We reviewed all English-language articles on associations among circulating levels of the insulin-like growth factors (IGF) and their binding proteins (IGFBP), polymorphisms in their genes, and breast cancer risk. In premenopausal women, five of eight IGF-I studies and four of six IGFBP-3 studies of circulating levels found that women in the highest quantile had more than twice the risk of developing breast cancer of those in the lowest, although in some this effect was only apparent at young ages. In postmenopausal women, however, there was no consistent effect. A simple sequence length polymorphism 1 kb 5′ to IGF-I was examined in relation to circulating levels of IGF-I (12 studies) or breast cancer risk (4 studies), but there was no convincing evidence of any effect. For an A/C polymorphism 5′ to IGFBP-3, all three studies were consistent with a modest effect on circulating levels, but no evidence of a direct effect on breast cancer risk was seen in the only relevant study. Variation within the reference range of IGF-I and IGFBP-3 may confer only modest increases in breast cancer risk, and any single polymorphism may only account for a small proportion of that variation. Nevertheless, population attributable fractions for high circulating levels of IGF-I and IGFBP-3 and for common genetic variants could be substantial. Further large studies, or combined analysis of data from existing studies, are needed to quantify these effects more precisely.

The insulin-like growth factors (IGF) I and II are peptide growth hormones that promote cellular proliferation of normal breast epithelial cells (1, 2). In the circulation, IGF-I and IGF-II form complexes with one of six different binding proteins (IGFBP), the vast majority (>90%) being with IGFBP-3 and an additional acid-labile protein subunit (3). The biological actions of the IGFs are transduced by a series of cell surface receptors. Binding of the IGFs to their binding proteins increases their half-lives from 12 minutes to >12 hours and is thought to sequester them, preventing interaction with their cell surface receptors (2). In the circulation, these tertiary complexes remain intact due to the presence of binding protein–specific protease inhibitors. In extravascular tissues, where such inhibitors are absent, cleavage of the binding proteins by a family of specific proteases reduces the stability of these complexes and liberates IGF-I and IGF-II, allowing them to interact with their cell surface receptors (4).

The IGF axis has been shown to play a role in cellular transformation and mammary carcinogenesis (5). Transgenic mice, which overexpress IGF-I (6) and IGF-II (7) specifically in the mammary gland, have an increased incidence of breast adenocarcinoma (6). Regulation of expression of IGF-I and IGF-II is complex. IGF-I is synthesized primarily in the liver, in response to growth hormone secretion, but both IGF-I and IGF-II are produced at other sites, including the breast (1, 2). Increased local production of IGF-I and IGF-II by tumors during the progression of several cancers, including breast cancer (8), suggests that activation of the IGF-I receptor is important for neoplastic growth in vivo.

The first epidemiologic studies to investigate a relationship between circulating levels of IGF-I, IGF-II, and/or IGFBP-3 and breast cancer in human subjects were published in the early 1990s (9-11). Many subsequent studies have sought to replicate—or refute—the findings of these studies. A series of twin studies (12-16) have shown that serum levels of IGF-I, IGF-II, and IGFBP-3 are determined by a combination of genetic and environmental effects. For IGF-I, estimates for the proportion of variance that is explained by genetic effects range from >80% (12, 13) to 38% (14). Several more recent epidemiologic studies have examined the role of genetic polymorphisms within or around the structural genes for IGF-I, IGF-II, and IGFBP-3 in determining serum levels of these growth factors and/or risk of breast cancer.

The purpose of this study is to review all published studies that have sought (a) associations between circulating levels of IGF-I, IGF-II, and their binding proteins and risk of breast cancer; (b) associations between polymorphisms in the IGF system and circulating levels of the protein products; and (c) associations between polymorphisms in the IGF system and breast cancer risk.

Selection of Studies

Relevant English-language articles published between January 1966 and October 2003 (inclusive) were sought from PubMed and EMBASE using a combination of thesaurus and free-text terms. The databases were searched using the keywords “insulin-like growth factor,” “IGF*,” “serum level/s,” “plasma concentration/s,” “plasma level/s,” “circulating IGF*,” “IGF*, serum,” “IGF* concentration/s,” “IGF* level/s,” “plasma IGF*,” “polymorphism,” “genetic,” and “microsatellite” alone or in combination with “breast cancer risk” or “case control or cohort.” Searches using the MESH terms “breast neoplasms,” “insulin-like growth factor I,” “insulin-like growth factor II,” “polymorphism (genetics),” and “plasma” were also carried out. Reference lists within all relevant articles and reviews were searched to identify publications (including published abstracts from conferences) not captured by the computerized searches. (Details of the search strategy are available on request.)

English-language articles were reviewed and were included if they were based on human subjects and investigated (a) associations between circulating levels of IGF-I, IGF-II, and/or their binding proteins and the risk of breast cancer; (b) associations between polymorphisms in or near the transcribed sequences of the genes coding for the IGFs/IGFBPs and circulating levels of their protein products; (c) associations between these polymorphisms and breast cancer. Articles that only investigated the role of IGFs/IGFBPs as prognostic markers rather than predictors of breast cancer risk or measured expression of IGFs/IGFBPs at the mRNA rather than the protein level were not considered in this review.

Each publication was independently reviewed by two of the authors using a standardized data extraction sheet. For studies of IGFs/IGFPs in relation to breast cancer risk, odds ratio (OR) estimates were extracted for the highest versus lowest concentration categories, with 95% confidence intervals (95% CI) and Plinear trend. The change per unit was recorded if IGF/IGFBP levels were analyzed as a continuous variable. For studies that reported an OR for the highest category versus the lowest category and a 95% CI or equivalent measure of variance, a combined estimate was calculated using inverse variance weighting (17). Because of differences in study design, exposure categorization, and adjustment for confounders, the combined estimate cannot be interpreted as a quantitative estimate of any specific comparison and should be used simply as evidence for or against a positive association between circulating levels and breast cancer risk. The percentage of total variation across studies that was due to heterogeneity rather than chance was quantified as I2 (18). For studies examining genotype in relation to circulating IGF/IGFBP levels, the frequency of the common allele in the study populations was computed, and for studies of genotype in relation to breast cancer risk, the ORs for common homozygotes versus heterozygotes and for common homozygote versus rare homozygotes were extracted (or calculated) wherever possible. Disagreement between two investigators was resolved by discussion with all authors.

Circulating Levels of IGFs and Their Binding Proteins and Risk of Breast Cancer

The first study to examine the association between circulating IGF levels and breast cancer risk was published by Peyrat et al. (9). By the end of October 2003, 21 studies had reported on the association between circulating levels of IGFs/IGFBPs and breast cancer risk. Eight case-control studies (9, 11, 19-24) were excluded from this review because their sample sizes were small, study subjects were recruited opportunistically, and comparisons of means (or median) levels of IGFs/IGFBPs between cases and controls were not adjusted for possible confounders, and no OR estimate was provided (or could be calculated). In some (19-21, 23), women with benign breast disorders were taken as controls, but later evidence indicates that IGFs/IGFBPs may be implicated in the etiology of these diseases (25). (An appendix with further details is available on request).

Main Findings. The characteristics of the 13 studies included in this review are summarized in Table 1. The study by Jernström and Barrett-Connor (26) was nested within the Rancho Bernardo cohort, but as blood samples were collected only after the diagnosis of breast cancer this study was regarded as a case-control study.

Table 1.

Characteristics of the studies that examined risk of breast cancer in relation to circulating levels of IGFs and their binding proteins

ReferenceStudy detailsCases [n, age (y),* ethnicity]Controls [n, age (y),* ethnicity]Matching variablesExclusion criteria
Case-control studies      
    Bruning et al. (10) Nov 1986-Dec 1987, Amsterdam, the Netherlands 124, range 38-75, NA 294 (P), range 38-75, NA Residence, socioeconomic status Oral contraceptive/HRT use, recent weight change, endocrine disorders, pregnancy 
    Jernström and Barrett-Connor (26) 1972-1994, California, Rancho Bernardo cohort 45 postmenopausal, mean 74 (SD 8.3), Caucasian American 393 postmenopausal (P), mean 74 (SD 9.6), Caucasian American NA HRT/tamoxifen/insulin use 
    Agurs-Collins et al. (36) Sep 1992-Dec 1995, Washington 30 postmenopausal, mean 67 (SD 8.8), African American 30 postmenopausal (P), mean 67 (SD 8.8), African American Age, socioeconomic status (indirectly) Diabetes mellitus. Controls: history of cancer/gynecologic disease/psychiatric disorders 
    Petridou et al. (32) Feb-Sep 1998, Athens, Greece 75, 12% <45; 19% >74, NA 75 (H), 12% <45; 17% >74, NA Age, residence Cancer history 
    Li et al. (38) Oct 1998-Jul 1999, Louisiana 40, mean 52 (SD 9.6), 23 African American; 17 Caucasian 40 (H), mean 52 (SD 9.3), matching variable Age, ethnicity NA 
    Yu et al. (29) Aug 1996-Mar 1998, Shanghai, China 300, mean 49 (SD 8.3), Chinese 300 (P), mean 49 (SD 8.4), Chinese Age, blood collection date, menopausal status NA 
    Hirose et al. (33) Nov 2000-Sep 2002, Japan 187, mean 53 (SD 10.6), NA, presumably Japanese 190 (H), mean 52 (SD 10.9), NA, presumably Japanese Age None 
Prospective studies (case-control studies nested within cohort studies)      
    Hankinson et al. (27) 1989/1990-31 May 1994, Nurses' Health Study cohort 397, mean 62 (SD 5.3), NA 620, mean 62 (SD 4.9), NA Age, HRT use, time of/fasting status at blood collection, menopausal status NA 
    Toniolo et al. (35) 1985-1995, New York, New York University Women's Health Study cohort 287, mean 44.8 (SD 4.8), NA 706, mean 44.7 (SD 4.7), NA Age, menopausal status, phase/day of menstrual cycle at blood collection Breast cancer diagnosed within first 6 months of enrollment 
    Kaaks et al. (34) 1985-1998, Umeå, Sweden (Västerbotten Intervention Project, Monitoring of Trends and Cardiovascular Study, Population-Based Mammary Screening Project) 246, mean 55 (range 30-70), NA 454, mean 54 (range 30-70), NA Age, subcohort, blood collection date, HRT use. Some subcohorts: menopausal status, fasting time at blood collection Oral contraceptive use, unknown HRT use at blood collection 
 1991-1996, Malmö, Sweden (Malmö Diet and Cancer Study) 267, mean 57 (range 45-73), NA 533, mean 57 (range 45-73), NA   
    Krajcik et al. (30) 1964-1991, Kaiser Permanente cohort 126, range 19-73 at breast cancer diagnosis, White 126, range 19-73, White Age, date of examination, duration of follow-up Breast cancer history, pregnancy, breast cancer diagnosed within 2 y of blood collection 
    Muti et al. (31) 1987-1995, Varese, Italy ORDET study (Hormones and Diet in the Etiology of Breast Cancer prospective cohort) 133, range 35-69, NA 503, range 35-69, NA Age, menopausal status, recruitment center and period Metabolic conditions, bilateral oophorectomy, pregnancy, breast-feeding, oral contraceptive/HRT use at time of blood collection 
    Keinan-Boker et al. (37) 1993-2000, European Prospective Investigation into Cancer and Nutrition cohort 71 postmenopausal, range 50-69, NA 163 postmenopausal, range 50-69, NA Age, cohort, date of entry, residence (for PPHV cohort) HRT/insulin use, cancer history, <12 mo between enrollment and diagnosis of breast cancer 
 1987-2000, the Netherlands PPHV cohort (Dutch project aimed at monitoring risk factors for cardiovascular disorders) 78 postmenopausal, range 20-59, NA >170 postmenopausal, range 20-59, NA   
ReferenceStudy detailsCases [n, age (y),* ethnicity]Controls [n, age (y),* ethnicity]Matching variablesExclusion criteria
Case-control studies      
    Bruning et al. (10) Nov 1986-Dec 1987, Amsterdam, the Netherlands 124, range 38-75, NA 294 (P), range 38-75, NA Residence, socioeconomic status Oral contraceptive/HRT use, recent weight change, endocrine disorders, pregnancy 
    Jernström and Barrett-Connor (26) 1972-1994, California, Rancho Bernardo cohort 45 postmenopausal, mean 74 (SD 8.3), Caucasian American 393 postmenopausal (P), mean 74 (SD 9.6), Caucasian American NA HRT/tamoxifen/insulin use 
    Agurs-Collins et al. (36) Sep 1992-Dec 1995, Washington 30 postmenopausal, mean 67 (SD 8.8), African American 30 postmenopausal (P), mean 67 (SD 8.8), African American Age, socioeconomic status (indirectly) Diabetes mellitus. Controls: history of cancer/gynecologic disease/psychiatric disorders 
    Petridou et al. (32) Feb-Sep 1998, Athens, Greece 75, 12% <45; 19% >74, NA 75 (H), 12% <45; 17% >74, NA Age, residence Cancer history 
    Li et al. (38) Oct 1998-Jul 1999, Louisiana 40, mean 52 (SD 9.6), 23 African American; 17 Caucasian 40 (H), mean 52 (SD 9.3), matching variable Age, ethnicity NA 
    Yu et al. (29) Aug 1996-Mar 1998, Shanghai, China 300, mean 49 (SD 8.3), Chinese 300 (P), mean 49 (SD 8.4), Chinese Age, blood collection date, menopausal status NA 
    Hirose et al. (33) Nov 2000-Sep 2002, Japan 187, mean 53 (SD 10.6), NA, presumably Japanese 190 (H), mean 52 (SD 10.9), NA, presumably Japanese Age None 
Prospective studies (case-control studies nested within cohort studies)      
    Hankinson et al. (27) 1989/1990-31 May 1994, Nurses' Health Study cohort 397, mean 62 (SD 5.3), NA 620, mean 62 (SD 4.9), NA Age, HRT use, time of/fasting status at blood collection, menopausal status NA 
    Toniolo et al. (35) 1985-1995, New York, New York University Women's Health Study cohort 287, mean 44.8 (SD 4.8), NA 706, mean 44.7 (SD 4.7), NA Age, menopausal status, phase/day of menstrual cycle at blood collection Breast cancer diagnosed within first 6 months of enrollment 
    Kaaks et al. (34) 1985-1998, Umeå, Sweden (Västerbotten Intervention Project, Monitoring of Trends and Cardiovascular Study, Population-Based Mammary Screening Project) 246, mean 55 (range 30-70), NA 454, mean 54 (range 30-70), NA Age, subcohort, blood collection date, HRT use. Some subcohorts: menopausal status, fasting time at blood collection Oral contraceptive use, unknown HRT use at blood collection 
 1991-1996, Malmö, Sweden (Malmö Diet and Cancer Study) 267, mean 57 (range 45-73), NA 533, mean 57 (range 45-73), NA   
    Krajcik et al. (30) 1964-1991, Kaiser Permanente cohort 126, range 19-73 at breast cancer diagnosis, White 126, range 19-73, White Age, date of examination, duration of follow-up Breast cancer history, pregnancy, breast cancer diagnosed within 2 y of blood collection 
    Muti et al. (31) 1987-1995, Varese, Italy ORDET study (Hormones and Diet in the Etiology of Breast Cancer prospective cohort) 133, range 35-69, NA 503, range 35-69, NA Age, menopausal status, recruitment center and period Metabolic conditions, bilateral oophorectomy, pregnancy, breast-feeding, oral contraceptive/HRT use at time of blood collection 
    Keinan-Boker et al. (37) 1993-2000, European Prospective Investigation into Cancer and Nutrition cohort 71 postmenopausal, range 50-69, NA 163 postmenopausal, range 50-69, NA Age, cohort, date of entry, residence (for PPHV cohort) HRT/insulin use, cancer history, <12 mo between enrollment and diagnosis of breast cancer 
 1987-2000, the Netherlands PPHV cohort (Dutch project aimed at monitoring risk factors for cardiovascular disorders) 78 postmenopausal, range 20-59, NA >170 postmenopausal, range 20-59, NA   

