Background:

Case–control studies show that copper (Cu) is high and zinc (Zn) low in blood and urine of women with breast cancer compared with controls.

Methods:

To assess whether prediagnostic Cu and Zn are associated with breast cancer risk, OR of breast cancer according to Cu, Zn, and Cu/Zn ratio in plasma and urine was estimated in a nested case–control study within the ORDET cohort, using conditional logistic regression adjusted for multiple variables: First 496 breast cancer cases and matched controls, diagnosed ≥2 years after recruitment (to eliminate reverse causation) were analyzed. Then all eligible cases/controls were analyzed with stratification into years from recruitment to diagnosis.

Results:

For women diagnosed ≥2 years, compared with lowest tertiles, breast cancer risk was higher in the highest tertile of plasma Cu/Zn ratio (OR, 1.75; 95% CI, 1.21–2.54) and the highest tertile of both plasma and urine Cu/Zn ratio (OR, 2.37; 95% CI, 1.32–4.25). Risk did not vary with ER/PR/HER2 status. For women diagnosed <2 years, high Cu/Zn ratio was strongly associated with breast cancer risk.

Conclusions:

Our prospective findings suggest that increased Cu/Zn ratio in plasma and urine may be both an early marker of, and a risk factor for, breast cancer development. Further studies are justified to confirm or otherwise our results and to investigate mechanisms.

Impact:

Our finding that prediagnostic Cu/Zn ratio is a strong risk factor for breast cancer development deserves further investigation and, if confirmed, might open the way to interventions to reduce breast cancer risk in women with disrupted Cu/Zn homeostasis.

A growing body of evidence from case–control and cross-sectional studies has established a connection between disrupted copper (Cu) and zinc (Zn) homeostasis and the development and progression of breast cancer (1–3). A recent meta-analysis (4) of 16 case–control studies confirmed that women with breast cancer typically have high Cu/Zn ratio compared with healthy controls.

Cu and Zn are essential micronutrients. Cu is a component of ubiquitous and pluri-functional metalloenzymes that reduce molecular oxygen (5). Zinc is an essential cofactor for numerous enzymes and also has structural and regulatory functions (6). Circulating and stored levels of both metals are tightly regulated to avoid deficiency and toxic excess (5, 6). Nevertheless, elevated Cu together with depressed Zn is one of the most common trace metal imbalances encountered in the human body (7). Cu and Cu/Zn ratio are elevated in inflamed and malignant tissues, and increase with advancing age (8)—an increase proposed associated with reduced ability of older persons to regain homeostasis after a destabilizing event (8). In mouse models, limiting the availability of copper impairs cancer cell metabolism (9). In patients with breast cancer, high expression of Zn transporters is associated with better outcomes (10).

High prediagnostic Cu/Zn ratio has been reported associated with increased risk of developing (11) and dying (12) from several cancers, but not breast cancer. It is unclear whether the disrupted Cu and Zn homeostasis persistently found in women with breast cancer (4) is due to disease-driven changes or to conditions (e.g., exposure to risk factors) that occurred prior to disease onset.

Notwithstanding the abundant case–control and cross-sectional studies, no longitudinal studies have investigated associations between prediagnostic Cu/Zn imbalance and BC risk. We carried out the present prospective study to assess associations between plasma and urinary Cu, Zn, and Cu/Zn ratio, and subsequent risk of breast cancer in women participating in the ORDET (Hormones and Diet in the Etiology of Breast Cancer) longitudinal cohort study (13). Our main hypothesis is that high Cu compared with Zn is associated with increased breast cancer risk.

The ORDET cohort

We carried out a case–control study nested within the ORDET cohort study, which is described in detail elsewhere (14). Briefly, 10,786 women from the general population of Varese province, Lombardy, northern Italy, ages 35 to 69 years, volunteered for enrollment between 1987 and 1992. At baseline trained personnel collected data on menstrual, reproductive and occupational histories, education, socioeconomic status, family history of breast cancer, and other potential risk factors for breast cancer, using standardized procedures, adherence to which was checked periodically. Occupation was divided into five ordinal categories according to a priori estimates of association with metal exposure: 1, never worked; 2, low (e.g., teacher); 3, medium (e.g., shop worker); 4, high (e.g., craftsperson); 5, very high (e.g., industrial worker). Anthropometry was measured by standardized procedures. Dietary information was obtained by administering a validated food frequency questionnaire (FFQ; ref. 14) at baseline. This instrument ascertained the types (107 items) and quantities of foods consumed over the previous year (14). By computer linkage of FFQ data to ORDET food composition tables, the quantities of Zn and Cu consumed were estimated. Cu levels in food items were originally obtained from another Italian food composition table and subsequently inserted into ORDET food composition tables.

