Background: Vitamin B12, holo-haptocorrin, and the folate-pathway single-nucleotide polymorphisms MTR 2756A>G and SHMT1 1420C>T have been associated with an increased risk of prostate cancer. We investigated whether these and other elements of folate metabolism were associated with prostate-specific antigen (PSA) velocity (PSAV) as a proxy measure of prostate cancer progression in men with localized prostate cancer.

Methods: We measured plasma folate, B12, holo-haptocorrin, holo-transcobalamin, total transcobalamin, and total homocysteine at diagnosis in 424 men (ages 45-70 years) with localized prostate cancer in a U.K.-wide population-based cohort. Thirteen folate-pathway single-nucleotide polymorphisms were genotyped for 311 of these men. Postdiagnosis PSAV (continuous measure and with a threshold set a priori at 2 ng/mL/y) was estimated from repeat PSA measurements.

Results: Median follow-up time was 2.5 (range, 0.8-5.6) years. Vitamin B12, holo-haptocorrin, holo-transcobalamin, total transcobalamin, and total homocysteine were not associated with postdiagnosis PSAV. Folate was associated with an increased risk of PSAV >2 ng/mL/y [odds ratio (OR) per unit increase in loge concentration, 1.57; 95% confidence interval (95% CI), 0.98-2.51; P = 0.06]. MTRR 66A>G (rs1801394) was associated with a reduced risk (recessive model OR, 0.33; 95% CI, 0.11-0.97; P = 0.04), and SHMT1 1420C>T (rs1979277) with an increased risk (per-allele OR, 1.49; 95% CI, 0.93-2.37; P = 0.09) of PSAV >2 ng/mL/y.

Conclusions: We found weak evidence that higher folate levels may be associated with faster progression of localized prostate cancer.

Impact: Long-term follow-up is needed to test associations with metastases and mortality, and the observed genetic effects require replication. Cancer Epidemiol Biomarkers Prev; 19(11); 2833–8. ©2010 AACR.

Mechanisms postulated to link dietary intake of folate with cancer risk, including genome-wide hypomethylation, gene-specific hypermethylation, and DNA uracil misincorporation, have been observed in prostate tumor cells (1, 2). However, epidemiologic studies of prostate cancer risk based on dietary intakes of folate have generated contradictory findings. For example, secondary analysis of data from a randomized placebo-controlled trial of folic acid and aspirin for the prevention of colorectal adenomas showed a positive association between folic acid supplementation and prostate cancer risk but also suggested inverse associations with baseline dietary and plasma folate (3). Such contradictory findings could be explained by a dual effect of folate, in which low intakes are associated with an increased risk of prostate cancer initiation and high intakes with more rapid progression (4), or could be attributed to the adverse effects of excess folic acid (5).

We previously reported that two folate-pathway polymorphisms (MTR 2756A>G and SHMT1 1420C>T; ref. 6) and circulating concentrations of vitamin B12 and holo-haptocorrin (B12 bound to haptocorrin; ref. 7) were associated with an increased risk of prostate cancer. We also reported that baseline folate levels were associated with an increased risk of prostate cancer in prospective cohort studies (7). Given these associations and the “dual-effect” hypothesis, further investigation of folate metabolism in relation to prostate cancer progression is warranted.

Prostate-specific antigen velocity (PSAV) has emerged as a promising prognostic marker in men with localized prostate cancer (8, 9), supporting its use as a surrogate measure of tumor progression. In this study, we use data from a U.K. population–based cohort to investigate whether 13 common folate-pathway polymorphisms and circulating concentrations of folate, vitamin B12, total homocysteine (tHcy), and vitamin B12 bound to its transport proteins (holo-haptocorrin and holo-transcobalamin) are associated with PSAV over a 5-year period in men diagnosed with localized prostate cancer.

The study population, anthropometric and lifestyle covariates, ethical approval, blood sample handling, biochemical analyses, DNA extraction, and genotyping are as previously described (6, 7). In brief, between 2000 and 2008, men ages 45 to 70 years and registered at participating general practices were invited to undergo a prostate-specific antigen (PSA) test as part of a population-based U.K.-wide study (ISRCTN20141297; ref. 10) and then referred for biopsy if their PSA level was ≥3.0 ng/mL. The current observational cohort includes 507 men diagnosed with histologically proven and clinically localized prostate cancer who were managed by active monitoring (by random allocation or by preference), consisting of regular PSA testing with curative treatment if an increasing PSA level is confirmed at clinical review as indicative of worsening disease. All men in the cohort had follow-up PSA tests every 3 months for the first year, then at 6- or 12-month intervals thereafter, and were invited to attend a review appointment if their PSA level was assessed to be increasing (by >50% in 12 months), or if the patient or clinician was otherwise concerned. Our analysis was based only on pretreatment PSA test results. Biochemical analyses and genotyping were done on blood drawn at the first PSA test.

