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American Journal of Epidemiology Advance Access originally published online on October 5, 2006
American Journal of Epidemiology 2006 164(10):978-983; doi:10.1093/aje/kwj311
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.

Original Contribution

Prostate-specific Antigen Values in Diabetic and Nondiabetic US Men, 2001–2002

David M. Werny1, Mona Saraiya1 and Edward W. Gregg2

1 Division of Cancer Prevention and Control, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA
2 Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA

Correspondence to Dr. Mona Saraiya, National Center for Chronic Disease Prevention and Health Promotion, Mail Stop K-55, 4770 Buford Highway NE, Atlanta, GA 30341 (e-mail: msaraiya{at}cdc.gov).

Received for publication February 8, 2006. Accepted for publication April 28, 2006.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Recent studies have shown that diabetic men have a lower risk of prostate cancer and that this association may be related to time since diagnosis. The authors examined the association between diabetes and prostate-specific antigen (PSA) levels, controlling for potential confounders, in a nationally representative cross-sectional survey of the US population (National Health and Nutrition Examination Survey 2001–2002). Diabetes classification was self-reported, and undiagnosed diabetes was determined with fasting plasma glucose measurements. Controlling for age, men with self-reported diabetes had a 21.6% lower geometric mean PSA level than men without diabetes. The difference increased with years since diagnosis (>10 years: 27.5% lower geometric mean PSA level). Overweight men who had had diabetes for more than 10 years had a predicted geometric mean PSA level 40.8% lower than that of nondiabetic, normal-weight men. These results are consistent with the hypothesis that long-term diabetes is associated with a lower risk of prostate cancer. The mechanism of this association may involve the regulation of PSA by androgens, although the authors are unable to confirm this assertion. Better understanding of the determinants of PSA level is needed to make the distinction between factors affecting the PSA test's accuracy and those altering the risk of prostate cancer.

diabetes mellitus, type 2; prostate-specific antigen; prostatic neoplasms; testosterone

Abbreviations: BMI, body mass index; IGF-1, insulin-like growth factor 1; NHANES, National Health and Nutrition Examination Survey; PSA, prostate-specific antigen


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Recent studies have suggested an association between type 2 diabetes mellitus and lower risk of prostate cancer (1Go, 2Go). It has been hypothesized that men with long-term diabetes have a lower risk of prostate cancer than nondiabetic men, and recently diagnosed men have a higher risk (3Go, 4Go). In biologic models proposed to explain this association, researchers note the higher concentrations of insulin and insulin-like growth factor 1 (IGF-1) in early diabetes and the lower testosterone and IGF-1 levels and higher estrogen concentrations in long-term diabetes (5Go, 6Go). Whether diabetes influences levels of biomarkers such as prostate-specific antigen (PSA), which is involved in the detection of prostate cancer, is unknown.

Factors influencing serum PSA levels in men include age, benign prostatic hyperplasia, prostatitis, and body mass index (BMI) (7Go, 8Go). Still, we understand little about PSA, and its relations with comorbid conditions remain unexplored (9Go). Diabetes and PSA screening are prevalent among men aged 50 years or older, and no doubt many men this age with diabetes undergo PSA testing (10Go, 11Go). In this analysis, we examined whether PSA concentrations varied by diabetes status and duration of diabetes.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Survey
The 2001–2002 National Health and Nutrition Examination Survey (NHANES) was weighted to provide a nationally representative sample of the noninstitutionalized, civilian US population (12Go). In 2001–2002, participation rates for the NHANES interview and medical examination were 83.9 percent and 79.7 percent, respectively. An institutional review board approved the survey. Participants provided informed consent for PSA testing.

PSA assays were conducted on serum samples taken from all men aged ≥40 years who did not meet any of the PSA testing exclusion criteria, including current prostatitis or diagnosis of prostate cancer (n = 1,308) (13Go). PSA was measured using the Hybritech assay (Hybritech, Inc., San Diego, California) in the Beckman immunoassay system (Beckman Instruments, Inc., Fullerton, California).

