Abstract
Objective
Diabetes, characterized by perturbations in insulin production and signaling, is inversely associated with prostate cancer risk irrespective of stage. Obesity, a diabetes risk factor, is inversely associated with localized but positively with advanced disease. To understand the complex association between hyperinsulinemia and prostate cancer, we evaluated the association of plasma C-peptide, an insulin secretion marker, with prostate cancer risk in a case-control study nested in a prospective community cohort.
Methods
Prostate cancer cases (n=264) and matched controls (n=264) were identified in the CLUE II cohort between baseline in 1989 and 2002. C-peptide concentration was measured in baseline plasma by ELISA. Odds ratios (ORs) and 95% confidence intervals (CI) were estimated using conditional logistic regression adjusting for overweight/obesity and family history.
Results
Median C-peptide concentration was lower in cases (1,180 pmol/L) than controls (1,365 pmol/L; P=0.03). Men in the highest (versus lowest) fourth of C-peptide had a lower prostate cancer risk (OR=0.65, 95% CI 0.37–1.14; P-trend=0.08), primarily localized disease (OR=0.44, 95% CI 0.19–1.03; P-trend=0.04). Associations were similar to overall excluding cases diagnosed during the first five years of follow-up, men with diabetes, or men who had not had a PSA test.
Conclusions
C-peptide concentration was inversely associated with subsequent diagnosis of prostate cancer, primarily localized disease, not unlike the association for obesity. However, we cannot rule out detection bias that might result if men with higher C-peptide have lower PSA irrespective of whether prostate cancer is present.
Keywords: Prostate cancer, C-peptide, epidemiology, nested case-control study
INTRODUCTION
Obesity, a diabetes risk factor, may differently influence the development of more versus less aggressive prostate cancer (1–3). Metabolic perturbations as a result of obesity, such as insulin resistance and consequent hyperinsulinemia, have been hypothesized to mediate the association between obesity and prostate cancer (4). Insulin is a peptide hormone that signals the uptake of glucose into cells via the insulin receptor, and thus is involved in energy regulation (5). Insulin is also a well recognized mitogen that can bind to the insulin-like growth factor (IGF) receptor (5). Insulin receptors, IGF-receptors, and hybrids of the two have been reported to be present on primary human prostate adenocarcinomas, suggesting that both insulin and IGF-1 may enhance the progression of an extant cancer (6). In benign prostate tissue, IGF-receptors were also detected in epithelial and basal cells, but insulin receptors were less often present and only in basal cells (6).
In contrast to IGF-1, which is consistently positively associated with prostate cancer irrespective of stage and grade in a large number of prospective studies (11), few studies have evaluated the association between circulating insulin levels and prostate cancer: a case-control study observed a positive association (7), whereas three prospective studies observed no association (8–10). In addition, positive (11), null (12) and inverse (13) associations have been observed between C-peptide, a marker for insulin secretion, and total prostate cancer risk in studies of men who were not exclusively fasting at the time of blood draw. In two studies, C-peptide was reported to be positively associated with aggressive disease (11, 13). One study reported a positive association between pre-diagnostic C-peptide and prostate cancer death in a cohort of men diagnosed with prostate cancer (14). A number of studies have also evaluated the association between diabetes mellitus type 2 – often a sequela of obesity and characterized by hyperinsulinemia – and have consistently observed an inverse association with prostate cancer risk, irrespective of stage or grade (15). Prostate cancer risk also appears to decrease with increasing time since diagnosis (15–21), which may be, in part, explained by reduced insulin production with longer duration of diabetes (22). Despite some inconsistencies, when taken together these studies suggest hyperinsulinemia, like obesity, may be differently associated with aggressive and nonaggressive disease, whereas frank diabetes is inversely associated with prostate cancer risk irrespective of stage and grade.
To understand better the complex association of hyperinsulinemia with prostate cancer, we evaluated the association between circulating C-peptide concentration and subsequent prostate cancer risk, overall and by stage and grade, in a case-control study nested in the prospective CLUE II cohort. We measured plasma concentration of C-peptide as a surrogate for insulin. Insulin is produced by pancreatic beta cells as a pro-molecule from which C-peptide is cleaved. The cleavage of pro-insulin results in equimolar amounts of insulin and C-peptide. In CLUE II, 88.4% of the participants were not fasting, and in non-fasting individuals, C-peptide provides a more stable measurement of secreted insulin concentration (23–24).
METHODS
Study population
Prostate cancer cases and matched controls were identified among participants in the CLUE II cohort. CLUE II (from the slogan “Give us a Clue to Cancer and Heart Disease”) is an ongoing, community-based prospective cohort study conducted in Washington County, Maryland. The cohort includes 32,898 males and females, 22,887 of whom were adults living in the county at the time of enrollment. At baseline in 1989, participants donated a blood specimen and provided exposure and medical histories on a brief questionnaire. They were also asked to complete an abbreviated version of the Block food frequency questionnaire, which was returned by mail (25). Blood was collected in a heparinized Vacutainer tubes and refrigerated until centrifuged, aliquotted into plasma, erythrocytes, and buffy coat, and frozen at −70°C. The eligibility criteria for the cases and controls were being a male county resident without a history of cancer diagnosis prior to blood donation, except possibly for nonmelanoma skin cancer. Follow-up questionnaires were sent periodically to collect more detailed and updated exposure and medical histories.