NOTE: NA, not given in the original publication; H, hospital-based controls; P, population-based controls.

*

Age at entry into the study unless otherwise specified.

The terminology used throughout the text and tables is as reported in the original articles.

Menopausal status only given if the study is restricted to either premenopausal or postmenopausal women.

Menopausal status at the time of blood draw may modify the effect of IGFs and IGFBPs on breast cancer risk (27), so the main results from the studies summarized in Table 1 are presented for premenopausal and postmenopausal women separately wherever possible. IGF-I levels decline with age (28) and all studies that stratified on menopausal status at the time of blood draw reported higher levels at premenopausal ages.

For premenopausal women, one of three case-control studies and three of five prospective studies reported a significant linear trend and an OR of at least 2 for women who were in the highest category of circulating IGF-I level relative to those in the lowest (refs. 27, 29-31; Table 2; Fig. 1). The weighted average of the IGF-I effect estimates across the studies [after exclusion of the study by Petridou et al. (32) where IGF-I was analyzed as a continuous variable] was 1.6. There was, however, moderate evidence of heterogeneity between the studies (I2 = 51%), with two reporting ORs of <1.0 (33, 34). Restricting the analysis to women who were both premenopausal and aged <50 years at the time of blood draw (27) or to those who were premenopausal at the time of blood draw and aged <50 years at the time of diagnosis of breast cancer (35) increased the magnitude of the IGF-I effect (Table 2). In one study (31), however, the effect of premenopausal levels of IGF-I was stronger when the analysis was restricted to women who had breast cancer after age 48 years, but the point estimates were based on rather small numbers of cases (Table 2). Five studies were adjusted for circulating levels of IGFBP-3; the effect of IGF-I was strengthened after this adjustment in one study (27) but not in the others (refs. 29, 30, 33, 35; Table 2).

Table 2.

Circulating levels of IGF-I and IGF-II and breast cancer risk (estimates in bold are adjusted for circulating levels of IGFBP-3 and those in italics are for the IGF-I/IGFBP-3 ratio)

ReferenceCasesControls
OR* (95% CI)Plinear trendUnit or category of analysis
nMean (SD) or median (range), ng/mLnMean (SD) or median (range), ng/mL
IGF-I        
Premenopausal women        
    Case-control studies        
        Petridou et al. (32) 14 182 (50) 15 197 (62) 0.4AS (0.1-1.4) 0.16 per 1 SD 
        Yu et al. (29) 171 163 (41-334) 170 146 (69-299) 2.29ADR (1.20-4.37) 0.012 U3 vs L3 
     1.92ADR (0.88-4.20) 0.236 U3 vs L3 
        Hirose et al. (33) 88 190 (86-390) 79 190 (95-420) 0.88 (0.41-1.88) 0.741 U3 vs L3 
     0.86AR (0.32-2.29) 0.783 U3 vs L3 
    Prospective studies        
        Hankinson et al. (27) 76 204 (40-425) 105 184 (81-320) 2.33 (1.06-5.16) 0.08 U3 vs L3 
     2.88 (1.21-6.85) 0.02 U3 vs L3 
     2.13 (0.97-4.68) NA U3 vs L3 IGF-I/IGFBP-3 
 60 <51 y at blood draw 206 (78-425) 78 175 (85-320) 4.58 (1.75-12.0) 0.02 U3 vs L3 
     7.28 (2.40-22.0) 0.01 U3 vs L3 
     2.46 (0.97-6.24) NA U3 vs L3 IGF-I/IGFBP-3 
        Toniolo et al. (35) 172 215 (64) 486 213 (66) 1.60AR (0.91-2.81) 0.09 U4 vs L4 
     1.49AR (0.80-2.79) NA U4 vs L4 
 96 <51 y at dx 232 (60) 280 223 (67) 2.30AR (1.07-4.94) 0.03 U4 vs L4 
     1.90AR (0.82-4.42) NA U4 vs L4 
        Kaaks et al. (34) 116 NA 330 NA 0.63 (0.29-1.39) 0.51 U4 vs L4 
        Krajcik et al. (30) 66 258 (86) 66 244 (90) 3.49A (0.65-18.7) 0.051 U4 vs L4 
     2.01A (0.33-12.4) 0.24 U4 vs L4 
        Muti et al. (31) 69 170 (55) 265 159 (60) 3.12ARS (1.13-8.60) 0.01 U4 vs L4 
 36 <48 y at dx NA 138 NA 1.52A (0.50-4.60) NA U3 vs L3 
 33 >48 y at dx NA 127 NA 15.43A (3.25-73) NA U3 vs L3 
Postmenopausal women        
    Case-control studies        
        Jernström et al. (26)§ 45 120 (41) 393 127 (54) 1.00 (1.00-1.00) 0.44 Per rank 
        Agurs-Collins et al. (36) 30 167 (33) 30 133 (50) 1.183A (1.167-1.201) <0.05 Per 10 ng/mL 
        Petridou et al. (32) 61 144 (56) 60 142 (54) 1.1AS (0.7-1.7) 0.59 Per 1 SD 
        Yu et al. (29) 128 114 (31-280) 130 106 (34-350) 1.97ADR (0.93-4.19) 0.042 U3 vs L3 
     1.56ADR (0.68-3.57) 0.166 U3 vs L3 
        Hirose et al. (33) 99 160 (73-310) 111 160 (59-370) 1.48 (0.73-3.02) 0.277 U3 vs L3 
     1.30AR (0.48-3.42) 0.594 U3 vs L3 
    Prospective studies        
        Hankinson et al. (27) 305 142 (21-390) 483 153 (24-464) 0.85 (0.53-1.39) 0.63 U5 vs L5 
     0.89 (0.51-1.55) 0.99 U5 vs L5 
        Toniolo et al. (35) 115 167 (52) 220 173 (67) 0.95R (0.49-1.86) 0.87 U4 vs L4 
        Kaaks et al. (34) 274 NA 519 NA 1.29 (0.80-2.07) 0.15 U4 vs L4 
        Krajcik et al. (30) 60 227 (71) 60 243 (76) 0.77A (0.23-2.56) 0.067 U4 vs L4 
     1.22A (0.21-6.78) 0.74 U4 vs L4 
        Muti et al. (31) 64 124 (44) 238 130 (50) 0.58ARS (0.24-1.36) 0.25 U4 vs L4 
        Keinan-Boker et al. (37) 149 NA 333 NA 1.1AR (0.6-2.1) NA U4 vs L4 
     0.7AR (0.3-1.5) NA U4 vs L4 
All women        
    Case-control studies        
        Bruning et al. (10) 109 NA 279 NA 7.34AR (1.67-32.16) 0.006 U5 vs L5 IGF-I/IGFBP-3 
        Li et al. (38) 40 106 (40-253) 40 97 (39-202) 1.75 (0.70-4.37) 0.229 U2 vs L2 
     2.00R (0.43-9.28) 0.376 U2 vs L2 
     2.25R (0.72-7.01) 0.164 U2 vs L2 IGF-I/IGFBP-37 
IGF-II        
    Case-control studies        
        Li et al. (38) 40 premenopausal and postmenopausal 605 (255-1,020) 40 613 (267-900) 0.71 (0.29-1.72) 0.446 U2 vs L2 
     0.53R (0.15-1.83) 0.318 >U2 vs L2 
        Yu et al. (29) 171 premenopausal 852 (326-1,857) 170 867 (407-1,386) 1.50ADR (0.51-4.44) 0.439 U3 vs L3 
 128 postmenopausal 867 (362-1,472) 130 810 (454-1,430) 2.17ADR (0.60-7.90) 0.367 U3 vs L3 
ReferenceCasesControls
OR* (95% CI)Plinear trendUnit or category of analysis
nMean (SD) or median (range), ng/mLnMean (SD) or median (range), ng/mL
IGF-I        
Premenopausal women        
    Case-control studies        
        Petridou et al. (32) 14 182 (50) 15 197 (62) 0.4AS (0.1-1.4) 0.16 per 1 SD 
        Yu et al. (29) 171 163 (41-334) 170 146 (69-299) 2.29ADR (1.20-4.37) 0.012 U3 vs L3 
     1.92ADR (0.88-4.20) 0.236 U3 vs L3 
        Hirose et al. (33) 88 190 (86-390) 79 190 (95-420) 0.88 (0.41-1.88) 0.741 U3 vs L3 
     0.86AR (0.32-2.29) 0.783 U3 vs L3 
    Prospective studies        
        Hankinson et al. (27) 76 204 (40-425) 105 184 (81-320) 2.33 (1.06-5.16) 0.08 U3 vs L3 
     2.88 (1.21-6.85) 0.02 U3 vs L3 
     2.13 (0.97-4.68) NA U3 vs L3 IGF-I/IGFBP-3 
 60 <51 y at blood draw 206 (78-425) 78 175 (85-320) 4.58 (1.75-12.0) 0.02 U3 vs L3 
     7.28 (2.40-22.0) 0.01 U3 vs L3 
     2.46 (0.97-6.24) NA U3 vs L3 IGF-I/IGFBP-3 
        Toniolo et al. (35) 172 215 (64) 486 213 (66) 1.60AR (0.91-2.81) 0.09 U4 vs L4 
     1.49AR (0.80-2.79) NA U4 vs L4 
 96 <51 y at dx 232 (60) 280 223 (67) 2.30AR (1.07-4.94) 0.03 U4 vs L4 
     1.90AR (0.82-4.42) NA U4 vs L4 
        Kaaks et al. (34) 116 NA 330 NA 0.63 (0.29-1.39) 0.51 U4 vs L4 
        Krajcik et al. (30) 66 258 (86) 66 244 (90) 3.49A (0.65-18.7) 0.051 U4 vs L4 
     2.01A (0.33-12.4) 0.24 U4 vs L4 
        Muti et al. (31) 69 170 (55) 265 159 (60) 3.12ARS (1.13-8.60) 0.01 U4 vs L4 
 36 <48 y at dx NA 138 NA 1.52A (0.50-4.60) NA U3 vs L3 
 33 >48 y at dx NA 127 NA 15.43A (3.25-73) NA U3 vs L3 
Postmenopausal women        
    Case-control studies        
        Jernström et al. (26)§ 45 120 (41) 393 127 (54) 1.00 (1.00-1.00) 0.44 Per rank 
        Agurs-Collins et al. (36) 30 167 (33) 30 133 (50) 1.183A (1.167-1.201) <0.05 Per 10 ng/mL 
        Petridou et al. (32) 61 144 (56) 60 142 (54) 1.1AS (0.7-1.7) 0.59 Per 1 SD 
        Yu et al. (29) 128 114 (31-280) 130 106 (34-350) 1.97ADR (0.93-4.19) 0.042 U3 vs L3 
     1.56ADR (0.68-3.57) 0.166 U3 vs L3 
        Hirose et al. (33) 99 160 (73-310) 111 160 (59-370) 1.48 (0.73-3.02) 0.277 U3 vs L3 
     1.30AR (0.48-3.42) 0.594 U3 vs L3 
    Prospective studies        
        Hankinson et al. (27) 305 142 (21-390) 483 153 (24-464) 0.85 (0.53-1.39) 0.63 U5 vs L5 
     0.89 (0.51-1.55) 0.99 U5 vs L5 
        Toniolo et al. (35) 115 167 (52) 220 173 (67) 0.95R (0.49-1.86) 0.87 U4 vs L4 
        Kaaks et al. (34) 274 NA 519 NA 1.29 (0.80-2.07) 0.15 U4 vs L4 
        Krajcik et al. (30) 60 227 (71) 60 243 (76) 0.77A (0.23-2.56) 0.067 U4 vs L4 
     1.22A (0.21-6.78) 0.74 U4 vs L4 
        Muti et al. (31) 64 124 (44) 238 130 (50) 0.58ARS (0.24-1.36) 0.25 U4 vs L4 
        Keinan-Boker et al. (37) 149 NA 333 NA 1.1AR (0.6-2.1) NA U4 vs L4 
     0.7AR (0.3-1.5) NA U4 vs L4 
All women        
    Case-control studies        
        Bruning et al. (10) 109 NA 279 NA 7.34AR (1.67-32.16) 0.006 U5 vs L5 IGF-I/IGFBP-3 
        Li et al. (38) 40 106 (40-253) 40 97 (39-202) 1.75 (0.70-4.37) 0.229 U2 vs L2 
     2.00R (0.43-9.28) 0.376 U2 vs L2 
     2.25R (0.72-7.01) 0.164 U2 vs L2 IGF-I/IGFBP-37 
IGF-II        
    Case-control studies        
        Li et al. (38) 40 premenopausal and postmenopausal 605 (255-1,020) 40 613 (267-900) 0.71 (0.29-1.72) 0.446 U2 vs L2 
     0.53R (0.15-1.83) 0.318 >U2 vs L2 
        Yu et al. (29) 171 premenopausal 852 (326-1,857) 170 867 (407-1,386) 1.50ADR (0.51-4.44) 0.439 U3 vs L3 
 128 postmenopausal 867 (362-1,472) 130 810 (454-1,430) 2.17ADR (0.60-7.90) 0.367 U3 vs L3 

