Is Cadmium a Risk Factor for Breast Cancer – Results from a Nested Case–Control Study Using Data from the Malmö Diet and Cancer Study

It has been suggested that the heavy metal cadmium (Cd) is associated with increased risk of breast cancer, but results from epidemiologic studies are inconsistent, as reported below.

Many established risk factors for breast cancer are hormone- and pregnancy-related (1). It has been suggested that Cd is a metalloestrogen — a metal that binds to estrogen receptors (2), and mimics the actions of estrogen. The Cd concentration in breast cancer tissue seems to be higher than in normal breast tissue (3). Cd is proestrogenic in vivo (2, 4, 5). Cd is classified as a group 1 carcinogen by the International Agency for Research on Cancer (IARC) due to increased risk of lung cancer.

Nonsmokers in Sweden are exposed to Cd mainly through food (e.g., cereals, potatoes, vegetables, ref. 6). Cd in tobacco smoke contributes to higher blood Cd (BCd) levels (7). The uptake of Cd is higher among persons with iron deficiency, probably due to an upregulation of a common receptor in the intestine (8).

For dietary Cd (assessed using questionnaires), some studies found an association with breast cancer. In the population-based Swedish Mammography Cohort, the RR was 1.21, [95% confidence interval (CI), 1.07–1.36], for highest versus lowest tertile of dietary Cd intake (9). An Italian cohort study also showed an increased risk [HR, 1.54 (95% CI, 1.06–2.22)] for highest versus lowest quintile of dietary Cd (10). However, there are also studies showing no clear association (11–14).

Cd has a long half-life in the body (10–40 years) and exposure can be assessed using biomarkers of Cd in urine (UCd) or BCd (7). Using UCd, two case–control studies reported a doubled risk for breast cancer [OR, 2.29; 95% CI, 1.3–4.2 for UCd >0.58 vs. <0.26 μg/g creatinine (15), and OR, 2.69; 95% CI, 1.07–6.78, for UCd >0.60 vs. < 0.22 μg/g creatinine (16)]. There are, however, also studies showing no association (17, 18). Fewer studies have used BCd, but a recent study was negative, reporting a relative risk of breast cancer of 0.84 per μg/L (95% CI, 0.69–1.01; ref. 19). In summary, the literature on Cd and breast cancer is inconsistent.

The aim of this study is to investigate and quantify the association between breast-cancer risk and Cd exposure (measured by BCd in prediagnostic samples), using cases and controls from the large Swedish Malmo Diet and Cancer cohort with a follow-up time of around 20 years.

The population-based Malmö Diet and Cancer study (MDCS) has been described elsewhere (20, 21), but in short all men and women living in Malmö, Sweden, and born during 1923 to 1950 were invited between 1991 and 1996 to participate. A total of 17,035 women were recruited. Part of the cohort was reexamined during 2007 to 2012 (22).

Through the Swedish personal identification number, the women were linked to the Swedish Cancer Registry, and followed until first breast cancer diagnosis, death, emigration, or end of follow-up (December 31, 2014). Emigration was identified through the National Statistics registers. Breast cancer events, including carcinomia in situ, were assigned as International Classification of Diseases 7 (ICD7) = 170 (the Swedish cancer register uses the seventh revision of the ICD as the main code).

After excluding prevalent breast cancer cases (diagnosis before baseline), there were 1,292 women with a diagnosis registered after baseline and up to December 31, 2014. From the same cohort, 2,584 female controls were selected, frequency-matched for time of inclusion in MDCS (± 6 months), date of birth (± 6 months), and selected among those still alive at diagnosis of cases. This selection approach is called incidence density sampling (23).

Invasive tumors diagnosed between 1991 and 2004 were pathologically reevaluated regarding estrogen receptor (ER) and progesterone receptor (PgR) status by one senior pathologist (24, 25). For tumors from 2005 and onwards, this information was available from pathologic reports. ER and PgR were considered positive at a cutoff of >10% positively stained nuclei.