Prostate cancer cases and controls
Prostate cancer diagnoses were identified by linkage to the Washington County Cancer Registry and, since 1992, to the Maryland Cancer Registry. Information on age, stage and histologic grade at diagnosis was also abstracted. Cases were classified as clinically localized (T1–T2, N0, M0), advanced (T3, T4, N1, M1 or fatal), low grade (Gleason sum <7), and high grade (Gleason sum ≥7). Two hundred sixty-five cases were histologically confirmed as a first cancer diagnosis (except possibly for a prior diagnosis of nonmelanoma skin cancer) between time of blood draw and December 2002. One control who was alive at the date of the case’s diagnosis and also did not have a subsequent cancer diagnosis was selected for each case and matched on age at and date of blood donation, race, and number of hours between the last meal and blood donation.
C-peptide measurement
C-peptide concentration was measured by ELISA (ALPCO Diagnostics, Windham, NH) in the laboratory of Dr. Nader Rifai at Children’s Hospital Boston. The assay limit of detection was 15 pmol/L. At concentrations of 304, 818 and 1,803 pmol/L, the laboratory reported day-to-day variabilities of 6.8, 5.4, and 3.5%, respectively. Laboratory personnel were not aware of the case-control or quality control status of the samples. Mean intra-pair coefficient of variation calculated from seven blinded quality control pairs was 8.2%. Plasma volume was insufficient to run the assay for one case, thus leaving 264 pairs for the analysis.
Assessment of other factors
Self-reported marital status, attained education, cigarette smoking history, height, and weight at baseline and at age 21 years were obtained on the baseline questionnaire. Body mass index (BMI) was calculated by dividing weight in kilograms by the square of height in meters (kg/m2). Participants were also asked whether they had taken any medications within 48 hours of blood donation. A completed food frequency questionnaire was returned by 82.8% of the cases and controls. Information on family history of prostate cancer (father or brother) and PSA testing were obtained on the follow-up questionnaire mailed in 1996.
Statistical analysis
The distribution of C-peptide concentration among controls was right-skewed, thus we report medians and compare the distribution between matched cases and controls using the Wilcoxon sign-rank test. Multivariable-adjusted odds ratios (OR) of prostate cancer overall, as well as, by stage and grade and 95% confidence intervals (CI) were estimated from conditional logistic regression models with C-peptide concentration entered as a series of indicator variables for fourths based on the controls’ distribution. The models included terms for baseline BMI (overweight: 25–29.9, obese: ≥30 vs. normal: <25 kg/m2) and family history of prostate cancer (yes, missing vs. no). Individually adjusting for height; BMI at age 21; cigarette smoking history; daily intake of energy, alcohol, tomato-based foods, red or processed meat, fish, and calcium from diet and supplements; use of multivitamins and vitamin E supplements; and use of nonsteroidal anti-inflammatory drugs and diabetes medications in the past 48 hours of blood draw did not notably alter the point estimates for C-peptide, and thus, were not retained in the model. To test for trend, we entered into the model a single ordinal variable that had values equal to the median of the fourth into which a man’s C-peptide concentration fell.
We conducted a number of subanalyses for total and localized prostate cancer. To allow equal prostate cancer screening opportunity between cases and controls, we ran an analysis restricting to controls who reported ever having had a PSA test and their matched cases. Because the risk factors for prostate cancer may differ by age at onset, we ran conditional logistic regression models stratified by the median age at diagnosis (≤71, >71 years old). Given the complexity of the obesity, diabetes, and metabolic syndrome links with prostate cancer, we stratified by BMI at the median to obtain stable estimates (≤26.5, >26.5 kg/m2) and ran unconditional logistic regression models adjusting for matching factors and for residual variation in BMI within each stratum, and separately we restricted to men without diabetes and ran conditional logistic regression models. To test for statistical interaction, we entered the main effect terms for C-peptide (categorical) and age (binary) or BMI (continuous) along with a term for their product into appropriate multivariable models. The coefficient for the product term was evaluated by the Wald test. All analyses were conducted using SAS release 9.1 (SAS Institute, Cary, NC). P-values from two-sided tests are reported.
RESULTS
The mean age at prostate cancer diagnosis was 70.2 years (range: 46.1–92.3 years) and mean time between blood donation and diagnosis was 5.6 years (range: 0.3–12.1 years). The majority of cases were localized (69.0% of the 184 cases with stage information). Among the 208 cases where grade was available, 38.5% had a Gleason sum of ≥7. With the possible exception of diabetes medication use, which was less common in cases (P=0.09), and PSA testing, which was more common in cases (P=0.05), none of the other characteristics was significantly different between the cases and controls (Table 1).
