role of IGF-II in obesity-related chronic diseases also warrants additional investigation. Investigating associations of chronic disease outcomes with IGF genetic polymorphisms that alter lifelong exposure to biologically active IGF and IGFBP levels could increase our understanding of this issue (39).


   Acknowledgments
 
We are very grateful to the cohort members who participated in the follow-up study. We acknowledge all the research workers in the original survey in 1937–1939. We thank Professor John Pemberton for information concerning the conduct of the original survey; Professor Peter Morgan, director of the Rowett Research Institute, for the use of the archive, and Walter Duncan, honorary archivist to the Rowett; and Susie Potts for secretarial and administrative support to the study. We thank all the GPs who participated in the study and the following for providing clinic space in 2002: Dr. Nigel Williams (Clarkson Surgery, Wisbech), Professor Andrew Morris (Diabetes Centre, Ninewells Hospital, Dundee), Dr. Ali Jawad (Royal London, Mile End), Sylvia Hey (Human Nutrition Unit, Rowett Research Institute), Professor Frank Sullivan (Mill Practice, Dundee), and Professor Philip Hannaford (Aberdeen University). Jane Carter and Paul Savage performed the IGF analyses.


   Footnotes
 
This work was supported by the World Cancer Research Fund (Grant no. 2001/31), the Wellcome Trust (GR063779FR, research training fellowship to R.M.M.), and UK Survivors.

The hypothesis was developed by D.G., G.D.S., R.M.M., and J.M.P.H. The fieldwork was conducted by R.M.M. and Simone Watson under the direction of a steering group (Shah Ebrahim, G.D.S., Stephen Frankel, D.G., and J.M.P.H.). R.M.M. wrote the first draft and coordinated completion of the paper. R.M.M. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors critically commented on and edited earlier drafts and approved the final version of the paper.

Disclosure statement: The authors have nothing to disclose.

First Published Online June 20, 2006

Abbreviations: BMI, Body mass index; GP, general practitioner; HOMA, homeostasis model assessment; IGFBP, IGF-binding protein.

Received April 5, 2006.

