Finasteride Modifies the Relation between Serum C-Peptide and Prostate Cancer Risk: Results from the Prostate Cancer Prevention Trial Free

Read the Perspective on this article by Kaaks and Stattin, p. 259

Metabolic dysfunction is a recognized risk factor for carcinogenesis, including carcinogenesis of the prostate (1–3). Important examples of metabolic dysfunction include hyperinsulinemia and insulin resistance (1, 2). These clinical characteristics are of particular interest because insulin-activated signaling involves pathways known to be involved in carcinogenesis (4–7).

Several biologically plausible mechanisms may underlie the role of insulin in carcinogenesis. For example, insulin promotes cell division; thus, a metabolic milieu of hypersinsulinemia is likely to promote cellular proliferation and hyperplasia (8–10). Alternatively, insulin may facilitate carcinogenesis by favoring cell survival and inhibiting apoptosis through effects on AKT and mTOR (11, 12), allowing accumulation of genetic damage.

Increasing evidence suggests that obesity is a strong risk factor and/or adverse prognostic factor for cancer, including prostate cancer (13, 14). Whereas the mechanism for the obesity-prostate cancer association is not definitively identified, one possibility is that obesity-related hyperinsulinemia could lead to inappropriate survival and proliferation of insulin-receptor–positive at-risk and/or transformed prostate epithelial cells (8, 10, 15–17). Consistent with this view, plasma levels of C-peptide, which serve as a marker of insulin secretion, increase with obesity (8, 18, 19), and higher levels are associated with increased risk of prostate cancer–specific mortality (7). Other evidence suggests that insulin may influence cancer risk through effects on inflammation, oxidative stress, or sex hormones (5). Regardless of the mechanism, targeting the insulin axis for cancer prevention is attractive because insulin axis components are modifiable by diet, weight loss, physical activity, and pharmacologic approaches (20–22).

Biomarkers of metabolic dysfunction and insulin resistance include glucose, insulin, C-peptide, glycosylated hemoglobin, and calculated measures such as homeostasis model of assessment (23). Of these, C-peptide has been used as the biomarker of insulin secretion in many population-based studies (7). Proinsulin is synthesized by the pancreas and cleaved into insulin and C-peptide before release in the circulation. In nonfasting samples, C-peptide is considered a more reliable marker of insulin secretion because, compared with insulin, it has a longer half-life and is metabolically stable (7, 24).

Three investigations have examined these biomarkers (excess glucose, insulin, and insulin resistance) in relation to risk of breast and colorectal cancers (25–27), but relatively few studies have investigated insulin resistance or C-peptide with prostate cancer risk. A Swedish cohort study reported an inverse association of high versus low plasma C-peptide with overall prostate cancer risk. However, a nonsignificant increased risk of aggressive disease [defined as Gleason ≥8, lymph node metastasis, prostate-specific antigen (PSA) >50, bone metastasis, or prostate cancer death] was observed in the cohort (28). More recently, the Physicians' Health Study showed that men diagnosed with prostate cancer who had higher versus lower baseline C-peptide had a >2-fold risk of dying from their prostate cancer. These results became somewhat attenuated after controlling for body mass index (BMI) and clinical characteristics such as Gleason grade and stage (7). Further subgroup analysis revealed that overweight men who also had elevated C-peptide had a 4-fold increased risk of death from prostate cancer (7). A recent nested case-control study (n = 100 prostate cancer cases/400 controls) reported an odds ratio (OR) of 2.55 [95% confidence interval (95% CI), 1.18-5.51; P_trend = 0.02] for the fourth versus first quartile of serum insulin (29). In contrast, a multiethnic study of 114 prostate cancer cases and 484 controls reported no association of prediagnostic C-peptide with prostate cancer risk (30).

An alternative and equally plausible pathway by which obesity may increase prostate cancer risk is via adipokines. Adipose tissue synthesizes adipokines (such as leptin and adiponectin), and the more adipose tissue the greater the synthesis of these peptides (31, 32). Leptin was originally identified as a satiety signal, but other functions were subsequently identified, including involvement in glucose and fatty acid oxidation, complex orchestrating of the hormonal control of food intake, regulation of other hormones (i.e., growth hormones and reproductive hormones), and a role in the promotion of mitogenic activity (31, 32). In addition, in vitro studies have suggested that leptin inhibits apoptosis and promotes cell proliferation in prostate cancer cells (33–35), rendering it a potential mediator of the relationship between obesity and cancer risk.