Table 1.
Characteristics* of prostate cancer cases and matched controls, CLUE II, 1989
| Cases | Controls | P† | |
|---|---|---|---|
| Number of men | 264 | 264 | |
| Mean age at baseline (years) | 64.6 ± 9.0 | 64.6 ± 9.0 | Matched |
| African-American (%) | 2.3 | 2.3 | Matched |
| Married (%) | 88.3 | 85.2 | 0.30 |
| Smoking status (%) | |||
| Never | 39.4 | 38.3 | |
| Former | 51.9 | 53.0 | 0.89 |
| Current | 8.7 | 8.7 | |
| Family history of prostate cancer in 1996‡ (%) | 13.5 | 9.8 | 0.29 |
| PSA test as of 1996§ (%) | 89.1 | 81.6 | 0.05 |
| Mean attained education (years) | 12.4 ± 3.4 | 12.1 ± 3.4 | 0.35 |
| Mean height (inches) | 69.3 ± 2.6 | 69.5 ± 2.4 | 0.27 |
| Mean BMI (kg/m2) | 26.4 ± 3.5 | 26.7 ± 3.2 | 0.26 |
| BMI at age 21 (kg/m2)¥ | 22.4 ± 3.2 | 22.7 ± 3.2 | 0.35 |
| Mean intake|| | |||
| Energy (kcal/day) | 1,583 ± 626 | 1,559 ± 523 | 0.67 |
| Alcohol (g/day) | 6.5 ± 12.6 | 6.3 ± 12.8 | 0.86 |
| Tomato products (servings/day) | 0.45 ± 0.36 | 0.48 ± 0.41 | 0.50 |
| Red/processed meat (servings/day) | 0.71 ± 0.48 | 0.73 ± 0.48 | 0.63 |
| Fish (servings/day) | 0.09 ± 0.10 | 0.10 ± 0.13 | 0.30 |
| Calcium from diet plus supplements (mg/day) | 798 ± 467 | 819 ± 475 | 0.64 |
| Use of a vitamin E supplement (%)|| | 9.9 | 9.8 | 0.87 |
| Use of aspirin or nonsteroidal anti-inflammatory agents within 48 hours before blood draw (%) | 32.6 | 34.9 | 0.57 |
| Use of diabetes medications within 48 hours before blood draw (%) | 1.9 | 4.6 | 0.09 |
Baseline information unless otherwise specified
Paired t-test (continuous) or McNemar’s test (categorical)
Among men (170 cases, 173 controls) who responded to the 1996 questionnaire and provided information using the chi-square test
Among men (184 cases, 174 controls) who responded to the 1996 questionnaire and provided information using the chi-square test
Among men with information on BMI at age 21 (264 cases, 262 controls)
Among men who returned the food frequency questionnaire and provided valid data using the t-test
Median C-peptide concentration in cases (1,181 pmol/L) was statistically significantly lower than in controls (1,365 pmol/L; P=0.03). For total prostate cancer, compared with the lowest fourth, men in the highest fourth of C-peptide had a lower prostate cancer risk (OR=0.65, 95% CI 0.37–1.14; P-trend=0.08). To decrease the possibility that undiagnosed prostate cancer may have influenced C-peptide concentration, we excluded cases diagnosed five years after the date of blood donation; the inverse trend remained (P-trend=0.04) (Table 2).
Table 2.
Association* between pre-diagnostic plasma C-peptide concentration and prostate cancer, CLUE II, 1989–2002
| Fourth of the C-peptide distribution (pmol/L) |
P-trend† | ||||
|---|---|---|---|---|---|
| Lowest (<849) | Second (849–<1,367) | Third (1,367–<2,017) | Highest (≥2,017) | ||
| Cases (no.) | 79 | 75 | 53 | 57 | |
| Controls (no.) | 66 | 67 | 65 | 66 | |
| OR | 1.00 | 0.92 | 0.66 | 0.65 | 0.08 |
| 95% CI | Ref | 0.57–1.49 | 0.39–1.10 | 0.37–1.14 | |
| Excluding the first 5 years of follow-up | |||||
| Cases (no.) | 48 | 40 | 30 | 21 | |
| Controls (no.) | 39 | 36 | 33 | 31 | |
| OR | 1.00 | 0.83 | 0.69 | 0.40 | 0.04 |
| 95% CI | Ref | 0.43–1.63 | 0.35–1.36 | 0.17–0.96 | |
Adjusted for BMI (overweight, obese vs. normal) and family history of prostate cancer (yes, missing vs. no)
Entered in the model as a single ordinal variable with values corresponding to the median of the fourth into which a man’s C-peptide concentration fell, the coefficient for which was evaluated using the Wald test
The association between C-peptide and prostate cancer by stage and grade was also evaluated (Table 3). Compared with the lowest fourth, men in the highest fourth had a lower risk of organ-confined disease (OR=0.44, 95% CI 0.19–1.03; P-trend=0.04). We could not rule out a positive association for advanced disease (Table 3). C-peptide was possibly inversely associated with both low- and high-grade disease.
