Cytochrome P450 1A2 (CYP1A2) Activity and Risk Factors for Breast Cancer: A Cross-Sectional Study

Chi-Chen Hong; Bing-Kou Tang; Geoffrey L Hammond; David Tritchler; Martin Yaffe; Norman F Boyd

Disclosures

Breast Cancer Res. 2004;6(4) 

In This Article

Methods

The methods employed in the present study are published in detail elsewhere[13] and are only briefly described here. Ethical approval for the study protocol was given by the Human Subjects Review Committee at the University of Toronto, Canada.

Between 1994 and 1997, potential participants were identified from the mammographic units of Mount Sinai, Women's College, and St. Michael's Hospital in Toronto. The extent of mammographic density for all patients was visually estimated by a radiologist and expressed as a percentage of breast area on a five-point scale. The purpose of our approach to recruitment was to assemble a group of women without breast cancer and with a wide range of density levels by recruiting approximately equal numbers of women in each of five categories of density, with over-representation of the extreme categories. The number of patients recruited into each of the five categories of radiological density were as follows: <10%, n = 101; 10% to <25%, n = 62; 25% to <50%, n = 60; 50% to <75%, n = 60; and = 75%, n = 99.

Potential participants were sent a letter and subsequently telephoned about the study. Premenopausal women were eligible if they were menstruating regularly, not pregnant or breast-feeding, and had not had a hysterectomy or oophorectomy. Postmenopausal women were eligible if they had had spontaneous amenorrhea for at least 12 months, or had had a hysterectomy and were 50 years of age or older, or had had a bilateral oophorectomy at any age. A woman was excluded if she was taking any type of exogenous hormone preparation, had breast augmentation or reduction or a personal history of breast cancer, or was being investigated for breast cancer. In total, 382 women agreed to participate in the study, representing 88% of those who were contacted and found to be eligible.

Data and blood samples were collected after a 12-hour overnight fast, and during the luteal phase of the menstrual cycle (days 20–24) for premenopausal women. The mammogram closest to the time of the blood draw was used (mean difference 32 weeks).

Because examination of CYP1A2 activity was not a goal of the original study, patients were subsequently mailed a letter describing the goals of this component of the study, and written consent was obtained to measure CYP1A2 activity. Information on ethnicity was also obtained at this time. Of 382 eligible patients (193 premenopausal and 189 postmenopausal women), 357 (93%) gave consent. Eight women could not be contacted because they had moved and could not be traced through either telephone directories or their physicians. Sixteen women were nonresponders after a minimum of four telephone reminders, and one did not provide consent.

By questionnaire (see below), each patient was asked their country of birth, as well as the countries of birth for each of their parents and grandparents. They were also asked the question, 'What is your ethnic or cultural background?' and given instructions to mark all appropriate categories. Subjects were classified as follows: black; white (e.g. British, French, European, Latin/South American of European background); native/aboriginal people of North America (North American Indian, Inuit, Métis); East Asian (e.g. Chinese, Japanese, Korean, Vietnamese); South Asian (e.g. Indian from India, Pakistani, Punjabi, Tamil); other, with specification; and 'don't know'. Because of low numbers in groups other than Caucasians, the categories were collapsed and described as Caucasian (white), East Asians, Jewish, and other.

Information about epidemiologic risk factors for breast density and breast cancer was collected by questionnaire, and dietary information was obtained using a list-based food frequency questionnaire developed by Block and coworkers.[14] Each woman was weighed and measured for height, and waist and hip circumference.

Caffeine (1, 3, 7-trimethylxanthine) is metabolized by CYP1A2 and has been used to evaluate CYP1A2 activity in vivo.[15] The best urinary metabolic ratio appears to be (AFMU + 1X + 1U)/1,7U (i.e. the CMR).[16]

Urinary caffeine metabolites were measured by high-performance liquid chromatography as previously described,[15] except for a modification of the composition of the mobile phase. The mobile phase was composed of 1.3% isopropanol, 0.2% isonitrile, and 0.1% phosphoric acid. The caffeine metabolites were eluted at 1 ml/min and detected by ultraviolet absorbance (0.05) at 280 nm. The retention times of 1U, 1X and 1,7U and the internal standard (N-acetyl-p-aminophenol) were 9.9, 11.9, 29.8, and 14.2 min, respectively.

Urinary AFMU was first deformylated to stable 5-acetylamino-6-amino-3-methyluracil (AAMU) and then measured using the high-performance liquid chromatography method reported by Tang and coworkers.[15] The mobile phase consisted of 0.075% acetic acid and 0.075% phosphoric acid. AAMU and the internal standard (hydrobenzyl alcohol) were eluted at a flow of 0.9 ml/min and monitored by ultraviolet absorbance (0.04) at 263 nm. The retention times of AAMU and the internal standard were 13 and 36 min, respectively.

A standard urine sample with known caffeine metabolite concentrations was analyzed across all days of sample analyses with an interassay coefficient of variation of 9%. Accuracy of the CYP1A2 measurement did not vary with caffeine intake. After adjustment for smoking status, age, body mass index (BMI), waist–hip ratio (WHR), and ethnicity, coefficient of variations associated with mean CYP1A2 function across quartiles of caffeine intake were 56.8%, 55.3%, 57.0%, and 46.9% for premenopausal women, and 39.8%, 44.1%, 43.5%, and 42.4% for postmenopausal women. Kashuba and coworkers[17] investigated the intraindividual variability in CYP1A2 activity over a 3-month period and the median coefficient of variation was 16.8% (range 4.5–49.3%).