Women with a history of cancer, bilateral ovariectomy, chronic or acute liver disease, or who had received hormone therapy in the 3 months before recruitment, were excluded. After exclusion of these women and those who were lost to follow-up immediately after recruitment, 10,669 participants remained. The study was approved by the ethics committee of the National Cancer Institute of Milan (Istituto Nazionale dei Tumori).

Specimen collection

Blood and 12-hour urine samples were collected at recruitment (baseline). The women were instructed to collect overnight 12-hour urine, maintained at room temperature, and to bring it with them on the morning of specimen collection. Twelve-hour urine is a valid alternative to the 24-hour urine gold standard (15). Urine volume was recorded (range 150–1980 mL, mean 613 mL). Blood (40 mL) was drawn from an antecubital vein between 7:30 and 9:30 a.m. (exact time recorded) after overnight fasting. The blood samples were processed by standardized procedures to obtain serum, plasma, red blood cell membranes, and buffy coat. Aliquots of blood and urine were stored in freezers at −80°C for a mean of 17 years. There were no thawing accidents.

Breast cancer cases and controls

Between 1987 and 2012, 613 new primary breast cancer cases were diagnosed in the cohort by record linkage to the Lombardy Cancer Registry. Cases were confirmed by consulting pathology reports: there were 39 cases of in situ carcinoma (ICD-10 code D05) and 574 cases of malignant neoplasm of the breast (ICD-10 code C50).

For each case, one control was chosen using an incidence density sampling protocol (16) from cohort members alive and free of cancer (except nonmelanoma skin cancer) at the time of case diagnosis. Controls could include women who later became cases, and could also serve as controls more than once: 16 women served as both as cases and controls (15 women served once as control and later as a case, the other woman served twice as control and then as a case). Other matching criteria were: age at recruitment (±3 years); menopausal status (pre-, peri-, or postmenopausal), time of blood collection (±180 days), and laboratory batch.

Receptor status

Estrogen receptor (ER), progesterone receptor (PR), and HER2 status were obtained from pathology reports. If this information was absent, IHC determinations were performed on paraffin-embedded blocks of tumor tissue, if available, archived in pathology laboratories in the province of Varese (13). ER, PR, and HER2 status could not be determined in 60, 86, and 99 breast cancer cases, respectively.

Analytical methods

A total 1,213 plasma (600 breast cancer cases and 613 matched controls) and 1,226 urine samples (613 cases and 613 matched controls) were assayed for Cu and Zn by inductively coupled plasma mass spectrometry (ICP-MS; ref. 17). All samples were prepared under a clean hood to prevent contamination by atmospheric particulate matter. Calibration curves using standard solutions containing 300, 75, 18.75, 4.69, and 1.17 µg/L of either Zn or Cu (for plasma), and 100, 25, 6.25, 1.56, and 0.39 µg/L of either Zn or Cu (for urine) were prepared. Gallium-71 was used as internal standard. After sample vortexing, 200 µL aliquots of plasma were diluted 10-fold with 0.1% (v/v) HCl in ultrapure water (18 MΩ/cm); 200 µL aliquots of urine were diluted five-fold with ultrapure water. The diluted samples were injected into a Thermo Fischer iCAP Q mass spectrometer equipped with a PFA-ST nebulizer and cooled Peltier quartz spray chamber, coupled to a Cetac ASX-520 autosampler. The analyses were performed in kinetic energy discrimination mode with helium gas to minimize polyatomic interference. Cu and Zn concentrations were determined directly by Qtegra software supplied with the ICP-MS.