PSAV was estimated by linear regression of the five most recent PSA measurements against elapsed time (in days) from the date of the first PSA test. We chose the five most recent PSA measurements because these provide a balance between sensitivity to upturns in velocity and insensitivity to random fluctuations (11). In sensitivity analyses, we determined whether results were similar if PSAV was derived from the four most recent PSA measurements (PSAVfour) and from all available (men with a minimum of five) PSA measurements (PSAVall). We used linear regression models to estimate associations of metabolite concentrations (loge transformed to normalize skewed distributions) with PSAV, adjusted for age and Gleason score. Linear regression was also used to estimate similarly adjusted associations of PSAV with genotype in additive [coding genotype as A/A = 0 (reference), a/A = 1, a/a = 2], dominant [(a/a | a/A) versus AA], and recessive [aa versus (a/A | A/A)] models. We also created a dichotomous PSAV variable, using an a priori cutoff point of 2 ng/mL/y. This threshold, when measured before radical prostatectomy, was associated with an increased risk of death due to prostate cancer in 10 years of post-procedure follow-up (12) and has been independently validated (13). We used logistic regression to estimate odds ratios (OR) for this dichotomous variable per unit loge metabolite concentration and in the additive, dominant, and recessive genotype models. Confounding and effect modification by anthropometric (body mass index) and lifestyle (smoking and alcohol consumption) covariates of associations of vitamin and metabolite concentrations with PSAV were assessed in the linear and logistic regression models. All statistical analyses were done using Stata Release 11 (StataCorp, 2009).

Baseline characteristics

The mean age of the 507 men available for the current analysis was 62.6 (SD, 5.1) years, the median follow-up time was 2.1 years [interquartile range (IQR), 1.2-3.0 years; range, 0.0-5.6 years), and the median number of repeat PSA tests was 7 (IQR, 4-9; range, 0-18). Overall, 424 men (84%) had five or more PSA tests and contributed to the final analysis with a median follow-up time of 2.5 years (IQR, 1.7-3.2 years; range, 0.8-5.6 years); of these 424 men, genotype data were available for 311 men (73%). An additional 39 men had four PSA test results, of whom 10 had genotype data, and contributed to the sensitivity analysis. Gleason score was “6” for 79.3% (336/424) and “7” for 19.6% (83 of 424) of men; a small number of men had scores outside this range: “5” for 0.2% (1 of 424) and “8” for 0.9% (4 of 424) of men. Anthropometric (body mass index) and lifestyle (current smoking and alcohol consumption over the previous 12 months) data were available for 243 men, representing a 60% response rate to “Diet, Health and Lifestyle” postal questionnaires, which all men were asked to complete.

PSAV was positively skewed with a mean of 0.74 (SD, 2.03) ng/mL/y and a median of 0.43 ng/mL/y (IQR, −0.10 to 1.19 ng/mL/y; range, −5.86 to 22.2 ng/mL/y). Tertiles of PSAV were determined by cutoff points at 0.06 and 0.85 ng/mL/y (i.e., one third of men were below, between, and above these PSAV thresholds).

The main PSAV measure (here denoted PSAVmain) differed from the PSAV measures used in our sensitivity analyses: PSAVfour = 0.86 (SD, 2.61) ng/mL/y (paired t test compared with PSAVmain, P = 0.05); PSAVall = 0.56 (SD, 1.44) ng/mL/y (paired t test compared with PSAVmain, P = 0.001). The different PSAV measures were highly correlated, indicating that men were ranked similarly within each measure: Spearman's ρ = 0.79 (P < 0.001) for correlation between PSAVfour and PSAVmain; Spearman's ρ = 0.84 (P < 0.001) for correlation between PSAVall and PSAVmain. Eighty-four of 463 men (18.1%) had PSAVfour >2 ng/mL/y and 38 of 424 men (9.0%) had PSAVall >2 ng/mL/y. None of the PSAV measures were associated with age or length of follow-up, but all were strongly positively associated with Gleason score at the time of diagnosis [age-adjusted change in mean PSAVmain per increment in Gleason score, 0.77; 95% confidence interval (95% CI), 0.33-1.21 ng/mL/y; P = 0.001].