Table 3.
Association* between pre-diagnostic plasma C-peptide concentration and prostate cancer by stage and grade, CLUE II, 1989–2002
| Fourth of the C-peptide distribution (pmol/L) |
P-trend† | ||||
|---|---|---|---|---|---|
| Lowest (<849) | Second (849–<1,367) | Third (1,367–<2,017) | Highest (≥2,017) | ||
| Localized (127 pairs) | |||||
| OR | 1.00 | 0.77 | 0.50 | 0.44 | 0.04 |
| 95% CI | Ref | 0.361–1.66 | 0.24–1.05 | 0.19–1.03 | |
| Advanced (57 pairs) | |||||
| OR | 1.00 | 1.39 | 1.50 | 1.83 | 0.39 |
| 95% CI | Ref | 0.47–4.09 | 0.47–4.82 | 0.50–6.78 | |
| Low grade (128 pairs) | |||||
| OR | 1.00 | 1.13 | 0.92 | 0.68 | 0.28 |
| 95% CI | Ref | 0.56–2.31 | 0.44–1.91 | 0.30–1.54 | |
| High grade (80 pairs) | |||||
| OR | 1.00 | 1.00 | 0.32 | 0.57 | 0.15 |
| 95% CI | Ref | 0.37–2.68 | 0.11–0.97 | 0.18–1.83 | |
Adjusted for BMI (overweight, obese vs. normal) and family history of prostate cancer (yes, missing vs. no)
Entered in the model as a single ordinal variable with values corresponding to the median of the fourth into which a man’s C-peptide concentration fell, the coefficient for which was evaluated using the Wald test
Restricting to controls who reported ever having had a PSA test and their matched cases by 1996 (n=142 pairs) the results were similar to overall: compared with the lowest fourth, ORs were 0.91, 0.84 and 0.55 for the second, third, and highest fourths, respectively (P-trend=0.16) for total prostate cancer and 0.97, 0.98, 0.50 respectively (P-trend=0.29) for localized disease. Including only matched pairs who did not use medication to treat diabetes at the time of blood donation (n=231 pairs), the results were similar to overall: the ORs for total prostate cancer were 0.92, 0.57, and 0.62 for the second, third and highest fourths of plasma C-peptide concentration, respectively, compared with the lowest fourth (P-trend=0.06). For localized disease, the ORs were 0.79, 0.44, and 0.44 for the second, third and highest fourths of C-peptide, respectively, compared with the lowest fourth (P-trend=0.04). When evaluating the association between C-peptide and prostate cancer by strata of age (P-interaction=0.82) or BMI (P-interaction=0.29), we observed no statistically significant differences in the association between the strata (Table 4); effect modification also was not observed by age (P-interaction=0.78) or BMI (P-interaction=0.28) for localized disease.
Table 4.
Association between pre-diagnostic C-peptide concentration and prostate cancer by age at diagnosis and body mass index, CLUE II, 1989–2002
| Fourth of the C-peptide distribution (pmol/L) |
P-trend* | P-interaction | ||||
|---|---|---|---|---|---|---|
| Lowest (<849) | Second (849–<1,367) | Third (1,367–<2,017) | Highest (≥2,017) | |||
| Age at diagnosis† | ||||||
| ≤71 years (137 pairs) | ||||||
| OR | 1.00 | 1.06 | 0.91 | 0.70 | 0.34 | |
| 95% CI | Ref | 0.54–2.07 | 0.43–1.92 | 0.30–1.62 | ||
| >71 years (127 pairs) | 0.81 | |||||
| OR | 1.00 | 0.77 | 0.46 | 0.51 | 0.09 | |
| 95% CI | Ref | 0.37–1.62 | 0.21–1.01 | 0.22–1.19 | ||
| Body mass index‡ | ||||||
| <26.5 kg/m2 (142 cases, 132 controls) | ||||||
| OR | 1.00 | 0.75 | 0.59 | 0.64 | 0.16 | |
| 95% CI | Ref | 0.40–1.43 | 0.30–1.17 | 0.32–1.28 | ||
| ≥26.5 kg/m2 (122 cases, 132 controls) | 0.29 | |||||
| OR | 1.00 | 1.25 | 0.85 | 0.95 | 0.65 | |
| 95% CI | Ref | 0.60–2.57 | 0.40–1.81 | 0.45–2.01 | ||
Entered in the model as a single ordinal variable with values corresponding to the median of the fourth into which a man’s C-peptide concentration fell, the coefficient for which was evaluated using the Wald test
The median was 71 years old. Adjusted for BMI (overweight, obese vs. normal) and family history of prostate cancer (yes, missing vs. no)
The median was 26.5 kg/m2. Adjusted for BMI (continuous) and family history of prostate cancer (yes, missing vs. no)
DISCUSSION
In this prospective study, we observed an inverse association between C-peptide and subsequent prostate cancer risk, the trend for which was statistically significant for localized disease and for total prostate cancer diagnosed more than five years after the time of blood donation. The findings were similar when the analysis was restricted to men who had had a PSA test or to men without diabetes at the time of blood donation. The association between C-peptide and prostate cancer did not differ by strata of age or BMI. Although based on few cases, we could not rule out a positive association for advanced stage disease.
