Vol.:(0123456789) Cancer Causes & Control https://doi.org/10.1007/s10552-021-01511-4 ORIGINAL PAPER Differences in breast cancer‑risk factors between screen‑detected and non‑screen‑detected cases (MCC‑Spain study) Marta Hernández‑García1 · Ana Molina‑Barceló1  · Mercedes Vanaclocha‑Espi1 · Óscar Zurriaga2,3,4,5 · Beatriz Pérez‑Gómez5,6 · Nuria Aragonés5,7 · Pilar Amiano5,8,9 · Jone M. Altzibar8,10 · Gemma Castaño‑Vinyals5,10,11,12 · María Sala11,13 · María Ederra5,14,15 · Vicente Martín5,16 · Inés Gómez‑Acebo5,17 · Carmen Vidal5,18,19 · Adonina Tardón5,20 · Rafael Marcos‑Gragera5,21,22 · Marina Pollán5,23 · Manolis Kogevinas5,10,11,12 · Dolores Salas1,4,5 Received: 30 December 2020 / Accepted: 15 October 2021 © The Author(s) 2021 Abstract Purpose The variation in breast cancer (BC)-risk factor associations between screen-detected (SD) and non-screen-detected (NSD) tumors has been poorly studied, despite the interest of this aspect in risk assessment and prevention. This study ana- lyzes the differences in breast cancer-risk factor associations according to detection method and tumor phenotype in Spanish women aged between 50 and 69. Methods We examined 900 BC cases and 896 controls aged between 50 and 69, recruited in the multicase–control MCC- Spain study. With regard to the cases, 460 were detected by screening mammography, whereas 144 were diagnosed by other means. By tumor phenotype, 591 were HR+, 153 were HER2+, and 58 were TN. Lifestyle, reproductive factors, family history of BC, and tumor characteristics were analyzed. Logistic regression models were used to compare cases vs. controls and SD vs. NSD cases. Multinomial regression models (controls used as a reference) were adjusted for case analysis accord- ing to phenotype and detection method. Results TN was associated with a lower risk of SD BC (OR 0.30 IC 0.10–0.89), as were intermediate (OR 0.18 IC 0.07–0.44) and advanced stages at diagnosis (OR 0.11 IC 0.03–0.34). Nulliparity in postmenopausal women and age at menopause were related to an increased risk of SD BC (OR 1.60 IC 1.08–2.36; OR 1.48 IC 1.09–2.00, respectively). Nulliparity in postmenopausal women was associated with a higher risk of HR+ (OR 1.66 IC 1.15–2.40). Age at menopause was related to a greater risk of HR+ (OR 1.60 IC 1.22–2.11) and HER2+ (OR 1.59 IC 1.03–2.45) tumors. Conclusion Reproductive risk factors are associated with SD BC, as are HR+ tumors. Differences in BC-risk factor associa- tions according to detection method may be related to prevailing phenotypes among categories. Keywords Breast neoplasm · Risk factors · Early detection of cancer · Phenotype Introduction Breast cancer (BC) is the most common tumor among women worldwide. An estimated 2,261,419 new cases occurred globally in 2020 [1]. The main associated risk fac- tors include nulliparity, early menarche, and late menopause (all three factors related to endogenous hormone exposure), as well as alcohol consumption, being overweight, obesity, and lack of physical activity [2–6]. High-penetrance gene mutations (BRCA1, BRCA2, TP53, PTEN) considerably increase BC risk, however, they only account for a small proportion of the disease burden given their low frequency in the population [2, 4, 5]. Breast cancer is a heterogeneous disease; it includes sev- eral entities with a different natural history and response to treatment. Breast tumors can be classified into pheno- types based on the expression of hormone receptors and human epidermal growth factor receptor 2 in tumor cells [7]. Reproductive behavior influences the development of HR+ (hormone receptor positive) tumors as it modifies estrogen levels, whereas lifestyle-related factors (body mass index [BMI], tobacco, alcohol, and