Associations of plasma folate, vitamin B12, tHcy, and B12 transport proteins with PSAV

In the linear regression models adjusted for age and Gleason score, none of the vitamins or metabolites were associated with PSAV as a continuous or dichotomous measure (Table 1; Supplementary Table S1) with the exception of plasma folate, which was weakly associated with a 57% higher odds of PSAV >2 ng/mL/y (OR, 1.57; 95% CI, 0.98-2.51 per unit increase in loge folate concentration; P = 0.06). This result was accentuated when PSAV was derived from all available (men with five or more) PSA measurements (N = 424; OR, 1.83; 95% CI, 1.03-3.24; P = 0.04), but was nullified when PSAV was derived from the four most recent PSA measurements (N = 463; OR, 0.99; 95% CI, 0.67-1.47; P = 1.0). The corresponding linear regression coefficients were as follows: change in mean PSAV, 0.14; 95% CI, −0.18 to 0.45 ng/mL/y; P = 0.4; change in mean PSAVall, 0.11; 95% CI, −0.11 to 0.33 ng/mL/y; P = 0.3; change in mean PSAVfour, 0.03; 95% CI, −0.37 to 0.43 ng/mL/y; P = 0.9. In a model adjusted for levels of vitamin B12, holo-transcobalamin, total transcobalamin, and tHcy, plasma folate was more strongly associated with increased odds of PSAV >2 ng/mL/y (OR, 1.75; 95% CI, 1.02-3.00 per unit increase in loge folate concentration; P = 0.04). Anthropometric (body mass index) and lifestyle (smoking and alcohol consumption) covariates (data available for 243 men) did not confound or modify any associations of vitamin and metabolite concentrations with PSAV.

Table 1.

Associations of circulating folate, vitamin B12, homocysteine, and vitamin B12 transport proteins measured at the time of diagnosis of localized prostate cancer with subsequent PSAV (N = 424)

MetaboliteMedian PSAV (ng/mL/y) by quartile of circulating vitamin and metabolite concentrationChange in mean PSA velocity (ng/mL/y) per unit increase in loge metabolite concentration*OR for PSA velocity >2 vs ≤2 ng/mL/y per unit increase in loge metabolite concentration*
Q1Q2Q3Q4Coefficient (95% CI)OR (95% CI)
Folate 0.37 0.38 0.46 0.49 0.14 (−0.18 to 0.45), P = 0.4 1.57 (0.98-2.51), P = 0.06 
Vitamin B12 0.35 0.51 0.45 0.42 −0.08 (−0.59 to 0.43), P = 0.8 0.64 (0.30-1.35), P = 0.2 
Holo-haptocorrin 0.38 0.47 0.39 0.43 −0.08 (−0.55 to 0.40), P = 0.7 0.63 (0.32-1.25), P = 0.2 
Holo-transcobalamin 0.32 0.29 0.55 0.41 −0.04 (−0.43 to 0.34), P = 0.8 1.00 (0.57-1.77), P = 1.0 
Total transcobalamin 0.43 0.53 0.31 0.38 −0.30 (−1.14 to 0.53), P = 0.5 0.79 (0.23-2.77), P = 0.7 
Total homocysteine 0.38 0.54 0.39 0.30 0.07 (−0.68 to 0.82), P = 0.9 0.80 (0.26-2.46), P = 0.7 
MetaboliteMedian PSAV (ng/mL/y) by quartile of circulating vitamin and metabolite concentrationChange in mean PSA velocity (ng/mL/y) per unit increase in loge metabolite concentration*OR for PSA velocity >2 vs ≤2 ng/mL/y per unit increase in loge metabolite concentration*
Q1Q2Q3Q4Coefficient (95% CI)OR (95% CI)
Folate 0.37 0.38 0.46 0.49 0.14 (−0.18 to 0.45), P = 0.4 1.57 (0.98-2.51), P = 0.06 
Vitamin B12 0.35 0.51 0.45 0.42 −0.08 (−0.59 to 0.43), P = 0.8 0.64 (0.30-1.35), P = 0.2 
Holo-haptocorrin 0.38 0.47 0.39 0.43 −0.08 (−0.55 to 0.40), P = 0.7 0.63 (0.32-1.25), P = 0.2 
Holo-transcobalamin 0.32 0.29 0.55 0.41 −0.04 (−0.43 to 0.34), P = 0.8 1.00 (0.57-1.77), P = 1.0 
Total transcobalamin 0.43 0.53 0.31 0.38 −0.30 (−1.14 to 0.53), P = 0.5 0.79 (0.23-2.77), P = 0.7 
Total homocysteine 0.38 0.54 0.39 0.30 0.07 (−0.68 to 0.82), P = 0.9 0.80 (0.26-2.46), P = 0.7 

*Adjusted for age and Gleason score.