Because insulin is a growth factor, we initially hypothesized that higher insulin, and thus, higher C-peptide, would be associated with an increased risk of prostate cancer. Our finding for total and localized prostate cancer was in the opposite direction to our hypothesis, but was possibly consistent for advanced-stage disease. Nevertheless, our findings are similar to those from a Swedish nested case-control study (392 cases and 392 controls) that found an inverse association between C-peptide and subsequent prostate cancer (13). More specifically, our results are similar to their observations of a strong inverse association for nonaggressive (organ-confined and low Gleason sum, n=278) and a suggestive positive association for aggressive (advanced or high Gleason sum, n=114) prostate cancer (13). Our results are not consistent with other studies, two nested case-control (8, 10) and one prospective cohort (9) that measured insulin, and one small case-control study (12) that measured C-peptide, that reported no association between insulin and total prostate cancer. One of these specifically reported no association for both nonaggressive and aggressive disease (10). A case-control study nested in the Prostate Cancer Prevention Trial observed modest increases in risk for total and low-grade disease, and a stronger increased risk high-grade disease among men in the placebo arm (11).
In addition, two studies have observed associations of higher insulin or C-peptide with prostate cancer risk (7) or risk of death from prostate cancer in cases (14). The former, a case-control study in Chinese men, observed an approximate 2.5-fold increase in total prostate cancer risk comparing the highest to the lowest tertile of insulin after adjusting for BMI, waist-to-hip ratio, leptin, and IGF-1 (7). Although the positive association was observed for both localized and regional disease (7), these cases were, for the most part, not PSA detected, indicating a high proportion of clinically-relevant disease and consistency with the literature on the association between obesity and aggressive prostate cancer. The latter study, a cohort of men diagnosed with prostate cancer in the Physician’s Health Study, observed a 1.72-fold increase in risk of prostate-cancer specific death comparing the top to bottom fourths of pre-diagnostic C-peptide after adjusting for age, BMI, clinical stage, Gleason sum, fasting status, and time from blood donation to diagnosis (14). When the results of these epidemiologic studies, including ours, are taken together, they support that insulin may influence risk of the development of aggressive and its progression disease, but either does not influence or possibly may reduce the risk of prostate cancer overall. This pattern is also remarkably compatible with the differences in the extent of insulin receptor expression on prostate cancer cells versus normal prostate cells (6). This pattern also appears to be similar to that for obesity (inverse for localized, positive for advanced/fatal prostate cancer), but not diabetes (inverse, irrespective stage at diagnosis).
One possible explanation for the observed inverse association between C-peptide and prostate cancer, primarily localized disease, in our study may be related to the links among body fat and concentrations of insulin and testosterone. Men with higher C-peptide concentration, and thus, insulin concentration, may be more likely to be overweight or obese (26–29). Obese men have a lower risk of being diagnosed with localized prostate cancer (1–2), and compared with lean men, obese men convert a greater proportion of their testosterone to estradiol because aromatase, the enzyme catalyzing this conversion, is expressed in adipose tissue. Because androgen is required for normal growth and maintenance of the prostate, greater androgenicity has been hypothesized to increase the risk of prostate cancer (30–31). Thus, men with higher insulin concentration who are overweight or obese may have a lower risk of prostate cancer due to lower testosterone. Because BMI and C-peptide were positively correlated among the controls (Spearman r=0.17; P=0.006), we adjusted for being overweight or obesity in our analysis. However, the association between C-peptide and prostate cancer overall and by stage or grade, remained similar before and after the adjustment for overweight/obesity and the association was similar in leaner and heavier men. However, because BMI may not capture the extent and distribution of body fat adequately, it is possible that residual confounding may still exist between C-peptide and prostate cancer that could at least, in part, explain the inverse association.
An alternative explanation for the observed inverse association between C-peptide and prostate cancer in our study may be detection bias as a consequence of the interrelationships among body fat and concentrations of insulin and PSA. In addition to lower testosterone, obese men have also been shown to have lower PSA (32–34). Men with higher C-peptide concentrations were also reported to have lower PSA levels (35). Because expression of PSA is under androgenic regulation, the lower testosterone level in obese men may explain their lower PSA level. An NHANES study reported that men with insulin resistance had lower PSA concentrations compared with men without insulin resistance, even after adjusting for BMI (36). Thus, detection bias may arise because men who are overweight/obese and/or are hyperinsulinemic have lower PSA and thus, are less likely to undergo biopsy to diagnose occult prostate cancer than lean men with normal insulin levels. Although we attempted to limit another possible source of detection bias (i.e., if the source population from which the controls were sampled had a different opportunity to have occult prostate cancer detected than the men who became the cases did), we could not address this possible source of detection bias. We do not believe that this possible PSA level-associated detection bias is fully explanatory, however, because Stocks et al. (13) also observed an inverse association between C-peptide and total and localized prostate cancer in their study conducted in Sweden, a country where PSA testing is not as widely implemented.