physical activity) similarly affect different tumor subtypes [8–10]. In addition, HR+ breast carcinomas often have smaller tumors and are in an * Ana Molina-Barceló molina_anabar@gva.es Extended author information available on the last page of the article Cancer Causes & Control 1 3 early stage at diagnosis, features that are associated with a better prognosis [9, 11]. In contrast, HER2+ (human epi- dermal growth factor receptor 2 positive) and TN (triple negative) carcinomas have larger tumors and are in a more advanced stage at diagnosis [9]. Additionally, TN is associ- ated with aggressive metastatic breast cancer, partly due to its contribution to the rapid progression of the disease [12]. Through European Council Recommendation of 2 December 2003 on cancer screening [13], the EU advises the implementation of systematic population-based breast can- cer screening programs as part of prevention strategies. By performing diagnostic tests on an asymptomatic population, screening allows for early detection of the disease, thereby helping to reduce the impact on mortality [14–16]. In Spain, these programs have been included in the Spanish National Health System’s service portfolio since 2014, although they have been progressively implemented since the 1990s in dif- ferent regions. They consist of biennial mammography and the target population is women aged 50–69. According to 2017 data [17], coverage is approximately 85% and the par- ticipation rate is over 70%. Nevertheless, uptake is unequal among individuals; women living in the least deprived areas are more likely to participate in breast cancer screening pro- grams than women living in the most deprived areas. This is not only due to their limited knowledge about mammogra- phy or misconceptions, but also to practical barriers such as difficulties attending mammography appointments or family commitments [18]. Apart from population-based screening programs, part of the target population participates in oppor- tunistic breast cancer screening practices [19, 20]. These are individual, non-systematic population-based interventions carried out at the patient’s request or arising from a visit for another medical reason. Tumor characteristics according to detection method have been previously studied. By comparison, non-screen- detected BC (either due to a failure to participate in screen- ing programs or the appearance of interval cancer) is asso- ciated with more advanced stages at diagnosis, aggressive tumors, and poorer prognoses, whereas screen-detected tumors are more frequently diagnosed at an early stage, are of a small size, and express hormone receptors [21–24]. A previous study in the United States has shown dif- ferences in breast cancer-risk factor associations by the method of detection [25]. However, there are few studies on variation in breast cancer-risk factor associations between screen-detected and non-screen-detected tumors, and there- fore there is little information available on this matter. This study examines whether the screening method affects risk factor associations, regardless of tumor phenotype. Thus, we aim to analyze the differences in breast cancer- risk factor associations according to method of detection and tumor phenotype in a population of Spanish women aged 50–69. Material and methods Study population MCC-Spain is a population-based multicase–control study aimed at investigating the influence of environmental and genetic factors on different tumors (gastric, colorectal, breast, prostate, and chronic lymphocytic leukemia) [26]. Cases and population controls were recruited between 2008 and 2013 in twelve Spanish regions. Incident cases were actively recruited at 23 collaborating