Originally published as JCO Early Release 10.1200/JCO.2006.07.6356 on March 19 2007

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A Prospective Study of Plasma Prolactin Concentrations and Risk of Premenopausal and Postmenopausal Breast Cancer

Shelley S. Tworoger, A. Heather Eliassen, Patrick Sluss, Susan E. Hankinson

From the Channing Laboratory, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School; Department of Epidemiology, Harvard School of Public Health; Reproductive Endocrinology Unit Laboratory, Massachusetts General Hospital, Boston, MA

Address reprint requests to Shelley S. Tworoger, PhD, Channing Laboratory, 181 Longwood Ave, 3rd Floor, Boston, MA 02115; e-mail: nhsst{at}channing.harvard.edu

	ABSTRACT

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Purpose: Epidemiologic studies suggest that prolactin is associated with breast cancer risk in older women. Because of limited prospective data, particularly in younger women, we examined whether prolactin concentrations were associated with breast cancer risk among women 42 to 55 years (68% premenopausal) from the Nurses’ Health Study (NHS), and then conducted a pooled analysis of three studies.

Patients and Methods: The analysis included 377 cases of breast cancer diagnosed after blood draw and before June 2000; two controls were matched per case on age, menopausal status at blood draw and diagnosis, fasting status, and time of day and month of blood collection. These data were pooled with two previously published data sets from the NHS and NHSII cohorts (n = 1,539 cases, 2,681 controls; ages 32 to 70 years).

Results: Prolactin was modestly associated with an increased breast cancer risk (relative risk [RR], top v bottom quartile = 1.3; 95% CI, 0.9 to 1.9; P for trend = .12). Risk estimates did not vary by menopausal status, tumor invasiveness, or estrogen receptor (ER) status. In the pooled analysis, the overall RR was 1.3 (95% CI, 1.1 to 1.6; P for trend = .002), and did not vary by menopausal status (P for interaction = .95). The risk was strongest for women with ER+ tumors (RR = 1.6; 95% CI, 1.2 to 2.0; P for trend < .001). Correcting for within-person variability, the RR comparing the median of the top versus the bottom prolactin quartile increased from 1.3 to 1.7 for all women and from 1.5 to 2.1 for ER+ cases.

Conclusion: These data, in conjunction with experimental studies, indicate that prolactin likely is important in breast cancer etiology, particularly ER+ tumors, over a wide range of ages.

	INTRODUCTION

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Epidemiologic evidence suggests that circulating prolactin concentrations are associated with increased breast cancer risk. In two large prospective studies, the Nurses’ Health Study (NHS; n 850) and a Swedish cohort (n 170 cases), postmenopausal women in the top versus the bottom fourth of prolactin levels had a 30% increased risk,^1,2 with an 80% increased risk for estrogen receptor (ER)/progesterone receptor (PR) –positive tumors.¹ One large study in younger women (NHSII; n 315 cases) reported similar associations.³

Experimental evidence also supports prolactin involvement in breast cancer etiology^4-6 by increasing cell proliferation^7-15 and inhibiting apoptosis.^6,16 Prolactin may enhance tumorigenesis in mice,^9,17 and increase cell motility,¹⁸ metastasis,⁹ and tumor vascularization.^19,20 Further, prolactin may increase cellular responsiveness to estrogenic effects.^10,15,21

Because of limited prospective data in younger women, we examined whether prolactin concentrations were associated with breast cancer risk among NHS women (ages 42 to 55 years). Further, to examine this relationship in greater detail across a wide age range, we combined these data with those from two previously published data sets from the NHS¹ and NHSII,³ which cumulatively comprise approximately 80% of cases from published prospective studies to date.

	PATIENTS AND METHODS

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Study Population
The NHS cohort was established in 1976 among 121,700 US female registered nurses, ages 30 to 55 years, and the NHSII was established in 1989 among 116,609 female registered nurses, ages 25 to 42 years. All women completed an initial questionnaire and have been followed biennially by questionnaire to update exposure status and disease diagnoses.

From 1989 to 1990, 32,826 NHS participants (ages 43 to 70 years) provided blood samples and completed a short questionnaire²²; follow-up was 99% in 2000. Briefly, women arranged to have their blood drawn and shipped with an icepack, via overnight courier, to our laboratory, where it was processed and separated into plasma, RBC, and WBC components.