Accepted June 8, 2006.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP 2001 The continuing epidemics of obesity and diabetes in the United States. JAMA 286:1195–1200[Abstract/Free Full Text]
  2. McCarthy HD, Ellis SM, Cole TJ 2003 Central overweight and obesity in British youth aged 11–16 years: cross sectional surveys of waist circumference. BMJ 326:624
  3. Srinivasan SR, Myers L, Berenson GS 2002 Predictability of childhood adiposity and insulin for developing insulin resistance syndrome (syndrome X) in young adulthood: the Bogalusa Heart Study. Diabetes 51:204–209[Abstract/Free Full Text]
  4. Sinha R, Fisch G, Teague B, Tamborlane WV, Banyas B, Allen K, Savoye M, Rieger V, Taksali S, Barbetta G, Sherwin RS, Caprio S 2002 Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. N Engl J Med 346:802–810[Abstract/Free Full Text]
  5. Thissen JP, Ketelslegers JM, Underwood LE 1994 Nutritional regulation of the insulin-like growth factors. Endocr Rev 15:80–101[Abstract]
  6. Laughlin GA, Barrett-Connor E, Criqui MH, Kritz-Silverstein D 2004 The prospective association of serum insulin-like growth factor I (IGF-I) and IGF-binding protein-1 levels with all cause and cardiovascular disease mortality in older adults: the Rancho Bernardo Study. J Clin Endocrinol Metab 89:114–120[Abstract/Free Full Text]
  7. Sugimoto T, Nakaoka D, Nasu M, Kanzawa M, Sugishita T, Chihara K 1998 Age-dependent changes in body composition in postmenopausal Japanese women: relationship to growth hormone secretion as well as serum levels of insulin-like growth factor (IGF)-I and IGF-binding protein-3. Eur J Endocrinol 138:633–639[Abstract]
  8. Gapstur SM, Kopp P, Chiu BCH, Gann PH, Colangelo LA, Liu K 2004 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. Cancer Epidemiol Biomarkers Prev 13:2208–2216[Abstract/Free Full Text]
  9. Cruickshank JK, Heald AH, Anderson S, Cade JE, Sampayo J, Riste LK, Greenhalgh A, Taylor W, Fraser W, White A, Gibson JM 2001 Epidemiology of the insulin-like growth factor system in three ethnic groups. Am J Epidemiol 154:504–513[Abstract/Free Full Text]
  10. DeLellis K, Rinaldi S, Kaaks RJ, Kolonel LN, Henderson B, Le Marchand L 2004 Dietary and lifestyle correlates of plasma insulin-like growth factor-I (IGF-I) and IGF binding protein-3 (IGFBP-3): the multiethnic cohort. Cancer Epidemiol Biomarkers Prev 13:1444–1451[Abstract/Free Full Text]
  11. Kajantie E, Fall CH, Seppala M, Koistinen R, Dunkel L, Yliharsila H, Osmond C, Andersson S, Barker DJ, Forsen T, Holt RI, Phillips DI, Eriksson J 2003 Serum insulin-like growth factor (IGF)-I and IGF-binding protein-1 in elderly people: relationships with cardiovascular risk factors, body composition, size at birth, and childhood growth. J Clin Endocrinol Metab 88:1059–1065[Abstract/Free Full Text]
  12. Landin-Wilhelmsen K, Wilhelmsen L, Lappas G, Rosen T, Lindstedt G, Lundberg PA, Bengtsson BA 1994 Serum insulin-like growth factor I in a random population sample of men and women: relation to age, sex, smoking habits, coffee consumption and physical activity, blood pressure and concentrations of plasma lipids, fibrinogen, parathyroid hormone and osteocalcin. Clin Endocrinol (Oxf) 41:351–357[Medline]
  13. Krajcik RA, Borofsky ND, Massardo S, Orentreich N 2002 Insulin-like growth factor I (IGF-I), IGF-binding proteins, and breast cancer. Cancer Epidemiol Biomarkers Prev 11:1566–1573[Abstract/Free Full Text]
  14. Chang S, Wu X, Yu H, Spitz MR 2002 Plasma concentrations of insulin-like growth factors among healthy adult men and postmenopausal women: associations with body composition, lifestyle, and reproductive factors. Cancer Epidemiol Biomarkers Prev 11:758–766[Abstract/Free Full Text]
  15. Tricoli JV, Winter DL, Hanlon AL, Raysor SL, Watkins-Bruner D, Pinover WH, Hanks GE 1999 Racial differences in insulin-like growth factor binding protein-3 in men at increased risk of prostate cancer. Urology 54:178–182[CrossRef][Medline]
  16. Ben Shlomo Y, Holly J, McCarthy A, Savage P, Davies D, Gunnell D, Davey Smith G 2003 An investigation of fetal, postnatal and childhood growth with insulin-like growth factor I and binding protein 3 in adulthood. Clin Endocrinol (Oxf) 59:366–373[CrossRef][Medline]