Associations of folate-pathway polymorphisms with PSAV

Nine of the 13 folate-pathway polymorphisms were not associated with PSAV as either a continuous or dichotomous measure (Table 2; Supplementary Table S2). A recessive protective effect of the MTRR 66A>G polymorphism was evident for PSAV as a dichotomous (OR, 0.33; 95% CI, 0.11-0.97; P = 0.04) and continuous measure (change in mean PSAV, −0.61; 95% CI, −1.24 to 0.02 ng/mL/y; P = 0.06), and these effects were robust to sensitivity testing using PSAV as a continuous measure (change in mean PSAVall, −0.44; 95% CI, −0.86 to −0.02 ng/mL/y; P = 0.04; change in mean PSAVfour, −0.66; 95% CI, −1.47 to 0.15; P = 0.11) but were less robust to sensitivity testing using PSAV >2 ng/mL as a threshold (PSAVall OR, 0.44; 95% CI, 0.12-1.55; P = 0.20; PSAVfour OR, 0.58; 95% CI, 0.26-1.32; P = 0.20).

Table 2.

Associations of folate-pathway polymorphisms with PSA velocity after diagnosis of localized prostate cancer (N = 311)

GeneSNP IDEffectMedian PSA velocity (ng/mL/y) by genotypeChange in mean PSA velocity (ng/mL/y)OR for PSA velocity >2 vs ≤2 ng/mL/y
A/AA/aa/aCoefficient (95% CI)*OR (95% CI)*
MTHFR 677C>T rs1801133 Per minor allele 0.54 0.37 0.21 −2.20 (−6.43 to 2.03), P = 0.3 0.93 (0.58-1.49), P = 0.8 
Dominant −0.50 (−0.99 to −0.01), P = 0.05 0.94 (0.50-1.76), P = 0.8 
Recessive 0.06 (−0.68 to 0.79), P = 0.9 0.85 (0.31-2.33), P = 0.8 
MTHFR 1298A>C rs1801131 Per minor allele 0.37 0.48 0.60 0.08 (−0.30 to 0.46), P = 0.7 1.21 (0.75-1.96), P = 0.4 
Dominant 0.10 (−0.39 to 0.59), P = 0.7 1.38 (0.73-2.59), P = 0.3 
Recessive 0.11 (−0.77 to 0.99), P = 0.8 1.01 (0.33-3.12), P = 1.0 
MTR 2756A>G rs1805087 Per minor allele 0.35 0.60 0.63 0.06 (−0.38 to 0.50), P = 0.8 0.99 (0.56-1.75), P = 1.0 
Dominant 0.06 (−0.45 to 0.58), P = 0.8 1.04 (0.54-2.00), P = 0.9 
Recessive 0.09 (−1.23 to 1.40), P = 0.9 0.63 (0.08-5.16), P = 0.7 
MTRR 66A>G rs1801394 Per minor allele 0.33 0.56 0.21 −0.17 (−0.52 to 0.18), P = 0.3 0.85 (0.54-1.33), P = 0.5 
Dominant 0.05 (−0.48 to 0.57), P = 0.9 1.27 (0.64-2.53), P = 0.5 
Recessive −0.61 (−1.24 to 0.02), P = 0.06 0.33 (0.11-0.97), P = 0.04 
MTHFD1 1958G>A rs2236225 Per minor allele 0.37 0.38 0.65 −0.14 (−0.49 to 0.21), P = 0.4 1.16 (0.74-1.81), P = 0.5 
Dominant −0.36 (−0.90 to 0.19), P = 0.2 0.95 (0.48-1.89), P = 0.9 
Recessive 0.02 (−0.57 to 0.62), P = 0.9 1.57 (0.78-3.18), P = 0.2 
SLC19A1 80G>A rs1051266 Per minor allele 0.45 0.37 0.67 −0.08 (−0.43 to 0.27), P = 0.7 1.08 (0.69-1.69), P = 0.7 
Dominant −0.21 (−0.73 to 0.32), P = 0.4 1.08 (0.55-2.11), P = 0.8 
