Abstract
Background: Risk factors for prostate cancer are not well understood. Red blood cell, platelet, and white blood cell indices may be markers of a range of exposures that might be related to prostate cancer risk. Therefore, we examined the associations of hematologic parameters with prostate cancer risk.
Methods: Complete blood count data from 209,686 male UK Biobank participants who were free from cancer at study baseline were analyzed. Participants were followed up via data linkage. After a mean follow-up of 6.8 years, 5,723 men were diagnosed with prostate cancer and 323 men died from prostate cancer. Multivariable-adjusted Cox regression was used to estimate adjusted HRs and 95% confidence intervals (CI) for prostate cancer incidence and mortality by hematologic parameters, and corrected for regression dilution bias.
Results: Higher red blood cell (HR per 1 SD increase = 1.09, 95% CI, 1.05–1.13) and platelet counts (HR = 1.07, 1.04–1.11) were associated with an increased risk of prostate cancer. Higher mean corpuscular volume (HR = 0.90, 0.87–0.93), mean corpuscular hemoglobin (HR = 0.90, 0.87–0.93), mean corpuscular hemoglobin concentration (HR = 0.87, 0.77–0.97), and mean sphered cell volume (HR = 0.91, 0.87–0.94) were associated with a lower prostate cancer risk. Higher white blood cell (HR = 1.14, 1.05–1.24) and neutrophil count (HR = 1.27, 1.09–1.48) were associated with prostate cancer mortality.
Conclusions: These associations of blood indices of prostate cancer risk and mortality may implicate shared common causes, including testosterone, nutrition, and inflammation/infection among several others in prostate cancer development and/or progression.
Impact: These associations provide insights into prostate cancer development and progression.
This article is featured in Highlights of This Issue, p. 1513
Introduction
Prostate cancer is the second most common cancer in men and the fifth leading cause of cancer mortality worldwide (1). Established risk factors for prostate cancer include age, family history, ethnicity, and genetic factors (2). Although there are large differences in the global incidence of prostate cancer, little is known regarding potential modifiable risk factors.
Testosterone, folate, vitamin B12, and iron are fundamental to the generation of red blood cells, and low concentrations of any of these factors can lead to changes in red blood cell counts and morphologies (3–10). Low free testosterone concentrations may reduce prostate cancer risk (11), and there is some evidence that folate, vitamin B12, and iron may be positively associated with prostate cancer risk, though these associations are inconsistent (12, 13).
Inflammation and infection stimulate increases in platelet and white blood cell production (10, 14). Previous studies indicate that chronic inflammation and/or the immune response may be associated with overall cancer incidence (15, 16), but it is unclear whether these factors are associated specifically with prostate cancer risk (17, 18).
Although some prospective studies have previously examined the associations between a limited number of hematologic parameters and overall cancer risk (19–23), few have had the power to examine these exposures in relation to prostate cancer diagnosis or mortality (19, 20, 24). Moreover, these studies have relied on a single measure at study baseline. The UK Biobank has measured complete blood count and reticulocyte indices across the entire cohort, as well as repeat measurements in approximately 9,000 men, and thus provides a unique opportunity to investigate these associations reliably. Therefore, we aimed to assess the association of hematologic indices as possible surrogate markers of exposures including testosterone, dietary factors, and inflammation/infection, with risk of prostate cancer incidence and mortality.
Materials and Methods
Study design
UK Biobank is a large prospective study designed to be a resource for research into the causes of disease. Further details of the study protocol and data collection are available online (http://www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf) and in the literature (25).
In brief, all participants were registered with the National Health Service (NHS) and lived within 40 km of one of the UK Biobank assessment centers. Approximately 9.2 million people were initially invited to participate. Overall, about 503,000 men and women ages 40–69 years consented to join the cohort and attended one of 22 assessment centers throughout England, Wales, and Scotland between 2006 and 2010, resulting in a participation rate of 5.5% (25).
The UK Biobank study was approved by the North West Multi-Centre Research Ethics Committee (reference number 06/MRE08/65), and at recruitment all participants gave written informed consent to participate and be followed-up.