Finding effective measures for prostate cancer prevention is an important clinical and public health priority, particularly for high-grade disease that typically has a poor prognosis. Therefore, we sought to better understand both of these potential mechanisms (insulin secretion/insulin resistance and leptin) in relation to prostate cancer risk in the Prostate Cancer Prevention Trial (PCPT).

The PCPT was a randomized, placebo-controlled trial testing whether the 5α-reductase inhibitor finasteride could reduce the 7-y period prevalence of prostate cancer. Details regarding study design and participant characteristics have been described previously (36). Briefly, at 221 clinical centers across the United States, 18,880 men, ages 55 y and older and with a normal digital rectal exam and prostate-specific antigen (PSA) level <3.0 ng/mL, as well as no history of prostate cancer, severe benign prostatic hyperplasia, or clinically significant comorbid conditions that would have precluded successful completion of the study protocol, were randomized to receive finasteride (5 mg/d) or placebo. During the course of the PCPT, men underwent annual digital rectal exam and PSA measures and a prostate biopsy was recommended for all men with an abnormal digital rectal exam or a finasteride-adjusted PSA of ≥4.0 ng/mL (37). At the final study visit, all men without a previous diagnosis of prostate cancer were offered an end-of-study biopsy. Biopsies were collected under transrectal ultrasonographic guidance, and a minimum of six biopsy specimens (cores) were collected from each participant. All biopsies were reviewed by a local study pathologist and by a central study pathologist (38, 39). Discordant pathology interpretations were arbitrated by a referee pathologist and concordance was achieved in all cases (36, 38, 39). Pathologists were blinded to the randomization arm of all participants. Tumors were graded with the Gleason system by central pathology review at the Prostate Diagnostic Laboratory (Denver, CO). Low-grade prostate cancer included tumors with Gleason score <7 and high-grade tumors were those with Gleason score ≥7 (36). Study procedures were approved by the Institutional Review Boards of the participating clinical centers, the Southwest Oncology Group (San Antonio, TX) and the Southwest Oncology Group Data and Statistical Center (Fred Hutchinson Cancer Research Center, Seattle, WA). All men signed informed consent. An independent data safety and monitoring committee met every 6 mo throughout the course of the trial to review data on safety, adherence, and diagnosis of prostate cancer (36).

This report presents data from a nested-case control study in the PCPT. Cases were men with biopsy-determined prostate cancer identified either during an interim or end-of-study biopsy and who had baseline serum available for analysis (n = 1,803). We retained the low-grade and high-grade classifications as used in the original trial report for these analyses (low grade, Gleason <7; high grade, Gleason ≥7; ref. 36). Controls were selected from men who completed the end-of-study biopsy procedure, had no evidence of prostate cancer, and had archived baseline serum samples (n = 1,797). Controls were frequency matched to cases on distributions of age (in 5-y age groups), PCPT treatment arm (finasteride versus placebo), and positive family history for first-degree relative with prostate cancer; controls were oversampled to include all non-whites.

Nonfasting blood specimens were collected at screening (∼3 mo before randomization) and yearly thereafter. Venous blood was drawn into collection tubes without anticoagulant, refrigerated, and shipped via overnight courier to the PCPT specimen repository where they were centrifuged, aliquoted, and stored at −70°C until analysis (40).

Serum C-peptide and leptin concentrations were assayed with a standard ELISA using a single production lot of reagents (Diagnostic Systems Limited). All assays were conducted in duplicate and the means of the duplicate measures were reported. Two sets of QC samples (from pooled specimens) were included for quality control and the coefficients of variation from these QC pools were 5.6% and 3.8% for the C-peptide assays and 4.9% and 4.5% for the leptin assays. Laboratory technicians were blinded to the randomization assignment and case-control status of all participants.

Demographic, personal medical history, family history of prostate cancer, and lifestyle habits such as smoking, alcohol, and physical activity were collected by self-report at baseline; the measurement characteristics of many of these self-assessment tools are published (41–43). Height and weight were assessed at the baseline clinic visit using a standard protocol (44) and weight was assessed annually thereafter. BMI was calculated as weight (kg)/[height (m)]² and standard clinical cutoff points categorized BMI as normal (BMI <25.0), overweight (BMI 25.0 to <30.0), and obese (BMI ≥30.0; ref. 45). Circumferences of the abdomen, waist, hip, and thigh were measured at 1 y post-randomization (46). As the body circumference measurements were voluntary, some clinical centers did not participate, resulting in missing data for n = 348 (10%) of the participants.