NOTE: dx, diagnosis of breast cancer; L2 (U2), L3 (U3), L4 (U4), L5 (U5), lowest (highest) half, third, quarter, and fifth of the IGF-I/IGF-II distributions.

*

Cases and controls matched as indicated in Table 1; analysis further adjusted for anthropometric (A), dietary (D), reproductive (R), and socioeconomic (S) variables.

P for linear trend, unless there were only two categories (U2 vs L2) being compared.

Menopausal status as ascertained at the time of blood collection expect for Krajcik et al. (30) where it refers to the time of diagnosis of breast cancer. The study by Kaaks et al. (34) did not stratify by menopausal status but provided data separately for premenopausal age (<50 y) and postmenopausal age (≥55 y). Results for all women combined are shown here only when no data stratified by menopausal status were available in the original articles.

§

This study was nested within a large prospective study but was classified here as a case-control study because blood samples were collected after breast cancer diagnosis.

Women were ranked according to their IGF-I levels from lowest to highest. ORs adjusted for age. Further adjustment for anthropometric and reproductive variables did not affect the results (but no OR value is given in the article).

Figure 1.

Circulating levels of IGF-I and IGFBP-3 by menopausal status at the time of blood draw and risk of breast cancer.

Figure 1.

Circulating levels of IGF-I and IGFBP-3 by menopausal status at the time of blood draw and risk of breast cancer.

Close modal

None of the six prospective studies examining IGF-I and breast cancer risk in postmenopausal women found an OR that was statistically significantly different from one (Table 2; Fig. 1). Two of the five case-control studies found a positive trend in the OR of breast cancer with increasing levels of IGF-I (29, 36). The weighted average of the study-specific IGF-I effect estimates (after exclusion of the studies where data were analyzed as a continuous variable; refs. 26, 32, 36) was 1.09 and there was no evidence of heterogeneity between studies (I2 = 1.5%). Five studies adjusted for circulating levels of IGFBP-3, but this adjustment made little difference to the results (refs. 27, 29, 30, 33, 37; Table 2).

Two case-control studies provided data for women with no stratification by menopausal status or age (Table 2). One showed a strong positive association between the IGF-I/IGFBP-3 ratio, a marker of IGF-I bioavailability, and breast cancer risk, with women in the top quintile having a 7-fold increase in risk relative to those in the bottom one (10). The other reported nonsignificant positive associations with levels of IGF-I and the IGF-I/IGFBP-3 ratio but based on a small sample (only 40 cases; ref. 38).

Only two studies, both with a case-control design, have examined the role of circulating levels of IGF-II on breast cancer risk and neither found statistically significant evidence of an association, although the point estimate from the largest (29) was consistent with a positive effect (Table 2).

One of the two case-control studies that examined the effect of premenopausal IGFBP-3 levels on breast cancer risk and three of the four prospective studies reported OR estimates of at least 2 (refs. 29-31, 35; Table 3; Fig. 1), but some had wide 95% CI, and in one study (35), a borderline statistically significant effect was seen only when the analysis was restricted to women who were ages <51 years at the time of breast cancer diagnosis (Table 3). The weighted average of the study-specific IGFBP-3 effect estimates was 1.62, consistent with a positive association, but with evidence of moderate heterogeneity between studies (I2 = 54%). In addition to the data presented in Table 3 or Fig. 1, Bruning et al. (10) reported statistically significantly lower circulating levels of IGFBP-3 in premenopausal cases than in controls (P = 0.028) but gave no estimate of the effect size.

Table 3.

Summary of findings from studies that examined breast cancer risk in relation to circulating levels of IGFBPs (estimates given in bold are adjusted for circulating levels of IGF-I)

ReferenceCases
Controls
OR* (95% CI)Plinear trendUnit or category of analysis
nMean (SD) or median (range), ng/mLnMean (SD) or median (range), ng/mL
IGFBP-3        
Premenopausal women        
    Case-control studies        
        Yu et al. (29) 171 4,224 (2,100-9,767) 170 3,901 (2,263-10,740) 3.71ADR (1.67-8.26) 0.002 U3 vs L3 
     2.69ADR (1.12-6.47) 0.022 U3 vs L3 
        Hirose et al. (33) 88 2,860 (2,360-3,410) 79 2,890 (2,270-3,410) 0.68 (0.30-1.54) 0.385 U3 vs L3 
     0.88AR (0.31-2.49) 0.762 U3 vs L3 
    Prospective studies        
        Toniolo et al. (35) 172 3,420 (787) 486 3,310 (661) 1.18AR (0.66-2.08) 0.63 U4 vs L4 
 96 <51 y at dx 3,530 (784) 280 3,310 (669) 2.17AR (0.99-4.76) 0.14 U4 vs L4 
        Kaaks et al. (34) 121 NA 230 NA 1.37 (0.65-2.91) 0.88 U4 vs L4 
        Krajcik et al. (30) 66 2,510 (700) 66 2,310 (670) 2.37 (0.85-6.55) 0.075 U4 vs L4 
     5.28A (1.13-24.7) 0.033 U4 vs L4 
        Muti et al. (31) 69 3,754 (965) 265 3,549 (753) 2.31ARS (0.97-5.53) 0.02 U4 vs L4 
Postmenopausal women        
    Case-control studies        
        Yu et al. (29) 128 4,597 (2,209-11,810) 130 4,189 (1,513-10,730) 2.60ADR (1.03-6.56) 0.044 U3 vs L3 
     2.11ADR (0.76-5.87) 0.178 U3 vs L3 
        Hirose et al. (33) 99 2,890 (1,680-3,940) 111 2,890 (1,630-3,900) 0.91 (0.47-1.77) 0.784 U3 vs L3 
     0.60AR (0.24-1.47) 0.266 U3 vs L3 
    Prospective studies        
        Toniolo et al. (35) 115 3,180 (751) 220 3,200 (742) 1.08R (0.54-2.16) 0.83 U4 vs L4 
        Kaaks et al. (34) 274 NA 519 NA 1.46 (0.92-2.32) 0.30 U4 vs L4 
        Krajcik et al. (30) 60 2,220 (530) 60 2,420 (660) 0.44 (0.15-1.28) 0.10 U4 vs L4 
     0.32A (0.07-1.41) 0.09 U4 vs L4 
        Muti et al. (31) 64 3,690 (1,026) 238 3,740 (806) 0.73ARS (0.30-1.74) 0.53 U4 vs L4 
        Keinan-Boker et al. (37) 149 NA 333 NA 1.6AR (0.7-3.5) NA U4 vs L4 
     1.4AR (0.6-3.4) NA U4 vs L4 
All women        
    Case-control studies        
        Li et al. (38)
 
40
 
3,020 (1,130-4,910)
 
40
 
2,720 (1,360-4,480)
 
1.12 (0.42-2.94)
 
0.824
 
U2 vs L2
 
     0.65 (0.16-2.58) 0.536 U2 vs L2 
IGFBP-1        
Prospective studies        
    Kaaks et al. (34) 246 premenopausal and postmenopausal NA 454 premenopausal and postmenopausal NA 1.65 (0.90-3.02) 0.17 U4 vs L4 
    Krajcik et al. (30) 66 premenopausal 46 (36) 66 premenopausal 42 (33) 1.0 (0.38-2.66) 0.56 U4 vs L4 
     2.40A (0.61-9.51) 0.18 U4 vs L4 
 60 postmenopausal 44 (35) 60 postmenopausal 43 (29) 1.0 (0.36-2.80) 0.85 U4 vs L4 
     1.96A (0.35-10.9) 0.75 U4 vs L4 
    Muti et al. (31) 69 premenopausal 32 (21) 265 premenopausal 30 (18) 0.96ARS (0.39-2.38) 0.76 U4 vs L4 
 64 postmenopausal 39 (19) 238 postmenopausal 38 (21) 1.70ARS (0.70-4.15) 0.50 U4 vs L4 
    Keinan-Boker et al. (37)
 
144 postmenopausal
 
NA
 
333 postmenopausal
 
NA
 
0.7AR (0.3-1.3)
 