Plasma and urine samples from five healthy adult human volunteers (not study participants) were pooled to obtain samples that were analyzed repeatedly to determine the interday and intraday variation in the analytical method. Intraday and interday coefficients of variation (CV%) for plasma samples were 2.61% and 1.05% for Cu, respectively, and 2.62% and 1.33% for Zn, respectively. Intraday and interday CV% for urine samples were 4.93% and 8.6% for Cu, respectively, and 4.27% and 6.73% for Zn, respectively. Case and control samples were assayed together (in the same batch, each batch containing from three to 16 paired samples). Laboratory personnel were blind to case versus control status.

Cu and Zn levels in plasma and urine

The Cu and Zn concentrations (µg/L) determined in plasma were used in the statistical models. The Cu and Zn concentrations in urine were normalized before use in the models to account for between-women variations in urine production: the urine concentration obtained (µg/L) was multiplied by the 12-hour urine volume, so that levels in urine were expressed as the quantity excreted in 12 hours (µg-12h). The Cu/Zn ratio in plasma was calculated as the concentration of Cu divided by that of Zn. The Cu/Zn ratio in urine was calculated as the 12-hour excretion of Cu divided by the 12-hour excretion of Zn.

To explore associations of Cu and Zn in plasma and urine with breast cancer risk, plasma-urine indices were defined as follows: women in the upper tertile of both plasma and urine were assigned a plasma-urine index of 3; women in the lower tertile of both plasma and urine were assigned a plasma-urine index of 1; all others were assigned a plasma-urine index of 2.

Statistical methods

Conditional logistic regression models with adjustment for age (continuous) were used to estimate ORs, with 95% confidence intervals (CI) of being diagnosed with breast cancer in relation to tertiles (of the distribution in controls) of plasma and urine Cu, Zn, and Cu/Zn ratio (model 1). Fully-adjusted models (model 2) were additionally adjusted for BMI (continuous), years of education (<5, 5, 8, 10, 12, >12 years); alcohol intake (<0.25, 6–20, ≥20 g/day), lifetime cigarette smoking (continuous), history of breast cancer in first degree relatives (yes/no), and number of full-term pregnancies. To avoid losing participants, for 46 cases and 46 matched controls with missing covariates a “missing” category was added to the following covariates: education (one missing), BMI (seven missing), smoking (thirty-one missing), alcohol (six missing).

To lessen chances that reverse causality contributed to the relation between metal levels and breast cancer risk, 117 breast cancer cases (and 117 controls) in whom breast cancer was diagnosed within 2 years of baseline were excluded from the main models. As a consequence, these models for urine included 992 women (496 cases and 496 matched controls). An additional nine controls (and nine cases) with no plasma sample were excluded from the plasma models, which included 974 women (487 cases and 487 controls; Supplementary Fig. S1).

Models were also run that included as covariates: occupational exposure, inflammatory markers (C-reactive protein, interleukins 6, 8, 10), and medical conditions declared by the women at recruitment and considered pertinent (hypertension, liver cirrhosis, gallbladder stones, diabetes, myocardial infarction, stroke, and kidney stones). None of these covariates had any effect on associations and were not retained in the fully-adjusted models (model 2).

Finally, to elucidate how breast cancer risk changed over time, model 2 analyses were carried out on the entire series of cases and controls (613 cases and controls for urine analyses and 600 cases and controls for the plasma and plasma plus urine analyses) stratified into years passed (<2, 2–4, 5–9, 10–19, 20+) from recruitment to diagnosis.

Tests for linear trend across tertiles were performed assigning an ordinal value to each tertile. To test for differences by receptor status, a polytomous logistic regression model adjusted for matching variables was used, followed by a Wald test to compare relative risk (RR) of breast cancer per tertile of metal level in relation to receptor status (e.g., ER+ vs. ER–). Stata version 16 was used for all statistical analyses. All statistical tests were two-tailed. The study protocol was developed following the Declaration of Helsinki. Written informed consent was obtained from all participants before the enrolment in the study.

Data availability statement

The study data are held by the corresponding author, however their availability is restricted for ethical reasons. The Ethical Committee of the Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy, does not allow public sharing of data pertaining to individuals. However, aggregated data are available to other researchers, upon request. Requests should be sent to Dr Vittorio Krogh (vittorio.krogh@istitutotumori.mi.it).