Baseline assessment
At baseline, participants provided data on a range of sociodemographic, physical, lifestyle, and health-related factors and summary diet information (including supplement and medication use) via self-completed touch-screen questionnaires and a computer-assisted personal interview (25). Body mass index (BMI) was measured at the assessment center.
Blood samples were taken from all participants except for a small proportion who declined, were unable to, or where the attempt was abandoned for either technical or health reasons (0.3%; ref. 25).
To prevent the blood from clotting, it was collected into ethylenediaminetetraacetic acid vacutainers and shipped to the central processing laboratory in temperature-controlled shipping boxes (at 4°C; ref. 26). On arrival at the central laboratory, the blood contents were measured on a Beckman-automated hematology analyzer, and reticulocyte parameters were measured on a COULTER LH 750 System, typically within 24 hours of blood draw (26). A maximum of 31 hematologic parameters were measured by the machine, including a mixture of measured and calculated values (further details are available from https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/haematology.pdf; ref. 27).
For red blood cell and platelet parameters, the values analyzed here are as measured by the machines. For subtypes of white blood cells (neutrophils, eosinophils, basophils, monocytes, lymphocytes), we calculated the counts from the measured proportions of these cells.
Repeat assessment
Repeat assessment data were collected in a subset of participants (∼9,000 men) between August 2012 and June 2013 at the UK Biobank Co-ordinating Centre in Stockport. Participants who lived within a 35 km radius were invited to attend, with an overall response rate of 21% (27, 28); for further details, see: https://biobank.ctsu.ox.ac.uk/~bbdatan/Repeat_assessment_doc_v1.0.pdf).
Exclusion criteria
There were up to 209,686 men included in this analysis, after excluding 9,871 men with prevalent malignant cancer (except C44: nonmelanoma skin cancer), 213 participants who were identified as being genetically female, and 9,364 men who did not have blood data available.
Participant follow-up
Cancer registration data were provided via record linkage to the NHS Central Register, until the censoring date (March 31, 2016, in England and Wales and October 31, 2015, in Scotland). Death data for England and Wales were provided by NHS Digital and for Scotland by the Information and Statistics Division (censoring dates January 31, 2018 and November 30, 2016, respectively).
In the analyses of incident prostate cancer, the endpoint was defined as the first diagnosis of prostate cancer, or death from prostate cancer [International Classification of Diseases Tenth revision code (ICD-10) C61; ref. 29], whichever was recorded first. Prostate cancer cases identified via death records were included only if death from prostate cancer preceded the NHS Central Register censoring date. Person-years were calculated from the date of recruitment to the date of the first cancer registration [excluding nonmelanoma skin cancer (ICD-10 C44; ref. 29)], death, or censoring date, whichever occurred first. In analyses of prostate cancer mortality, the endpoint was defined as being prostate cancer as the primary cause of death.
Statistical analysis
Partial correlations between the blood indices were estimated after adjustment for region (10 UK regions), age at recruitment (<45, 45–49, 50–54, 55–59, 60–64, and ≥65 years), and BMI [<25, ≥25–<30, ≥30–<35, ≥35 kg/m2, unknown (0.4%)].
HRs and 95% confidence intervals (CI) of prostate cancer diagnosis and mortality were estimated using Cox proportional hazards models, with age as the underlying time variable. Analyses were stratified by geographic region of recruitment (10 UK regions) and age at recruitment (categories as defined above), and adjusted for Townsend deprivation score [fifths, unknown (0.1%)], racial/ethnic group [White, mixed background, Asian, Black, other, and unknown (0.6%)], height [<170, ≥170–<175, ≥175–<180, ≥180 cm, and unknown (0.4%)], lives with a wife or partner (no, yes), BMI (categories as defined above), cigarette smoking [never, former, current light smoker (1–14 cigarettes per day), current heavy smoker (≥15 cigarettes per day), current (number of cigarettes per day unknown), and smoking status unknown (0.6%)], alcohol consumption (nondrinkers, <1–≤9, ≥10–<20, ≥20 g ethanol/day, unknown (0.5%)], and self-reported diabetes [no, yes, and unknown (0.5%)]. Categories of the adjustment covariates were defined a priori based on previous analyses using UK Biobank data (30).