NA
 
U4 vs L4
 
IGFBP-2        
Prospective studies        
    Kaaks et al. (34) 246 premenopausal and postmenopausal NA 454 premenopausal and postmenopausal NA 1.09 (0.57-2.09) 0.97 U4 vs L4 
    Krajcik et al. (30) 66 premenopausal 376 (208) 66 premenopausal 393 (226) 1.00 (0.37-2.67) 0.54 U4 vs L4 
     1.10 (0.30-4.07) 0.69 U4 vs L4 
 60 postmenopausal 470 (247) 60 postmenopausal 550 (253) 0.29 (0.09-0.92) 0.007 U4 vs L4 
     0.11 (0.02-0.66) 0.002 U4 vs L4 
    Muti et al. (31) 69 premenopausal 416 (189) 265 premenopausal 428 (343) 0.66ARS (0.26-1.64) 0.48 U4 vs L4 
 64 postmenopausal 436 (251) 238 postmenopausal 410 (194) 0.87ARS (0.39-1.92) 0.36 U4 vs L4 
    Keinan-Boker et al. (37) 149 postmenopausal NA 333 postmenopausal NA 1.1AR (0.5-2.4) NA U4 vs L4 
ReferenceCases
Controls
OR* (95% CI)Plinear trendUnit or category of analysis
nMean (SD) or median (range), ng/mLnMean (SD) or median (range), ng/mL
IGFBP-3        
Premenopausal women        
    Case-control studies        
        Yu et al. (29) 171 4,224 (2,100-9,767) 170 3,901 (2,263-10,740) 3.71ADR (1.67-8.26) 0.002 U3 vs L3 
     2.69ADR (1.12-6.47) 0.022 U3 vs L3 
        Hirose et al. (33) 88 2,860 (2,360-3,410) 79 2,890 (2,270-3,410) 0.68 (0.30-1.54) 0.385 U3 vs L3 
     0.88AR (0.31-2.49) 0.762 U3 vs L3 
    Prospective studies        
        Toniolo et al. (35) 172 3,420 (787) 486 3,310 (661) 1.18AR (0.66-2.08) 0.63 U4 vs L4 
 96 <51 y at dx 3,530 (784) 280 3,310 (669) 2.17AR (0.99-4.76) 0.14 U4 vs L4 
        Kaaks et al. (34) 121 NA 230 NA 1.37 (0.65-2.91) 0.88 U4 vs L4 
        Krajcik et al. (30) 66 2,510 (700) 66 2,310 (670) 2.37 (0.85-6.55) 0.075 U4 vs L4 
     5.28A (1.13-24.7) 0.033 U4 vs L4 
        Muti et al. (31) 69 3,754 (965) 265 3,549 (753) 2.31ARS (0.97-5.53) 0.02 U4 vs L4 
Postmenopausal women        
    Case-control studies        
        Yu et al. (29) 128 4,597 (2,209-11,810) 130 4,189 (1,513-10,730) 2.60ADR (1.03-6.56) 0.044 U3 vs L3 
     2.11ADR (0.76-5.87) 0.178 U3 vs L3 
        Hirose et al. (33) 99 2,890 (1,680-3,940) 111 2,890 (1,630-3,900) 0.91 (0.47-1.77) 0.784 U3 vs L3 
     0.60AR (0.24-1.47) 0.266 U3 vs L3 
    Prospective studies        
        Toniolo et al. (35) 115 3,180 (751) 220 3,200 (742) 1.08R (0.54-2.16) 0.83 U4 vs L4 
        Kaaks et al. (34) 274 NA 519 NA 1.46 (0.92-2.32) 0.30 U4 vs L4 
        Krajcik et al. (30) 60 2,220 (530) 60 2,420 (660) 0.44 (0.15-1.28) 0.10 U4 vs L4 
     0.32A (0.07-1.41) 0.09 U4 vs L4 
        Muti et al. (31) 64 3,690 (1,026) 238 3,740 (806) 0.73ARS (0.30-1.74) 0.53 U4 vs L4 
        Keinan-Boker et al. (37) 149 NA 333 NA 1.6AR (0.7-3.5) NA U4 vs L4 
     1.4AR (0.6-3.4) NA U4 vs L4 
All women        
    Case-control studies        
        Li et al. (38)
 
40
 
3,020 (1,130-4,910)
 
40
 
2,720 (1,360-4,480)
 
1.12 (0.42-2.94)
 
0.824
 
U2 vs L2
 
     0.65 (0.16-2.58) 0.536 U2 vs L2 
IGFBP-1        
Prospective studies        
    Kaaks et al. (34) 246 premenopausal and postmenopausal NA 454 premenopausal and postmenopausal NA 1.65 (0.90-3.02) 0.17 U4 vs L4 
    Krajcik et al. (30) 66 premenopausal 46 (36) 66 premenopausal 42 (33) 1.0 (0.38-2.66) 0.56 U4 vs L4 
     2.40A (0.61-9.51) 0.18 U4 vs L4 
 60 postmenopausal 44 (35) 60 postmenopausal 43 (29) 1.0 (0.36-2.80) 0.85 U4 vs L4 
     1.96A (0.35-10.9) 0.75 U4 vs L4 
    Muti et al. (31) 69 premenopausal 32 (21) 265 premenopausal 30 (18) 0.96ARS (0.39-2.38) 0.76 U4 vs L4 
 64 postmenopausal 39 (19) 238 postmenopausal 38 (21) 1.70ARS (0.70-4.15) 0.50 U4 vs L4 
    Keinan-Boker et al. (37)
 
144 postmenopausal
 
NA
 
333 postmenopausal
 
NA
 
0.7AR (0.3-1.3)
 
NA
 
U4 vs L4
 
IGFBP-2        
Prospective studies        
    Kaaks et al. (34) 246 premenopausal and postmenopausal NA 454 premenopausal and postmenopausal NA 1.09 (0.57-2.09) 0.97 U4 vs L4 
    Krajcik et al. (30) 66 premenopausal 376 (208) 66 premenopausal 393 (226) 1.00 (0.37-2.67) 0.54 U4 vs L4 
     1.10 (0.30-4.07) 0.69 U4 vs L4 
 60 postmenopausal 470 (247) 60 postmenopausal 550 (253) 0.29 (0.09-0.92) 0.007 U4 vs L4 
     0.11 (0.02-0.66) 0.002 U4 vs L4 
    Muti et al. (31) 69 premenopausal 416 (189) 265 premenopausal 428 (343) 0.66ARS (0.26-1.64) 0.48 U4 vs L4 
 64 postmenopausal 436 (251) 238 postmenopausal 410 (194) 0.87ARS (0.39-1.92) 0.36 U4 vs L4 
    Keinan-Boker et al. (37) 149 postmenopausal NA 333 postmenopausal NA 1.1AR (0.5-2.4) NA U4 vs L4 

NOTE: Two studies were not included in this table because they did not provide ORs for IGFBP-3. The study by Bruning et al. (10) only provided data on the IGF-I/IGFBP-3 ratio (see Table 2) and the study by Hankinson et al. (27) reported a “nonsignificant inverse association between IGFBP-3 and breast cancer risk,” but no effect estimates were given. L2 (U2), L3 (U3), L4 (U4), and L5 (U5), lowest (highest) half, third, quarter, and fifth of the IGFBP distributions.

*

Cases and controls matched as indicated in Table 1; analysis further adjusted for anthropometric (A), dietary (D), reproductive (R), and socioeconomic (S) variables.

P for linear trend, unless there were only two categories (U2 vs L2) being compared.

Menopausal status as ascertained at the time of blood collection expect for Krajcik et al. (30) where it refers to the time of diagnosis of breast cancer. The study by Kaaks et al. (34) did not stratify the analyses by menopausal status but presented data separately for premenopausal age (<50 y) and postmenopausal age (≥55 y). Results for all women combined are shown here only when no data stratified by menopausal status were available in the original articles.

Among postmenopausal women, most studies found no association of IGFBP-3 levels with breast cancer risk (Table 3; Fig. 1), although one case-control study (29) reported a statistically significant positive linear trend. The weighted average of the estimates for IGFBP-3 was consistent with an OR of 1.0 and evidence of moderate heterogeneity between studies (I2 = 35%). In addition, Bruning et al. (10) reported similar IGFBP-3 levels in postmenopausal cases and controls (P = 0.86). No overall association with IGFBP-3 levels was observed in the only study that did not stratify by menopausal status (38). Hankinson et al. (27) reported a “nonsignificant inverse association between IGFBP-3 and breast cancer risk,” but no effect estimates were provided.

Few studies have examined the association of circulating levels of other IGFBPs with breast cancer risk (Table 3). Four prospective studies analyzed IGFBP-1, but none found a statistically significant effect; however, one study (37) collected nonfasting blood samples. Four prospective studies investigated the role of IGFBP-2, and of these, one study (30) reported a statistically significant protective effect, which was restricted to postmenopausal women.

Study Design and Study Populations. Studies where controls were recruited “opportunistically,” using convenience samples, such as other hospital patients without breast cancer or employees, may be subject to selection bias (38). Studies where controls were drawn from approximately the same population as cases are less likely to be biased (10, 29). Case-control comparisons nested within large prospective studies are the most informative, as they reduce the potential for selection bias as well as measure IGFs/IGFBPs levels before disease onset.

In studies with a non-nested case-control design, the possibility of reverse causality cannot be excluded, as IGF/IGFBP measurements were done only after breast cancer had been diagnosed and sometimes after treatment. Although breast tumor cells express and secrete IGFs, particularly IGF-II and IGFBPs, it is unlikely that tumor production significantly affects postdiagnostic measurements of IGFs and IGFBP-3, as the basal circulating levels of these proteins are very high and their clearance from circulation is very slow. IGFBP-2 is an exception, as it normally circulates at much lower levels. Of much greater concern in case-control studies is the fact that circulating concentrations of IGF-I and IGFBP-3 progressively decline in patients with cancer, consistent with a catabolic host response in which metabolism can be substantially affected even if the tumor is extremely small in relation to body mass. Treatment effects and postdiagnostic changes in lifestyle, particularly diet, further affect circulating levels of the IGFs and their binding proteins (39).

All prospective studies in this review were based on incident cases, but for many case-control studies, it is unclear whether incident or prevalent cases (or both) were recruited. Studies based on prevalent cases (26) could have been affected by survival bias, as circulating levels of IGFs/IGFBPs may predict prognosis of breast cancer (24).

Case and control definitions varied between studies. Some were restricted to histologically confirmed cases (32, 34, 36, 38) or to early disease (10) or operable disease (33). Two studies (27, 34) included both invasive and in situ tumors, but only one presented data separately for each one of these type of tumors (34).

Potential Confounding Variables and/or Effect Modifiers. Mean circulating levels of IGF-I and IGFBP-3 increase from birth to puberty and progressively decline throughout the remainder of life in both sexes (28). The majority of studies took account of the potential confounding effect of age either by matching cases and controls on this variable or by adjusting for it (Tables 1-3).

Ethnic differences in circulating levels of IGF-I and IGFBP-3 have been reported (40, 41). Only five studies provided explicit information on the ethnic origin of their study subjects. Of these, four studies were restricted to a single ethnic group [African American (36), Chinese (29), Caucasian American (26), or White women (30)] and one (38) was matched on ethnicity.

Some studies (26, 29-33, 35-37) adjusted for nutritional intake and/or anthropometric measurements, but adjusted and unadjusted estimates were similar in the few studies that provided both. In principle, however, such adjustment would not be appropriate if these factors lie in the causal pathway between IGFs/IGFBPs and breast cancer. Nutritional intake is a strong determinant of IGF-I plasma concentration in humans, with high-energy diets increasing and energy restriction decreasing circulating levels (42, 43). Adult height and weight are associated with breast cancer risk (44), and some studies (45-47), but not all (46-49), have reported associations of circulating levels of IGF-I and/or IGFBP-3 in adults with height in childhood and adulthood and with adult body mass index. Thus, circulating levels of IGF-I and IGFBP-3 could reflect the relationship among nutrition, growth, and breast cancer risk (42, 50).

Exclusion criteria also varied (Table 1). Some studies excluded women with conditions that are thought to affect circulating levels of IGFs/IGFBPs, such as diabetes mellitus, hepatic disorders, endocrine dysfunction, or nutritional-related problems. Oral contraceptives (51) and hormone replacement therapy (HRT; ref. 47) decrease circulating levels of IGF-I; therefore, the use of these hormones should be taken into account when investigating the role of IGF-I on breast cancer risk. However, the IGF-I effect estimates from the few studies that excluded users (10, 26) or matched/adjusted for hormone use (27, 31, 34, 37) were not consistent (Table 1).

Sample Size. The total number ranged from 30 (36) to 513 (34) for cases and from 30 (36) to 987 (34) for controls, with four case-control studies (26, 32, 36, 38) but no prospective study, having <100 breast cancer cases. Few studies were large enough to satisfy conventional criteria of adequate statistical power, however. For example, ∼300 or 800 cases and equal numbers of controls would be required in each menopausal stratum to ensure that the study would have 90% power to detect an OR (comparing the top quartile with the bottom quartile) of 2.0 or 1.5, respectively, at the 5% significance level. Few studies have these many cases and controls in each menopausal stratum.

Blood Sample Collection and Laboratory Assays. Some studies collected samples after a period of fasting or at particular times of the day. A fasting sample is essential for measurement of IGFBP-1, as circulating levels change acutely, under insulin regulation, throughout the day. There is much less variation for IGF-I, IGFBP-3, and IGFBP-2. Small nighttime variations in IGF-I and IGFBP-3 levels have been described, but these are probably due to posture-related fluid redistribution (52).

Samples were stored at temperatures ranging from −20°C to −80°C. Only a few studies provide information on storage time, but all prospective studies and one case-control study (29) matched cases and controls on time of sample collection. The case-control study by Petridou et al. (32) was not matched on storage time but adjust for it in the analyses. In some studies (e.g., ref. 30), samples had been thawed previously and then refrozen, and this may have affected measurements, particularly of IGFBP-3, which is susceptible to degradation.

IGF/IGFBP concentrations were determined by a variety of assays, including RIA, immunoradiometric assay, and ELISA, either using commercial kits or in-house assays. Commercial kits for measuring IGF-I and IGFBP-3 were developed for the diagnosis of growth hormone disorders, such as acromegaly, growth hormone deficiency, or resistance rather than the investigation of relatively small interindividual variations within normal populations. It is now apparent that the performance of many assays is far from optimal when used to rank individuals within the normal concentration range. The large variation seen between assays, and between batches for some assays, is likely to attenuate the ability of epidemiologic studies to detect statistically significant exposure-disease associations and to provide precise point estimates of the effect.