Mean Cu levels in plasma and urine were similar in cases and controls. Mean Zn levels in plasma and urine were higher in controls than cases (Table 1). Correlations between urinary and serum levels of Cu, Zn, and Cu/Zn were low (Spearman's r = 0.11, 0.20, and 0.14, respectively). Baseline Cu and Zn levels in controls according to various characteristics are shown in Supplementary Table S1. Cu levels in plasma and urine, and Zn in urine, increased with age. Both metals in plasma and urine increased with BMI. Comparison of the baseline characteristics of excluded (for missing plasma or because diagnosed within 2 years of recruitment) and included participants did not suggest selection bias.

Table 1.

Baseline characteristics of women participating in nested case–control study within the ORDET cohort.

Cases (N = 496)Matched controls (N = 496)
Age (years), mean (SD) 49 (8)a 
Menopausal status, % (n
 Pre 56% (276)a 
 Peri 6% (29)a 
 Post 39% (191)a 
BMI (kg/m²), mean (SD) 25 (4) 26 (4) 
Education (years of schooling), % (n
 <5 6% (31) 5% (24) 
 5 45% (221) 43% (214) 
 8 22% (110) 23% (112) 
 10 8% (39) 9% (45) 
 12 11% (57) 11% (57) 
 >12 7% (37) 9% (44) 
Smoking status, % (n
 Never 67% (332) 64% (319) 
 Former 13% (62) 16% (78) 
 Current 21% (102) 20% (99) 
Occupational exposureb, % (n
 Never worked 8% (42) 5% (26) 
 Low 16% (78) 19% (95) 
 Medium 25% (126) 27% (133) 
 High 7% (34) 9% (46) 
 Very high 44% (216) 40% (196) 
Plasma concentration, mean (SD) 
 Cu (µg/L) 1,156 (197) 1,151 (203) 
 Zn (µg/L) 882 (180) 905 (185) 
 Cu/Zn ratio 1.35 (0.30) 1.31 (0.30) 
 C reactive protein (mg/dL) 1.82 (2.42) 1.73 (2.55) 
 IL6 (pg/mL) 15 (123) 16 (106) 
 IL8 (pg/mL) 191 (496) 225 (506) 
 IL10 (pg/mL) 0.33 (0.41) 0.33 (0.34) 
Urinary excretionc, mean (SD) 
 Cu (µg-12 h) 12 (7) 12 (6) 
 Zn (µg-12 h) 172 (113) 181 (126) 
 Cu/Zn ratio 0.094 (0.093) 0.088 (0.066) 
Cases (N = 496)Matched controls (N = 496)
Age (years), mean (SD) 49 (8)a 
Menopausal status, % (n
 Pre 56% (276)a 
 Peri 6% (29)a 
 Post 39% (191)a 
BMI (kg/m²), mean (SD) 25 (4) 26 (4) 
Education (years of schooling), % (n
 <5 6% (31) 5% (24) 
 5 45% (221) 43% (214) 
 8 22% (110) 23% (112) 
 10 8% (39) 9% (45) 
 12 11% (57) 11% (57) 
 >12 7% (37) 9% (44) 
Smoking status, % (n
 Never 67% (332) 64% (319) 
 Former 13% (62) 16% (78) 
 Current 21% (102) 20% (99) 
Occupational exposureb, % (n
 Never worked 8% (42) 5% (26) 
 Low 16% (78) 19% (95) 
 Medium 25% (126) 27% (133) 
 High 7% (34) 9% (46) 
 Very high 44% (216) 40% (196) 
Plasma concentration, mean (SD) 
 Cu (µg/L) 1,156 (197) 1,151 (203) 
 Zn (µg/L) 882 (180) 905 (185) 
 Cu/Zn ratio 1.35 (0.30) 1.31 (0.30) 
 C reactive protein (mg/dL) 1.82 (2.42) 1.73 (2.55) 
 IL6 (pg/mL) 15 (123) 16 (106) 
 IL8 (pg/mL) 191 (496) 225 (506) 
 IL10 (pg/mL) 0.33 (0.41) 0.33 (0.34) 
Urinary excretionc, mean (SD) 
 Cu (µg-12 h) 12 (7) 12 (6) 
 Zn (µg-12 h) 172 (113) 181 (126) 
 Cu/Zn ratio 0.094 (0.093) 0.088 (0.066) 

aMatching variable: values the same in cases and controls.

bFive ordinal categories from 1 (low metal exposure, e.g., those who have never worked) to 5 (high metal exposure, e.g., industrial worker).

cOvernight urinary excretion, over 12 hours.