hospitals and population controls were contacted by telephone and invited to take part in the study, after being randomly selected from primary care center lists within the catch- ment area of these 23 hospitals. Cases had no prior his- tory of cancer and they were identified as soon as possi- ble after the diagnosis was made through an active search that included regular visits to the collaborating hospital departments. Only histologically confirmed incident can- cer cases, including breast cancer, were included in the study (International Classification of Diseases 10th Revi- sion: C50, D05.1, D05.7). Controls were frequency-matched to cases by age, sex, and region, ensuring that there was at least one control of the same sex and within the same five-year age interval for each case in each region. Participants had resided within the hospital catchment areas for at least 6 months. Cases and controls answered an epidemiological questionnaire in a personal interview. The questionnaire (available at http:// www. mccsp ain. org) focused on socio-demographic characteristics, lifestyle, medical history, and family history of cancer. Additionally, subjects were provided with a semi-quantitative Food Fre- quency Questionnaire (FFQ), which was a modified ver- sion of a previously validated instrument in Spain [27]. It estimated regular dietary intake during the previous year. Clinical and pathological information on the diagnosis and treatment of tumors was collected from hospital records. We included 1.796 women in the study in this analy- sis, 896 controls and 900 BC cases. Inclusion criteria for cases were BC tumor histologically diagnosed during the recruitment period, home address in one of the collabo- rating hospitals’ catching area, age range 50–69. Cases exclusion criteria were BC diagnosis previous to patient recruitment period, inability to communicate in Spanish, and physical incapacity to take part in the study. By phe- notype, 591 were HR+ tumors, 153 were HER2+, and 58 were TN (phenotype data were missing for 98 cases). By self-referred detection method, 460 cases were screen- detected either by organized or spontaneous screening mammography (average age 59,04 years old), and 144 cases were non-screen-detected (average age 57,88 years Cancer Causes & Control 1 3 old). Detection method data were missing and/or incon- sistent for 296 cases. The age range was defined in accord- ance with European Council recommendations on cancer screening programs [13]. Definition of variables. Detection method: BC cases were classified into screen- detected and non-screen-detected tumors, based on the answers provided in the epidemiological questionnaire. BC cases were considered screen-detected whenever participants declared that the tumor had been diagnosed by screening (organized or spontaneous screening mammography per- formed in the absence of symptoms) and, additionally, that this mammogram had been performed in the last 6 months. On the contrary, BC cases were classified as non-screen- detected when patients declared that the tumor had not been diagnosed by mammogram screening and, additionally, that they had not undergone a screening mammography in the last 6 months. Using a simplified version of the St Gallen International Consensus [28], breast tumors were classified into the fol- lowing phenotypes: HR+ (hormone receptor positive), HER2+ (human epidermal growth factor receptor 2 positive, regardless of hormone receptors), and TN (triple negative, i.e., negative for progesterone and estrogen receptors, and human epidermal growth factor receptor 2 negative). Other tumor characteristics included the stage at diagnosis (early − 0, IA, IB −; intermediate − IIA, IIB −; and