The blood parameters investigated were chosen a priori. The red blood cell parameters were: red blood cell (erythrocyte) count, red blood cell distribution width, hematocrit, hemoglobin concentration, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean sphered cell volume (MSCV), and reticulocyte count. Platelets parameters were platelet count, platelet distribution width, and mean platelet (thrombocyte) volume. White blood cells parameters were white blood cell (leukocyte) count, neutrophil count, eosinophil count, basophil count, monocyte count, and lymphocyte count. Summary descriptions of hematologic parameters and measurements are in Table 1.
Summary description of hematologic parameters and measurement/calculation methods by UK Biobank.
Due to measurement error and within-person variability, using single measures at baseline may lead to substantial underestimation of potential associations with disease risk (i.e., regression dilution bias; ref. 31). UK Biobank repeated the blood measurements during follow-up in a subsample of 8,667 men. HRs for trend were estimated per one unit increase in SD and were corrected for regression dilution bias using the MacMahon–Peto method (32). This method divides the subjects into fifths at baseline measurement and calculates the difference between the mean parameter measurement in the top and bottom quintile groups for both the baseline and a second measurement in order to estimate a ratio of ranges (32). In the categorical analysis, hematologic data were categorized into fifths of the distribution of the whole cohort at study baseline. HRs were calculated relative to the lowest fifth of each blood parameter. The variance of the log risk in each group was calculated (from the variances and covariances of the log risk) and used to obtain group-specific 95% CIs, which enabled comparisons across different exposure categories (33).
The proportional hazards assumption was tested using Schoenfeld residuals and revealed no evidence of deviation from the assumption.
Stratified analyses
To examine whether associations with prostate cancer risk differed for cancers diagnosed shortly after recruitment or in men who were diagnosed at a younger age, heterogeneity in the associations of hematologic indices with incident prostate cancer was tested by time from recruitment to diagnosis and mortality (<4; ≥4 years) and age at diagnosis (<65; ≥65 years). Stratified Cox models were fitted based on competing risks, and heterogeneity in the risk coefficients and standard errors in the two subgroups was tested using the χ2 test of heterogeneity (11).
For noncase-defined characteristics, heterogeneity in the associations with incident prostate cancer by age at blood collection (<60, ≥60 years), BMI (<30, ≥30 kg/m2), smoking (never, ever smokers), and diabetes status (yes, no) was tested using the χ2 test of interaction between subgroups.
Sensitivity analyses
Family history of prostate cancer was not included as an adjustment factor in the primary analysis due to the limited availability of data (45.1% missing/unknown). Family history of prostate cancer was further adjusted for as a sensitivity analysis [no, yes (brother or father), and unknown]. To adjust for the possibility of laboratory drift, analyses were repeated following additional adjustment for year of blood collection (continuous).
The primary analysis was repeated after: (i) excluding men who reported that they were regularly taking folic acid, folate, or multivitamin supplementation at baseline (n = 41,323); (ii) excluding men who reported that they were regularly taking prescription hydroxyurea, interferon, heparin, erythropoietin, anemia treatment, immunosuppressants, antihistamines, or anti-inflammatories at baseline (n = 64,284); and (iii) after removing outliers [outside the range of (lower quartile – 3 × interquartile range, upper quartile + 3 × interquartile range)], the number of outliers identified ranged from n = 195 for mean platelet volume to n = 5,199 for basophils.
All tests of significance were two-sided, and P values <0.01 were considered statistically significant. Analyses were performed using Stata version 14.1 (Stata Corporation), and figures were created in R version 3.2.3.
Results
Note that 209,686 men were included in this analysis, and after a mean follow-up of 6.8 years (SD = 1.3 years), 5,723 (2.7%) were diagnosed with prostate cancer and 323 died from the disease (numbers of incident cases and deaths are smaller for some exposure variables). Table 2 summarizes the baseline characteristics of study participants. Mean age at recruitment was 56.6 years (SD = 8.2), and men had a mean BMI of 27.8 kg/m2. Note that 12% of men were current smokers, and 43% reported drinking ≥20 g alcohol per day. Twenty-eight percent reported having a PSA test prior to baseline, and 7.5% had a family history of prostate cancer.