Conclusions. Overall, the findings are consistent with a positive association between premenopausal levels of IGF-I and IGFBP-3 and subsequent risk of breast cancer. For IGF-I, this is consistent with findings from laboratory-based research and with studies showing similar effects on breast mammographic density (25). For IGFBP-3, the picture is more complex, as the original hypothesis was that high circulating levels of IGFBP-3 would protect against breast cancer by sequestering IGF-I and preventing it from interacting with cell surface receptors. The epidemiologic evidence, however, suggests that in premenopausal women at least high levels of IGFBP-3 may be associated, independently or as a marker of other biological processes, with an increased risk of breast cancer. IGFBP-3 has been found to exert dual regulatory effects on IGF-I action. By binding IGF-I, IGFBP-3 also increases the half-life of IGF-I, protecting it from degradation and hence increasing the amount that can reach local tissues. Thus, although IGFBP-3 can inhibit the action of IGF-I on cell proliferation and apoptosis, it may also enhance its effects by increasing pericellular concentrations of IGF-I (53).

Although the relative risks associated with high levels of IGF-I and IGFBP-3 are likely to be relatively modest, this exposure could still account for a considerable proportion of breast cancer cases, as the percentage of premenopausal women exposed to high circulating levels of these proteins is high. Assuming a linear association between premenopausal levels of these proteins and risk of subsequent breast cancer, with OR estimates in the second, third, and fourth quartiles relative to the bottom quartile of 1.2, 1.4, and 1.6, respectively (the latter being consistent with the weighted average of study-specific effect estimates found here), the population attributable risk fraction for high levels of each one of these proteins would be ∼20%. These calculations are, however, rather simplistic, as the effects of IGF-I and IGFBP-3 on breast cancer risk may not be independent.

5′ Simple Tandem Repeat and Serum Levels of IGF-I

Main Findings. Twelve of the 13 articles that investigated circulating levels of IGF-I in relation to polymorphisms focused on a simple tandem repeat (STR) that lies 1 kb 5′ to the IGF-I gene transcriptional start site. This 5′ STR was first identified by Rotwein et al. (54). In normal Caucasian populations, the common allele is the 19 CA repeat allele, which occurs at a frequency estimated to be between 59% (55) and 70% (56), except in one study based on a sample of 56 individuals (57) in which it was estimated to be 30%. Overall, the repeat lengths vary from a minimum of 10 repeats to a maximum of 23 repeats with two alleles, the 19 repeat allele and the 20 repeat allele predominating in Caucasians (70% and 17%, respectively; ref. 58). In other ethnic groups, the distribution of alleles was less extreme. In Black women, for instance, the 18, 19, 20, and 21 repeat alleles occurred at frequencies of 16%, 38%, 19%, and 14%, respectively (56).

Rosen et al. (55) first reported that the 19/19 genotype was associated with decreased levels of serum IGF-I in men and women. Table 4 (and Fig. 2 for Caucasians) summarize the characteristics of this and the 11 subsequent publications that have sought to replicate this association. These 12 publications represent 10 independent comparisons; Jernström et al. (56, 58) report analyses on overlapping samples, as do Vaessen et al. (59) and Rietveld et al. (60). Each study has carried out a slightly different main comparison. Some compared homozygotes for the 19 repeat allele (19/19) with all other genotypes combined (19/− and −/−), some compared all 19 allele carriers (19/19 and 19/−) with those who had no copies of the 19 repeat allele (−/−), and some compared homozygotes for the 19 repeat allele (19/19) with heterozygotes (19/−) and non–19 allele carriers (−/−) separately. Most studies adjusted for age and gender, and several studies (56-58, 60-63) adjusted for additional covariates. Of these 10 comparisons, 3 (55, 58, 64) reported a significant (P < 0.05) association between the 19 allele and lower circulating levels of IGF-I, 5 (57, 62, 63, 65, 66) found no genotype effect, and 2 (59-61) reported a significant association between the 19 allele and higher circulating IGF-I levels (Table 4).

Table 4.

Studies investigating polymorphisms in the IGF-I, IGF-II, and IGFBP-3 genes in relation to measurements of serum levels of their protein products

ReferenceStudy detailsStudy populationExclusion criteriaAllele frequency*Variables matched or adjusted forArithmetic mean* (SE or 95% CI), ng/mLAnalysis
IGF-I (CA 5′ STR)        
Rosen et al. (55) Study period NA, Richmond, VA. Observational study of hormonal determinants of bone mineral density 30 men, age mean 48.0 y Conditions and medications known to affect serum IGF-I or bone density CA19: 59% for all three combined. Ethnicity Caucasian. HWE NA None for main analysis. Some analysis stratified by gender 129 (7) vs 154 (9); P = 0.03 in 59 men and 57 women combined 19/19 vs (19/− and −/−) 
 Study period NA, Maine, randomized controlled trial of calcium supplementation 37 postmenopausal women, age mean 72.3 y    158.6 (9.0) vs 188.8 (8.6); P = 0.02 in men only (n = 89) 19/19 vs (19/− and −/−) 
 Study period NA, location NA. Cardiology referrals 59 men, 20 women, age mean 58.6 y    “No difference” between 19/− and any other genotype  
Jernström et al. (58) Study period NA, Toronto, Canada. Healthy volunteers 311 women, age mean 25.4 y (range 17-35) Non-Caucasian, history of pregnancy/cancer/diabetes, hysterectomy, current estrogen non–oral contraceptive use CA19: Allele frequency or genotypes for entire group NA. From graphs, −/− (n = 26) and “one or two 19” alleles (n = 263; 309 total). Ethnicity Caucasian. HWE NA Age, estrogen dose (in oral contraceptive users) 264 vs 315; P = 0.025 in oral contraceptive users (19/19 and 19/−) vs −/− 
     Effect only given stratified by oral contraceptive use 305 vs 287 in non–oral contraceptive users (approximate figures from graphs, exact figures NA) (19/19 and 19/−) vs −/− 
     Test for interaction between genotype and oral contraceptive use P = 0.04   
Jernström et al. (56) Study period NA, Toronto, Canada. Healthy volunteers 507 women, age mean NA (range, 17-35 y) Mixed ethnicity, history of pregnancy/cancer/diabetes, hysterectomy, current estrogen non–oral contraceptive use CA19: White§ 70.2%, Black 37.8%, Asian 40.8%, Indian/Pakistani 56.0%. Ethnicity 329 White, 78 Black, 71 Asian, 25 Indian/Pakistani. HWE NA Age, estrogen dose (in oral contraceptive users), not ethnicity 266 vs 338; P = 0.00007 in oral contraceptive users (19/19 and 19/−) vs −/− 
     Effect only given stratified by oral contraceptive use 305 vs 312 in non–oral contraceptive users (approximate figures from graphs, exact figures NA) (19/19 and 19/−) vs −/− 
     Test for interaction between genotype and oral contraceptive use P = 0.002   
Vaessen et al. (59) 1990-1993, Rotterdam, the Netherlands. Population-based cohort (myocardial infarction cases) 55 men, 95 women. 50 each of 19/19, 19/−, and −/−, age mean 60.7 y Age <55 or >75 y, medication for non-insulin-dependent diabetes mellitus/hormone conditions CA19: 67.6%. Ethnicity Caucasian. HWE P = 0.76 Age 157.7 vs 128.5 (geometric means) 19/19 vs −/− 
      29.2 (−46.2 to −10.0; P = 0.003) Mean difference 
Allen et al. (62) 1994-1997, Oxford, United Kingdom. European Prospective Investigation into Cancer and Nutrition 660 men, age mean 47 y (range 20-78) Cancer history, certain medications, estimated energy intake <3.1 or >18.4 mJ/d CA19: 64.2%. Ethnicity Caucasian. HWE P = 0.95 Age, body mass index dietary group, smoking, days between blood collection and processing 151.5 (148.5-156.9) vs 150.8 (146.2-155.4) vs 150.0 (140.8-158.5; Plinear trend = 0.66) 19/19 vs 19/− vs −/− 
       Also tested 19/19 and 19/− vs −/− (NS) and repeat length vs serum levels 
Frayling et al. (64) 1997-1999, Barry and Caerphilly, Wales. Adult offspring of mothers in randomized controlled trial of milk token supplementation 342 men, 298 women, age 25 y (all) Insulin-dependent diabetes mellitus CA19: 62.9%. Ethnicity Caucasian. HWE P = 0.99 Age, gender 133.2 (127.2-139.5) vs 143.5 (137.6-149.6) vs 142.8 (133.4-152.5; Plinear trend = 0.01) 19/19 vs 19/− vs −/− 
Giovannucci et al. (65) 1989-Jun 1994, Nurses' Healthy Study cohort (colorectal adenoma cases) 202 cases, 202 controls, age mean NA Family history of colorectal cancer, history of cancer or adenoma CA19: 61.8%. Ethnicity NA. HWE NA Age, month of/fasting status at blood draw, year of (and indications for) endoscopy 168.6 vs 167.7 (P = 0.88), cases and controls combined 19/19 vs (19/− and −/−) 
      168.6 vs 166.4 (P = 0.76) cases and controls combined 19/19 vs 19/20 
      (figures for controls only NA)  
Kim et al. (63) Study period NA, Seoul, Korea. Attending menopause clinic for bone mineral density measurement 229 postmenopausal women, age mean NA (range 45-75 y) Premenopausal, bilateral oophorectomy, hepatic/renal disease, medications affecting bone metabolism CA19: 14.3%. Ethnicity Korean. HWE NA Age, body mass index, years since menopause 140 vs 150 vs 160 (P > 0.05; approximate figures from graphs, exact figures NA) 19/19 vs 19/− vs −/− 
      Higher levels in women with 20/20 genotype (P < 0.005) 20/20 vs 20/− vs −/− 
Missmer et al. (61) 1989-Jun 1994, Nurses' Health Study cohort 418 control women, age mean NA Cancer history CA19: 63.3% (n = 622). Ethnicity all but eight women (cases + controls) Caucasian. HWE NA Age, HRT use, menopausal status, time of/fasting status at blood draw 173 vs 146 (P = 0.005) all women combined 19/19 vs 19/− vs −/− 
     Stratified by fasting status, time of blood draw, HRT use 183 vs 175 vs 155 (P = 0.10) premenopausal women 19/19 vs −/− 
      Values NA (P = 0.78) premenopausal women 19/19 vs 19/− 
DeLellis et al. (66) 1993-1996, Hawaii and Los Angeles, Multiethnic cohort 230 postmenopausal women, age mean NA (range 45-75 y) Cancer history, HRT use in previous 2 wk CA19: African American 40.8%, Japanese American 39.0%, non-Latino White 69.1%, Latino White 64.0%. Genotypes 19/19 vs 19/− vs −/− differed “significantly” between ethnic groups. Ethnicity 65 African American, 50 Japanese American, 47 non-Latino White, 68 Latino White. HWE no evidence for departure (P = NA) Crude and age-adjusted estimates presented African American 150 (106-194) vs 166 (142-190) vs 150 (121-180; P ≥ 0.05) 19/19 vs 19/− vs −/− 
      Japanese American 146 (101-190) vs 148 (130-167) vs 144 (119-169; P > 0.05) 19/19 vs 19/− vs −/− 
      Non-Latino White 146 (125-167) vs 142 (121-162) vs 160 (104-216; P > 0.05) 19/19 vs 19/− vs −/− 
      Latino White 118 (93-142) vs 126 (101-151) vs 149 (107-192; P > 0.05) 19/19 vs 19/− vs −/− 
Kato et al. (57) 1998-1999, Louisiana, Prostate screening program, breast clinic attendees, hospital employees 60 men, age mean NA (range 48-86). 53 women, age mean NA (range 22-73) Diabetes, cancer history, “any other serious condition” CA19: Caucasians 30.4%, African Americans 18.4%. Frequency of 19 allele higher in Caucasian (P = 0.04). Ethnicity 56 Caucasian, 57 African American. HWE tested in all ethnic groups combined (P = 0.219) Age, ethnicity matching to prostate/breast cancer cases: (a) hormone use, age, smoking, log height (African American women); (b) hormone use and age <40 (Caucasian women), (c) log-height, log body mass index (African American men) NA (Caucasian men) African American women 110.8 vs 95.5 (P = 0.35) 19/− vs −/− 
      Caucasian women 88.2 vs 92.3 (P = 0.79) 19/− vs −/− 
      African American men 106.9 vs 94.9 (P = 0.43) 19/− vs −/− 
      Caucasian men 94.8 vs 118.7 (P = 0.091) 19/− vs −/− 
Rietveld et al. (60) 1990-1993, Rotterdam, the Netherlands. Population-based cohort (myocardial infarction cases) 80 men, 88 women, age mean 67.4 y Age <55 or >75 y CA19: 63.7% for random sample. Ethnicity Caucasian. HWE NA Age group to myocardial infarction cases, gender, body mass index 143.8 (134.8-152.8) vs 126.9 (117.9-135.9; P = 0.01). Combined figures for random sample (n = 168) and for those selected on genotype (n = 150) 19/19 vs −/− 
  150 selected on IGF-I genotype (59), age NA
 
Age <55 or >75 y, diabetes, HRT
 
    
IGF-I (intronic STR)        
Arends et al. (70)
 