We measured C-peptide as a surrogate for insulin production in this mostly non-fasting cohort. C-peptide has a longer half-life in circulation than insulin (37–38), thus providing a more stable estimate of insulin secretion. The assay we used was reliable as indicated by the low coefficient of variation for the quality control samples. We measured C-peptide at one point in time; it is unknown whether this measurement may accurately capture the men’s usual C-peptide levels or the etiologically relevant measurement of daily insulin regulation (e.g., peak or nadir level, area under the insulin level curve) over the long-term or levels during the etiologically relevant window of time. Men with high C-peptide may be those who are prediabetic or early in their natural history of diabetes whereas men with lower C-peptide may be those who are not prediabetic or diabetic, those who are late in their natural history of diabetes, or are diabetics who have good glucose control. In the main analysis comparing men with high to low C-peptide, our ability to interpret the inverse association is complicated by including in the reference group both men who have had a long duration of diabetes and normal men. Because insulin levels change over the natural history of pre-diabetes to early diabetes to late diabetes, treatment and adherence influences insulin levels, and because diabetic men likely were in different points in the natural history of their diabetes at the time of blood donation we attempted to reduce this source of mismeasurement of the relevant C-peptide level in a subanalysis by excluding men whose diabetes was adequately severe to require medication. In that subanalysis, the men remaining men were most likely those without insulin perturbations or who had hyperinsulinemia and we still observed an inverse association for high versus low C-peptide with total and localized disease.
Other aspects of the study warrant discussion as well. The prospective design of the cohort limits the possibility of selection bias. Because most of the cases had early prostate cancer, it is unlikely that extant but undiagnosed disease influenced C-peptide concentration. This assumption is supported by our results when analysis was restricted to cases diagnosed more than 5 years after blood draw. We also limited the possibility of detection bias that might result if the cases and controls had a different opportunity to have occult prostate cancer detected by running a subanalysis restricting to controls who reported ever having had a PSA test and matched cases. Our hypothesis focused on insulin, however many other metabolic and hormonal systems are altered in people with insulin production and signaling perturbations. Because we did not measure other correlates of obesity, diabetes, and the metabolic syndrome we cannot determine whether C-peptide independently explained the association with prostate cancer. Finally, we had low power to assess the association for advanced or aggressive disease or by strata of age and BMI.
In conclusion, we found that men with higher C-peptide concentration were less likely to be diagnosed with prostate cancer. Whether this finding reflects a lower risk of developing prostate cancer or merely reduced accuracy of detecting occult prostate cancer in men with higher C-peptide remains to be determined. Also, our study cannot rule out a potential positive association of C-peptide with advanced stage disease. With the continuing rise in the prevalence of obesity and diabetes, additional mechanistic and epidemiologic focus on the metabolic perturbations underlying these states is needed to understand the complex links of obesity, diabetes, the metabolic syndrome with prostate cancer.
Acknowledgments
We thank the research staff of the CLUE II study for their continued help in conducting the study. We also thank Gary Bradwin of the Clinical and Epidemiological Research Laboratory, Children’s Hospital Boston.