Baseline characteristics and blood indices of men free from prostate cancer and men who developed prostate cancer.
Median and interquartile range values for hematologic data are displayed in Table 2. Characteristics of men by red blood cell, platelet, and white blood cell counts by 1st, 3rd, and 5th fifths at baseline are displayed in Table 3. Regression dilution ratios are displayed in Supplementary Table S1. Basophil count and MCHC had the lowest regression dilution ratios (0.20 and 0.24, respectively). Regression dilution ratios for the other hematologic parameters ranged between 0.53 and 0.83 (Supplementary Table S1).
Baseline characteristics of 209,686 men in UK Biobank according to observed red blood cell, platelet, and white blood cell count at initial assessment.
Correlations between blood indices
Red blood cell count was negatively correlated with MCV and MCH (r = −0.50 and −0.54, respectively). Platelet count was negatively correlated with mean platelet volume and platelet distribution width (r = −0.47 and −0.35, respectively). White blood cell counts were at least weakly positively correlated with each other, and with platelet count (r = 0.24; Supplementary Fig. S1).
Associations between hematologic data and prostate cancer incidence and mortality
Estimates are corrected for regression dilution bias; uncorrected results are displayed in Supplementary Table S2.
Higher total red blood cell count was associated with an elevated risk of prostate cancer (HR per 1 SD increase = 1.09, 95% CI, 1.05–1.13; Ptrend < 0.001). Higher MCV (HR per 1 SD increase = 0.90, 95% CI, 0.87–0.93), MCH (0.90, 0.87–0.93), MCHC (0.87, 0.77–0.97), and MSCV (0.91, 0.87–0.94) were associated with a lower prostate cancer risk (Fig. 1). Red blood cell indices were not associated with prostate cancer mortality (Supplementary Fig. S2).
HR (95% CIs) of prostate cancer diagnosis by fifths of red blood cell indices. HRs are stratified by region and age at recruitment and adjusted for age (underlying time variable), Townsend deprivation score, racial/ethnic group, height, lives with a wife or partner, BMI, cigarette smoking, alcohol consumption, and diabetes. HRs per 1 SD increase are adjusted for regression dilution bias.
Higher platelet count (HR per 1 SD increase = 1.07, 95% CI, 1.04–1.11) was associated with increased risk of prostate cancer diagnosis (Fig. 2). Platelet indices were not associated with prostate cancer mortality (Supplementary Fig. S3).
HR (95% CIs) of prostate cancer diagnosis by fifths of platelet indices. HRs are stratified by region and age at recruitment and adjusted for age (underlying time variable), Townsend deprivation score, racial/ethnic group, height, lives with a wife or partner, BMI, cigarette smoking, alcohol consumption, and diabetes. HRs per 1 SD increase are adjusted for regression dilution bias.
White blood cell counts were not associated with prostate cancer diagnosis (Fig. 3), but higher white blood cell (HR per 1 SD increase = 1.14, 95% CI, 1.05–1.24) and neutrophil counts (1.27, 1.09–1.48) were associated with an increased risk of prostate cancer mortality (Fig. 4).
HR (95% CIs) of prostate cancer diagnosis by fifths of white blood cell indices. HRs are stratified by region and age at recruitment and adjusted for age (underlying time variable), Townsend deprivation score, racial/ethnic group, height, lives with a wife or partner, BMI, cigarette smoking, alcohol consumption, and diabetes. HRs per 1 SD increase are adjusted for regression dilution bias.
HR (95% CIs) of prostate cancer mortality by fifths of white blood cell indices. HRs are stratified by region and age at recruitment and adjusted for age (underlying time variable), Townsend deprivation score, racial/ethnic group, height, lives with a wife or partner, BMI, cigarette smoking, alcohol consumption, and diabetes. HRs per 1 SD increase are adjusted for regression dilution bias.