Recessive 0.04 (−0.59 to 0.68), P = 0.9 1.14 (0.51-2.55), P = 0.8 
SHMT1 1420C>T rs1979277 Per minor allele 0.38 0.49 0.51 −0.14 (−0.51 to 0.23), P = 0.4 1.49 (0.93-2.37), P = 0.1 
Dominant −0.20 (−0.69 to 0.30), P = 0.4 1.64 (0.84-3.20), P = 0.1 
Recessive −0.16 (−0.92 to 0.61), P = 0.7 1.74 (0.73-4.17), P = 0.2 
FOLH1 484T>C rs202676 Per minor allele 0.46 0.31 0.54 −0.16 (−0.56 to 0.23), P = 0.4 0.73 (0.42-1.29), P = 0.3 
Dominant −0.28 (−0.76 to 0.21), P = 0.3 0.61 (0.31-1.21), P = 0.2 
Recessive 0.12 (−0.89 to 1.13), P = 0.8 1.15 (0.31-4.32), P = 0.8 
FUT2 204A>G rs492602 Per minor allele 0.43 0.45 0.39 −0.23 (−0.59 to 0.13), P = 0.2 0.91 (0.58-1.43), P = 0.7 
Dominant 0.33 (−0.23 to 0.88), P = 0.2 1.21 (0.59-2.49), P = 0.6 
Recessive 0.28 (−0.34 to 0.90), P = 0.4 1.06 (0.49-2.29), P = 0.9 
TCN2 776C>G rs1801198 Per minor allele 0.43 0.53 0.37 0.23 (−0.09 to 0.56), P = 0.2 1.32 (0.87-2.00), P = 0.2 
Dominant 0.61 (0.08 to 1.14), P = 0.02 1.53 (0.74-3.15), P = 0.3 
Recessive 0.01 (−0.54 to 0.56), P = 1.0 1.42 (0.72-2.81), P = 0.3 
TCN1 372T>C rs526934 Per minor allele 0.37 0.48 0.48 0.15 (−0.08 to 0.38), P = 0.2 1.20 (0.64-2.27), P = 0.6 
Dominant 0.38 (−0.10 to 0.86), P = 0.1 1.28 (0.67-2.46), P = 0.5 
Recessive −0.22 (−1.11 to 0.68), P = 0.6 0.76 (0.21-2.73), P = 0.7 
CUBN 758C>T rs1801222 Per minor allele 0.48 0.49 0.38 0.04 (−0.17 to 0.25), P = 0.7 1.28 (0.72-2.29), P = 0.4 
Dominant 0.16 (−0.33 to 0.65), P = 0.5 1.57 (0.80-3.11), P = 0.2 
Recessive −0.04 (−0.71 to 0.64), P = 0.9 1.38 (0.60-3.14), P = 0.4 
MUT 1595G>A rs1141321 Per minor allele 0.46 0.45 0.53 −0.07 (−0.31 to 0.17), P = 0.6 1.00 (0.55-1.80), P = 1.0 
Dominant 0.14 (−0.37 to 0.64), P = 0.6 1.38 (0.71-2.69), P = 0.3 
Recessive 0.11 (−0.58 to 0.79), P = 0.2 1.14 (0.49-2.69), P = 0.8 
GeneSNP IDEffectMedian PSA velocity (ng/mL/y) by genotypeChange in mean PSA velocity (ng/mL/y)OR for PSA velocity >2 vs ≤2 ng/mL/y
A/AA/aa/aCoefficient (95% CI)*OR (95% CI)*
MTHFR 677C>T rs1801133 Per minor allele 0.54 0.37 0.21 −2.20 (−6.43 to 2.03), P = 0.3 0.93 (0.58-1.49), P = 0.8 
Dominant −0.50 (−0.99 to −0.01), P = 0.05 0.94 (0.50-1.76), P = 0.8 
Recessive 0.06 (−0.68 to 0.79), P = 0.9 0.85 (0.31-2.33), P = 0.8 
MTHFR 1298A>C rs1801131 Per minor allele 0.37 0.48 0.60 0.08 (−0.30 to 0.46), P = 0.7 1.21 (0.75-1.96), P = 0.4 
Dominant 0.10 (−0.39 to 0.59), P = 0.7 1.38 (0.73-2.59), P = 0.3 
Recessive 0.11 (−0.77 to 0.99), P = 0.8 1.01 (0.33-3.12), P = 1.0 
MTR 2756A>G rs1805087 Per minor allele 0.35 0.60 0.63 0.06 (−0.38 to 0.50), P = 0.8 0.99 (0.56-1.75), P = 1.0 