advanced − IIIA, IIIB, IIIC, IV −), extension (in situ, invasive), and histology (ductal, lobular, other). Epidemiological factors included reproductive factors (age at menarche, nulliparity, age at first birth, menopausal status, and age at menopause), history of breast cancer in first-degree relatives, and lifestyles factors (alcohol con- sumption over the year prior to the interview, tobacco use a month before the interview, leisure-time physical activity over a ten-year period up to two years prior to the interview, BMI at recruitment, and fruit and vegetable consumption over the year prior to the interview). Statistical analysis A descriptive analysis of tumor characteristics and epide- miological factors was conducted for screen-detected/non- screen-detected cases, total cases/controls, screen-detected cases/controls, non-screen-detected cases/controls, HR+ cases/controls, HER2+ cases/controls, and TN/controls. Frequencies and percentages were calculated, and signifi- cant differences were tested using the Chi-square test for categorical variables and the Wilcoxon test for continuous variables. Adjusted odds ratios (OR) and 95% confidence intervals (CI) were calculated using multivariate logistic regression models to analyze the association between epidemiologi- cal factors and overall BC risk, as well as to investigate the differences in screen-detected/non-screen-detected cases related to tumor characteristics and epidemiological factors. A multinomial logistic regression model was conducted to examine whether risk factor associations vary according to method of detection. An additional multinomial logistic regression model was used to analyze variation in risk factor associations by tumor phenotype. In both models, controls were used as a reference group. The exponential multinomial logarithmic coefficient provided an estimate of relative risk, expressed here as the relative risk (RR) coefficient. p values were considered statistically significant below 0.05. All statistical analyses were performed using the sta- tistical software R (version 3.6.0). Analyses were adjusted by region, educational level, age, and menopausal status. In order to assess the age-related risk factors of meno- pause and nulliparity, analyses were performed for the overall sample, for postmenopausal women, and for parous women. Results Sample description Table 1 shows the results of the descriptive analysis. Statis- tically significant differences are observed in the associa- tion with epidemiological factors and tumor characteristics (only for cases), with heterogeneous results depending on the method of detection and phenotype. Comparative analysis of tumor characteristics for screen‑detected and non‑screen‑detected breast cancer The multivariate logistic regression analysis for screen- detected/non-screen-detected tumors (Table  2) shows significant differences in terms of tumor characteristics. Screen-detected breast cancers are less likely to be diag- nosed at intermediate (OR = 0.23; 95%CI 0.11–0.47) and advanced stages (OR = 0.14; 95%CI 0.06–0.36) than non-screen-detected breast cancers. Similar results are obtained for postmenopausal women and parous women, both for intermediate (OR = 0.18; 95%CI 0.07–0.44 and OR = 0.21; 95%CI 0.09–0.52, respectively) and advanced stages (OR = 0.11; 95%CI 0.03–0.34 and OR = 0.18; 95%CI 0.06–0.53, respectively). In comparison with HR+ tumors, TN tumors (OR = 0.30; 95%CI 0.10–0.89) are less likely to be screen-detected. Similar results are found for parous women (OR = 0.18; 95%CI 0.05–0.67). Cancer Causes & Control 1 3 Table 1 Sample description Cases by detection method Cases by tumor phenotype Controls Cases SD NSD HR+ HER2+ TN n (%) n (%) n (%) n (%) n(%) n(%) n(%) Total 896 900 460 144 591 153 58 Tobacco use  Never 491 (54.9) 509 (56.7) 258 (56.2) 88 (61.5) 331 (56.2) 91 (59.5) 36 (62.1)  Former smoker 227 (25.4) 244 (27.2) 127 (27.7) 33 (23.1) 157 (26.7) 41 (26.8) 18 (31)  Smoker 177 (19.8) 145 (16.1) 74 (16.1) 22 (15.4) 101 (17.1) 21 (13.7) 4 (6.9) Alcohol, g/ day (median, Q1–Q3) 1.9 (0.0–8.8) 2.0 (0.0–9.6) 1.9 (0.0–9.1) 1.8 (0.0–9.7) 2.4 (0.0–9.6) 1.2 (0.0–8.9) 1.2 (0.0–8.2) BMI, kg/m2 (median, Q1–Q3) 25.2 (22.5–28.6) 26.2 (23.4– 29.7)a 26.1 (23.4– 29.2)b 26.7 (23.9– 29.9)b 26.2 (23.5– 29.7)c 26.0 (23.3– 29.3)c 26.1 (23.3– 29.8) Physical activ- ity (MET*h/ week)  Inactivity 346 (38.8) 346 (38.4) 146 (31.7)b 69 (47.9)b 225 (38.1) 68 (44.4) 18 (31.0)  Low activity 151 (16.9) 152 (16.9) 71 (15.4) 33 (22.9) 96 (16.2) 29 (19.0) 9 (15.5)  Moderate activity 134 (15.0) 114 (12.7) 55 (12.0) 16 (11.1) 75 (12.7) 19 (12.4) 8 (13.8)  High activity 261 (29.3) 288 (32.0) 188 (40.9) 26 (18.1) 195 (33) 37 (24.2) 23 (39.7) Fruits and vegetables intake, g/ day (median, Q1-Q3) 556.7 (393.7–718.4) 556.7 (393.6–718.4) 534.2 (368.6–742.9) 547.3 (387.8–732.5) 537.4 (364.2–756.3) 548.2 (406.9–729.8) 559.8 (456.3–769.6) Age at menarche  ≤ 12 years 390 (45.3) 419 (47.0) 211 (46.1) 74 (52.1) 278 (47.4) 68 (45.0) 24 (41.4)  > 12 years 471 (54.7) 472 (53.0) 247 (53.9) 68 (47.9) 308 (52.6) 83 (55.0) 34 (58.6) Nulliparity  Yes 133 (14.9) 157 (17.5) 89 (19.3)b 20 (14.1) 486 (82.4) 125 (82.2) 49 (84.5)  No 762 (85.1) 741 (82.5) 371 (80.7) 122 (85.9) 104 (17.6) 27 (17.8) 9 (15.5) Age at first birth1 (median, Q1-Q3) 25 (23–28) 25 (23–28) 26 (23–29)b 26 (22–27)b, d 25 (23–28) 25 (22–29) 26 (23–30) Age at menopause2  ≤ 50 years 446 (65.3) 402 (57.3)a 208 (57.3)b 67 (62.6) 256 (56)c 71 (55.9) 32 (71.1)  > 50 years 237 (34.7) 299 (42.7) 155 (42.7) 40 (37.4) 201 (44) 56 (44.1) 13 (28.9) Education level  Less than primary school 113 (12.6) 126 (14.0) 59 (12.8) 23 (16.0) 91 (15.4) 20 (13.1) 6 (10.3)  Primary school 324 (36.2) 344 (38.2) 173 (37.6) 53 (36.8) 225 (38.1) 58 (37.9) 24 (41.4)  Secondary school 293 (32.7) 286 (31.8) 155 (33.7) 47 (32.6) 186 (31.5) 49 (32.0) 19 (32.8)  University and higher 166 (18.5) 144 (16.0) 73 (15.9) 21 (14.6) 89 (15.1) 26 (17.0) 9 (15.5) Family history of BC Cancer Causes & Control 1 3 Table 1 (continued) Cases by detection method Cases by tumor phenotype Controls Cases SD NSD HR+ HER2+ TN n (%) n (%) n (%) n (%) n(%) n(%) n(%)  Yes 89 (10.3) 118 (13.5)a 63 (14.2)b 12 (8.7) 499 (87.4) 128 (84.8) 49 (87.5)  No 778 (89.7) 754 (86.5) 381 (85.8) 126 (91.3) 72 (12.6) 23 (15.2) 7 (12.5) Phenotype – – – –  HR+ 591 (73.7) 303 (75.2) 85 (63.9)d  HER2+ 153 (19.1) 73 (18.1) 31 (23.3)  TN 58 (7.2) 27 (6.7) 17 (12.8) Stage at diagnose –  Early 390 (54.2) 226 (61.6) 43 (37.7)d 273 (53.6) 53 (48.2) 20 (44.4)  Intermediate 245 (34.0) 109 (29.7) 49 (43.0) 175 (34.4) 41 (37.3) 17 (37.8)  Advanced 85 (11.8) 32 (8.7) 22 (19.3) 61 (12.0) 16 (14.5) 8 (17.8) Extension –  In situ 111 (12.7) 64 (14.1) 11 (8.2)d 26 (4.5) 18 (12.0) 1 (1.8)e  Invasive 765 (87.3) 390 (85.9) 123 (91.8) 550 (95.5) 132 (88.0) 54 (98.2) Histology –  Ductal 671 (86.3) 340 (86.3) 113 (87.6) 471 (84.1) 127 (96.2) 49 (87.5)e  Lobular 55 (7.1) 25 (6.3) 9 (7.0) 47 (8.4) 5 (3.8) 2 (3.6)  Other 52 (6.7) 29 (7.4) 7 (5.4) 42 (7.5) 0 (0.0) 5 (8.9) 1 Only parous women 2 Only postmenopausal women a p value < 0.05, Chi-squared/ Wilcoxon test between control population and cases b p value < 0.05, Chi-squared/ Wilcoxon test between control population and cancer subtype according to detection method c p value < 0.05, Chi-squared/ Wilcoxon test between control population and cancer subtype according to phenotype d p value < 0.05, Chi-squared/Wilcoxon test between cancer