Stratified analyses
There was some evidence of heterogeneity in the associations of red blood cell and platelet parameters with prostate cancer risk by time to diagnosis, with stronger associations observed in men diagnosed ≥4 years from baseline blood collection for MCH and MSCV parameters (Phet all < 0.01; Table 4). There was also some evidence that associations between white blood cell counts and prostate cancer mortality were stronger with a longer follow-up time, but numbers of prostate cancer deaths were limited (Supplementary Table S3). There was no evidence of heterogeneity in the associations with prostate cancer risk by age at diagnosis (Supplementary Table S4), age at recruitment, smoking, BMI, or diabetes status (Phet > 0.01 for all).
Multivariable-adjusted HRs (95% CIs) for prostate cancer by time to diagnosis in relation to hematologic indices.
Sensitivity analyses
Further adjustment for family history of prostate cancer and date of blood collection made no appreciable difference to the associations. Associations remained consistent following the exclusion of: (i) men who reported regularly taking multivitamins/folate supplementation, (ii) medications that are known to influence hematologic parameters, and (iii) following the exclusion of outliers. White blood cell and reticulocyte counts were positively skewed; however, log-transformation did not materially alter the results.
Discussion
The findings from this large prospective study in British men provide evidence for the associations of hematologic parameters with prostate cancer risk. Higher red blood cell and platelet counts were associated with an elevated risk of prostate cancer diagnosis, and higher MCV, MCH, MCHC, and MSCV were associated with a lower risk of prostate cancer diagnosis, but red blood cell and platelet parameters were not associated with prostate cancer mortality. In contrast, white blood cell counts were not associated with risk of prostate cancer diagnosis, but higher white blood cell and neutrophil counts were associated with an increased risk of prostate cancer mortality.
The observed associations of higher red blood cell counts, but lower measures of red blood cell volumes with prostate cancer risk may support the hypothesized roles of testosterone, folate, vitamin B12, and/or iron in prostate cancer development. Testosterone and dietary factors (folate, vitamin B12, and iron) play a role in red blood cell production and deficiencies in any of these factors can cause anemia (5–10, 34–37). We observed an association between a greater number of red blood cells, but lower red blood cell volumes and an elevated risk of prostate cancer diagnosis. Macrocytic anemia is a subtype of anemia which is characterized by larger red cell volumes and is commonly associated with folate and vitamin B12 deficiencies (38); therefore, our results are compatible with prior hypotheses that folate and/or B12 may have a positive association with prostate cancer risk, whereas iron deficiencies are more commonly associated with smaller red blood cell volumes, i.e., microcytic anemia (39). Although testosterone is associated with red blood cell production, any possible role in determining red blood cell size is not well described.
Red blood cell production is primarily controlled by the kidneys (via erythropoietin; ref. 3). Previous epidemiological studies have observed a reduced risk of prostate cancer in men with chronic kidney disease (40); therefore, the associations of red blood cell indices with prostate cancer risk may be related to the correlates of kidney function [including testosterone (41), insulin-like growth factor-I (42), iron (43), and metabolic syndrome (44)].
The finding of a positive association of higher platelet count with risk of prostate cancer diagnosis, and higher white blood cell and neutrophil counts with increased risk of prostate cancer mortality may support the hypothesized roles of chronic inflammation and/or infection in prostate cancer development and/or progression (16, 45). Previous studies have reported that inflammatory markers were associated with an increased risk of several cancers (20, 22, 23), but the associations with prostate cancer were inconsistent (20, 23, 46). Furthermore, the associations of intraprostatic inflammation with prostate tumor prognosis remain unclear (47).
It is possible that the associations of blood cells with prostate cancer may indicate causal patterns relating to general cell behavior (48, 49). Future analyses, such as Mendelian randomization, may be beneficial in identifying the possible causal components that may account for these associations (50).
It is possible that the associations observed in the current study may be at least partially explained by reverse causation. Cancer can cause anemia by producing cytokines, which lead to iron sequestration (51). Tumors can also increase platelet indices, even during early cancer (52). However, the associations of red blood cell and platelet indices with prostate cancer diagnosis were stronger with a longer follow-up time, which may suggest that they are etiologically relevant markers. Tumors may also increase the half-life of neutrophils, which can then promote tumor growth and metastasis (53, 54). In this analysis, associations of white blood cell counts with prostate cancer mortality were stronger with longer durations of follow-up. This suggests that these associations with prostate cancer death may not be explained by reverse causation, although there was limited statistical power to assess heterogeneity by time to prostate cancer death.