Metal levels in plasma and urine were not associated with dietary Cu, Zn, or fiber intake (Supplementary Table S2).

Women in the highest tertile of plasma Cu/Zn ratio had greater breast cancer risk than those in the lowest tertile (OR, 1.75; 95% CI, 1.21–2.54; model 2, Ptrend = 0.003). Women whose Cu/Zn ratio was in the highest tertile of both plasma and urine were at even higher risk of breast cancer (OR, 2.37; 95% CI, 1.32–4.25; model 2, Ptrend < 0.004; Table 2).

Table 2.

Breast cancer risk in relation to Cu and Zn levels in plasma and urinea.

Model 1cModel 2d
NbOR (95% CI)OR (95% CI)
Plasma concentration (µg/L)e Mean (range)    
Cu 1st tertile 938 (511–1,057) 297 1.00 (—) 1.00 (—) 
 2nd tertile 1,137 (1,058–1,225) 361 1.45 (1.05–1.98) 1.42 (1.01–1.99) 
 3rd tertile 1,379 (1,225–1,942) 316 1.15 (0.82–1.63) 1.12 (0.77–1.63) 
 Ptrendf   0.459 0.553 
Zn 1st tertile 717 (487–802) 337 1.00 (—) 1.00 (—) 
 2nd tertile 872 (803–944) 321 0.99 (0.70–1.39) 0.95 (0.66–1.37) 
 3rd tertile 1,103 (945–1,827) 316 0.75 (0.49–1.12) 0.72 (0.47–1.10) 
 Ptrendf   0.155 0.130 
Cu/Zn ratio 1st tertile 1.00 (0.46–1.17) 289 1.00 (—) 1.00 (—) 
 2nd tertile 1.30 (1.18–1.43) 335 1.40 (1.00–1.96) 1.44 (1.01–2.05) 
 3rd tertile 1.65 (1.44–2.61) 350 1.69 (1.18–2.40) 1.75 (1.21–2.54) 
 Ptrendf   0.004 0.003 
Urine (µg-12 h)g Mean (range) n   
Cu 1st tertile 7.44 (3.44–9.10) 318 1.00 (—) 1.00 (—) 
 2nd tertile 10.62 (9.11–12.42) 344 0.92 (0.68–1.25) 0.97 (0.71–1.34) 
 3rd tertile 17.92 (12.43–70.89) 330 1.03 (0.75–1.41) 1.10 (0.78–1.56) 
 Ptrendf   0.872 0.575 
Zn 1st tertile 89 (15–127) 370 1.00 (—) 1.00 (—) 
 2nd tertile 159 (128–196) 317 0.70 (0.52–0.96) 0.68 (0.49–0.94) 
 3rd tertile 301 (197–1,179) 305 0.73 (0.53–1.00) 0.70 (0.50–0.97) 
 Ptrendf   0.035 0.035 
Cu/Zn ratio 1st tertile 0.04 (0.01–0.05) 304 1.00 (—) 1.00 (—) 
 2nd tertile 0.07 (0.06–0.09) 335 1.22 (0.90–1.66) 1.29 (0.93–1.79) 
 3rd tertile 0.16 (0.10–1.02) 353 1.20 (0.88–1.65) 1.29 (0.93–1.80) 
 Ptrendf   0.228 0.131 
Plasma and urine category Category scoreh    
Cu Low plasma and urine 105 1.00 (—) 1.00 (—) 
 Intermediate 758 1.20 (0.79–1.81) 1.27 (0.82–1.96) 
 High plasma and urine 111 1.47 (0.87–2.51) 1.64 (0.91–2.94) 
 Ptrendi   0.154 0.100 
      