Sample adults were asked, "Have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?" Men answering affirmatively (n = 173) were classified as having diabetes. We calculated time since diagnosis (in years) by subtracting age at diagnosis from current age, producing categories of 0–5, 6–10, and >10 years (3Go). Men who reported being diabetic were also asked whether they were currently taking "diabetic pills to lower blood sugar."

A subset of our 1,308 participants were instructed to fast before the medical examination (n = 605). In NHANES, separate weights are assigned to ensure that this fasting sample is nationally representative. Among participants not self-classified as diabetic, we designated a fasting plasma glucose level of ≥126 mg/dl as undiagnosed diabetes, and we created separate groups for persons with fasting plasma glucose levels of <100 mg/dl, 100–110 mg/dl, and 111–125 mg/dl. Self-classified diabetic men were categorized by years since diagnosis, as noted above. Fasting has not been shown to affect PSA levels (14Go), and there was no statistically significant difference between PSA values in the fasting and nonfasting groups (p = 0.415).

Measures
We categorized participants by measured BMI (weight (kg)/height (m)2) as normal-weight (BMI <25), overweight (BMI ≥25–<30), or obese (BMI ≥30) (15Go). Persons with a measured systolic blood pressure of ≥140 mmHg or a diastolic blood pressure of ≥90 mmHg were considered hypertensive (16Go). We defined measured high density lipoprotein cholesterol with a gender-specific 0.9-mmol/liter threshold (17Go). Serum insulin concentrations were measured for the men who had fasted. The QUICKI index of insulin sensitivity was calculated for men in the fasting subsample (1/[log insulin (µU/ml) + log glucose (mg/dl)]) (18Go). Ever having been diagnosed with benign prostatic hyperplasia was self-reported. We categorized age by decade (40–49, 50–59, 60–69, 70–79, and 80–85 years) in the univariate analysis but controlled for age continuously in our multivariate models. We categorized race/ethnicity as Mexican-American, non-Hispanic White, non-Hispanic Black, or other. For all variables, responses such as "refused" or "don't know" were considered missing data.

Statistical analyses
PSA values were natural-log-transformed to improve normality. Log PSA was used as the dependent variable for the bivariate and linear regression analyses. In all analyses, we used SUDAAN, version 9.0 (Research Triangle Institute, Research Triangle Park, North Carolina), to account for the complex sampling design. All p values (from t tests) were two-sided. We analyzed log-transformed PSA values, and accordingly we present geometric means in table 1. We controlled for age when presenting results because of the known association between PSA and age. Variables for the categories of years since diagnosis of diabetes were coded as separate terms in the multivariate analyses. In constructing the multiple linear regression model (table 2), we first assessed variables using a series of models containing only the variable in question, the diabetes terms, including time since diagnosis, and an interaction term for the interaction between the two. Variables that were themselves significant predictors in the presence of the diabetes terms were included in the multiple linear regression model, as were any confounders that altered the predicted effect of the diabetes terms by more than 10 percent (19Go). Interactions between the diabetes terms and BMI, high density lipoprotein cholesterol, race/ethnicity, blood pressure, age, and benign prostatic hyperplasia were nonsignificant. The race/ethnicity terms were included in the model because of previous reports that showed variation in PSA levels by race/ethnicity (20Go).


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  		TABLE 1. Predicted geometric mean prostate-specific antigen values for men aged 51 years, by demographic and clinical characteristics, National Health and Nutrition Examination Survey 2001–2002*

 

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  		TABLE 2. Predictors of prostate-specific antigen (PSA) concentration obtained using multivariate linear regression, National Health and Nutrition Examination Survey 2001–2002*

 