Dominant 0.06 (−0.45 to 0.58), P = 0.8 1.04 (0.54-2.00), P = 0.9 
Recessive 0.09 (−1.23 to 1.40), P = 0.9 0.63 (0.08-5.16), P = 0.7 
MTRR 66A>G rs1801394 Per minor allele 0.33 0.56 0.21 −0.17 (−0.52 to 0.18), P = 0.3 0.85 (0.54-1.33), P = 0.5 
Dominant 0.05 (−0.48 to 0.57), P = 0.9 1.27 (0.64-2.53), P = 0.5 
Recessive −0.61 (−1.24 to 0.02), P = 0.06 0.33 (0.11-0.97), P = 0.04 
MTHFD1 1958G>A rs2236225 Per minor allele 0.37 0.38 0.65 −0.14 (−0.49 to 0.21), P = 0.4 1.16 (0.74-1.81), P = 0.5 
Dominant −0.36 (−0.90 to 0.19), P = 0.2 0.95 (0.48-1.89), P = 0.9 
Recessive 0.02 (−0.57 to 0.62), P = 0.9 1.57 (0.78-3.18), P = 0.2 
SLC19A1 80G>A rs1051266 Per minor allele 0.45 0.37 0.67 −0.08 (−0.43 to 0.27), P = 0.7 1.08 (0.69-1.69), P = 0.7 
Dominant −0.21 (−0.73 to 0.32), P = 0.4 1.08 (0.55-2.11), P = 0.8 
Recessive 0.04 (−0.59 to 0.68), P = 0.9 1.14 (0.51-2.55), P = 0.8 
SHMT1 1420C>T rs1979277 Per minor allele 0.38 0.49 0.51 −0.14 (−0.51 to 0.23), P = 0.4 1.49 (0.93-2.37), P = 0.1 
Dominant −0.20 (−0.69 to 0.30), P = 0.4 1.64 (0.84-3.20), P = 0.1 
Recessive −0.16 (−0.92 to 0.61), P = 0.7 1.74 (0.73-4.17), P = 0.2 
FOLH1 484T>C rs202676 Per minor allele 0.46 0.31 0.54 −0.16 (−0.56 to 0.23), P = 0.4 0.73 (0.42-1.29), P = 0.3 
Dominant −0.28 (−0.76 to 0.21), P = 0.3 0.61 (0.31-1.21), P = 0.2 
Recessive 0.12 (−0.89 to 1.13), P = 0.8 1.15 (0.31-4.32), P = 0.8 
FUT2 204A>G rs492602 Per minor allele 0.43 0.45 0.39 −0.23 (−0.59 to 0.13), P = 0.2 0.91 (0.58-1.43), P = 0.7 
Dominant 0.33 (−0.23 to 0.88), P = 0.2 1.21 (0.59-2.49), P = 0.6 
Recessive 0.28 (−0.34 to 0.90), P = 0.4 1.06 (0.49-2.29), P = 0.9 
TCN2 776C>G rs1801198 Per minor allele 0.43 0.53 0.37 0.23 (−0.09 to 0.56), P = 0.2 1.32 (0.87-2.00), P = 0.2 
Dominant 0.61 (0.08 to 1.14), P = 0.02 1.53 (0.74-3.15), P = 0.3 
Recessive 0.01 (−0.54 to 0.56), P = 1.0 1.42 (0.72-2.81), P = 0.3 
TCN1 372T>C rs526934 Per minor allele 0.37 0.48 0.48 0.15 (−0.08 to 0.38), P = 0.2 1.20 (0.64-2.27), P = 0.6 
Dominant 0.38 (−0.10 to 0.86), P = 0.1 1.28 (0.67-2.46), P = 0.5 
Recessive −0.22 (−1.11 to 0.68), P = 0.6 0.76 (0.21-2.73), P = 0.7 
CUBN 758C>T rs1801222 Per minor allele 0.48 0.49 0.38 0.04 (−0.17 to 0.25), P = 0.7 1.28 (0.72-2.29), P = 0.4 
Dominant 0.16 (−0.33 to 0.65), P = 0.5 1.57 (0.80-3.11), P = 0.2 
Recessive −0.04 (−0.71 to 0.64), P = 0.9 1.38 (0.60-3.14), P = 0.4 
MUT 1595G>A rs1141321 Per minor allele 0.46 0.45 0.53 −0.07 (−0.31 to 0.17), P = 0.6 1.00 (0.55-1.80), P = 1.0 
Dominant 0.14 (−0.37 to 0.64), P = 0.6 1.38 (0.71-2.69), P = 0.3 
Recessive 0.11 (−0.58 to 0.79), P = 0.2 1.14 (0.49-2.69), P = 0.8 