Zn Low plasma and urine 167 1.00 (—) 1.00 (—) 
 Intermediate 681 0.67 (0.47–0.96) 0.63 (0.43–0.93) 
 High plasma and urine 126 0.60 (0.37–0.99) 0.56 (0.33–0.95) 
 Ptrendi   0.047 0.031 
Cu/Zn ratio Low plasma and urine 104 1.00 (—) 1.00 (—) 
 Intermediate 735 1.31 (0.85–2.01) 1.41 (0.90–2.23) 
 High plasma and urine 135 2.11 (1.21–3.66) 2.37 (1.32–4.25) 
 Ptrendi   0.008 0.004 
Model 1cModel 2d
NbOR (95% CI)OR (95% CI)
Plasma concentration (µg/L)e Mean (range)    
Cu 1st tertile 938 (511–1,057) 297 1.00 (—) 1.00 (—) 
 2nd tertile 1,137 (1,058–1,225) 361 1.45 (1.05–1.98) 1.42 (1.01–1.99) 
 3rd tertile 1,379 (1,225–1,942) 316 1.15 (0.82–1.63) 1.12 (0.77–1.63) 
 Ptrendf   0.459 0.553 
Zn 1st tertile 717 (487–802) 337 1.00 (—) 1.00 (—) 
 2nd tertile 872 (803–944) 321 0.99 (0.70–1.39) 0.95 (0.66–1.37) 
 3rd tertile 1,103 (945–1,827) 316 0.75 (0.49–1.12) 0.72 (0.47–1.10) 
 Ptrendf   0.155 0.130 
Cu/Zn ratio 1st tertile 1.00 (0.46–1.17) 289 1.00 (—) 1.00 (—) 
 2nd tertile 1.30 (1.18–1.43) 335 1.40 (1.00–1.96) 1.44 (1.01–2.05) 
 3rd tertile 1.65 (1.44–2.61) 350 1.69 (1.18–2.40) 1.75 (1.21–2.54) 
 Ptrendf   0.004 0.003 
Urine (µg-12 h)g Mean (range) n   
Cu 1st tertile 7.44 (3.44–9.10) 318 1.00 (—) 1.00 (—) 
 2nd tertile 10.62 (9.11–12.42) 344 0.92 (0.68–1.25) 0.97 (0.71–1.34) 
 3rd tertile 17.92 (12.43–70.89) 330 1.03 (0.75–1.41) 1.10 (0.78–1.56) 
 Ptrendf   0.872 0.575 
Zn 1st tertile 89 (15–127) 370 1.00 (—) 1.00 (—) 
 2nd tertile 159 (128–196) 317 0.70 (0.52–0.96) 0.68 (0.49–0.94) 
 3rd tertile 301 (197–1,179) 305 0.73 (0.53–1.00) 0.70 (0.50–0.97) 
 Ptrendf   0.035 0.035 
Cu/Zn ratio 1st tertile 0.04 (0.01–0.05) 304 1.00 (—) 1.00 (—) 
 2nd tertile 0.07 (0.06–0.09) 335 1.22 (0.90–1.66) 1.29 (0.93–1.79) 
 3rd tertile 0.16 (0.10–1.02) 353 1.20 (0.88–1.65) 1.29 (0.93–1.80) 
 Ptrendf   0.228 0.131 
Plasma and urine category Category scoreh    
Cu Low plasma and urine 105 1.00 (—) 1.00 (—) 
 Intermediate 758 1.20 (0.79–1.81) 1.27 (0.82–1.96) 
 High plasma and urine 111 1.47 (0.87–2.51) 1.64 (0.91–2.94) 
 Ptrendi   0.154 0.100 
      
Zn Low plasma and urine 167 1.00 (—) 1.00 (—) 
 Intermediate 681 0.67 (0.47–0.96) 0.63 (0.43–0.93) 
 High plasma and urine 126 0.60 (0.37–0.99) 0.56 (0.33–0.95) 
 Ptrendi   0.047 0.031 
Cu/Zn ratio Low plasma and urine 104 1.00 (—) 1.00 (—) 
 Intermediate 735 1.31 (0.85–2.01) 1.41 (0.90–2.23) 
 High plasma and urine 135 2.11 (1.21–3.66) 2.37 (1.32–4.25) 
 Ptrendi   0.008 0.004 

aBreast cancer risk as ORs estimated by conditional logistic regression analysis of women with breast cancer and matched controls.

b487 breast cancer cases and 487 controls for models that investigated trace metals in plasma; 496 breast cancer cases and 496 controls for models that explored trace metals in urine.