  17. Juul A, Scheike T, Davidsen M, Gyllenborg J, Jorgensen T 2002 Low serum insulin-like growth factor I is associated with increased risk of ischemic heart disease: a population-based case-control study. Circulation 106:939–944[Abstract/Free Full Text]
  18. Jernstrom H, Deal C, Wilkin F, Chu W, Tao Y, Majeed N, Hudson T, Narod SA, Pollak M 2001 Genetic and nongenetic factors associated with variation of plasma levels of insulin-like growth factor-I and insulin-like growth factor-binding protein-3 in healthy premenopausal women. Cancer Epidemiol Biomarkers Prev 10:377–384[Abstract/Free Full Text]
  19. Gunnell D, Oliver SE, Donovan JL, Peters TJ, Gillatt D, Persad R, Hamdy FC, Neal DE, Holly JM 2004 Do height-related variations in insulin-like growth factors underlie the associations of stature with adult chronic disease? J Clin Endocrinol Metab 89:213–218[Abstract/Free Full Text]
  20. Holmes MD, Pollak MN, Hankinson SE 2002 Lifestyle correlates of plasma insulin-like growth factor I and insulin-like growth factor binding protein 3 concentrations. Cancer Epidemiol Biomarkers Prev 11:862–867[Abstract/Free Full Text]
  21. Langlois JA, Rosen CJ, Visser M, Hannan MT, Harris T, Wilson PWF, Kiel DP 1998 Association between insulin-like growth factor I and bone mineral density in older women and men: the Framingham Heart Study. J Clin Endocrinol Metab 83:4257–4262[Abstract/Free Full Text]
  22. O’Connor KG, Tobin JD, Harman SM, Plato CC, Roy TA, Sherman SS, Blackman MR 1998 Serum levels of insulin-like growth factor-I are related to age and not to body composition in healthy women and men. J Gerontol Ser A Biol Sci Med Sci 53:M176–M182
  23. Probst-Hensch NM, Wang H, Goh VHH, Seow A, Lee HP, Yu MC 2003 Determinants of circulating insulin-like growth factor I and insulin-like growth factor binding protein 3 concentrations in a cohort of Singapore men and women. Cancer Epidemiol Biomarkers Prev 12:739–746[Abstract/Free Full Text]
  24. Saitoh H, Kamoda T, Nakahara S, Hirano T, Nakamura N 1998 Serum concentrations of insulin, insulin-like growth factor (IGF)-I, IGF binding protein (IGFBP)-1 and -3 and growth hormone binding protein in obese children: fasting IGFBP-1 is suppressed in normoinsulinaemic obese children. Clin Endocrinol (Oxf) 48:487–492[CrossRef][Medline]
  25. van den Beld AW, Blum WF, Pols HA, Grobbee DE, Lamberts SW 2003 Serum insulin-like growth factor binding protein-2 levels as an indicator of functional ability in elderly men. Eur J Endocrinol 148:627–634[Abstract]
  26. Fall CH, Clark PM, Hindmarsh PC, Clayton PE, Shiell AW, Law CM 2000 Urinary GH and IGF-I excretion in nine year-old children: relation to sex, current size and size at birth. Clin Endocrinol (Oxf) 53:69–76[CrossRef][Medline]
  27. Fall CH, Pandit AN, Law CM, Yajnik CS, Clark PM, Breier B, Osmond C, Shiell AW, Gluckman PD, Barker DJ 1995 Size at birth and plasma insulin-like growth factor-1 concentrations. Arch Dis Child 73:287–293[Abstract]
  28. Ong K, Kratzsch J, Kiess W, Dunger D 2002 The ALSPAC Study Team. Circulating IGF-I levels in childhood are related to both current body composition and early postnatal growth rate. J Clin Endocrinol Metab 87:1041–1044[Abstract/Free Full Text]
  29. Hoppe C, Rovenna Udam T, Lauritzen L, Molgaard C, Juul A, Fleischer Michaelsen K 2004 Animal protein intake, serum insulin-like growth factor I, and growth in healthy 2.5-y-old Danish children. Am J Clin Nutr 80:447–452[Abstract/Free Full Text]
  30. Löfqvist C, Andersson E, Gelander L, Rosberg S, Hulthen L, Blum WF, Wikland KA 2005 Reference values for insulin-like growth factor-binding protein-3 (IGFBP-3) and the ratio of insulin-like growth factor-I to IGFBP-3 throughout childhood and adolescence. J Clin Endocrinol Metab 90:1420–1427[Abstract/Free Full Text]
  31. Helle SI, Ekse D, Holly JM, Lonning PE 2002 The IGF-system in healthy pre- and postmenopausal women: relations to demographic variables and sex-steroids. J Steroid Biochem Mol Biol 81:95–102[CrossRef][Medline]
  32. Voskuil DW, Bas Bueno de Mesquita H, Kaaks R, van Noord PAH, Rinaldi S, Riboli E, Grobbee DE, Peeters PHM 2001 Determinants of circulating insulin-like growth factor (IGF)-I and IGF binding proteins 1–3 in premenopausal women: physical activity and anthropometry (Netherlands). Cancer Causes Control 12:951–958[CrossRef][Medline]