The control group was oversampled with minorities to include all control-eligible non-whites. Thus, baseline demographic and lifestyle characteristics were adjusted for race (white versus non-white) using linear regression to calculate least squares means for continuous variables and adjusted percents for categorical variables.

Serum concentrations of C-peptide and leptin were categorized into quartiles based on the distribution in the controls. Logistic regression was used to calculate ORs and 95% CIs for risk of total prostate cancer, and polytomous logistic regression was used to calculate ORs and 95% CIs of both low-grade and high-grade prostate cancers. The polytomous regression with a generalized logit link permits a model including both low-grade and high-grade cancers as outcomes in the same model, contrasted with no cancer. Model covariates were carefully selected based on a priori information about potential confounding as well as diagnostic procedures completed as part of our modeling exercises. Final covariates included age, race (white/non-white), family history of first-degree relative with prostate cancer, insulin use at baseline, BMI, and smoking (pack-years of smoking). Because this nested case-control study was part of a randomized, controlled trial, we had an a priori hypothesis that there may be effect modification of the active agent in the trial, finasteride, on C-peptide or leptin in relation to prostate cancer risk. We also hypothesized that there could be effect modification by measures of adiposity (e.g., BMI and waist circumference). Tests for multiplicative interaction were conducted by entering cross-product terms of C-peptide and finasteride or measures of adiposity and testing these terms with the Wald test. We conducted a mediating factors analysis with BMI and leptin to determine whether any observed BMI-prostate cancer risk associations were explained by leptin. All statistical tests were two-sided, with P < 0.05 considered statistically significant. SAS (version 9.0) was used for all statistical analyses.

Cases and controls did not differ with respect to race-adjusted BMI, age, body circumferences, family history of prostate cancer, current smoking status, or alcohol habits. Controls reported higher levels of physical activity than cases (10.6% versus 8.2% were very active, P = 0.02). Controls also had more total pack-years of smoking (P = 0.02) and were more likely to report a diagnosis of diabetes or use insulin and other diabetes medications at baseline compared with cases (P = 0.05; Table 1).

Unadjusted baseline serum C-peptide concentrations did not differ between cases and controls (Table 2). To determine whether finasteride influenced serum C-peptide, we assayed the blood specimens of a random subset of participants (n = 267) at both baseline and 2 years later. Whereas mean and median concentrations of C-peptide increased for all men between baseline and year 2, there was no evidence that finasteride differentially affected these changes. Mean and median baseline-year 2 differences did not vary by treatment arm, even after adjustment for years since baseline blood draw and participant weight change from baseline to year 2 (data not shown).

Our initial modeling included the combined arms of the trial [intervention (finasteride) and placebo] and the interaction term between treatment arm and C-peptide. Notably, we observed a statistically significant interaction of C-peptide with treatment arm (P_interaction = 0.04 for all cancers and P = 0.03 for Gleason ≥7). Therefore, all remaining models were stratified by treatment arm. Additionally, because men with self-reported diabetes at baseline might have higher circulating C-peptide due to disease-associated poor glycemic control, we conducted additional analyses excluding diabetic men to determine whether they were driving the observed associations. However, because these analyses did not materially alter the results or their interpretation, men with diabetes at baseline were retained in the final model.

Table 3 gives the results for multivariate-adjusted associations of serum C-peptide with prostate cancer risk, stratified by treatment arm. In the placebo arm, C-peptide was associated with modest increases in risk for total and low-grade disease (OR, 1.21; 95% CI, 0.93-1.57; P_trend = 0.11 for all cancers). Findings for high-grade disease were considerably stronger. For men in the placebo arm, those with higher baseline serum C-peptide concentrations had a nearly 2-fold, statistically significant increased risk of high-grade prostate cancer, compared with men with lower baseline serum C-peptide (OR, 1.88; 95% CI, 1.19-2.97; P_trend = 0.004). Whereas the PCPT defined high-grade as Gleason ≥7, analyses where high grade was restricted to Gleason ≥8 were equally strong, but cell sizes were very small and confidence intervals very wide. Therefore, data presented are for Gleason scores <7 and ≥7 only. We next examined C-peptide as a continuous variable. In analyses where C-peptide was modeled as log(C-peptide), associations with high-grade disease were strong. For every unit increase in log(C-peptide), men randomized to placebo had a 39% increased risk of high-grade prostate cancer (P < 0.01).