This work was funded by: Public Health Service research grants U01 CA86308 (Early Detection Research Network) and P50 CA58236 (Prostate Cancer SPORE) from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. Gabriel Lai was funded by a National Research Service Award T32 CA009314 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
References
- 1.Hsing AW, Sakoda LC, Chua S., Jr Obesity, metabolic syndrome, and prostate cancer. Am J Clin Nutr. 2007;86:s843–57. doi: 10.1093/ajcn/86.3.843S. [DOI] [PubMed] [Google Scholar]
- 2.MacInnis RJ, English DR. Body size and composition and prostate cancer risk: systematic review and meta-regression analysis. Cancer Causes Control. 2006;17:989–1003. doi: 10.1007/s10552-006-0049-z. [DOI] [PubMed] [Google Scholar]
- 3.Freedland SJ, Giovannucci E, Platz EA. Are findings from studies of obesity and prostate cancer really in conflict? Cancer Causes Control. 2006;17:5–9. doi: 10.1007/s10552-005-0378-3. [DOI] [PubMed] [Google Scholar]
- 4.Giovannucci E, Michaud D. The role of obesity and related metabolic disturbances in cancers of the colon, prostate, and pancreas. Gastroenterology. 2007;132:2208–25. doi: 10.1053/j.gastro.2007.03.050. [DOI] [PubMed] [Google Scholar]
- 5.Nandeesha H. Insulin: a novel agent in the pathogenesis of prostate cancer. Int Urol Nephrol. 2009;41:267–72. doi: 10.1007/s11255-008-9440-x. [DOI] [PubMed] [Google Scholar]
- 6.Cox ME, Gleave ME, Zakikhani M, et al. Insulin receptor expression by human prostate cancers. Prostate. 2009;69:33–40. doi: 10.1002/pros.20852. [DOI] [PubMed] [Google Scholar]
- 7.Hsing AW, Chua S, Jr, Gao YT, et al. Prostate cancer risk and serum levels of insulin and leptin: a population-based study. J Natl Cancer Inst. 2001;93:783–9. doi: 10.1093/jnci/93.10.783. [DOI] [PubMed] [Google Scholar]
- 8.Stattin P, Soderberg S, Hallmans G, et al. Leptin is associated with increased prostate cancer risk: a nested case-referent study. J Clin Endocrinol Metab. 2001;86:1341–5. doi: 10.1210/jcem.86.3.7328. [DOI] [PubMed] [Google Scholar]
- 9.Hubbard JS, Rohrmann S, Landis PK, et al. Association of prostate cancer risk with insulin, glucose, and anthropometry in the Baltimore longitudinal study of aging. Urology. 2004;63:253–8. doi: 10.1016/j.urology.2003.09.060. [DOI] [PubMed] [Google Scholar]
- 10.Chen C, Lewis SK, Voigt L, Fitzpatrick A, Plymate SR, Weiss NS. Prostate carcinoma incidence in relation to prediagnostic circulating levels of insulin-like growth factor I, insulin-like growth factor binding protein 3, and insulin. Cancer. 2005;103:76–84. doi: 10.1002/cncr.20727. [DOI] [PubMed] [Google Scholar]
- 11.Neuhouser ML, Till C, Kristal A, et al. Finasteride modifies the relation between serum C-peptide and prostate cancer risk: results from the Prostate Cancer Prevention Trial. Cancer Prev Res. 2010;3:279–89. doi: 10.1158/1940-6207.CAPR-09-0188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Borugian MJ, Spinelli JJ, Sun Z, et al. Prediagnostic C-peptide and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 2007;16:2164–5. doi: 10.1158/1055-9965.EPI-07-0495. [DOI] [PubMed] [Google Scholar]
- 13.Stocks T, Lukanova A, Rinaldi S, et al. Insulin resistance is inversely related to prostate cancer: a prospective study in Northern Sweden. Int J Cancer. 2007;120:2678–86. doi: 10.1002/ijc.22587. [DOI] [PubMed] [Google Scholar]
- 14.Ma J, Li H, Giovannucci E, et al. Prediagnostic body-mass index, plasma C-peptide concentration, and prostate cancer-specific mortality in men with prostate cancer: a long-term survival analysis. Lancet Oncol. 2008;9:1039–47. doi: 10.1016/S1470-2045(08)70235-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Kasper JS, Giovannucci E. A meta-analysis of diabetes mellitus and the risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 2006;15:2056–62. doi: 10.1158/1055-9965.EPI-06-0410. [DOI] [PubMed] [Google Scholar]
- 16.Gong Z, Neuhouser ML, Goodman PJ, et al. Obesity, diabetes, and risk of prostate cancer: results from the prostate cancer prevention trial. Cancer Epidemiol Biomarkers Prev. 2006;15:1977–83. doi: 10.1158/1055-9965.EPI-06-0477. [DOI] [PubMed] [Google Scholar]
- 17.Calton BA, Chang SC, Wright ME, et al. History of diabetes mellitus and subsequent prostate cancer risk in the NIH-AARP Diet and Health Study. Cancer Causes Control. 2007;18:493–503. doi: 10.1007/s10552-007-0126-y. [DOI] [PubMed] [Google Scholar]
- 18.Velicer CM, Dublin S, White E. Diabetes and the risk of prostate cancer: the role of diabetes treatment and complications. Prostate Cancer Prostatic Dis. 2007;10:46–51. doi: 10.1038/sj.pcan.4500914. [DOI] [PubMed] [Google Scholar]