   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Geometric mean PSA levels increased with age (40–49 years: 0.75 ng/ml; 50–59 years: 0.93 ng/ml; 60–69 years: 1.11 ng/ml; 70–79 years: 1.76 ng/ml; 80–85 years: 2.15 ng/ml). Results are shown in table 1 after controlling for age (median age, 51 years). Both the overweight and obese BMI groups showed lower predicted geometric mean PSA levels than the comparison group (BMI <25) (table 1). No significant differences in predicted geometric mean PSA levels were seen by race/ethnicity, blood pressure, high density lipoprotein cholesterol, or past diagnosis of benign prostatic hyperplasia. The predicted geometric mean PSA value was lower in the diabetic group than in the nondiabetic group (p < 0.001) and was lowest in men who had been diagnosed with diabetes more than 10 years previously. The trend for decreased PSA by diabetes status (beginning with nondiabetic men and ending with men diagnosed more than 10 years previously) was statistically significant (p = 0.001). Diabetic men taking medication to control blood glucose levels had lower PSA levels than nondiabetic men, but in a comparison with diabetic men not taking medication, the p value was not statistically significant (p = 0.073).

We examined the association between PSA and diabetes covariates in a fasting subsample of our study population, adjusting for age as in table 1. There was no association between PSA and fasting insulin levels (p = 0.182; data not shown), as well as no change in predicted geometric mean PSA by category of fasting plasma glucose among the nondiabetic men (p = 0.800; data not shown). Additionally, there was no association between the QUICKI index of insulin sensitivity and PSA (p = 0.122; data not shown). The predicted geometric mean PSA for self-reporting diabetic men was lower than that of self-reporting nondiabetic men (n = 540) (0.62 ng/ml vs. 0.88 ng/ml; p = 0.012). There was no statistically significant difference between self-reporting diabetic men (n = 65) and men with undiagnosed diabetes (n = 45) (0.57 ng/ml vs. 0.77 ng/ml; p = 0.146). When we compared the group of diagnosed and undiagnosed diabetic men (n = 109) with those self-reporting nondiabetic men who had a fasting plasma glucose level less than 100 mg/dl (n = 250), there was no statistically significant difference (0.70 ng/ml vs. 0.86 ng/ml; p = 0.120). In contrast to the results in the larger sample, the lowest predicted geometric mean PSA level in the fasting subsample was for persons diagnosed with diabetes within the previous 5 years (n = 28) (0.56 ng/ml; in comparison with nondiabetic men with a fasting plasma glucose level less than 100 mg/dl, p = 0.019).

In the multiple linear regression model, neither BMI nor race/ethnicity confounded the association between the diabetes terms and PSA (table 2). We used the model to examine the joint associations of BMI and diabetes with PSA. The predicted geometric mean PSA for all 51-year-old nondiabetic men with a BMI less than 25 was 1.03 ng/ml—significantly higher than the value for their same-age peers with diabetes of more than 10 years' duration who had a BMI greater than or equal to 25 (0.61 ng/ml, a 40.8 percent reduction; p = 0.001).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
Diabetes and an overweight BMI are independently associated with lower geometric mean PSA levels. Together they are associated with as much as a 40.8 percent lower predicted geometric mean PSA level relative to nondiabetic, normal-weight men. Diabetic men have lower androgen levels than nondiabetic men, and this may partially explain their lower PSA levels (6Go). Our finding of a significant downward trend of PSA (from no diabetes to diabetes of more than 10 years' duration) is consistent with a possible association between progressing diabetes and decreasing testosterone concentrations, which may lower the risk of prostate cancer (21Go). Despite the existence of functional androgen-responsive elements upstream of the PSA gene promoter, previous investigations have not shown an association between serum testosterone and PSA levels (22Go). However, PSA may be associated with testosterone in men with subnormal levels of the sex hormone (23Go), as is the case with diabetic men. Diabetes might also alter PSA values through impaired kidney function (24Go) or as a consequence of diabetes medication use. Metformin, a hypoglycemic agent, has been shown to decrease testosterone levels in nondiabetic men but not in diabetic men (25Go, 26Go). In our study, diabetic men taking medication appeared to have lower PSA levels than diabetic men not taking medication, but our small sample size limited our statistical power to detect a difference. This association can be examined more appropriately with additional data.