  33. Keinan-Boker L, Bueno de Mesquita HB, Kaaks R, Van Gils CH, van Noord PA, Rinaldi S, Riboli E, Seidell JC, Grobbee DE, Peeters PH 2003 Circulating levels of insulin-like growth factor I, its binding proteins -1, -2, -3, C-peptide and risk of postmenopausal breast cancer. Int J Cancer 106:90–95[CrossRef][Medline]
  34. Lukanova A, Soderberg S, Stattin P, Palmqvist R, Lundin E, Biessy C, Rinaldi S, Riboli E, Hallmans G, Kaaks R 2002 Nonlinear relationship of insulin-like growth factor (IGF)-I and IGF-I/IGF-binding protein-3 ratio with indices of adiposity and plasma insulin concentrations (Sweden). Cancer Causes Control 13:509–516[CrossRef][Medline]
  35. Nam SY, Lee EJ, Kim KR, Cha BS, Song YD, Lim SK, Lee HC, Huh KB 1997 Effect of obesity on total and free insulin-like growth factor (IGF)-1, and their relationship to IGF-binding protein (BP)-1, IGFBP-2, IGFBP-3, insulin, and growth hormone. Int J Obes Relat Metab Disord 21:355–359[CrossRef][Medline]
  36. Attia N, Tamborlane WV, Heptulla R, Maggs D, Grozman A, Sherwin RS, Caprio S 1998 The metabolic syndrome and insulin-like growth factor I regulation in adolescent obesity. J Clin Endocrinol Metab 83:1467–1471[Abstract/Free Full Text]
  37. Kaaks R, Lukanova A 2001 Energy balance and cancer: the role of insulin and insulin-like growth factor-I. Proc Nutr Soc 60:91–106[Medline]
  38. Sandhu MS, Heald AH, Gibson JM, Cruickshank JK, Dunger DB, Wareham NJ 2002 Circulating concentrations of insulin-like growth factor-I and development of glucose intolerance: a prospective observational study. Lancet 359:1740–1745[CrossRef][Medline]
  39. Davey Smith G, Ebrahim S 2005 What can Mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ 330:1076–1079[Free Full Text]
  40. Jeffreys M, Davey Smith G, Martin RM, Frankel S, Gunnell D 2004 Childhood body mass index and later cancer risk: a 50-year follow-up of the Boyd Orr study. Int J Cancer 112:348–351[CrossRef][Medline]
  41. Gunnell DJ, Frankel S, Nanchahal K, Peters TJ, Davey Smith G 1998 Childhood obesity and adult cardiovascular mortality: a 57-y follow-up study based on the Boyd-Orr cohort. Am J Clin Nutr 67:1111–1118[Abstract]
  42. Martin RM, Gunnell DJ, Pemberton J, Frankel S, Davey Smith G 2005 The Boyd Orr cohort: an historical cohort study based on the 65 year follow-up of the Carnegie Survey of Diet and Health (1937–9). Int J Epidemiol 34:742–749[Free Full Text]
  43. Bray I, Gunnell D, Holly JM, Middleton N, Davey Smith G, Martin RM 2006 Associations of childhood and adulthood height and the components of height with insulin-like growth factor (IGF) levels in adulthood: 65 year follow-up of the Boyd Orr cohort. J Clin Endocrinol Metab 91:1382–1389[Abstract/Free Full Text]
  44. Cheetham TD, Holly JM, Baxter RC, Meadows K, Jones J, Taylor AM, Dunger DB 1998 The effects of recombinant human IGF-I administration on concentrations of acid labile subunit, IGF binding protein-3, IGF-I, IGF-II and proteolysis of IGF binding protein-3 in adolescents with insulin-dependent diabetes mellitus. J Endocrinol 157:81–87[Abstract]
  45. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC 1985 Homeostasis model assessment: insulin resistance and ß-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419[CrossRef][Medline]
  46. Rogers WH 1993 Regression standard errors in clustered samples. Stata Tech Bull Reprints 3:88–94
  47. Bereket A, Lang CH, Wilson TA 1999 Alterations in the growth hormone-insulin-like growth factor axis in insulin dependent diabetes mellitus. Horm Metab Res 31:172–181[Medline]
  48. Kaaks R, Lundin E, Rinaldi S, Manjer J, Biessy C, Soderberg S, Lenner P, Janzon L, Riboli E, Berglund G, Hallmans G 2002 Prospective study of IGF-I, IGF-binding proteins, and breast cancer risk, in northern and southern Sweden. Cancer Causes Control 13:307–316[CrossRef][Medline]
  49. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH 2000 Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320:1240–1243[Abstract/Free Full Text]
  50. Frankel S, Gunnell DJ, Peters TJ, Maynard M, Davey Smith G 1998 Childhood energy intake and adult mortality from cancer: the Boyd Orr cohort study. BMJ 316:499–504[Abstract/Free Full Text]
  51. Renehan AG, Zwahlen M, Minder PC, O’Dwyer ST, Shalet PS, Egger PM 2004 Insulin-like growth factor (IGF)-I, IGF binding protein-3, and cancer risk: systematic review and meta-regression analysis. Lancet 363:1346–1353[CrossRef][Medline]