Abbreviation: SNP, single-nucleotide polymorphism.

*Adjusted for age and Gleason score.

For SHMT1 1420C>T, there was marginal evidence of a positive association with PSAV >2 ng/mL per year (OR, 1.49; 95% CI, 0.93-2.37 per T allele; P = 0.09). This result was reasonably robust to sensitivity analyses (PSAVfour OR, 1.46; 95% CI, 0.95-2.23 per T allele, P = 0.08; PSAVall OR, 1.59; 95% CI, 0.88-2.89 per T allele, P = 0.12), but the confidence intervals for these two measures and for the main PSAV measure did not exclude null, and no effects were observed for PSAV as a continuous measure.

For MTHFR 677C>T, there was marginal evidence in the dominant effect model of an inverse association of the T allele with PSAV as a continuous measure (change in mean PSAV, −0.50; 95% CI, −0.99 to −0.01; P = 0.05), but no effect was observed for PSAV >2 ng/mL or in any of the sensitivity analyses. For TCN2 776C>G, there was evidence in the dominant effect model of a positive association of the G allele with PSAV as a continuous measure (change in mean PSAV, 0.61; 95% CI, 0.08-1.14; P = 0.02), but no effect was observed using PSAV >2 ng/mL as a threshold or in any of the sensitivity analyses.

In the first study of its kind, we have found that men with the highest levels of plasma folate measured just before PSA-based diagnosis of localized prostate cancer were more likely to exceed an a priori PSAV threshold of 2 ng/mL/y during subsequent follow-up. However, this positive association was not observed for PSAV as a continuous measure and was not robust to sensitivity analysis. Plasma concentrations of vitamin B12, tHcy, and B12 bound to haptocorrin and transcobalamin were not associated with postdiagnosis PSAV. PSAV is a potential prognostic marker in men with localized prostate cancer undergoing active monitoring (8, 9). Hence, our findings may be important in the context of current controversies over the potentially harmful long-term effects of folic acid and vitamin B12 fortification (5, 14).

We previously reported that circulating folate was positively associated with prostate cancer risk in prospective cohort studies (based mainly on clinically detected cases) but not in the ProtecT case-control study (based on PSA-detected cases; ref. 7). We suggested that this discrepancy was consistent with the dual-effect hypothesis in which folate is positively associated with the rate of progression, but not the initiation, of localized prostate cancer. Our finding that plasma folate is associated with an increased risk of exceeding a PSAV threshold of 2 ng/mL/y provides tentative evidence to support this hypothesis. This PSAV threshold has been consistently associated with adverse posttreatment outcomes, including prostate cancer mortality (15). Most notably, D'Amico et al. (12) reported a 10-fold increase in the odds of dying within 10 years of radical prostatectomy, based on PSAV estimated from measurements taken during the 12 months before diagnosis and surgery. D'Amico et al. (12) selected the threshold of >2 ng/mL/y on the basis of statistical tests within one cohort, and thus caution is required, but at least one prospective study has validated these findings, most strongly for associations with biochemical recurrence after radical prostatectomy, suggesting that this threshold may be a valid prognostic marker (13).

A recessive protective effect of the methionine synthase reductase (MTRR) 66A>G polymorphism (i.e., comparing G/G versus A/A + A/G) was evident for PSAV as a continuous and dichotomous measure, and this effect was reasonably robust to sensitivity analysis. This polymorphism was not associated with prostate cancer risk in our earlier meta-analysis (recessive effect pooled OR, 0.93; 95% CI, 0.76-1.13). Wettergren et al. (16) reported that colorectal cancer–specific survival was worse in patients with MTRR 66 A/A or A/G genotypes when the colorectal mucosa was positive for p16 gene promoter hypermethylation (hazard ratio, 2.7; 95% CI, 1.2-6.4), whereas this effect was not observed in patients with the G/G phenotype. Hubner et al. (17) reported a significantly reduced risk of colorectal neoplasia recurrence in patients with one or two MTRR 66 G alleles (relative risk, 0.71; 95% CI, 0.53-0.96), an effect which was enhanced for recurrence of advanced neoplasia (relative risk, 0.55; 95% CI, 0.36-0.86). That MTRR 66A>G had previously been associated with an increased risk of colorectal cancer (18, 19) might be consistent with the dual-effect hypothesis of colorectal cancer initiation and progression (4). Whether the same could be true for MTRR 66A>G and prostate cancer cannot be deduced from our data or from the literature.