Study period NA, location NA. Family-based association study comparing transmission of alleles
 
124 children born small for gestational age and their parents, age mean NA Neonatal complications, endocrine and metabolic disorders (children)
 
NA (not population-based). Ethnicity 113 Caucasians, 1 Asian, 1 Indo-Mediterranean, 4 mixed
 
 Showed preferential transmission of 191-bp allele (P = 0.02) and lower IGF-I levels in children with this allele. −1.1 vs −0.5 SD scores (P = 0.03)  
IGF-II (A/G ApaI RFLP in 3′ untranslated region)        
O'Dell et al. (73)
 
Study period NA, Northwick Park, United Kingdom. Northwick Park Heart cohort
 
92 men (48 common, 44 rare homozygotes), age mean NA (range 45-65 y)
 
NA
 
A 0.28, G 0.72. Ethnicity Caucasian. HWE “applied”
 
 614.0 ± 124.0 vs 683.3 ± 146.9 (P = 0.01)
 
GG vs AA
 
IGFBP-3 (A/C single nucleotide polymorphism at nucleotide −202)        
Deal et al. (75) 1982-1995, Physicians' Health Study (colorectal cancer cases) 478 men (cases and controls), age mean NA (range 40-84 y) History of cancer, myocardial infarction, stroke, transient ischemic attack, current liver/renal disease, peptic ulcer, gout, vitamin A use, β-carotene supplement at start of study A 0.46, C 0.54. Ethnicity NA. HWE tested in cases and controls combined R2 for polymorphism was 0.077 3,274 vs 2,753 (SD NA) for controls only AA vs CC 
      3180 vs 3000 vs 2760 in cases and controls combined (approximate figures from graphs, exact figures NA) AA vs AC vs CC 
Jernström et al. (56) Study period NA, Toronto, Canada. Healthy volunteers 311 women, age 25.4 y (range 17-35) Non-Caucasian, ever pregnant, cancer, diabetes, hysterectomy, estrogen non–oral contraceptive use A 0.47, C 0.53. Ethnicity Caucasian. HWE NA  4,390 vs 4,130 vs 3,840 AA vs AC vs CC 
Schernhammer et al. (80) 1989-Jun 1996, Nurses' Health Study cohort (breast cancer cases) 943 women (cases and controls), age mean NA Cancer history A 0.46, C 0.54 in cases and controls combined. Ethnicity NA. HWE tested in cases and controls combined (P > 0.90) Age, menopausal status, HRT use, blood draw details. R2 for polymorphism was 0.06 4,426 (4,291-4,561) vs 4,060 (3,970-4,150) vs 3,697 (3,581-3,813) in cases and controls combined (Plinear trend < 0.001) AA vs AC vs CC 
ReferenceStudy detailsStudy populationExclusion criteriaAllele frequency*Variables matched or adjusted forArithmetic mean* (SE or 95% CI), ng/mLAnalysis
IGF-I (CA 5′ STR)        
Rosen et al. (55) Study period NA, Richmond, VA. Observational study of hormonal determinants of bone mineral density 30 men, age mean 48.0 y Conditions and medications known to affect serum IGF-I or bone density CA19: 59% for all three combined. Ethnicity Caucasian. HWE NA None for main analysis. Some analysis stratified by gender 129 (7) vs 154 (9); P = 0.03 in 59 men and 57 women combined 19/19 vs (19/− and −/−) 
 Study period NA, Maine, randomized controlled trial of calcium supplementation 37 postmenopausal women, age mean 72.3 y    158.6 (9.0) vs 188.8 (8.6); P = 0.02 in men only (n = 89) 19/19 vs (19/− and −/−) 
 Study period NA, location NA. Cardiology referrals 59 men, 20 women, age mean 58.6 y    “No difference” between 19/− and any other genotype  
Jernström et al. (58) Study period NA, Toronto, Canada. Healthy volunteers 311 women, age mean 25.4 y (range 17-35) Non-Caucasian, history of pregnancy/cancer/diabetes, hysterectomy, current estrogen non–oral contraceptive use CA19: Allele frequency or genotypes for entire group NA. From graphs, −/− (n = 26) and “one or two 19” alleles (n = 263; 309 total). Ethnicity Caucasian. HWE NA Age, estrogen dose (in oral contraceptive users) 264 vs 315; P = 0.025 in oral contraceptive users (19/19 and 19/−) vs −/− 
     Effect only given stratified by oral contraceptive use 305 vs 287 in non–oral contraceptive users (approximate figures from graphs, exact figures NA) (19/19 and 19/−) vs −/− 
     Test for interaction between genotype and oral contraceptive use P = 0.04   
Jernström et al. (56) Study period NA, Toronto, Canada. Healthy volunteers 507 women, age mean NA (range, 17-35 y) Mixed ethnicity, history of pregnancy/cancer/diabetes, hysterectomy, current estrogen non–oral contraceptive use CA19: White§ 70.2%, Black 37.8%, Asian 40.8%, Indian/Pakistani 56.0%. Ethnicity 329 White, 78 Black, 71 Asian, 25 Indian/Pakistani. HWE NA Age, estrogen dose (in oral contraceptive users), not ethnicity 266 vs 338; P = 0.00007 in oral contraceptive users (19/19 and 19/−) vs −/− 
     Effect only given stratified by oral contraceptive use 305 vs 312 in non–oral contraceptive users (approximate figures from graphs, exact figures NA) (19/19 and 19/−) vs −/− 
     Test for interaction between genotype and oral contraceptive use P = 0.002   
Vaessen et al. (59) 1990-1993, Rotterdam, the Netherlands. Population-based cohort (myocardial infarction cases) 55 men, 95 women. 50 each of 19/19, 19/−, and −/−, age mean 60.7 y Age <55 or >75 y, medication for non-insulin-dependent diabetes mellitus/hormone conditions CA19: 67.6%. Ethnicity Caucasian. HWE P = 0.76 Age 157.7 vs 128.5 (geometric means) 19/19 vs −/− 
      29.2 (−46.2 to −10.0; P = 0.003) Mean difference 
Allen et al. (62) 1994-1997, Oxford, United Kingdom. European Prospective Investigation into Cancer and Nutrition 660 men, age mean 47 y (range 20-78) Cancer history, certain medications, estimated energy intake <3.1 or >18.4 mJ/d CA19: 64.2%. Ethnicity Caucasian. HWE P = 0.95 Age, body mass index dietary group, smoking, days between blood collection and processing 151.5 (148.5-156.9) vs 150.8 (146.2-155.4) vs 150.0 (140.8-158.5; Plinear trend = 0.66) 19/19 vs 19/− vs −/− 
       Also tested 19/19 and 19/− vs −/− (NS) and repeat length vs serum levels 
Frayling et al. (64) 1997-1999, Barry and Caerphilly, Wales. Adult offspring of mothers in randomized controlled trial of milk token supplementation 342 men, 298 women, age 25 y (all) Insulin-dependent diabetes mellitus CA19: 62.9%. Ethnicity Caucasian. HWE P = 0.99 Age, gender 133.2 (127.2-139.5) vs 143.5 (137.6-149.6) vs 142.8 (133.4-152.5; Plinear trend = 0.01) 19/19 vs 19/− vs −/− 
Giovannucci et al. (65) 1989-Jun 1994, Nurses' Healthy Study cohort (colorectal adenoma cases) 202 cases, 202 controls, age mean NA Family history of colorectal cancer, history of cancer or adenoma CA19: 61.8%. Ethnicity NA. HWE NA Age, month of/fasting status at blood draw, year of (and indications for) endoscopy 168.6 vs 167.7 (P = 0.88), cases and controls combined 19/19 vs (19/− and −/−) 
      168.6 vs 166.4 (P = 0.76) cases and controls combined 19/19 vs 19/20 
      (figures for controls only NA)  
Kim et al. (63) Study period NA, Seoul, Korea. Attending menopause clinic for bone mineral density measurement 229 postmenopausal women, age mean NA (range 45-75 y) Premenopausal, bilateral oophorectomy, hepatic/renal disease, medications affecting bone metabolism CA19: 14.3%. Ethnicity Korean. HWE NA Age, body mass index, years since menopause 140 vs 150 vs 160 (P > 0.05; approximate figures from graphs, exact figures NA) 19/19 vs 19/− vs −/− 
      Higher levels in women with 20/20 genotype (P < 0.005) 20/20 vs 20/− vs −/− 
Missmer et al. (61) 1989-Jun 1994, Nurses' Health Study cohort 418 control women, age mean NA Cancer history CA19: 63.3% (n = 622). Ethnicity all but eight women (cases + controls) Caucasian. HWE NA Age, HRT use, menopausal status, time of/fasting status at blood draw 173 vs 146 (P = 0.005) all women combined 19/19 vs 19/− vs −/− 
     Stratified by fasting status, time of blood draw, HRT use 183 vs 175 vs 155 (P = 0.10) premenopausal women 19/19 vs −/− 
      Values NA (P = 0.78) premenopausal women 19/19 vs 19/− 
DeLellis et al. (66) 1993-1996, Hawaii and Los Angeles, Multiethnic cohort 230 postmenopausal women, age mean NA (range 45-75 y) Cancer history, HRT use in previous 2 wk CA19: African American 40.8%, Japanese American 39.0%, non-Latino White 69.1%, Latino White 64.0%. Genotypes 19/19 vs 19/− vs −/− differed “significantly” between ethnic groups. Ethnicity 65 African American, 50 Japanese American, 47 non-Latino White, 68 Latino White. HWE no evidence for departure (P = NA) Crude and age-adjusted estimates presented African American 150 (106-194) vs 166 (142-190) vs 150 (121-180; P ≥ 0.05) 19/19 vs 19/− vs −/− 
      Japanese American 146 (101-190) vs 148 (130-167) vs 144 (119-169; P > 0.05) 19/19 vs 19/− vs −/− 
      Non-Latino White 146 (125-167) vs 142 (121-162) vs 160 (104-216; P > 0.05) 19/19 vs 19/− vs −/− 
      Latino White 118 (93-142) vs 126 (101-151) vs 149 (107-192; P > 0.05) 19/19 vs 19/− vs −/− 
Kato et al. (57) 1998-1999, Louisiana, Prostate screening program, breast clinic attendees, hospital employees 60 men, age mean NA (range 48-86). 53 women, age mean NA (range 22-73) Diabetes, cancer history, “any other serious condition” CA19: Caucasians 30.4%, African Americans 18.4%. Frequency of 19 allele higher in Caucasian (P = 0.04). Ethnicity 56 Caucasian, 57 African American. HWE tested in all ethnic groups combined (P = 0.219) Age, ethnicity matching to prostate/breast cancer cases: (a) hormone use, age, smoking, log height (African American women); (b) hormone use and age <40 (Caucasian women), (c) log-height, log body mass index (African American men) NA (Caucasian men) African American women 110.8 vs 95.5 (P = 0.35) 19/− vs −/− 
      Caucasian women 88.2 vs 92.3 (P = 0.79) 19/− vs −/− 
      African American men 106.9 vs 94.9 (P = 0.43) 19/− vs −/− 
      Caucasian men 94.8 vs 118.7 (P = 0.091) 19/− vs −/− 
Rietveld et al. (60) 1990-1993, Rotterdam, the Netherlands. Population-based cohort (myocardial infarction cases) 80 men, 88 women, age mean 67.4 y Age <55 or >75 y CA19: 63.7% for random sample. Ethnicity Caucasian. HWE NA Age group to myocardial infarction cases, gender, body mass index 143.8 (134.8-152.8) vs 126.9 (117.9-135.9; P = 0.01). Combined figures for random sample (n = 168) and for those selected on genotype (n = 150) 19/19 vs −/− 
  150 selected on IGF-I genotype (59), age NA
 
Age <55 or >75 y, diabetes, HRT
 
    
IGF-I (intronic STR)        
Arends et al. (70)
 
Study period NA, location NA. Family-based association study comparing transmission of alleles
 
124 children born small for gestational age and their parents, age mean NA Neonatal complications, endocrine and metabolic disorders (children)
 
NA (not population-based). Ethnicity 113 Caucasians, 1 Asian, 1 Indo-Mediterranean, 4 mixed
 
 Showed preferential transmission of 191-bp allele (P = 0.02) and lower IGF-I levels in children with this allele. −1.1 vs −0.5 SD scores (P = 0.03)  
IGF-II (A/G ApaI RFLP in 3′ untranslated region)        
O'Dell et al. (73)
 
Study period NA, Northwick Park, United Kingdom. Northwick Park Heart cohort
 
92 men (48 common, 44 rare homozygotes), age mean NA (range 45-65 y)
 
NA
 
A 0.28, G 0.72. Ethnicity Caucasian. HWE “applied”
 
 614.0 ± 124.0 vs 683.3 ± 146.9 (P = 0.01)
 
GG vs AA
 
IGFBP-3 (A/C single nucleotide polymorphism at nucleotide −202)        
Deal et al. (75) 1982-1995, Physicians' Health Study (colorectal cancer cases) 478 men (cases and controls), age mean NA (range 40-84 y) History of cancer, myocardial infarction, stroke, transient ischemic attack, current liver/renal disease, peptic ulcer, gout, vitamin A use, β-carotene supplement at start of study A 0.46, C 0.54. Ethnicity NA. HWE tested in cases and controls combined R2 for polymorphism was 0.077 3,274 vs 2,753 (SD NA) for controls only AA vs CC 
      3180 vs 3000 vs 2760 in cases and controls combined (approximate figures from graphs, exact figures NA) AA vs AC vs CC 
Jernström et al. (56) Study period NA, Toronto, Canada. Healthy volunteers 311 women, age 25.4 y (range 17-35) Non-Caucasian, ever pregnant, cancer, diabetes, hysterectomy, estrogen non–oral contraceptive use A 0.47, C 0.53. Ethnicity Caucasian. HWE NA  4,390 vs 4,130 vs 3,840 AA vs AC vs CC 
Schernhammer et al. (80) 1989-Jun 1996, Nurses' Health Study cohort (breast cancer cases) 943 women (cases and controls), age mean NA Cancer history A 0.46, C 0.54 in cases and controls combined. Ethnicity NA. HWE tested in cases and controls combined (P > 0.90) Age, menopausal status, HRT use, blood draw details. R2 for polymorphism was 0.06 4,426 (4,291-4,561) vs 4,060 (3,970-4,150) vs 3,697 (3,581-3,813) in cases and controls combined (Plinear trend < 0.001) AA vs AC vs CC 

NOTE: 19/19, homozygous for 19 allele; 19/−, heterozygous for 19 allele; −/−, no copies of 19 allele.