During baseline visits, anthropometric measurements were performed, and lifestyle factors, socioeconomic status, and medication were assessed using established questionnaires. Age at menarche, age at menopause, number of children were assessed. Menopausal status at baseline was defined as postmenopausal if menstruation had stopped more than 2 years prior to baseline, or if the woman had undergone bilateral oophorectomy. The status was defined as perimenopausal if the menstruation had stopped less than 2 years prior to baseline. For 2% of the women, the above information was not available and then the woman was classified as perimenopausal if she was 42 to 55 years at baseline, and as postmenopausal if she was over 55 at baseline (26). Smoking status was assessed at baseline (never smokers, former smokers, or current smokers). Number of smoking years and number of cigarettes per day were used to calculate pack-years. Diet data were collected through a 7-day menu book and a dietary questionnaire (27, 28), and fiber intake (g per day) was used in the present study. Leisure-time physical activity was classified from 18 questions (29, 30), and a summary score combined the intensity factors for each activity with the time spent on the activity, and the score was categorized into quartiles. Highest education was categorized into ≤9 years of schooling, 10 to 12 years, or university. Socioeconomic index was based on type of work (manual, nonmanual, employer, other). Information on oophorectomy was obtained from the Swedish National in-patient register. Fasting blood samples were drawn and stored in a biobank.

The fasting blood samples at baseline were drawn in heparin tubes and erythrocytes were stored frozen in cryovials. To measure the Cd concentrations, thawed erythrocytes were used, for all cases and controls in this study. In short, Cd was analyzed by inductively coupled plasma mass spectrometry with an octopole reaction system, operated in the helium collision cell mode (details published previously in ref. 31). The Cd level in blood was calculated by multiplying with hematocrit. Samples were analyzed in different rounds. Four lots of external quality control (QC) samples were included (Seronorm Trace Elements Whole Blood, Sero AS). The results compared with recommended limits were 0.34 ± 0.02 μg/L (N = 70) versus 0.32–0.40 μg/L (L-1, Lot no.1103128), 5.7 ± 0.18 μg/L (N = 70) versus 5.4–6.2 μg/L (L-2, Lot no.1103129), 0.27 ± 0.014 μg/L (N = 35) versus 0.17–0.40 μg/L (L-1, Lot no.1406263), 5.19 ± 0.181 μg/L (N = 35) versus 4.00–6.02 μg/L (L-2, Lot no.1406264). The imprecision was 9.6% (the coefficient of variation for 50 duplicate samples) and the detection limit, calculated as three times the SD of the blank, was 0.02 μg/L. None of the samples were below the limit of detection. An interlaboratory comparison showed good agreement (Pearson correlation coefficient 0.99, slope 1.04).

Multiple logistic regression was used (both conditional and unconditional) and BCd was included both as a continuous and categorical variable. Nonlinear associations were explored by specifying a spline (B-spline basis) in the EFFECT statement of the PROC LOGISTIC procedure (SAS Institute Inc). Comparisons between cases and controls were made using t tests or χ² tests.

Some of the risk factors for breast cancer, considered to have strong scientific support using the GRADE system (32), are late menopause, hormone replacement therapy, mutations in the BRCA1 and BRCA2 genes, and obesity (for postmenopausal breast cancer; Supplementary Table S1). Lifestyle factors are alcohol consumption (positive association), long breast-feeding, and physical activity (negative association; ref. 33).