In contrast to the C-peptide risk associations observed in the placebo arm, among men randomized to finasteride, there was no evidence for C-peptide–associated risk with total prostate cancer or low-grade or high grade-disease, even for men with high serum C-peptide. For low-grade disease, there was a suggestion of lowered risk among finasteride users (OR for high versus low C-peptide, 0.77; 95% CI, 0.54-1.09). However, for high-grade disease, prostate cancer risk estimates hovered around unity. Results were similar when high grade was defined as Gleason ≥8. Likewise, when C-peptide was modeled as a continuous variable, there was a modest suggestion of lowered risk for low-grade disease; for each unit increase of log(C-peptide), the OR for prostate cancer was 0.87 (95% CI, 0.72-1.05; P_trend = 0.16). In the continuous model, no association was observed between C-peptide and high-grade prostate cancer for men taking finasteride. All results in Table 3 were repeated, restricting the sample to men who were black or non-Hispanic white (i.e., excluding all other race groups). The results and their interpretation did not differ from models where all race/ethnicities were included. We also found no differences in risk across race groups, albeit sample sizes were small (data not shown). Therefore, the final models as presented included all non-white and white men.

Adiposity strongly influences insulin resistance and C-peptide. In this study sample, the correlations of BMI and waist circumference with serum C-peptide were r = 0.25 and r = 0.27, respectively. To investigate whether these adiposity measures modified the association between C-peptide and prostate cancer risk, we modeled C-peptide with the following interaction terms in separate models: (a) BMI 25.0-29.9 (overweight), BMI ≥30.0 (obese); (b) elevated waist circumference (≥102 cm); (c) elevated waist-hip ratio (≥1.0); and (d) a category termed “very high risk of metabolic dysfunction” defined as BMI ≥30.0 + waist circumference ≥102 cm, or BMI ≥35.0 (45). We restricted these analyses to the placebo arm and we presented high-grade disease only because this was the group with the elevated risk in the Table 3 models. The results presented in Table 4 were for low and high C-peptide (above and below the median concentration), and for each model the reference group is the lowest risk category (i.e., low C-peptide + normal BMI, or low C-peptide + waist-hip ratio <1.0). We observed no evidence for effect modification of C-peptide by measures of adiposity.

We conducted several other exploratory analyses in an attempt to further understand why the C-peptide/high-grade disease association was observed only in the PCPT placebo arm. First, we examined whether results may have been biased by the fact that PCPT participants had end-of-study biopsies in addition to biopsies by indication throughout the trial. However, analyses stratified by end-of-study versus for-cause biopsy and analyses stratified by year of diagnosis did not change the interpretation of results. Similarly, neither adjusting for time since blood draw, prostate volume, trends in PSA nor changes in participant weight over time altered the results or their interpretation (data not shown).

To fully understand the associations of obesity-related factors with prostate cancer risk, particularly high-grade disease, we next investigated whether serum leptin was associated with prostate cancer risk (Table 5). Specifically, we conducted a mediating factors analysis to understand whether leptin mediates the association of obesity with prostate cancer risk, as suggested by in vitro studies (33, 35). Our initial modeling did not show an interaction of finasteride with leptin, and thus for these models, the intervention and placebo arms are combined. When BMI is modeled as the primary exposure, the OR for high-grade prostate cancer among obese men is 1.39 (95% CI, 1.03-1.87; P_trend = 0.03). Obesity was associated with a modest inverse association with low-grade disease (OR, 0.80; 95% CI, 0.64-1.0; P_trend = 0.04). Leptin alone was likewise inversely associated with low-grade, but not high-grade, prostate cancer. In the mediating factors analysis, the BMI association with high-grade disease remains strong, even after including leptin in the model. Finally, we reran the C-peptide models again (from Table 3) but controlling for leptin. C-peptide continued to be associated with high-grade disease in the placebo arm (but not finasteride arm) even after controlling for leptin (OR, 1.99; 95% CI, 1.24-3.19; P_trend < 0.001; data not shown). The association of obesity with high-grade prostate cancer risk does not seem to be mediated by leptin.