In diabetes, IGF-1 levels increase during hyperinsulinemia and decrease as insulin production drops (27Go). Previous studies have found a peak in prostate cancer risk during early diabetes, and a recent study (28Go) found a modest positive association between serum PSA and serum IGF-1 that was proposed to be due to IGF-1-induced benign prostatic hyperplasia in older men (3Go, 4Go). We did not observe a positive association between insulin and PSA, even after restricting the analysis to men aged 60 years or older. Insulin levels may be a poor proxy for long-term IGF-1, however, and this misclassification may have weakened our chances of finding an association. We also found no association between PSA and the QUICKI measure of insulin resistance (p = 0.121). However, interpreting these results is made difficult by our inability to control for serum testosterone, which is associated inversely with insulin, fasting glucose, and insulin resistance (29Go).

It is unclear whether the lowered PSA levels in diabetic men accurately reflect a decreased risk of prostate cancer in the diabetic population or whether their lower PSA levels result in a reduced likelihood of receiving a diagnostic workup for detection of asymptomatic prostate cancers, as has been suggested for obese men (7Go). If the latter were true, diabetic men might well be diagnosed with later-stage tumors and have poorer treatment outcomes, and overweight diabetic men would have later-stage tumors than normal-weight diabetic men. However, data from CaPSURE (Cancer of the Prostate Strategic Urologic Research Endeavor), a registry of men with prostate cancer, have not shown later stages at presentation or worse clinical outcomes for diabetic men (30Go).

The principal limitation of this analysis is the small number of men self-reporting with diabetes. These men had a lower PSA level than men without diabetes in both our fasting (n = 605) and nonfasting (n = 703) subsamples, although only in the fasting subsample did the difference attain statistical significance (fasting: p = 0.012; nonfasting: p = 0.078). Examining the trend in PSA by duration of diabetes in the two subsamples revealed two different trends. We do not know whether these discrepancies arise from less stable estimates in the separated groups, whether fasting itself influences the relation between PSA and diabetes, or whether there are other differences between the groups that may explain these results. Future data from NHANES surveys will enable investigators to make more stable estimates of geometric mean PSA levels among diabetic men.

An additional limitation of this study is that the number of participants with undiagnosed cancer was unknown, and thus we do not know whether differences in PSA were due to undiagnosed prostate cancer in the diabetic and nondiabetic groups. Furthermore, without clinical data, we cannot be sure that these differences in PSA result in missed biopsy opportunities, nor can we recommend alternate biopsy thresholds for diabetic men. A third limitation is that self-reported measures of diabetes status have been shown to be highly specific but only 66 percent sensitive (31Go). We addressed this concern by analyzing the fasting subsample, although there may have been additional diabetic men in the NHANES who reported being nondiabetic. This misclassification would have biased our results towards the null. Although we were unable to separate men with type 1 diabetes from men with type 2 diabetes in our study, excluding the six persons who were diagnosed before age 30 years and were currently taking insulin did not significantly alter our results. In another potential source of misclassification, participants were given the option to report having "borderline" diabetes. We categorized these men as nondiabetic (n = 29). The analysis was repeated with these men categorized as diabetic; no substantial changes were noted.

Some investigators have recommended age- and race-specific thresholds for prostate cancer detection based on population-level differences (20Go). While this study was unable to demonstrate that modified PSA thresholds for diabetic men would result in greater accuracy, future studies should investigate whether diabetes status, duration of diabetes, and obesity should be considered when interpreting a PSA test result. Better understanding of the determinants of PSA levels is needed to make the distinction between factors affecting the test's accuracy and those altering the risk of prostate cancer.


   ACKNOWLEDGMENTS
 
This research was performed while David Werny was a fellow in the Centers for Disease Control and Prevention Research Participation Program, administered by the Oak Ridge Institute for Science and Education under contract DE-AC05-00OR22750 between the US Department of Energy and Oak Ridge Associated Universities.

Mr. Werny thanks Dr. Kevin Sullivan for his guidance.

Conflict of interest: none declared.


   References
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 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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