  52. Rudman D, Kutner MH, Rogers CM, Lubin MF, Fleming GA, Bain RP 1981 Impaired growth hormone secretion in the adult population: relation to age and adiposity. J Clin Invest 67:1361–1369[Medline]
  53. Blum WF, Horn N, Kratzsch J, Jorgensen JO, Juul A, Teale D, Mohnike K, Ranke MB 1993 Clinical studies of IGFBP-2 by radioimmunoassay. Growth Regul 3:100–104[Medline]
  54. Clemmons DR, Snyder DK, Busby Jr WH 1991 Variables controlling the secretion of insulin-like growth factor binding protein-2 in normal human subjects. J Clin Endocrinol Metab 73:727–733[Abstract]
  55. Harel Z, Tannenbaum GS 1995 Long-term alterations in growth hormone and insulin secretion after temporary dietary protein restriction in early life in the rat. Pediatr Res 38:747–753[Medline]
  56. Ben-Shlomo Y, Holly J, McCarthy A, Savage P, Davies D, Davey Smith G 2005 Pre and post natal milk supplementation and adult insulin-like growth factor I: long term follow-up of a randomised controlled trial. Cancer Epidemiol Biomarkers Prev 14:1336–1339[Abstract/Free Full Text]
  57. Martin RM, Holly JMP, Davey Smith G, Ness AN, Emmett P, Rogers I, Gunnell D 2005 Could associations between breastfeeding and insulin-like growth factors underlie associations of breastfeeding with adult chronic disease? The Avon Longitudinal Study of Parents and Children. Clin Endocrinol (Oxf) 62:728–737[CrossRef][Medline]
  58. Elias SG, Keinan-Boker L, Peeters PH, Van Gils CH, Kaaks R, Grobbee DE, van Noord PA 2004 Long term consequences of the 1944–1945 Dutch famine on the insulin-like growth factor axis. Int J Cancer 108:628–630[CrossRef][Medline]
  59. van Noord PA 2004 Breast cancer and the brain: a neurodevelopmental hypothesis to explain the opposing effects of caloric deprivation during the Dutch famine of 1944–1945 on breast cancer and its risk factors. J Nutr 134(Suppl 12):3399S–3406S
  60. Bateson P, Barker D, Clutton-Brock T, Deb D, D’Udine B, Foley RA, Gluckman P, Godfrey K, Kirkwood T, Lahr MM, McNamara J, Metcalfe NB, Monaghan P, Spencer HG, Sultan SE 2004 Developmental plasticity and human health. Nature 430:419–421[CrossRef][Medline]
  61. Waterland RA, Garza C 1999 Potential mechanisms of metabolic imprinting that lead to chronic disease. Am J Clin Nutr 69:179–197[Abstract/Free Full Text]
  62. Ahlgren M, Melbye M, Wohlfahrt J, Sorensen TIA 2004 Growth patterns and the risk of breast cancer in women. N Engl J Med 351:1619–1626[Abstract/Free Full Text]
  63. Baer HJ, Schnitt SJ, Connolly JL, Byrne C, Willett WC, Rosner B, Colditz GA 2005 Early life factors and incidence of proliferative benign breast disease. Cancer Epidemiol Biomarkers Prev 14:2889–2897[Abstract/Free Full Text]
  64. Frystyk J, Skjaerbaek C, Vestbo E, Fisker S, Orskov H 1999 Circulating levels of free insulin-like growth factors in obese subjects: the impact of type 2 diabetes. Diabetes Metab Res Rev 15:314–322[CrossRef][Medline]
  65. dos Santos Silva I, Johnson N, De Stavola B, Torres-Mejia G, Fletcher O, Allen DS, Allen NE, Key TJ, Fentiman IS, Holly JMP, Peto J 2006 The insulin-like growth factor system and mammographic features in premenopausal and postmenopausal women. Cancer Epidemiol Biomarkers Prev 15:449–455[Abstract/Free Full Text]
  66. Sandhu MS, Gibson JM, Heald AH, Dunger DB, Wareham NJ 2003 Low circulating IGF-II concentrations predict weight gain and obesity in humans. Diabetes 52:1403–1408[Abstract/Free Full Text]
  67. Heald AH, Karvestedt L, Anderson SG, McLaughlin J, Knowles A, Wong L, Grill V, Cruickshank JK, White A, Gibson JM, Brismar K 2006 Low insulin-like growth factor-II levels predict weight gain in normal weight subjects with type 2 diabetes. Am J Med 119:167.e9–167.e15
  68. Kaaks R, Toniolo P, Akhmedkhanov A, Lukanova A, Biessy C, Dechaud H, Rinaldi S, Zeleniuch-Jacquotte A, Shore RE, Riboli E 2000 Serum C-peptide, insulin-like growth factor (IGF)-I, IGF-binding proteins, and colorectal cancer risk in women. J Natl Cancer Inst 92:1592–1600[Abstract/Free Full Text]
  69. Toretsky JA, Helman LJ 1996 Involvement of IGF-II in human cancer. J Endocrinol 149:367–372[Abstract]
  70. Wei EK, Giovannucci E, Fuchs CS, Willett WC, Mantzoros CS 2005 Low plasma adiponectin levels and risk of colorectal cancer in men: a prospective study. J Natl Cancer Inst 97:1688–1694[Abstract/Free Full Text]