subtypes according to detection method (screen-detected vs. non-screen-detected) e p value < 0,05, Chi-squared/Wilcoxon test between cancer subtypes according to phenotype (HR+, HER2+, and TN) Table 2 Tumor characteristics according to detection method, for the overall sample, postmenopausal women, and parous women 1 OR adjusted for region, educational level, age, and epidemiological factors 2 OR adjusted for region, educational level, age, epidemiological factors (except menopausal status) 3 OR adjusted for region, educational level, age, epidemiological factors (except nulliparity) *p value < 0.05 Screen-detected BC vs. Non-screen-detected Overall sample Postmenopausal Parous women OR1 (95%CI) OR2 (95%CI) OR3 (95%CI) Phenotype (ref: HR+) HER2+ 1.14 (0.47–2.77) 0.70 (0.25–1.93) 1.11 (0.38–3.24) TN 0.30 (0.10–0.89)* 0.37 (0.10–1.34) 0.18 (0.05–0.67)* Stage (ref: early) Intermediate 0.23 (0.11–0.47)* 0.18 (0.07–0.44)* 0.21 (0.09–0.52)* Advanced 0.14 (0.06–0.36)* 0.11 (0.03–0.34)* 0.18 (0.06–0.53)* Histology (ref: ductal) Lobular 1.26 (0.31–5.11) 2.85 (0.36–22.89) 3.11 (0.47–20.73) Other 1.02 (0.25–4.21) 0.48 (0.09–2.52) 1.58 (0.23–10.70) Cancer Causes & Control 1 3 Breast cancer risk by detection method Table 3 shows breast cancer-risk factor associations accord- ing to detection method. The multinomial logistic regres- sion model for the overall sample reveals that BMI was associated with a 5% greater risk of screen-detected BC (RR = 1.05; 95%CI 1.02–1.08) and a 4% greater risk of non-screen-detected BC (RR = 1.04; 95%CI 1.00–1.08). Similar results are shown for BMI among postmenopausal women and parous women, although in both cases we found a borderline significant trend for risk association with non- screen-detected breast cancers. Likewise, BMI is associated with a slightly higher risk of BC for overall breast cancer cases regardless of the detection method, as shown in the supplementary table. Concerning reproductive factors, the association of nul- liparity and age at menopause with breast cancer risk varied by detection method for postmenopausal women only, with a 60% (RR = 1.60; 1.08–2.36) and 48% (RR = 1.48; 95%CI 1.09–2.00) greater risk of screen-detected BC, respectively. Similar results for nulliparity and age at menopause are found in postmenopausal women for overall breast cancer cases, irrespective of the detection method (see the supple- mentary table). Breast cancer‑risk variation by phenotype Concerning lifestyle factors, the risk factor associa- tion analysis by phenotype (Table 4) reveals that BMI is related to a marginally increased risk of HR+ tumors for the overall sample (RR = 1.04; 95%CI 1.01–1.07), but also for postmenopausal women (RR = 1.05; 95%CI 1.02–1.08) and parous women (RR = 1.04; 95%CI 1.01–1.07). In the overall sample, we also found a probable increased risk of HER2+ (RR = 1.03; 95%CI 1.00–1.07) and TN (RR = 1.05; 95%CI 1.00–1.10) for BMI, with borderline significant results. Additionally, BMI is associated with a risk of TN (RR = 1.05; 95%CI 1.01–1.10) among postmenopausal women. All in all, the results indicated that BMI is related to a small increase in breast cancer risk, regardless of tumor phenotype. Table 4 also shows an increased risk, albeit very marginal, of HR+ tumors associated with alcohol consumption in the overall sample. In a similar manner, a borderline significant Table 3 Breast cancer-risk variation by detection method. Multinomial logistic regression models (controls: reference groups); overall sample, postmenopausal women, and parous women *p value < 0.05 1 RR adjusted for region, educational level, and menopausal status; 2RR adjusted for region Overall sample, RR1 Postmenopausal women RR2 Parous women RR1 SD NSD SD