- 19.Darbinian JA, Ferrara AM, Van Den Eeden SK, Quesenberry CP, Jr, Fireman B, Habel LA. Glycemic status and risk of prostate cancer. Cancer Epidemiol Biomarkers Prev. 2008;17:628–35. doi: 10.1158/1055-9965.EPI-07-2610. [DOI] [PubMed] [Google Scholar]
- 20.Kasper JS, Liu Y, Giovannucci E. Diabetes mellitus and risk of prostate cancer in the health professionals follow-up study. Int J Cancer. 2009;124:1398–403. doi: 10.1002/ijc.24044. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Waters KM, Henderson BE, Stram DO, Wan P, Kolonel LN, Haiman CA. Association of diabetes with prostate cancer risk in the multiethnic cohort. Am J Epidemiol. 2009;169:937–45. doi: 10.1093/aje/kwp003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kasper JS, Liu Y, Pollak MN, Rifai N, Giovannucci E. Hormonal profile of diabetic men and the potential link to prostate cancer. Cancer Causes Control. 2008;19:703–10. doi: 10.1007/s10552-008-9133-x. [DOI] [PubMed] [Google Scholar]
- 23.Bonser AM, Garcia-Webb P. C-peptide measurement: methods and clinical utility. Crit Rev Clin Lab Sci. 1984;19:297–352. doi: 10.3109/10408368409165766. [DOI] [PubMed] [Google Scholar]
- 24.Hovorka R, Jones RH. How to measure insulin secretion. Diabetes Metab Rev. 1994;10:91–117. doi: 10.1002/dmr.5610100204. [DOI] [PubMed] [Google Scholar]
- 25.Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner L. A databased approach to diet questionnaire design and testing. Am J Epidemiol. 1986;124:453–69. doi: 10.1093/oxfordjournals.aje.a114416. [DOI] [PubMed] [Google Scholar]
- 26.Christlieb AR, Krolewski AS, Warram JH, Soeldner JS. Is insulin the link between hypertension and obesity? Hypertension. 1985;7:II54–7. doi: 10.1161/01.hyp.7.6_pt_2.ii54. [DOI] [PubMed] [Google Scholar]
- 27.Kahn BB, Flier JS. Obesity and insulin resistance. J Clin Invest. 2000;106:473–81. doi: 10.1172/JCI10842. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lazarus R, Sparrow D, Weiss S. Temporal relations between obesity and insulin: longitudinal data from the Normative Aging Study. Am J Epidemiol. 1998;147:173–9. doi: 10.1093/oxfordjournals.aje.a009431. [DOI] [PubMed] [Google Scholar]
- 29.Manicardi V, Camellini L, Bellodi G, Coscelli C, Ferrannini E. Evidence for an association of high blood pressure and hyperinsulinemia in obese man. J Clin Endocrinol Metab. 1986;62:1302–4. doi: 10.1210/jcem-62-6-1302. [DOI] [PubMed] [Google Scholar]
- 30.Hsing AW. Hormones and prostate cancer: what’s next? Epidemiol Rev. 2001;23:42–58. doi: 10.1093/oxfordjournals.epirev.a000795. [DOI] [PubMed] [Google Scholar]
- 31.Platz EA, Giovannucci E. The epidemiology of sex steroid hormones and their signaling and metabolic pathways in the etiology of prostate cancer. J Steroid Biochem Mol Biol. 2004;92:237–53. doi: 10.1016/j.jsbmb.2004.10.002. [DOI] [PubMed] [Google Scholar]
- 32.Baillargeon J, Pollock BH, Kristal AR, et al. The association of body mass index and prostate-specific antigen in a population-based study. Cancer. 2005;103:1092–5. doi: 10.1002/cncr.20856. [DOI] [PubMed] [Google Scholar]
- 33.Barqawi AB, Golden BK, O’Donnell C, Brawer MK, Crawford ED. Observed effect of age and body mass index on total and complexed PSA: analysis from a national screening program. Urology. 2005;65:708–12. doi: 10.1016/j.urology.2004.10.074. [DOI] [PubMed] [Google Scholar]
- 34.Werny DM, Thompson T, Saraiya M, et al. Obesity is negatively associated with prostate-specific antigen in U.S. men, 2001–2004. Cancer Epidemiol Biomarkers Prev. 2007;16:70–6. doi: 10.1158/1055-9965.EPI-06-0588. [DOI] [PubMed] [Google Scholar]
- 35.Fowke JH, Matthews CM, Buchowski MS, et al. Association between prostate-specific antigen and leptin, adiponectin, HbA1c or C-peptide among African-American and Caucasian men. Prostate Cancer Prostatic Dis. 2008;11:264–9. doi: 10.1038/sj.pcan.4501022. [DOI] [PubMed] [Google Scholar]
- 36.Parekh N, Lin Y, Marcella S, Kant AK, Lu-Yao G. Associations of lifestyle and physiologic factors with prostate-specific antigen concentrations: evidence from the National Health and Nutrition Examination Survey (2001–2004) Cancer Epidemiol Biomarkers Prev. 2008;17:2467–72. doi: 10.1158/1055-9965.EPI-08-0059. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Sundell IB, Hallmans G, Nilsson TK, Nygren C. Plasma glucose and insulin, urinary catecholamine and cortisol responses to test breakfasts with high or low fibre content: the importance of the previous diet. Ann Nutr Metab. 1989;33:333–40. doi: 10.1159/000177555. [DOI] [PubMed] [Google Scholar]
- 38.von Post-Skagegard M, Vessby B, Karlstrom B. Glucose and insulin responses in healthy women after intake of composite meals containing cod-, milk-, and soy protein. Eur J Clin Nutr. 2006;60:949–54. doi: 10.1038/sj.ejcn.1602404. [DOI] [PubMed] [Google Scholar]