Between 1996 and 1999, 29,611 NHSII participants (ages 32 to 54 years) provided blood samples and completed a short questionnaire³; follow-up was 98% in 2003. Briefly, premenopausal women (n = 18,521) who had not taken hormones, been pregnant, or lactated within 6 months provided blood samples drawn on the 3rd to 5th day of their menstrual cycle (follicular) and 7 to 9 days before the anticipated start of their next cycle (luteal, called timed samples). Follicular plasma was aliquoted by the participant and frozen. Other women (n = 11,090) provided a single 30-mL untimed blood sample. Luteal and untimed samples were shipped and processed identically to NHS samples.

Samples have been stored in liquid nitrogen freezers since collection. Prolactin is stable in whole blood for 24 to 48 hours.²³ These studies were approved by the Committee on the Use of Human Subjects in Research at the Brigham and Women's Hospital (Boston, MA).

We considered a woman to be premenopausal if (1) she gave timed samples, (2) her periods had not ceased, or (3) she had a hysterectomy with at least one ovary remaining and was 47 years or younger (nonsmokers) or 45 years or younger (smokers). We considered a woman to be postmenopausal if (1) her natural menstrual periods had ceased permanently, (2) she had a bilateral oophorectomy, or (3) she had a hysterectomy with at least one ovary remaining and was 56 years or older (nonsmokers) or 54 years or older (smokers).²⁴ The remaining women, most of whom had a simple hysterectomy and were 48 to 55 years old, were of unknown menopausal status.

Our analysis includes three data sets (Table 1). The first, for which results are presented here, are NHS women who were premenopausal or of unknown menopausal status at blood collection. The second data set, published previously,¹ contains NHS postmenopausal cases and controls. The third data set, published previously, contains all NHSII cases and controls.³ Women had no reported cancer diagnosis before blood collection.

NHS Postmenopausal
Briefly, 850 cases diagnosed after blood draw and before June 1, 2000, and 1,275 controls were available for analysis. Cases and controls were matched on age, recent postmenopausal hormone (PMH) use, and date, time of day, and fasting status of blood collection.

NHSII
Briefly, 312 cases of any menopausal status diagnosed after blood collection and before June 1, 2003, and 620 controls were available for analysis. Cases and controls were matched on age; menopausal status at blood collection and diagnosis; race/ethnicity; date, time of day, and fasting status at blood collection; and luteal day of the blood draw (timed cases only).

Laboratory Assays
Prolactin was measured by microparticle enzyme immunoassay. Samples were assayed at the Reproductive Endocrinology Unit Laboratory at the Massachusetts General Hospital in six batches (one for NHS premenopausal/unknown menopause, three for NHS postmenopausal, two for NHSII), using the AxSYM Immunoassay system (Abbott Diagnostics, Chicago, IL), except for 164 cases and 245 controls assayed by Christopher Longcope, MD (University of Massachusetts Medical Center, Worcester, MA), in three batches (NHS postmenopausal), using the IMx System (Abbott Laboratory, Abbott Park, IL). Although there was some batch-to-batch variation in prolactin values across data sets, the correlation between the two laboratories was 0.91, and across different batches within the same data set was more than 0.95.¹ The limit of detection was 0.6 ng/mL. In the NHS postmenopausal data set, prolactin values from the first five batches were recalibrated to have a comparable distribution to the sixth batch, as previously described.¹ Estradiol (NHSII timed and NHS postmenopausal samples) and testosterone (NHSII and NHS postmenopausal samples) were assayed as described previously²⁵; sex steroids were not available in the premenopausal women with untimed samples.

Case-control sets and follicular and luteal samples were assayed together, ordered randomly, and labeled to mask case-control status and follicular versus luteal samples. The coefficient of variation from blinded replicate samples was less than 12%.

Statistical Analysis
Outliers (> 99 ng/mL; n = 3) were excluded.²⁶ We had 377 cases and 786 controls available for analysis. RRs and 95% CIs were determined using conditional logistic regression comparing quartiles (cut points based on control distribution) of prolactin concentrations.²⁷ We stratified by tumor invasiveness, ER/PR status, and time between blood draw and diagnosis using polytomous unconditional logistic regression.^1,28 Secondary analyses excluded women with prolactin levels more than 24 ng/mL, taking antidepressants or PMH, or less than 45 years old, and stratified by menopausal status at baseline and diagnosis and current body mass index (BMI), using unconditional logistic regression adjusting for matching factors.

