Abstract
Background
Coenzyme Q10 (CoQ10) is considered to be a potential anti-cancer agent, but epidemiological evidence regarding CoQ10 and prostate cancer risk is lacking. We examined the association of circulating CoQ10 levels with prostate cancer risk using pre-diagnostic blood samples.
Methods
Each of the 307 cases was individually-matched to approximately 2 controls on age, ethnicity, geographic location, date/time of specimen collection, and hours of fasting, for a total of 596 controls. Logistic regression was used to compute odds ratios and 95% confidence intervals.
Results
There was no overall statistically significant association of plasma CoQ10 levels with prostate cancer risk (Ptrend = 0.50). However, after matched sets in which controls had possible undiagnosed prostate cancer (PSA > 4.0) were excluded, the odds ratios for quintiles 2–5 were all <1.0.
Conclusions
The results suggest the possibility that moderate levels of circulating CoQ10 may be optimal for the reduction of prostate cancer risk; however, the findings were weak and not statistically significant. Since this is the first epidemiologic study of the association between CoQ10 and prostate cancer, further research on this topic is needed.
Impact
If a nutritional factor like CoQ10 were determined to reduce prostate cancer risk, it would have considerable public health significance because of the very high incidence of this cancer.
Keywords: Coenzyme Q10 prostate cancer
Introduction
Coenzyme Q10 (CoQ10) is a component of the mitochondrial respiratory chain and is considered an important cellular antioxidant (1, 2); consequently, it plays a critical role in immunity, cell growth, and apoptosis. A few clinical studies of CoQ10 supplementation in prostate cancer patients have been reported (3, 4), but the relationship of CoQ10 to the development of prostate cancer has not been studied. To determine whether CoQ10 affects prostate cancer risk, we examined the association between pre-diagnostic plasma CoQ10 levels and prostate cancer in the Multiethnic Cohort Study (MEC).
Materials and Methods
We conducted a nested case-control study of prostate cancer from the biospecimen subcohort of MEC as previously described (5, 6). Cases of prostate cancer, diagnosed after blood collection, were identified through a linkage of the MEC with the Hawaii and California population-based SEER (Surveillance Epidemiology and End Results) cancer registries. For the cases, the years of diagnosis ranged from 1995 to 2005, and the average time between blood collection and cancer diagnosis was 1.9 years. Advanced prostate cancers were defined as: 1) having either regional or distant spread, and/or 2) having a Gleason score ≥ 7 irrespective of tumor stage. Approximately two controls for each case were randomly chosen from men in the biospecimen subcohort who were alive and free of prostate cancer at the age of the case’s diagnosis, and who matched the case on birth year (± 1 year), race/ethnicity (African-American, Japanese-American, Native Hawaiian, Latino, or white), location (Hawaii or California), date (± 6 months) and time (± 2 hours) of blood draw, and fasting hours (0 to <6, 6 to <8, 8 to ≪10, and 10+ hours). Plasma concentrations of total CoQ10 were analyzed by high-pressure liquid chromatography as previously described (7). Data analyses were performed on 307 cases (54 advanced cases) and 596 matched controls with complete data on CoQ10 and the adjustment variables described below.
Conditional logistic regression, with matched sets as strata, was used to compute odds ratios (OR) and 95% confidence intervals (CI). CoQ10 levels were categorized into quintiles based on the distribution of the overall study population, and a trend variable was created by assigning the median value of the appropriate category. The full model was adjusted for body mass index (BMI), family history of prostate cancer, and education, with additional adjustment for age at blood draw and fasting hours prior to blood draw as continuous variables to account for any possible systematic differences within matched sets. In addition, the model was run including only advanced prostate cases and their matched controls. Lastly, as pre-diagnostic PSA was measured on all sets, we also repeated the analyses excluding matched sets in which the controls had high prostate specific antigen (PSA) values (> 4.0 ng/mL), in order to reduce disease misclassification in the control group. Separate analyses were performed for subgroups defined by ethnicity, BMI, and smoking status. All analyses were conducted using SAS (Version 9.1; SAS Institute).
Results
Cases were similar to controls for most baseline characteristics, except that cases were more likely to have a family history of prostate cancer (Table 1).
Table 1.
Baseline characteristics of prostate cancer cases and controls in the Multiethnic Cohort Study*
| Cases (N= 307) | Controls (N=596) | |
|---|---|---|
| Age at blood draw (years), mean (SD)† | 69.0 (7.1) | 68.9 (7.2) |
| Fasting hours prior to blood draw, mean (SD)† | 13.6 (2.5) | 13.8 (2.6) |
| Ethnicity, n (%)† | ||
| African-American | 124 (40.4) | 239 (40.1) |
| Japanese-American | 70 (22.8) | 137 (23.0) |
| Latino | 49 (16.0) | 95 (15.9) |
| Native Hawaiian | 12 (3.9) | 22 (3.7) |
| White | 52 (16.9) | 103 (17.3) |
| Smoking status, n (%) | ||
| Never | 102 (33.2) | 186 (31.3) |
| Former | 162 (52.8) | 341 (57.3) |
| Current | 43 (14.0) | 68 (11.4) |
| Years of education, mean (SD) | 14.0 (2.7) | 13.9 (2.9) |
| Family history of prostate cancer, n (%) | 38 (12.4) | 52 (8.7) |
| Body mass index (kg/m2), mean (SD) | 26.1 (3.9) | 26.2 (4.0) |
| METS of activity per day (hours), mean (SD) | 1.6 (0.3) | 1.6 (0.3) |
| Alcohol consumption (% total energy), mean (SD) | 3.7 (7.4) | 4.1 (7.2) |
Cases and controls were matched on geographic location (California or Hawaii), ethnicity (African-American, Japanese-American, Latino, Native Hawaiian, or White), year of birth (± 1 year), date (± 6 months) and time (± 2 hours) of specimen collection, fasting status (0 to <6, 6 to <8, 8 to <10, and 10+ hours).