cModel 1: Adjusted for matching variables: age at enrollment (±3 years), menopausal status (pre-, peri-, postmenopausal), date of recruitment (±180 days), and laboratory batch; also adjusted for age (continuous).

dModel 2: Additionally adjusted for BMI (four categories), education (seven categories), alcohol intake (five categories), lifetime cigarette smoking (six categories), family history of breast cancer (yes/no), and number of full-term pregnancies (continuous).

eTrace metals in plasma (µg/L) measured using inductively coupled plasma mass spectrometry.

fTest for linear trend performed after assigning ordinal values to tertiles of metals.

gMetals in urine measured by inductively coupled plasma mass spectrometry. Levels (µg) in 12-hour urine were calculated as metal concentration (µg/L) in urine multiplied by quantity (L) of urine excreted over 12 hours.

hWomen in the lowest tertile of both plasma and urine levels were assigned score 1, women in the highest tertile of both plasma and urine levels were assigned score 3, everyone else scored 2.

iTest for linear trend performed after assigning ordinal values to categories of plasma plus urine levels.

High Cu levels were associated with increased breast cancer risk (OR, 1.64; 95% CI, 0.91–2.94 for women in the highest tertiles of Cu in plasma and urine compared with lowest). High Zn levels were associated with decreased breast cancer risk (OR, 0.56; 95% CI, 0.33–0.95 for women in the highest tertile of Zn in both plasma and urine compared with lowest; Table 2). Stratified polychotomous logistic regression modeling indicated no heterogeneity for breast cancer risk by plasma plus urine Cu/Zn category ratio in relation to either to ER, PR, or HER2 status (Supplementary Table S3).

Finally high Cu/Zn ratio was strongly associated with increased breast cancer risk in the 117 women diagnosed with breast cancer within 2 years of recruitment (OR, 5.73 95% CI, 1.37–23.87, highest tertile of both plasma and urine vs. lowest tertile; Ptrend = 0.017 model 2; Supplementary Table S4).

Our main finding is that women whose Cu/Zn ratio is >1.43 in plasma and also >0.09 in urine have more than twice the risk of subsequent breast cancer compared with women with ratios <1.17 in plasma and <0.05 in urine. We also found that Cu/Zn ratio was high in apparently healthy women who were nonetheless diagnosed with breast cancer within 2 years of recruitment. To our knowledge, no other studies have investigated Cu/Zn ratios in plasma and urine at the same time in relation to breast cancer risk. However a European cohort study (12) found that low plasma zinc combined with high plasma copper was associated with increased risk of all-site cancer mortality. Furthermore, a 2017 European nested case–control study found that high circulating Cu/Zn ratio was associated with increased risk of subsequent hepatocellular carcinoma (11); and a similar study on colorectal cancer found increased risk within 2 years of blood sampling (18).

Our finding that high Cu/Zn ratio is a strong risk factor for breast cancer development more than 2 years after sample donation deserves further investigation and if confirmed might open the way to interventions to reduce breast cancer risk in women with disrupted Cu/Zn homeostasis.

The second finding, that high Cu/Zn ratio is strongly associated with increased risk of breast cancer diagnosis within 2 years of recruitment is distinct from the now well-established finding that women with breast cancer typically have high circulating Cu, low Zn, and high Cu/Zn ratio (4) and suggests that high Cu/Zn ratio might be worth further investigation as a biomarker of breast cancer before it becomes clinically apparent.

We emphasize that although Cu was directly, and Zn inversely, related to breast cancer risk, these associations were considerably weaker than the increased risk associated with high Cu/Zn ratio, particularly when the ratio was high in both plasma and urine. Thus, high Cu/Zn ratio appears more important than levels of either of these trace metals alone in predicting breast cancer or increasing breast cancer risk. These findings are consistent with those of a case–control study (4) and an investigation of trace metal levels in urine as biomarker of breast cancer (19).

As regards the mechanisms of the link between altered Cu/Zn status and breast cancer, high circulating Cu and Cu/Zn ratio and low Zn have been associated with chronic inflammation in various human studies (20–25). There is considerable evidence that chronic inflammation in associated with increased risk of several cancers including breast cancer (26–28). A previous study on our ORDET cohort found that high levels of the inflammatory marker C reactive protein were associated with increased risk of postmenopausal breast cancer (29). However, in this study, adjustment for C reactive protein and also inflammatory interleukins did not modify associations of Cu and Zn with breast cancer risk.