Models were adjusted for BMI at age 18 years, weight change since age 18 years, family history of breast cancer, age at menarche, and history of benign breast disease. Further adjustment for hysterectomy, physical activity, antidepressant use, or oral contraceptive use did not substantially alter the results. We considered separate adjustment for age at first birth and parity, as this may be part of the biologic pathway through which prolactin affects breast cancer.⁶ Tests for trend were modeled using quartile medians and calculating the Wald statistic.²⁹

Pooled Analysis
For NHSII women with timed samples, we averaged the luteal and follicular values because results were similar when using each sample individually compared with averaged,³ and because prolactin levels do not substantially fluctuate within the menstrual cycle.³⁶ Because participants came from similar cohort studies, we combined the data via concatenation of the data sets, using data set–specific quartile cut points based on control distributions (Table A1, online only) to account for laboratory variability across data sets and maintain the same categorization as previously published.^1,3 Using conditional logistic regression, we compared models with and without data set interaction terms via the likelihood ratio test to assess heterogeneity across data sets.

We stratified by tumor invasiveness, ER/PR status, ductal/lobular, and time between blood draw and diagnosis using polytomous logistic regression.¹ Because prolactin levels differed by data set, we used probit scores for trend tests to standardize for between-batch variability²⁹; probit scores, unlike z-scores, have a normal distribution even if the original data are skewed. Scores were calculated as:

where is the cumulative distribution function for a standard normal distribution, i is the rank of the participant within an assay batch, and N is the number of participants in the batch. We stratified by menopausal status at diagnosis, age, parity, age at first birth, and years since first birth, and adjusted for covariates as described herein with further adjustment for age at menopause and data set. Additional adjustment for PMH use did not alter the RRs and, therefore, was not included in the final model. Continuous adjustment for BMI at age 18 years, weight change since age 18 years, and age at menarche did not alter the results compared with categoric adjustment; we used the categoric variables in the final model for consistency with previous analyses.

We used a reproducibility study to correct the point and interval estimates for laboratory measurement error and random within-person variation.³⁰ The reproducibility study contained 113 NHSII women, providing three timed blood samples over 2 to 3 years.³¹ We calculated the within-person variance using the reproducibility study and the between-person variance using the case-control study to estimate an intraclass correlation (ICC) separately for women with timed (follicular and luteal average) and untimed samples (follicular value, because the ICC = 0.46, which was similar to that in postmenopausal women [ICC = 0.49]¹). Uncorrected and corrected RRs were the average of the beta coefficients from the timed and untimed samples, weighted by the inverse of the variance. The standard deviation was equal to the square root of the sum of the timed and untimed sample variances. P values were two sided and considered statistically significant at less than or equal to .05.

	RESULTS

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NHS Premenopausal/Unknown Menopause
Women were 42 to 55 years old (mean, 49 years) at blood collection (Table 2). More cases than controls had a family history of breast cancer. Cases had a nonsignificantly higher median prolactin concentration compared with controls (P = .09).

Correcting for measurement error and within-person variability, the overall RR comparing the median of the top versus bottom quartile of prolactin concentrations increased from 1.3 (95% CI, 1.1 to 1.5) to 1.7 (95% CI, 1.2 to 2.3; Table 5). For ER+ cases, the RR increased from 1.5 (95% CI, 1.2 to 1.8) to 2.1 (95% CI, 1.4 to 3.2).

View this table: [in this window] [in a new window]   Table 5. Intraclass Correlation Coefficients* and Correction of Multivariate RRs and 95% CIs for Random Within-Person Measurement Error

	DISCUSSION

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Our findings for the NHS premenopausal/unknown menopause status women were similar to those in the younger NHSII women (RR, top v bottom quartile = 1.5).³ The relationship was slightly stronger among women older than 45 years in both studies, with a comparable 60% to 70% increased risk. In the NHSII, but not the current study, the association appeared to be stronger for ER+/PR+ tumors. This difference may be due to the relatively small number of ER+/PR+ cases (approximately 150) in each study.

With more than 1,500 cases in the pooled analysis, we observed a 30% increased breast cancer risk comparing the top versus the bottom quartile of prolactin concentrations, which was independent of estradiol and testosterone levels in a subgroup of women. This is consistent with a Swedish study (n = 173) observing a nonsignificant 30% increased risk.² Our results generally were consistent with case-control studies, although these studies can be biased due to their retrospective nature.⁴ Risk did not differ by menopausal status. Although women undergo substantial endocrinologic changes at menopause,³⁵ prolactin declines only slightly during this time.^3,35 Although the pattern of RRs across quartile categories was not clearly linear among either premenopausal or postmenopausal women, the trend tests were statistically significant, suggesting a moderately increasing risk with higher prolactin concentrations.