Evidence supporting the apparent positive association of SHMT1 1420C>T with PSAV >2 ng/mL/y was weak (P = 0.09) and subject to the caveats discussed above about the use of a PSAV threshold. Although the result for SHMT1 1420C>T was reasonably robust to sensitivity analysis, the polymorphism was not associated with PSAV as a continuous measure. We previously reported that SHMT1 1420C>T was associated with 24% higher odds of localized prostate cancer in a model comparing the T/T and C/C genotypes (pooled OR, 1.24; 95% CI, 1.02-1.51; ref. 6). SHMT1 (serine hydroxymethyltransferase-1) reversibly catalyzes the conversion of tetrahydrofolate to 5,10-methylenetetrahydrofolate and of serine to glycine in a reaction that requires vitamin B6 as a cofactor. Kasperzyk et al. (20) reported that higher B6 intake at the time of diagnosis of prostate cancer was associated with a lower risk of subsequent prostate cancer mortality. This inverse association was attributable to a strong effect among men diagnosed with localized cancer (Ptrend < 0.001), whereas no effect was observed among men diagnosed with advanced disease (Pheterogeneity < 0.001). Whether B6 has an effect on prostate cancer progression and whether any such effect is mimicked by genetic variation in SHMT1 are purely speculative. The inverse association of MTHFR 677C>T with PSAV would be consistent with a positive association of circulating folate with PSAV, but the apparent dominant effects of this polymorphism and the TCN 776C>G polymorphism were not robust to sensitivity analysis.

The key finding of our earlier study was that high circulating concentrations of vitamin B12, particularly B12 bound to haptocorrin, were associated with increased prostate cancer risk (7). We proposed three mechanisms of reverse causation: prostate tumor cells having an increased demand for B12 due to increased biosynthesis of polyamines; increased production of haptocorrin by prostate tumor cells; and overexpression by prostate tumor cells of the multidrug resistance protein gene (MRP1), which has a role in cellular efflux of B12. Although vitamin B12 measured just before diagnosis of localized prostate cancer was not associated with subsequent PSAV in the present study, repeat measurements of circulating B12 after diagnosis would be needed to discount these mechanisms. None of the polymorphisms known to affect the levels of B12 (FUT2 204A>G, CUBN 758C>T, and MUT 1595G>A) was associated with any measure of PSAV in our study, as would be expected from reverse causation. Kasperzyk et al. (20) found no association of vitamin B12 (or folate) intake measured at the time of diagnosis of prostate cancer with subsequent prostate cancer mortality.

The sensitivity of our results to the number of PSA measurements used raises the issue of the reliability of PSAV estimates, although the correlation between PSAV calculated from the five most recent, four most recent, and all available PSA test results was high. We cannot discount the role of chance in our findings, given the strength of evidence for the associations observed and the number of statistical tests performed. Finally, PSA kinetics are proxy measures of disease progression. Although they are consistently associated with adverse outcomes (for pretreatment PSA kinetics: mortality, relapse, and pathologic stage; for active monitoring PSA kinetics: mortality and progression; ref. 15), we would need to follow up our cohort for a longer period (5-10 years) to determine their associations with clinical outcomes.

Our study provides weak evidence that higher folate levels may be associated with faster progression, and the MTRR 66A>G polymorphism with slower progression, of localized prostate cancer. Given that folic acid supplementation (3) and baseline folate levels (7) have been associated with an increased risk of prostate cancer, we suggest that longer-term follow-up with clinical outcomes such as metastases and death is required to establish whether folate plays a role in accelerating prostate cancer progression.

No potential conflicts of interest were disclosed.

We would like to acknowledge the tremendous contribution of all members of the ProtecT study research group, and especially the following who were involved in this research: Prasad Bollina, Sue Bonnington, Debbie Cooper, Andrew Doble, Alan Doherty, Emma Elliott, David Gillatt, Pippa Herbert, Peter Holding, Joanne Howson, Liz Down, Mandy Jones, Roger Kockelbergh, Howard Kynaston, Teresa Lennon, Norma Lyons, Hilary Moody, Philip Powell, Stephen Prescott, Liz Salter, and Pauline Thompson. We thank Cynthia Prendergast (University of Oxford) for her invaluable help with the biochemical analyses.

Grant Support: World Cancer Research Fund UK grants 2004/18 and 2007/07. The National Cancer Research Institute (administered by the Medical Research Council) provided support for the development of the ProtecT epidemiologic database through the ProMPT (Prostate Mechanisms of Progression and Treatment) collaborative. The ProtecT study is supported by the UK NIHR Health Technology Assessment Programme (projects 96/20/06 and 96/20/99). Support for the ProtecT biorepository in Cambridge is provided by NIHR through the Biomedical Research Centre. DNA sample preparation in ProtecT was part-funded by a U.S. Department of Defense award to A. Cox (award no. W81XWH-04-1-0280). GDS works within a centre, CAiTE, supported by the MRC (G0600705) and the University of Bristol. The funders were nonprofit organizations with no participating role in the study.

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

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Supplementary data