This article has been cited by other articles:


Home page
 J. Clin. Endocrinol. Metab.Home page
R. M. Martin, D. Gunnell, E. Whitley, A. Nicolaides, M. Griffin, N. Georgiou, G. Davey Smith, S. Ebrahim, and J. M. P. Holly
Associations of Insulin-Like Growth Factor (IGF)-I, IGF-II, IGF Binding Protein (IGFBP)-2 and IGFBP-3 with Ultrasound Measures of Atherosclerosis and Plaque Stability in an Older Adult Population
J. Clin. Endocrinol. Metab., April 1, 2008; 93(4): 1331 - 1338.
[Abstract] [Full Text] [PDF]

Home page
 Endocr Relat CancerHome page
E. S Schernhammer, S. S Tworoger, A H. Eliassen, S. A Missmer, J. M Holly, M. N Pollak, and S. E Hankinson
Body shape throughout life and correlations with IGFs and GH
Endocr. Relat. Cancer, September 1, 2007; 14(3): 721 - 732.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
91/9/3287    most recent
Author Manuscript (PDF)
Right arrow Submit a related Letter to the Editor
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow Request Copyright Permission
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Martin, R. M.
Right arrow Articles by Gunnell, D.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Martin, R. M.
Right arrow Articles by Gunnell, D.
Related Collections
Right arrow <