Three regression models were fitted. In model 1, similar factors as in a recent study on breast cancer and the micronutrient selenium (26) were included, namely body mass index (BMI <20, 20–25, 25–30, or >30 kg/m²), age at first childbirth (<20, 20–25, 25–30, or >30 years), age at menarche (years), age at baseline (years), education (3 categories as described above), socioeconomic index (4 categories as described above), marriage (yes/no), number of children (0, 1, 2, 3, 4, or more), oophorectomy (yes/no), menopausal status (pre, peri, post), hormone replacement therapy (HRT, yes/no), use of oral contraceptives (yes/no), alcohol consumption (No alcohol, <15 g/day, 15–30 g/day, >30 g/day, infrequent use), smoking habits (current, former, never). Model 2 was based on the directed acyclic graph (DAG) in Supplementary Material, Supplementary Fig. S1, and adjusted for BMI, socioeconomic index, physical activity (4 categories as described above), menopausal status, age at first childbirth, age at baseline, and HRT. This was our main model. The third model included risk factors for breast cancer and determinants for Cd exposure, such as smoking and fiber consumption (metal concentrations are generally higher in whole grains than in refined products; ref. 34). This Model 3 included number of children, age at first childbirth, BMI, alcohol consumption, HRT, physical activity, and late menopause (above 50) as well as smoking and fiber consumption (g/MJ, proxy for Cd intake through food) and socioeconomic index.

First, the exposure-outcome curve was estimated (i) under assumption of a constant risk increase (per μg/L) and (ii) allowing the association to be nonlinear, through both categorization and a spline function. The associations in different receptor status groups were compared. Secondly, subgroup analyses were performed, based on smoking status, BMI, age at event, and for postmenopausal women. Between-group heterogeneity was assessed using the Cochran Q statistic, and tested by a χ² test.

The study was approved by the Ethics Committee at Lund University, Sweden (# 2015/283 and 2017/859).

BCd could be determined in 3,846 women (2,572 controls and 1,274 cases, of which 99 were in situ). The cases and controls had the same mean age at baseline, 56 years, and similar distributions on many variables, but HRT and nonmanual work was more common among the cases (Table 1). In the Supplementary Material, the available breast cancer risk factors (from Supplementary Table S1) are presented by BCd level among controls (Supplementary Table S2).

There were 3,559 women with complete data on all the variables (1,147 cases and 2,412 controls). In the conditional logistic regression, only the cases with at least one control are included in the analysis, leaving 3,290 observations. BCd was included as a linear effect, or categorized on the basis of the BCd cutoffs 0.40, 0.80, and 1.20 μg/L.

When all observations were used (n = 3,559), the OR was approximately 1.2 per μg/L of BCd (Table 2). But excluding the 5% highest BCd observations resulted in a lower risk estimate [OR, 1.14; 95% CI, 0.91–1.44 per μg/L (using Model 2 Supplementary Table S3)]. Among those with the highest BCd values, >1.20 μg/L, the OR was 1.34 (95% CI, 1.05–1.73; Table 3).

The association was further explored by a spline. The left panel of Fig. 1 shows an increasing OR above 1 μg/L, and the right panel shows that the OR is significantly increased above BCd 1.2 μg/L.

As the uptake of Cd is higher among persons with iron deficiency, hemoglobin was used as a marker for iron status (neither ferritin nor serum-Fe was available). Persons with hemoglobin <120 g/L were considered to be anemic. Excluding 95 women with anemia did not change the OR, compared with Tables 2 and 3 (Supplementary Table S4A and S4B).

The ER status among invasive tumors (excluding cancer in situ and bilateral tumors) was classified as ER⁺ in 762 cases (Table 1). The median BCd was 0.28 μg/L in ER⁺ cases and 0.25 μg/L in the 90 ER⁻ cases. Sixty percent were classified as PgR⁺ (Table 1). The median BCd was 0.28 μg/L both for PgR⁺ and PgR⁻ cases. For the 762 ER-positive tumors (and their controls), the OR was 1.34 (95% CI, 0.98–1.84) for BCd > 1.20 μg/L, which is similar to the overall OR. The regression estimates for the small ER-negative group were unstable. For PgR-positive and -negative tumors, the CIs were wide: for BCd > 1.20 versus BCd < 0.40 μg/L; OR, 1.42 (95% CI, 0.96–2.10) for PgR⁺, and OR, 1.12 (95% CI, 0.69–1.82) for PgR⁻ (details in Supplementary Tables S5A and S5B).