In the combined analysis, no differences were observed by invasiveness or cancer type. However, we observed a positive association for ER+/PR+ and ER+/PR– tumors, but not ER–/PR– tumors. Experimental data suggest that prolactin likely is associated with ER+ tumors. Transgenic mice with constant prolactin expression developed ER+ and ER– tumors, even though ER+ tumors are extremely rare in this model system.¹⁷ Similarly, in vitro studies have reported that long-term prolactin exposure increases ER expression.^10,21 We did not note any confounding by estradiol levels, likely because the correlation between the two hormones is weak (r = 0.10). Further, the prolactin association did not vary by estradiol levels, which is consistent with another study reporting that high levels of multiple hormones did not further increase breast cancer risk.³⁶ This suggests that mammotropic hormones may only sustain breast tumor growth, but, clearly, this warrants additional study.

The strongest relationship between prolactin and breast cancer was among women diagnosed less than 4 years after blood collection; however, in no individual study or in the combined analysis was the P for heterogeneity statistically significant. Our data suggest nearly a doubling in risk for women diagnosed less than 4 years after blood collection, and a 40% to 50% increased risk for those diagnosed later. This may be because subclinical breast tumors can secrete prolactin, and could increase circulating levels,^12,37,38 possibly suggesting that prolactin could be a tumor marker as well as a risk marker. Another explanation is that prolactin is most important in late-stage breast cancer development, which would attenuate risk estimates in later years.

This study has several limitations. First, we used data set–specific quartile cut points for our analysis, precluding the ability to relate absolute levels to risk. However, although mean levels differed, the correlation between samples assayed in different batches was high. We observed similar associations in each data set, suggesting that our estimates are robust. Second, the immunoassay measured multiple prolactin isoforms, which may have different biologic activities^39,40; thus, we cannot identify which isoforms are most important for breast cancer risk. Third, prolactin has a strong circadian rhythm,⁴¹ increasing after a noontime meal⁴²; however, we matched cases and controls carefully on time of day of blood draw and fasting status to minimize misclassification.

Although breast cancer risk estimates for prolactin are lower than those for estradiol and testosterone,⁴³ prolactin is more variable within a woman over time (ie, years), such that a single blood sample does not provide as good a measure of average long-term levels. The ICCs over 3 years in our study ranged from 0.46 to 0.64, which are lower than for many steroid hormones, and can attenuate the observable risk estimates. Therefore, we used a reproducibility study to estimate the true RRs based on the ICC and the observed RR. The corrected estimates generally are similar to those observed for testosterone (even after adjustment for measurement error because the ICC over 3 years is high, approximately 0.80),^25,31,44,45 but are lower than for estradiol.^43,46 Overall, our results suggest that prolactin may play a somewhat larger role in breast cancer etiology than was previously thought.

In a prospective study with more that 1,500 cases, we observed a 30% higher breast cancer risk, which increased to 70% after measurement error correction, for the women with the highest versus the lowest prolactin concentrations independent of estradiol and testosterone concentrations in cycling premenopausal women and postmenopausal women. Risk was similar by menopausal status, and was confined to ER+ breast tumors, with RRs of 1.7 to 2.3, comparing the highest versus lowest prolactin levels. In conjunction with experimental data,⁶ this more complete analysis of cases and controls in the NHS and NHSII provides important evidence that prolactin is a moderate to strong, and, likely, independent, risk factor for breast cancer. Future studies should explore the role of prolactin in breast cancer etiology by examining genetic variation in the prolactin and prolactin receptor genes, measuring different prolactin isoforms, elucidating the role of the prolactin-binding protein,⁴⁷ and better understanding determinants of prolactin concentrations.

	AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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The authors indicated no potential conflicts of interest.

	AUTHOR CONTRIBUTIONS

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Conception and design: Shelley S. Tworoger, A. Heather Eliassen, Susan E. Hankinson

Financial support: Susan E. Hankinson

Provision of study materials or patients: Shelley S. Tworoger, Susan E. Hankinson

Collection and assembly of data: Shelley S. Tworoger, Patrick Sluss, Susan E. Hankinson

Data analysis and interpretation: Shelley S. Tworoger, A. Heather Eliassen, Susan E. Hankinson

Manuscript writing: Shelley S. Tworoger, A. Heather Eliassen, Patrick Sluss, Susan E. Hankinson

Final approval of manuscript: Shelley S. Tworoger, A. Heather Eliassen, Patrick Sluss, Susan E. Hankinson

	Appendix

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	NOTES

 
published online ahead of print at www.jco.org on March 19, 2007.

Supported by the National Institutes of Health Grants No. CA67262, CA50385, P01 CA87969, CA49449, P50 CA089393, and DAMD-17-02-1-0692. A.H.E. was supported by Cancer Education and Career Development Grant No. R25 CA 098566-2 from the National Cancer Institute.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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Submitted May 25, 2006; accepted January 16, 2007.

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