*

Where data are from case-control studies and where possible HWE Ps, allele frequencies and serum measurements for controls only are given unless stated otherwise.

Where serum levels were quoted as nmol/L, we converted to ng/mL by dividing by 0.13 (IGF-I) or 0.035 (IGFBP-3) (DSL product information, DSL-10-5600 and DSL-10-6600).

Crude effect estimates unless stated otherwise.

§

329 White women genotyped, but Table 2 of original article shows results for only 655 alleles.

Kim et al. (63) discuss a discrepancy between the IGF-I nucleotide sequence observed in their Korean women (absence of GA immediately 3′ to the CA microsatellite) compared with the IGF-I sequence in the National Center for Biotechnology Information database Genbank M12659 (presence of GA immediately 3′ to the CA microsatellite). These two (GA) nucleotides are not present in the IGF-I sequence on the human chromosome 12 contig or in the IGF-I nucleotide sequence of two Caucasian subjects (N. Johnson, personal communication.) and possibly reflect an error in the original M12659 sequence.

Figure 2.

Difference in circulating levels of IGF-I by genotype among Caucasians.

Figure 2.

Difference in circulating levels of IGF-I by genotype among Caucasians.

Close modal

Study Design and Study Populations. Two studies (63, 65) only report results for cases and controls combined, although Giovannucci et al. (65) report that the results did not differ when cases and controls were analyzed separately. If, however, there were a relationship between serum IGF-I levels and disease status and some but not all of this variation were due to the genotype being studied, then the estimate of the genotype effect would be biased if it were estimated from data combining cases and controls.

Seven of the studies in Table 4 (55, 58-62, 64, 65) were based on Caucasian or predominantly (>99%) Caucasian populations. Kim et al. (63) studied Korean women and showed a dramatically different allele frequency for the 19 repeat allele (14% for Koreans versus 59-70% for Caucasians). Their study population, however, was taken from women attending a menopause clinic for bone density assessment and includes 158 (53%) women with osteopoenia and 75 (25%) women with osteoporosis.

Three of the studies (56, 57, 66) include men and women from more than one population of origin, and all three reported a significant difference in the frequency of the 19 repeat allele between one or more of their ethnic groups. In addition, one study (56) found a significant difference in serum IGF-I levels between Black women and White women, whereas another (66) found a significant difference between Latino White women and the other racial/ethnic groups in their multiethnic study. Thus, any analysis of genotype in relation to serum levels that does not take into account ethnic group, such as the analysis by Jernström et al. (56) may be confounded. Simply adjusting for ethnic group, however, may not be appropriate if the allele does not have the same effect in different ethnic groups and this cannot be tested unless the study is powered to test for interactions. This may be particularly important for polymorphisms of no functional significance where any effect is likely to be due to linkage disequilibrium with some other (functional) variant. Kato et al. (57) and DeLellis et al. (66) presented their results stratified by ethnic group, but the number of subjects in each stratum was small. The largest single group in the Kato et al. study was White men (n = 33) and the largest in the DeLellis et al. study was Latino White women (n = 68).

Method of Genotyping. In all of the studies, the region of IGF-I containing the 5′ STR was amplified by PCR using the same primers as those initially used by Rosen et al. (55). These primers generate a product of ∼180 to 200 bp depending on the number of CA repeats (19 CA repeats = 192 bp). Differently sized alleles were separated by PAGE, except in the article by Kato et al. (57) in which the size of the CA repeat was determined by DNA sequencing of the amplified product. The frequency of the 19 repeat allele was unusually low in this study (30% versus 59-70%) and there were no 19 repeat allele homozygotes at all. Sequencing of regions of highly repetitive DNA may cause difficulties, although this is most likely to be the case for heterozygotes where the electrophenogram may be difficult to interpret. Departures from Hardy-Weinberg equilibrium (HWE) in the control population may be indicative of genotyping problems (67). Some studies tested for departures from HWE in controls (59, 62, 64), but many did not.

Sample Size. In a recent letter examining the replication validity of genetic association studies, Ioannidis et al. (68) found that the first study published tended to report more extreme estimates of disease association than subsequent studies, particularly when the first study published had a relatively small sample size. The first association study examining the relationship between serum IGF-I levels and the STR 1 kb upstream of the IGF-I gene was based on measurements in 116 controls and reported a 19% (crude) difference between 19/19 homozygotes and 19/− and −/− genotypes combined. Only two of the subsequent studies (59, 61) reported differences of similar magnitude (>15%) between genotypes, but in both of these studies, the comparison was for 19/19 versus −/− and the difference was in the opposite direction.

Based on the data in Table 2, and assuming a multiplicative, codominant model, with a frequency of 0.65 for the 19 allele, between 550 (assuming mean serum levels of ∼200 ng/mL) and 1,000 controls (assuming mean serum levels of 150 ng/mL) are required for 90% power to detect a difference of 7.5% between each genotype at 5% significance. Two of the studies from Table 4 genotyped and measured circulating levels of IGF-I in >500 controls from a single population of origin.

Other IGF-I Polymorphisms Data on other polymorphisms in or around the IGF-I gene are sparse. Rasmussen et al. (69) found no nonsense, frameshift, or missense mutations in the coding sequences of the IGF-I gene in 82 probands of type II diabetics. Arends et al. (70) investigated three STRs: the 5′ STR (see above), a STR in the second intron of the IGF-I gene, and a STR (D12S318) that lies 3′ to IGF-I. They showed greater than expected (P = 0.02) transmission of the 191-bp allele of the intronic STR in their family based study of 124 children born small for gestational age. They also showed lower mean serum levels of IGF-I (expressed as SD scores) in children carrying the 191-bp allele.

The National Center for Biotechnology Information public dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/) has mapped two additional single nucleotide polymorphisms upstream of the human IGF-I gene. One is an A/T polymorphism at −421, and the other is a C/T polymorphism at −1229 with respect to the major transcription start site in promoter 1. Promoter 1 (upstream of exon 1) is the major promoter used in mammals; it is active in all tissues in which IGF-I is expressed and produces ∼75% of transcripts in the liver (71). We did not identify any studies examining these polymorphisms in relation to serum levels.

Polymorphisms and Serum Levels of IGF-II and IGFBP-3 Four studies have investigated polymorphisms in and around either the IGF-II gene or the IGFBP-3 gene (Table 4). For IGF-II, several polymorphisms 5′ to the gene, 3′ to the gene, and within intronic sequences have been identified (72). Serum levels, however, have only been investigated in relation to one of these, an A/G single nucleotide polymorphism in the 3′ untranslated region (73). For this single nucleotide polymorphism, AA homozygotes had higher mean serum IGF-II levels than GG homozygotes (683.3 ± 146.9 versus 614.0 ± 124.0 ng/mL; P = 0.01).

For the IGFBP-3 gene, we identified three studies, all of which examined an A/C polymorphism at −202 bp relative to the transcriptional start site. This polymorphism occurs close to the basal promoter within a region that in the rat and bovine genes is rich in binding sites for hormone receptors, including growth hormone, estrogen, thyroid hormone, and glucocorticoids, many of which are conserved in the human IGFBP-3 gene (74). In all three studies, circulating levels of IGFBP-3 decreased as the number of copies of the A allele decreased (AA > AC > CC). Consistent with these in vivo findings, Deal et al. (75) showed that in an in vitro transient transfection assay the C allele had 50% lower activity compared with the A allele.

Conclusions. For the IGF-I 5′ STR, there is no convincing evidence of an effect of genotype on serum levels of IGF-I. The IGFBP-3 −202 polymorphism occurs in a location that could plausibly affect expression levels of the gene and in the three studies investigating this polymorphism in relation to circulating levels of IGFBP-3 the evidence of a modest effect is consistent. The single study investigating the ApaI polymorphism in the IGF-II 3′ untranslated region is suggestive but further data will be needed to confirm or refute this effect.

IGF-I 5′ STR and Breast Cancer

Main Findings. Four studies have sought a relationship between IGF-I 5' STR and cancer risk (Table 5). Of these, only the first published study (76) found a statistically significant OR (95% CI) of 0.47 (0.21-1.00) for a comparison based on the 19 repeat allele. The only comparison made was of women with one copy of the 19-bp allele versus women with no copies of the 19 allele as, in addition to the unusually low frequency of the common 19 allele in this study (30% versus 59-70% in other studies), there were no common homozygotes (19/19).

Table 5.

Studies investigating polymorphisms in the IGF-I and IGFBP-3 genes in relation to breast cancer risk

ReferenceStudy detailsCasesControlsExclusion criteriaVariables matched or adjusted forEffect size* (95% CI)Type of analysis
IGF-I (CA 5′ STR)        
Case-control studies        
Yu et al. (76) Oct 1998-Nov 1999, Louisiana n = 53, age mean 52 y (range 29-79). Ethnicity 32 African American, 21 Caucasian n = 53, age mean 51 y (range 22-73). Ethnicity 30 African American, 23 Caucasian. HWE NA NA (4 pairs not matched) Age, ethnicity, alcohol intake, menopausal variables 0.47 (0.21-1.00) 19/− vs −/− 
     Stratified by menopausal status, plasma IGFBP-3 0.35 (0.14-0.86) adjusted 19/− vs −/− 
      1.09 (0.43-2.79) adjusted >17 vs ≤17 
      0.55 (0.23-1.30) adjusted <21 vs =21 
      Plinear trend > 0.05 =19 vs 18.5 vs ≤18 
Figer et al. (79) Period NA, Sheba and Ramban, Israel n = 212 (sporadic), age mean 50.0 y (SD 12.6). Ethnicity 122 Ashkenazi n = 144. age range 22-44 y. Ethnicity 56 Ashkenazi. HWE NA NA No matching or adjustment 0.98 (0.62-1.55) 19/19 vs 19/− 
  n = 56 (BRCA1/2 mutation carriers), age mean 42.3 y (SD 8.5). Ethnicity 55 Ashkenazi   Stratified by ethnicity (Ashkenazi vs non-Ashkenazi) 0.50 (0.23-1.05) 19/19 vs −/− 
      0.49 (0.28-0.87) (21 + 22) repeats vs rest 
      1.99 (0.59-6.67) (17 + 16 + 11) repeats vs rest 
Case-control studies nested within cohort studies        
Missmer et al. (61) 1989-Jun 1994, Nurses' Health Study Cohort n = 463 (included ca in situ), age mean 58 y (SD 7.1). Ethnicity 8 non-Caucasian (cases and controls) n = 622, age matched. Ethnicity 8 non-Caucasian (cases and controls). HWE NA Cancer history Age, menopausal status, HRT use, time of/fasting status at blood collection, breast cancer risk factors, anthropometric variables 1.05 (0.81-1.36) 19/19 vs 19/− 
      0.91 (0.62-1.33) 19/19 vs −/− 
      P > 0.05 19/19 vs each of 6 other genotypes and one “other” category 
DeLellis et al. (66)
 
1993-1996, Hawaii and Los Angeles, Multiethnic Cohort Study
 
n = 320 postmenopausal, age range 45-75 y. Ethnicity 81 African American, 76 Japanese American, 81 Latino White, 82 non-Latino White
 
n = 373 postmenopausal, age range 45-75 y. Ethnicity 91 African American, 94 Japanese American, 96 Latino White, 92 non-Latino White. HWE “no evidence for departure in any ethnic group”
 