Dietary caffeine intake was assessed by summing the molar concentrations of AAMU, 1, 7U, 1U, and 1, 3-dimethylurate (1,3U) in urine and multiplying them by the 24-hour urine volume to obtain total amount of caffeine metabolites excreted in moles. This amount was multiplied by the molecular weight of caffeine (194.19 g/mol) and divided by 1000 to obtain caffeine intake in milligrams. A correction factor of 1.49 (1/0.67) was applied because the above metabolites, on average, account for 67% of all excreted caffeine metabolites.[18]

Serum estradiol in premenopausal women and sex hormone binding globulin (SHBG) levels were measured at the London Regional Cancer Center in Ontario, Canada in the laboratory of Geoffrey Hammond.[19] Percentage free estradiol was estimated from a nomogram describing the relationship between serum SHBG levels and percentages of free estradiol in a reference population of premenopausal and postmenopausal women of normal weight.[20] These values were used to calculate the free estradiol concentrations from the total estradiol measurements. Serum estradiol levels in postmenopausal women, and IGF-1, IGF binding protein (IGFBP)-3, and GH levels were measured by Esoterix Center for Clinical Trials (Calabasas Hills, CA, USA).[13] Insulin and progesterone levels were measured by the Clinical Biochemistry Laboratory at the Wellesley Hospital in Toronto, Canada. Measurements of lipids and lipoproteins were performed at the J Alick Little Lipid Research Laboratory (St. Michael's Hospital, Toronto), using the standardized Lipid Research Clinic method.[21] For hormone measurements, the percentage coefficient of variation within hormone assays was less than 7% for all (except for progesterone, which was 8.7%), and between assays it was less than 10% for all (except progesterone, which was 11.9%). The interassay coefficient of variation was less than 4% for total cholesterol, triglycerides, and high-density lipoprotein (HDL)-cholesterol.

Data were analyzed using the SAS statistical software package (version 6.12; SAS Institute Inc., Cary, NC, USA).[22] Data were inspected for normality and, when necessary, transformed to approximate a normal distribution. Details of the transformations used are given in the footnotes of tables. Differences in results were found for premenopausal and postmenopausal women; thus, all data are presented stratified by menopausal status.

Relationships between CYP1A2 activity and hormone levels, anthropometric measurements, blood lipids, and diet were explored using multiple regression analysis. Ethnicity, age, body size (BMI and WHR), and smoking status as determinants of CYP1A2 activity[7] were included in all models as potential confounders. Further adjustments were made to control for potential confounding variables, and details are given in the tables and figures. Because the data analysis was exploratory in nature, the goal of the study was to provide results for all relationships that might potentially be associated with CYP1A2 activity. Subsequent research will be needed to confirm any relationships identified here. Differences were thus considered significant at P ≤ 0.05, but for descriptive purposes quartile least square means were determined for variables associated with CYP1A2 activity at P ≤ 0.20, and tests for trends were performed as an additional method of assessing the data for these variables. Although these results are based on analyses of transformed data, they are presented in their original units with 95% confidence intervals.

Stepwise and maximum R regression analyses were used to identify covariates related to CYP1A2 activity after controlling for potential confounders. If variables were highly correlated (R ≥ 0.75), then the variable most strongly associated with CYP1A2 activity (from the analyses presented in Table 3 and Table 4 ) was included in the model. In general, variables correlated at less than 0.8 will not pose problems in regression analysis.[23] Percentage free estradiol was not included in the models as a potential covariate because values were calculated from total serum estradiol and SHBG levels.[24] Low-density lipoprotein (LDL)-cholesterol levels were not included as a potential covariate because of high correlations with total cholesterol level (R > 0.84). Total energy intake was not included in the models because of high correlations with protein, fat, and carbohydrate intake (R > 0.79). Variables identified from the stepwise and maximum R regressions with P ≤ 0.15 were re-analyzed with linear regression, and only variables that continued to be significant at the P ≤ 0.15 level were reported. For descriptive purposes, each variable included in the final model was also categorized, in turn, into quartiles so that least square (adjusted) means for CYP1A2 activity could be determined. P values for trend across quartiles of each variable were determined.

Values for GH were missing in 19 (premenopausal, n = 3; postmenopausal, n = 16) and GH was undetectable in 120 (premenopausal, n = 53; postmenopausal, n = 67) of 357 (34%) women in the study. A missing value occurred when the volume of serum available for a participant was insufficient for both a GH and IGF-1 analysis; in such instances, IGF-1 values were determined and GH assays were not performed. Nondeterminate values were assumed to be due to the episodic and pulsatile nature of GH release, which results in considerable variability in basal hormone levels. A value of 0.2 ng/l was assigned to the 120 undetectable measurements and represents the lower limit of sensitivity for the assay. To determine whether we were introducing a bias into the results, we looked for a relationship between GH detectability and CYP1A2 activity, and found that none existed (P = 0.71).

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