Including only the invasive tumors and their controls gave OR = 1.33 (95% CI, 1.01–1.75) for BCd > 1.20 versus BCd < 0.40 μg/L (Supplementary Tables S6A and S6B), which was similar to the results based on all observations.

As mentioned above, smoking is an important determinant for BCd levels, and separate analyses were conducted for never-smokers, ex-smokers, and current smokers. The distribution of BCd is very different between the smoking groups, making it difficult to find a common categorization. When the cut-offs 0.40, 0.80, and 1.20 were used (resulting in 443, 266, and 42 events respectively in the reference category for each smoking group), only the smokers had high BCd values and an OR of 1.34 (95% CI, 0.85–2.12). Common BCd categories for the 3 smoking groups means that OR for BCd > 1.20 μg/L could not be estimated among never- and ex-smokers (Table 4, Supplementary Table S7A). When assuming a constant risk increase per μg/L of BCd, no association was seen among never-smokers [OR, 1.00 per μg/L of BCd (95% CI, 0.49–2.03)]. Among ex-smokers, OR = 1.12 per μg/L of BCd (95% CI, 0.54–2.34). Among current smokers the OR was 1.25 per μg/L of BCd (95% CI, 1.02–1.53; (Supplementary Table S7B). At the reexamination from 2007 to 2012, smoking history was reassessed. In a sensitivity analysis, only those with a consistent smoking status (those where status did not change) were included and cases diagnosed before the reexamination were excluded (163 events) together with their controls, leaving 2,281 individuals with a complete set of covariates. The results (Supplementary Table S8A and S8B) were slightly stronger than for the whole group, especially when comparing BCd>1.20 with BCd<0.40 μg/L [OR, 1.53 (95% CI, 1.09–2.14)]. Adjusting for pack-years attenuated the association with breast cancer slightly (see Fig. 2).

Among never-smokers, 53% had a BMI above 25 (16% had BMI>30), 37% among current smokers (10% had BMI>30), and 49% among ex-smokers (14% had BMI>30). The association between BCd and breast cancer was stronger among women with BMI above 25, see Fig. 2.

At baseline, 61% of the women were classified as having postmenopausal status and 8% as perimenopausal (Table 1). The median BCd levels among the postmenopausal women were very similar for the 730 cases (0.26 μg/L) and the 1,570 controls (0.27 μg/L). Compared with the whole group (n = 3,559), the OR was lower when analyzing the postmenopausal group (Fig. 2; complete results in Supplementary Table S9A and S9B).

The median age at event was 66 years (range 45.7–91.4). In a comparison based on the age at event of the cases (≤66 vs. >66 years at event), the OR was higher in the older group [OR, 1.80 (95% CI, 1.22–2.65); see Fig. 2].

In this Swedish population-based case–control study, we found an association between breast cancer risk and BCd, but only among women with BCd>1.20 μg/L. This association was mainly due to an association in smokers. At BCd<1.0 μg/L, the OR was not increased. Among the cases, only 8% were classified as perimenopausal at baseline, and more than 50% as postmenopausal, and in postmenopausal women adipose tissue is a source of estrogen production. Among women with BMI above 25 in our study, the OR was 1.50 (95% CI, 1.04–2.19) when comparing BCd>1.20 and BCd<0.40 μg/L. Only 11% of the invasive tumors were classified as ER⁻ and thus no clear conclusion could be made regarding differential risk due to estrogen receptor status, only that the ER⁺ group had higher BCd.