Cancer history, missing anthropometric information
 
Age group, ethnicity. Stratified by ethnicity
 
1.15 (0.79-1.68) all ethnic groups combined
 
19/19 vs 19/−
 
      1.34 (0.89-2.04) all ethnic groups combined 19/19 vs −/− 
      1.14 (0.78-1.68) adjusted 19/19 vs 19/− 
      1.40 (0.90-2.20) adjusted 19/19 vs −/− 
IGFBP-3 (A/C single nucleotide polymorphism at nucleotide −202)        
Case-control studies nested within cohort studies        
Schernhammer et al. (80) 1989-Jun 1996, Nurses' Health Study Cohort n = 677 (included ca in situ), age range 43-69 y, ethnicity NA n = 834, age range 43-69 y, ethnicity NA, HWE tested in cases and controls combined (P > 0.90) Cancer history Age, menopausal status, time of fasting status at blood collection, breast cancer risk factors, anthropometric variables, exogenous hormone use 0.99 (0.77-1.26) all ages CC vs AC 
      0.97 (0.72-1.30) all ages CC vs AA 
      0.95 (0.43-2.14) age <50 y CC vs AC 
      0.87 (0.29-2.60) age <50 y CC vs AA 
ReferenceStudy detailsCasesControlsExclusion criteriaVariables matched or adjusted forEffect size* (95% CI)Type of analysis
IGF-I (CA 5′ STR)        
Case-control studies        
Yu et al. (76) Oct 1998-Nov 1999, Louisiana n = 53, age mean 52 y (range 29-79). Ethnicity 32 African American, 21 Caucasian n = 53, age mean 51 y (range 22-73). Ethnicity 30 African American, 23 Caucasian. HWE NA NA (4 pairs not matched) Age, ethnicity, alcohol intake, menopausal variables 0.47 (0.21-1.00) 19/− vs −/− 
     Stratified by menopausal status, plasma IGFBP-3 0.35 (0.14-0.86) adjusted 19/− vs −/− 
      1.09 (0.43-2.79) adjusted >17 vs ≤17 
      0.55 (0.23-1.30) adjusted <21 vs =21 
      Plinear trend > 0.05 =19 vs 18.5 vs ≤18 
Figer et al. (79) Period NA, Sheba and Ramban, Israel n = 212 (sporadic), age mean 50.0 y (SD 12.6). Ethnicity 122 Ashkenazi n = 144. age range 22-44 y. Ethnicity 56 Ashkenazi. HWE NA NA No matching or adjustment 0.98 (0.62-1.55) 19/19 vs 19/− 
  n = 56 (BRCA1/2 mutation carriers), age mean 42.3 y (SD 8.5). Ethnicity 55 Ashkenazi   Stratified by ethnicity (Ashkenazi vs non-Ashkenazi) 0.50 (0.23-1.05) 19/19 vs −/− 
      0.49 (0.28-0.87) (21 + 22) repeats vs rest 
      1.99 (0.59-6.67) (17 + 16 + 11) repeats vs rest 
Case-control studies nested within cohort studies        
Missmer et al. (61) 1989-Jun 1994, Nurses' Health Study Cohort n = 463 (included ca in situ), age mean 58 y (SD 7.1). Ethnicity 8 non-Caucasian (cases and controls) n = 622, age matched. Ethnicity 8 non-Caucasian (cases and controls). HWE NA Cancer history Age, menopausal status, HRT use, time of/fasting status at blood collection, breast cancer risk factors, anthropometric variables 1.05 (0.81-1.36) 19/19 vs 19/− 
      0.91 (0.62-1.33) 19/19 vs −/− 
      P > 0.05 19/19 vs each of 6 other genotypes and one “other” category 
DeLellis et al. (66)
 
1993-1996, Hawaii and Los Angeles, Multiethnic Cohort Study
 
n = 320 postmenopausal, age range 45-75 y. Ethnicity 81 African American, 76 Japanese American, 81 Latino White, 82 non-Latino White
 
n = 373 postmenopausal, age range 45-75 y. Ethnicity 91 African American, 94 Japanese American, 96 Latino White, 92 non-Latino White. HWE “no evidence for departure in any ethnic group”
 
Cancer history, missing anthropometric information
 
Age group, ethnicity. Stratified by ethnicity
 
1.15 (0.79-1.68) all ethnic groups combined
 
19/19 vs 19/−
 
      1.34 (0.89-2.04) all ethnic groups combined 19/19 vs −/− 
      1.14 (0.78-1.68) adjusted 19/19 vs 19/− 
      1.40 (0.90-2.20) adjusted 19/19 vs −/− 
IGFBP-3 (A/C single nucleotide polymorphism at nucleotide −202)        
Case-control studies nested within cohort studies        
Schernhammer et al. (80) 1989-Jun 1996, Nurses' Health Study Cohort n = 677 (included ca in situ), age range 43-69 y, ethnicity NA n = 834, age range 43-69 y, ethnicity NA, HWE tested in cases and controls combined (P > 0.90) Cancer history Age, menopausal status, time of fasting status at blood collection, breast cancer risk factors, anthropometric variables, exogenous hormone use 0.99 (0.77-1.26) all ages CC vs AC 
      0.97 (0.72-1.30) all ages CC vs AA 
      0.95 (0.43-2.14) age <50 y CC vs AC 
      0.87 (0.29-2.60) age <50 y CC vs AA 
*

Crude effect estimates unless stated otherwise.

Figures calculated by the authors from data provided in the original articles.

Method of Genotyping. As with studies of circulating levels of IGF-I in relation to genotype, all four studies used the same PCR primers as those initially used by Rosen et al. (55) and only one (76) used DNA sequencing rather than size fractionation by PAGE. Case genotypes would not generally be expected to conform to HWE, except in a codominant model where risk alleles are assumed to act multiplicatively (77, 78). Only one study (66) reported testing for HWE in the control population.

Ethnicity. Combining data from more than one population of origin has the potential for confounding by population stratification in association studies. The majority of cases and controls were matched on ethnic group in two studies (57, 76), and the analysis was stratified by ethnic group in another (66). All women in the study by Figer et al. (79) were of Israeli Jewish origin, but the proportion who were of Ashkenazi origin differed among sporadic cases (58%), BRCA1/BRCA2 carriers (98%), and controls (39%).

Sample Size. Under a multiplicative codominant model with a frequency of 0.65 for the 19 allele, ∼400 cases and 400 controls are required for 90% power to detect an OR of 2.0 between women homozygous for the 19 repeat allele and women with two non–19 alleles at a significance of 5% (based on a test for trend of all three genotypes). Substantially larger numbers would be needed to detect more modest effects. Approximately 1,200 cases and 1,200 controls would be needed to detect an OR of 1.5. The study by Missmer et al. (61) genotyped 463 cases and 622 controls from a single population of origin; therefore, if a true association between breast cancer risk and the 19 repeat allele in Caucasians does exist, it is likely to be more modest than the estimate of a 2-fold risk originally suggested by Yu et al. (76).

IGF-II and IGFBP-3 Polymorphisms and Breast Cancer We were unable to find any studies investigating breast cancer risk in relation to polymorphisms in the IGF-II gene. Only one study has sought an association with the −202 IGFBP-3 polymorphism (80). The ORs (95% CI) reported by this study [0.99 (0.77-1.26) for CC versus AC and 0.97 (0.72-1.30) for CC versus AA in which 677 cases and 834 controls were genotyped] were entirely compatible with random variation.

Conclusions. Despite the extreme OR found for the IGF-I 5′ STR in the relatively small study by Yu et al. (76), none of the three subsequent studies found a statistically significant difference in breast cancer risk for any comparison of 19 repeat allele with any other allele/genotype. The evidence available for the A/C polymorphism 5′ of the IGFBP-3 gene is also consistent with chance. Modest relative risks in mozygotes with two copies of these alleles versus noncarriers cannot be ruled out.

The evidence from the studies included in this review suggests a positive association between premenopausal levels of IGF-I and IGFBP-3 and subsequent breast cancer risk. For postmenopausal levels of IGF-I and IGFBP-3, the available evidence is consistent with no effect.

A limitation of any systematic review is the possibility of publication bias. There is, however, no reason to suppose that studies that suggest a protective effect would be more or less likely to be published than those showing an increased risk or that publication bias is more likely in studies of premenopausal women than in those conducted on postmenopausal women. The consensus of a modest risk conferred by higher IGF-I levels and higher IGFBP-3 levels in premenopausal women does, therefore, seem plausible, although the IGFBP-3 effect is opposite to that originally proposed.

Almost all of the studies in this review defined menopausal status by the time of 30), which analyzed menopausal status at the time of diagnosis of breast cancer rather than at the time of blood collection. The prospective studies indicate that premenopausal levels of IGF-I and IGFBP-3 may affect the risk of both premenopausal and postmenopausal breast cancer, raising the possibility that IGF-I/IGFBP-3 levels at younger ages are more biologically relevant possibly because of a synergistic relationship with endogenous sex hormones (81). The association of breast cancer risk with adult height, which in turn is a marker of circulating IGF-I levels in childhood, would be consistent with a hypothesis that IGF-I/IGFBP-3 measurements at younger ages are more relevant. Alternatively, because IGF-I and IGFBP-3 levels decline with age (28), the failure to observe an effect for postmenopausal levels may simply reflect larger assay error when circulating levels are low. Future studies should analyze the effect of age at blood collection as well as age at breast cancer diagnosis to resolve this issue. An improvement in the validity of current laboratory assays would also contribute to more accurate estimation of the IGF/IGFBP effects on breast cancer risk in these subgroups of women.

Circulating levels of IGFs are determined by clearance as well as production and hence are influenced by the six high-affinity IGFBPs and their complex interactions. Tissue concentrations in the breast are affected by local production and clearance rates as well as by circulating levels, and there is evidence of local expression of both IGF-I (82) and IGF-II (83, 84) in breast cancer patients. For logistical and ethical reasons, epidemiologic studies rely on measurements of levels of IGFs/IGFBPs in the serum or plasma, but local concentrations in breast tissue itself are likely to be the more relevant.

Serum levels of IGF-I, IGF-II, and IGFBP-3 are determined by a combination of genetic and environmental effects (12-16), and polymorphisms that influence the level of expression of the structural genes are likely to affect lifetime exposure to IGFs/IGFBPs by both endocrine and autocrine mechanisms. Unfortunately, polymorphisms in sequences that directly affect gene expression are difficult to find (85). This may account for the fact that all of the studies examining genotype, and either serum IGF-I levels or breast cancer risk directly examine a single STR upstream of the IGF-I gene. It is not clear why the length of this STR should influence serum levels. An association between rare alleles of a STR upstream of the HRAS gene and common cancers, including breast cancer, has been reported (86), but for breast cancer at least, this observation remains contentious (87, 88). An in vitro study (89) reports that the length of a STR proximal to an enhancer element in the first intron of the EGFR gene influences transcription levels. There are no known regulatory elements near the IGF-I STR, although it does lie at the 3′ border of a region of high sequence identity between man and mouse. The paucity of data on other polymorphisms that might influence expression levels may be due to a genuine lack of such variants. Despite the evidence of a genetic effect on serum IGF-I levels, there is no reason to believe that this effect operates in cis. The production of IGF-I by the liver is regulated by a variety of factors, including growth hormone and insulin (2), and clearance is influenced by IGF-II, IGFBPs, and acid-labile protein subunit (3, 53).

A functional effect of the IGFBP-3 −202 A/C polymorphism is biologically plausible, and both in vitro and in vivo evidence is consistent with reduced expression from the C allele and it is surprising that only one study has examined this polymorphism in relation to breast cancer risk. Investigators may have been deterred by the large sample sizes required to estimate small relative risks. Even if the relative risk in AA homozygotes was 1.3 (the upper bound of the 95% CI for the point estimate), ∼2,500 cases and 2,500 controls would be required for 90% power at 5% significance. As the frequency of the “risk” allele is so high (∼0.5), a relative risk of 1.3 in AA homozygotes would correspond to a population attributable fraction of 12%, and even a relative risk of 1.10 would correspond to a population attributable fraction of ∼5%. Risk alleles with a lower population frequency may confer a higher risk to the individual but will account for a much lower population attributable fraction. For instance, a germ line mutation (1100delC) in the cell cycle checkpoint kinase CHEK2 has been shown to confer a relative risk of ∼2-fold, but the frequency of this variant is probably 1% (90) or less (91) in the general population, resulting in a population attributable fraction for this variant of only ∼1%.

Epidemiologic studies into the role of circulating levels of IGFs and/or polymorphisms in/around IGF-I, IGF-II, and IGFBP-3 genes on breast cancer risk must be adequately designed. Large studies (or combined analyses of data from consortia of smaller groups) are essential. Only prospective studies can exclude reverse causality in relation to circulating IGF/IGFBP levels and breast cancer risk, but cross-sectional studies on healthy individuals are appropriate for investigating associations between polymorphisms and circulating levels of IGFs/IGFBPs, and case-control studies are the most cost-effective choice for examining the effect of polymorphisms on breast cancer risk.

Grant support: Cancer Research UK, Breakthrough Breast Cancer Research Centre, and Association for International Cancer Research.

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

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