In the PCPT, men in the placebo arm of the trial who had higher serum C-peptide had a nearly 2-fold increased risk of high-grade prostate cancer, relative to men with lower serum C-peptide. The results suggest that metabolic abnormalities are associated with prostate cancer risk, but the mechanism seems to be through insulin resistance and not via adipokines, such as leptin. The C-peptide findings were independent of the principal risk factors for prostate cancer: age, race, and family history of prostate cancer (47). The observed associations were also independent of adiposity measures, such as BMI and waist circumference, which recent reports suggest are important prostate cancer risk factors (14, 48, 49). For every unit increase in log(C-peptide), there was a 39% increased risk of high-grade prostate cancer. These findings are strengthened by the fact that all men underwent either interim (cases only) or end-of-study (cases and controls) biopsies in the PCPT (36). Thus, the control group is not contaminated with any preclinical cases, as may be the case with other studies.

In contrast, men taking finasteride had no apparent increased risk for high-grade prostate cancer, even for men with high serum C-peptide. The strength of the interaction of C-peptide with finasteride was somewhat unexpected. Finasteride inhibits 5α-reductase, which converts testosterone to the more potent and biologically active dihydrotestosterone (36, 50). We do not have evidence that finasteride affects C-peptide per se because analysis of the small subset (n = 267) of specimens assayed for both baseline and year 2 serum C-peptide did not support a C-peptide–reducing effect by finasteride. There is some evidence, however, that finasteride may indirectly affect metabolic function. Two previous reports showed that finasteride slows weight gain trajectories (51, 52). If the finasteride-induced decreased weight gain slope affected some of the downstream events that are upregulated by C-peptide (e.g., AKT and mTOR; refs. 12, 17), then finasteride could essentially override C-peptide–driven events without affecting C-peptide itself. Another possible explanation is that proliferation of neoplastic cells in the prostate may depend on both insulin and androgens. Because androgen levels (such as dihydrotestosterone) are reduced among men on finasteride, neoplastic progression could be slowed despite high insulin levels. It is also possible that multiple mechanisms are at work. Investigations using methods developed for genetic epidemiology that simultaneously model metabolic and hormonal pathways, as well as the role of finasteride in those pathways, may be a particularly useful approach to use in the future to understand these complex relationships (53–55). Regardless of the underlying biological mechanism, the evidence that men with higher serum C-peptide who used finasteride, but did not have an increased prostate cancer risk, may be clinically meaningful and should be investigated further.

The results from the PCPT placebo arm are consistent with most, but not all, previous studies of C-peptide and prostate cancer risk. The Physicians' Health Study reported that risk of death from prostate cancer was >2-fold higher for men with higher versus lower C-peptide (7). Unlike the PCPT, however, the Physicians' Health Study observed an interaction of BMI with C-peptide such that men in the highest C-peptide quartile who were also overweight (BMI >25.0) had a 4-fold increased risk of prostate cancer mortality, but those with high C-peptide who were not overweight had no increased risk of prostate cancer death (7). It is possible that the PCPT did not have sufficient variation in adiposity measures to detect an interaction effect with C-peptide because only 25% of the study sample was of normal BMI. Other studies have reported on insulin and insulin resistance measures such as homeostasis model of assessment. Two studies conducted in Chinese men support a positive association of insulin or insulin resistance with prostate cancer risk (5, 6), as does one recent study of Finnish men (29). In contrast to the positive reports for C-peptide and other measures of metabolic dysfunction, Borugian et al. (30) reported no association of serum C-peptide with prostate cancer risk, but the study was very small (n = 57 cases) and no results were reported by disease stage or Gleason grade.

Despite the increasing evidence in this and other reports that metabolic dysfunction increases prostate cancer risk (5, 7, 13, 48), one somewhat paradoxical observation still exists. Several observational studies have reported inverse associations of diabetes with prostate cancer risk (14, 56, 57). Because diabetes is a disease of disordered glucose metabolism, and often the consequence of obesity (18, 58), evidence suggesting that diabetes reduces prostate cancer risk is curious. Diabetic men also have altered sex hormone profiles as a consequence of their disease (59), which could possibly explain the relationship, but how these complex pathways weave together to affect disease risk is yet to be determined. A particularly complex piece of this clinical picture is the marked heterogeneity of diabetes. For example, many patients with type II diabetes initially have hyperinsulinemia, but in later stages of the disease actually have hypoinsulinemia, a state that may further reduce androgens and potentially reduce prostate cancer risk (14, 60). Alternatively, the link between diabetes and decreased prostate cancer risk may relate to drugs used to treat diabetes rather than diabetes itself. Metformin is very commonly used in the treatment of type II diabetes (61), and metformin has recently been shown to have potent antiproliferative properties (62). These may relate not only to metformin-induced reduction in the hyperinsulinemia seen in type II diabetics but also to direct mechanisms of growth inhibition related to metformin activation of the AMP kinase signaling pathway (63–66). In addition, many diabetics routinely use statins due to comorbid cardiovascular symptoms; statins have been noted to lower prostate cancer risk (67, 68). Future studies, rather than simply examining relationships between diabetes and prostate cancer, may provide more insight into mechanisms by exploring individually the specific relationships between risk and the levels of various hormones (including insulin), glucose, measures of obesity, and antidiabetic drug use. Although not definitive, several recent studies have raised the question of increased cancer risk among diabetics who use insulin, in contrast to possibly reduced risk among those who use metformin (69, 70).