Cd is considered to have estrogen-like properties, (35), and to mimic estradiol by binding to the ER alpha and activating it (36–39). According to IARC, it is not yet known whether or not Cd promotes tumor growth by an estrogen-mediated mechanism (40). Other suggested mechanisms behind Cd as a carcinogen are disturbances of DNA repair and changes in DNA methylation patterns (40–42). Cd levels among women are often higher than for men, as a result of low iron stores (43). Previous epidemiologic studies, where the exposure assessment was based on dietary intake or UCd, have shown mixed results for Cd as a risk factor for breast cancer. Several case–control studies have reported increased risk: for highest versus lowest tertile of Cd intake, (44) reported OR = 1.23 (95% CI, 0.76–2.00), and for high versus low UCd levels, (45) reported OR = 6.05 (95% CI, 2.90–12.62) and (46) reported OR = 1.62 (95% CI, 1.19–2.21). U.S. studies compared UCd approximately above 0.6 to approximately below 0.24 μg/g creatinine and reported a pooled OR of 2.4 (95% CI, 1.55–3.72; ref. 47). But there are also studies that report no clear association with UCd (18, 48, 49), nor with dietary Cd (49).

Studies where the exposure was measured by BCd have also shown contradictory results. A Chinese case–control study reported higher BCd concentration among cases compared with controls (2.28 and 1.77 μg/L, respectively; ref. 50). A National Health and Nutrition Survey (NHANES)-based study indicated a higher self-reported prevalence of breast cancer, OR = 1.29 (95% CI, 0.73–2.28), among women with BCd>0.6 compared with BCd<0.3 μg/L (51). Our results, however, indicated a higher risk of breast cancer among women with BCd levels (above 1.2 μg/L), OR = 1.34 (95% CI, 1.05–1.73), which was not found in the meta-analysis by Gaudet, where the RR was 0.59 (95% CI, 0.39–0.91) when comparing BCd levels above 2 μg/L and below 0.49 μg/L (19). In our study, the BCd levels (median 0.28 μg/L) are considerably lower than in studies from China, where median blood concentrations around 2–3 μg/L was reported among patients with breast cancer, and median 1.77 μg/L among controls (50, 52).

Assessing Cd exposure using dietary questionnaires provide only a rough estimate of long-term exposure to Cd, (53), whereas both UCd and BCd are well established, and provide reliable measures of exposure and body burden (7, 54). The half-life of UCd is longer than that of BCd (7).

In our study, the median BCd was higher among ER-positive tumors (0.28 μg/L) than in ER-negative tumors (0.25 μg/L). Since most of the classified tumors were ER-positive (762 of 852), we had no power to compare the receptor groups. For the ER⁺ group, OR was 1.34 (95% CI, 0.98–1.84) for BCd>1.20 μg/L, which is similar to the results for all the tumors. Two previous case-control studies both reported stronger associations among ER-positive breast cancers: OR = 1.79 (1.30–2.46) when comparing UCd>0.33 with UCd<0.18 μg/g creatinine, (55), and using Cd intake, OR = 1.94 (95% CI, 1.04–3.63) when comparing high (31.5 μg/day) and low (21.4 μg/day) (44). For BCd>1.20 compared with BCd<0.40 μg/L, our study consistently showed higher (nonsignificant) risk estimates in the PgR⁺ group, compared with PgR⁻. This was also reported in ref. 55, where OR was 1.88 (95% CI, 1.30–2.74), for UCd > 0.33 versus UCd < 0.18 μg/g creatinine. Also in a prospective study, the risk estimates (HR) were higher in the receptor-positive groups, although the heterogeneity was not significant (10).

A previous cohort study reported overrepresentation of ER-negative tumors among smokers and former smokers (56). In our study, however, the proportion of ER-negative tumors was only 9% among current and former smokers, and 12% among never smokers. If smokers are more likely to have ER-negative tumors, then the mechanism would not be through binding to the ER. Smoking is complicated, since it is an important source of Cd as mentioned above, and therefore a vast majority of the women with the highest BCd levels (>1.20 μg/L) were smokers, 311 of 322. For another estrogen-based cancer, endometrial cancer, cigarette smoking was associated with a reduced risk, especially among postmenopausal women (57). However, as obesity is a possible factor for endometrial cancer, (58), the reduced risk among smokers might be due to less adipose tissue and therefore decreased estrogen levels.