Matching variables.
Overall, no significant association between plasma CoQ10 levels and prostate cancer risk was observed for all cases (Ptrend = 0.50; Table 2) or for cases with advanced tumors (Ptrend = 0.73; data not shown). After excluding matched sets in which controls had PSA > 4.0 ng/mL, the odds ratios for quintiles 2–5 were all <1.0, with the lowest odds ratio in the middle quintile (Table 2). However, none of these odds ratios was statistically significant. Stratification by ethnicity, BMI, and smoking status had little influence on the relation of CoQ10 with prostate cancer risk (data not shown).
Table 2.
Odds ratios and 95% confidence intervals (CI) for risk of prostate cancer across quintiles of plasma coenzyme Q10 (CoQ10)
| CoQ10 quintiles (ng/mL) | Cases | Controls* | Odds Ratio† (95% CI) | P for trend‡ | |
|---|---|---|---|---|---|
| All cases | Q1 (≤ 1017) | 64 | 117 | 1.00 | |
| Q2 (1017–1288) | 61 | 120 | 0.93 (0.59–1.47) | ||
| Q3 (1288–1623) | 52 | 128 | 0.73 (0.44–1.18) | ||
| Q4 (1623–2106) | 66 | 115 | 1.13 (0.69–1.80) | ||
| Q5 (≥ 2106) | 64 | 116 | 1.05 (0.63–1.75) | 0.50 | |
| Excluding matched sets in which controls had PSA > 4.0 ng/mL | Q1 (≤ 1017) | 43 | 71 | 1.00 | |
| Q2 (1017–1288) | 43 | 76 | 0.88 (0.49–1.58) | ||
| Q3 (1288–1623) | 34 | 87 | 0.56 (0.30–1.05) | ||
| Q4 (1623–2106) | 41 | 82 | 0.78 (0.43–1.43) | ||
| Q5 (≥ 2106) | 44 | 80 | 0.89 (0.47–1.69) | 0.95 |
Controls were men matched to cases on geographic location (California or Hawaii), ethnicity (African-American, Japanese-American, Latino, Native Hawaiian, or White), year of birth (± 1 year), date (± 6 months) and time (± 2 hours) of specimen collection, fasting status (0 to <6, 6 to <8, 8 to <10, and 10+ hours).
Estimated by conditional logistic regression with matched sets as strata, with additional adjustment for age at blood draw and fasting hours prior to blood draw as continuous variables, as well as family history of prostate cancer, body mass index, and years of education (continuous).
Linear dose–response in the logit of risk was estimated by a Wald test for CoQ10 modeled as a trend variable assigned the median value of the appropriate category.
Discussion
The current study is the first epidemiologic study of which we are aware to assess the relation between circulating CoQ10 and prostate cancer risk. Our results showed no statistically significant association between pre-diagnostic CoQ10 and the subsequent occurrence of prostate cancer. However, the consistent inverse associations observed in quintiles 2–5 when the sets were limited to those in which the controls had PSA levels ≤ 4.0 suggest the possibility that there may be an optimal level of circulating CoQ10, at least with regard to prostate cancer risk reduction.
There is in vitro evidence that CoQ10 suppresses the growth of prostate cancer cells (8). Two clinical studies examined the effect of CoQ10 in combination with other antioxidants in prostate cancer patients. One study found that CoQ10 administered in combination with other antioxidants improved survival in patients with advanced prostate cancer (3). The second study was a randomized placebo-controlled trial in which the authors found no effect of a combination of CoQ10, vitamin E, vitamin C, and selenium on PSA or hormonal levels in prostate cancer patients (4). A recent analysis within the MEC also found no associations between serum concentrations of antioxidants (carotenoids, tocopherols, selenium) and prostate cancer risk; however, CoQ10 was not examined in that study (6).
The major strengths of our study were the prospective design and reasonable statistical power due to the relatively large overall sample size. Although this investigation had some limitations, including a relatively short follow-up time (average duration: 1.9 years) and a small number of advanced cases, the results suggest that further studies are needed to assess a possible protective effect of higher circulating CoQ10 levels on the risk of prostate cancer.
Acknowledgments
Financial support: This work was supported in part by National Cancer Institute (grants R03 CA132149, P01 CA33619, R37 CA54281, S10 RR020890, and P30 CA71789). WC was supported by a postdoctoral fellowship on grant R25 CA90956.
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