Our finding of no relationship of leptin to high-grade prostate cancer risk is a potentially important one, particularly with regard to formulating programs for prevention and control. Evidence to support an association of leptin with prostate cancer is inconsistent from observational studies. A Swedish case-control study reported a relative risk of 2.4 for high versus low serum leptin in relation to prostate cancer risk (71). However, two subsequent case-control studies in China and Norway reported no statistically significant association of leptin with prostate cancer (6, 72). A more recent case-control study in Texas also reported no association of leptin with prostate cancer risk (73). However, the PCPT results presented here are consistent with results from a study using the fatless A-ZIP/F-1 transgenic mouse model. These animals are insulin resistant and are in a chronic state of inflammation, but they lack white adipose tissue and thus have near undetectable levels of adipokines, such as leptin (74, 75). Nunuz et al. reported that the A-ZIP/F-1 mice produced more skin and mammary tumors than wild-type mice [using a classic topical 7,12-dimethylbenz(a)anthracene application for the skin cancers and breeding with C3(1)/T-Ag transgenic mice for the mammary cancers]. The tumors developed in spite of very low levels of adipokines but high levels of insulin and inflammatory factors and upregulation of insulin-regulated signaling pathways involved in carcinogenesis. The authors and others conclude that the obesity-cancer association may be mediated by insulin resistance and inflammation, rather than adipokines (74, 75). Our results in the PCPT are similar; C-peptide was strongly associated with increased risk of prostate cancer and the effects remained strong after controlling for obesity-related factors. The relationships were attenuated by finasteride. Conversely, the association of obesity with prostate cancer risk was not mediated by leptin. Such findings quite likely have relevance for future programs of prostate cancer prevention and control wherein it might be most effective to improve measures of insulin sensitivity as a measure of prevention and control.

This study has several strengths. The PCPT was a large placebo-controlled randomized trial. The trial design specified that prostate cancer outcomes would be based on biopsy results. As such, the control group used in these analyses all had negative prostate biopsies, largely eliminating the possibility that controls may have had undiagnosed or undetected disease. Other strengths include carefully collected data throughout the course of the trial and a central pathology laboratory for uniform adjudication of all cases (including adjudication of Gleason grade). Limitations should also be noted, including the fact that the PCPT included few minorities. Although we oversampled non-white controls to increase power for analyses by race, the power for any race-specific subgroups was limited. Further, few deaths from prostate cancer have occurred in the PCPT and thus we were unable to conduct analyses to examine mortality as an end point.

In conclusion, these results from the PCPT suggest that men with elevated C-peptide have an increased risk of high-grade prostate cancer. The lack of support in the PCPT for an association of leptin with prostate cancer suggests that the obesity-cancer associations may be mediated by insulin resistance rather than by adipose-derived factors, such as leptin. The findings reported here confirm and extend results from other cohorts (7). Because insulin resistance–type syndromes respond well to lifestyle and pharmacologic treatments, consideration should be given to preventive interventions to lower insulin resistance as a means of prostate cancer prevention. Our finding that men with elevated C-peptide who also used finasteride had no increased prostate cancer risk is novel. Further research is needed to understand the clinical significance and mechanisms underlying the finasteride/C-peptide interaction presented in this report. We cannot rule out the possibility that C-peptide levels may be of use in defining a subpopulation of men for whom finasteride-based prevention programs would be particularly useful.

No potential conflicts of interest were disclosed.

We thank the PCPT participants and clinic staff, and Drs. Scott Lippman, Ian Thompson, M. Scott Lucia, Regina Santella, and Catherine Tangen for their scientific contributions.

Grant Support: National Cancer Institute at the NIH, U.S. Department of Health and Human Services (P01 CA108964).

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