Smokers and nonsmokers differ regarding dietary habits, but the association between diet and breast cancer is not clear. For fat intake and breast cancer risk, a case–control study (nested within MDCS) found different associations depending on how fat intake was assessed (59, 60). According to a review (33), there is only limited evidence that intake of, for example, nonstarchy vegetables or dairy products, is related to breast cancer risk. It is difficult to assess how different diets in smokers and nonsmokers will affect associations between BCd and breast cancer risk. Smoking is not considered a risk factor according to recent reviews (33, 61), which would indicate that the increased BCd-related risk we found among smokers could be due to their high BCd levels.

The three covariate models mostly gave similar results, which indicates robustness. As smoking is a large source of Cd, it could be argued that models 1 and 3 imply an over-adjustment by including smoking. However, if we consider those results that had a P value for OR smaller than 0.10, the ORs are often slightly higher for models 1 and 3. Model 2 often has the narrowest CI, which is probably an effect of fewer variables included. Our study is a population-based case-control study, where the two matched controls were selected from the same cohort as the cases. Both unconditional and conditional logistic regression was used, where the latter uses the discordant pairs (the pairs where case and control have different exposure).

Some limitations should be acknowledged. The heparin tubes used for blood collection were not tested for possible contamination of Cd. The BCd concentration in this study are, however, consistent with results from other studies in Sweden, and no outlier with unexpectedly high concentrations were found. BCd and covariates were assessed at baseline, and may have changed before the breast cancer diagnosis date. Although BCd is affected partly by recent Cd exposure, it is also a good biomarker of body burden if smoking and dietary habits have not changed recently. For example, in US former smokers, the correlation with previous exposure (pack-years of smoking) was in fact higher for BCd than for UCd (62). Moreover, in this study, a sensitivity analysis based on only women with consistent smoking pattern, also showed an increased risk for BCd>1.20 μg/L [OR, 1.53 (95% CI, 1.09–2.14)]. The distribution of BCd is skewed with few high values, making the estimation of the exposure-risk curve difficult. In addition to including Cd as a continuous variable, we categorized Cd, and used a spline to illustrate the exposure–response relationship.

A strength is that we used prediagnostic exposure to avoid reverse causation. Bias was avoided by selecting all cases in the MDCS cohort, by selecting controls from the same cohort as the cases and by not having selection criteria related to the exposure (63). A further strength was that the controls were matched on birthday within +-6 months, ensuring similar age ranges. The cases were identified from the Swedish National Cancer Registry, which has shown a high level of completeness and a low underreporting for breast cancer (64).

In conclusion, the results provide support for a nonlinear dose–response relation between Cd exposure and breast cancer, with a threshold for increased risk at relatively high Cd exposure, which in the present cohort was the case mainly in smokers. On the individual level, Cd exposure can be reduced through smoking cessation. A reduction of Cd levels in common foods (cereals, potatoes) is warranted, for example, by using fertilizers with low Cd concentration.

E.M. Andersson reports grants from Swedish Research Council for Health, Working Life and Welfare (FORTE) during the conduct of the study. L. Barregard reports grants from The Sahlgrenska University Hospital, Region Västra Götaland under the ALF agreement during the conduct of the study. No disclosures were reported by the other authors.

E.M. Andersson: Software, formal analysis, funding acquisition, visualization, methodology, writing–original draft, project administration, writing–review and editing. M. Sandsveden: Investigation, writing–review and editing. N. Forsgard: Resources, investigation, writing–review and editing. G. Sallsten: Conceptualization, methodology, writing–review and editing. J. Manjer: Data curation, validation, methodology, writing–review and editing. G. Engström: Data curation, methodology, writing–review and editing. L. Barregard: Conceptualization, funding acquisition, methodology, writing–review and editing.

This study was funded by the Swedish Research Council for Health, Working Life and Welfare (FORTE, # 2017-00519), by the foundation “Allmänna sjukhusets i Malmö stiftelse för bekämpande av cancer”, and by The Sahlgrenska Univertsity Hospital, Region Västra Götaland under the ALF agreement (ALFGBG-924961).

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