This analysis has a number of strengths. With data on over 200,000 men, this is the largest prospective study to examine a wide range of hematologic measures in relation to prostate cancer–specific risk. Blood indices were measured across the entire cohort via standardized methods and participants were well characterized. The availability of repeat measures in a subsample of men allowed for correction for regression dilution bias, and therefore more accurate estimates of the associations with risk.
A limitation of this analysis was that it was not possible to rule out detection bias. These blood indices may be associated with health status [for instance, low red blood cell count is associated with hypertension, hypothyroidism, and congestive heart failure (55), whereas high white blood cell count is associated with increased total and cardiovascular mortality (56, 57)]. It is also difficult to rule out the possibility of residual confounding due to other potential lifestyle-related risk factors. Comorbidities, socioeconomic status, and poor health may affect PSA test attendance, but PSA testing attendance after baseline was not known. Prostate tumor stage and grade data are not currently available in UK Biobank. In the absence of data on tumor characteristics, associations with prostate cancer death may be considered more etiologically relevant than overall diagnosis, although the statistical power to examine associations with death was limited. The associations of white blood cell counts with prostate cancer death may indicate that these indices are associated with more clinically relevant forms of prostate cancer, or may be evidence of their role in tumor progression. UK Biobank participants are healthier and better educated than the sampling population, and have a higher rate of incident prostate cancer (25), which may be related to a greater PSA testing attendance (58). Therefore, risk estimates may not be generalizable to all populations. The UK Biobank prospective cohort study design is underpowered to detect associations with acute short-term infection and inflammation, and associations with inflammation and infection are likely to reflect chronic long-term exposure. However, acute short-term inflammation and infection may decrease regression dilution ratios. Although the blood measurements were generally consistent between baseline measurement and the repeat measurement, MCHC and basophil count had low regression dilution ratios. Lower regression dilution ratios will not affect the P value, but will increase the uncertainty of the risk estimates. Blood indices were used as surrogate markers of a range of possible risk factors. Circulating folate, vitamin B12, iron, and testosterone concentrations or markers of inflammation (such as C-reactive protein) were not available at the time of analysis; therefore, it was not possible to investigate further any potential mechanistic factors which may account for the observed associations.
In conclusion, this analysis of more than 200,000 men observed associations of several hematologic parameters with prostate cancer risk. The associations of blood indices with prostate cancer risk and mortality implicate shared common causes with prostate cancer. These relationships are compatible with the hypothesized relationships of testosterone, folate, vitamin B12, and inflammation with prostate cancer development. Future analyses, such as Mendelian randomization, may be beneficial in identifying the possible causal components that may account for these associations.
Disclosure of Potential Conflicts of Interest
No potential conflicts of interest were disclosed.
Authors' Contributions
Conception and design: E.L. Watts, A. Perez-Cornago, R.C. Travis, T.J. Key
Development of methodology: E.L. Watts, A. Perez-Cornago
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): R.C. Travis
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): E.L. Watts, J. Kothari
Writing, review, and/or revision of the manuscript: E.L. Watts, A. Perez-Cornago, J. Kothari, N.E. Allen, R.C. Travis, T.J. Key
Study supervision: A. Perez-Cornago, R.C. Travis, T.J. Key
Acknowledgments
This research has been conducted using the UK Biobank resource under application number 3282. Data analysis was supported by Cancer Research UK grants C8221/A19170, C8221/A29017, and C8221/A20986. E.L. Watts was supported by the Nuffield Department of Population Health Early Career Research Fellowship.
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.
Footnotes
Note: Supplementary data for this article are available at Cancer Epidemiology, Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).
Cancer Epidemiol Biomarkers Prev 2020;29:1615–26
- Received December 13, 2019.
- Revision received February 12, 2020.
- Accepted May 19, 2020.
- Published first May 26, 2020.
- ©2020 American Association for Cancer Research.
References
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