It is also possible that altered Cu and Zn may have increased breast cancer risk via involvement in oxidative stress (7, 30). There is good evidence that increased oxidative stress is involved in breast carcinogenesis (31,32). Animal studies indicate that excess Cu, not contrasted by Zn, increases lipid peroxidation and depletes the glutathione reserve (33). Furthermore, Cu and Zn are cofactors for the superoxide dismutase SOD3, which converts the superoxide radical into less active hydrogen peroxide and oxygen. It is possible that disrupted Cu/Zn may disrupt the anti-oxidative function of SOD3 (34) thereby increasing cancer risk. It is noteworthy that SOD3 has been found to suppress the growth of human breast cancer cells (35).

A further possibility is that Cu and Zn levels may be genetically determined. Genome-wide association studies have identified some loci affecting blood Cu and Zn levels (36), however these loci seem not to be associated with breast cancer risk (37).

Strengths of this study are its prospective design. Our decision to exclude breast cancer cases diagnosed within the 2 years of recruitment enabled us to exclude the possibility that Cu and Zn levels were influenced by undiagnosed breast cancer (reverse causation). On the other hand, stratification by years from recruitment to diagnosis revealed that cases diagnosed within 2 years were characterized by high Cu/Zn ratio, suggesting that reverse causation could have been at work in this subgroup. Furthermore, since the laboratory batch is one of the case–control matching variables, the interday variability of the analytical method is not relevant in our study although it remains a source of variability to be considered in clinical practice. Another study strength is that detailed lifestyle, dietary, anthropometric, and other information was available permitting adjustment of the models for a variety of risk factors (in addition to matching factors)—all of which had negligible effects on risk estimates. Nevertheless, in view of the observational nature of this study, residual confounding cannot be completely discounted.

A study limitation is that metal levels were only available for a single time point. It is known that circulating levels of these metals are reliable markers of recent Cu5 and Zn6 status, but data on the long-term stability of circulating levels of these metals appear to be unavailable. As regards metal levels in urine, urinary Zn has been suggested as a good indicator of Zn status (38), whereas there appear to be no data on the reliability of a single measurement of Cu in urine as an indicator of Cu status in healthy humans. Overall it is fair to say that data are insufficient to conclude that levels of Zn and Cu in urine accurately reflect the status of these metals in the body over the long term (39, 40).

To conclude, our prospective findings provide further evidence that Cu and Zn homeostasis is altered prior to breast cancer diagnosis and suggest that increased Cu/Zn ratio in plasma and urine may be both an early marker of, and a risk factor for, breast cancer. Further studies are justified to confirm or otherwise our results and investigate the mechanism by which Cu and Zn imbalance promote the development of breast cancer.

Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, those authors alone are responsible for the views expressed in this article, which do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer or WHO.

M. Vinceti reports grants from Italian Association for Cancer Research during the conduct of the study. S. Sieri reports grants from Italian Association for Cancer Research during the conduct of the study. No disclosures were reported by the other authors.

V. Pala: Conceptualization, formal analysis, methodology, writing–original draft. C. Agnoli: Conceptualization, data curation, validation, writing–review and editing. A. Cavalleri: Data curation, validation, writing–review and editing. S. Rinaldi: Methodology, writing–review and editing. R. Orlandi: Methodology, writing–review and editing. F. Segrado: Validation, writing–review and editing. E. Venturelli: Validation, writing–review and editing. M. Vinceti: Writing–review and editing. V. Krogh: Supervision, methodology, writing–review and editing. S. Sieri: Conceptualization, supervision, funding acquisition, project administration, writing–review and editing.

The Italian Association for Cancer Research (AIRC) provided financial support for this study (grant no 17572 awarded to S. Sieri). We thank the women who participated in ORDET and Don Ward for help with the English. The authors are solely responsible for study design, data collection, analysis and interpretation, writing the article, and the decision to submit the article for publication. All authors contributed and read and approved the manuscript. The sponsor had no role in study design, data collection, data analysis, data interpretation, report writing, or the decision to publish.

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