NSD SD NSD Tobacco use (ref: never) Ever 0.93 (0.70–1.22) 0.78 (0.50–1.22) 0.91 (0.67–1.24) 0.87 (0.51–1.49) 0.91 (0.67–1.23) 0.73 (0.44–1.20) Alcohol consumption (g/ day) 1.01 (0.99–1.02) 1.00 (0.99–1.02) 1.01 (0.99–1.02) 1.00 (0.98–1.02) 1.01 (0.99–1.02) 0.99 (0.97–1.02) BMI 1.05 (1.02–1.08)* 1.04 (1.00–1.08)* 1.05 (1.02–1.08)* 1.05 (1.00–1.10) 1.04 (1.01–1.08)* 1.03 (0.99–1.07) Physical Activity (ref: active) Inactive 0.87 (0.66–1.15) 1.36 (0.88–2.08) 0.92 (0.67–1.27) 1.29 (0.78–2.12) 0.90 (0.66–1.22) 1.34 (0.84–2.15) Fruits and vegetables intake (g/day) 1.00 (0.99–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.00) 1.00 (0.99–1.00) Age at menarche (ref: > 12 years) ≤ 12 years 1.06 (0.82–1.37) 1.15 (0.75–1.75) 1.02 (0.76–1.37) 1.27 (0.78–2.07) 1.04 (0.78–1.39) 0.98 (0.62–1.55) Nulliparity (ref: No) Yes 1.32 (0.94–1.86) 1.10 (0.61–1.98) 1.60 (1.08–2.36)* 1.28 (0.63–2.60) Age at menopause (ref: ≤ 50 years) > 50 years 1.48 (1.09–2.00)* 1.13 (0.68–1.85) Age at first birth 1.03 (0.99–1.07) 0.99 (0.95–1.03) Family history of BC (ref: no) Yes 1.33 (0.90–1.96) 0.69 (0.31–1.51) 1.03 (0.65–1.64) 0.59 (0.24–1.50) 1.44 (0.94–2.20) 0.74 (0.31–1.72) Cancer Causes & Control 1 3 trend is observed for all phenotypes, also in postmenopausal and parous women. A possible risk association between lack of physical activity and HER2+ breast cancer is observed in postmeno- pausal (RR = 1.54; 95%CI 0.98–2.41) and parous women (RR = 1.49; 95%CI 0.96–2.31), with borderline significant results. Regarding reproductive factors, in postmenopausal women the strength of association of nulliparity varied according to phenotype, with an approximately 60% greater risk of HR+ BC (RR = 1.66; 95%CI 1.15–2.40). Age at menopause also changed risk associations depend- ing on phenotype, with an approximately 60% greater risk of HR+ BC (RR = 1.60; 95%CI 1.22–2.11) and HER2+ BC (RR = 1.59; 95%CI 1.03–2.45). Additionally, in parous women, we also found that age at first birth was positively correlated to TN BC (RR = 1.07; 95%CI 1.01–1.12). Table 4 Breast cancer-risk variation by phenotype. Multinomial logistic regression models (controls: reference groups); overall sample, post- menopausal women, and parous women *p value < 0.05 1 RR adjusted for region, educational level, and menopausal status 2 RR adjusted for region Overall sample, RR1 Postmenopausal women RR2 Parous women RR1 HR+ HER2+ TN HR+ HER2+ TN HR+ HER2+ TN Tobacco use (ref: never) Ever 0.91 (0.71– 1.17) 0.77 (0.51– 1.17) 0.77 (0.41– 1.42) 1.01 (0.76– 1.34) 0.68 (0.42– 1.09) 0.65 (0.32– 1.32) 0.87 (0.66– 1.15) 0.67 (0.42– 1.07) 0.77 (0.39– 1.54) Alcohol cons. (g/ day) 1.01 (1.00– 1.02)* 1.00 (0.99– 1.02) 1.00 (0.98– 1.03) 1.01 (0.99– 1.02) 1.00 (0.99– 1.02) 0.99 (0.97– 1.03) 1.01 (0.99– 1.02) 1.01 (0.99– 1.02) 1.00 (0.97– 1.03) BMI 1.04 (1.01– 1.07)* 1.03 (1.00– 1.07)* 1.05 (1.00– 1.10)* 1.05 (1.02– 1.08)* 1.02 (0.99– 1.06) 1.05 (1.01– 1.10)* 1.04 (1.01– 1.07)* 1.03 (0.99– 1.07) 1.04 (0.99– 1.10) Physical Act. (ref: active) Inactive 1.16 (0.91– 1.49) 1.33 (0.90– 1.98) 0.78 (0.41– 1.49) 1.13 (0.85– 1.50) 1.54 (0.98– 2.41) 0.72 (0.34– 1.53) 1.18 (0.90– 1.55) 1.49 (0.96– 2.31) 0.78 (0.38– 1.61) Fru. Veg. intake (g/ day) 1.00 (1.00– 1.00) 1.00 (0.99– 1.00) 1.00 (0.99– 1.00) 1.00 (1.00– 1.00) 1.00 (0.99– 1.00) 1.00 (0.99– 1.00) 1.00 (0.99– 1.00) 1.00 (0.99– 1.00) 1.00 (0.99– 1.00) Age menarche (ref: > 12) ≤ 12 years 1.05 (0.83– 1.33) 1.02 (0.69– 1.50) 1.10 (0.61– 2.01) 1.07 (0.82– 1.40) 1.04 (0.67– 1.61) 1.02 (0.52– 2.02) 1.04 (0.80– 1.35) 1.09 (0.71– 1.68) 1.17 (0.61– 2.27) Nulliparity (ref: No) Yes 1.30 (0.94– 1.78) 1.16 (0.68– 1.95) 1.23 (0.54– 2.77) 1.66 (1.15– 2.40)* 1.39 (0.76– 2.55) 1.47 (0.60– 3.61) Menopause (ref: ≤ 50) > 50 years 1.60 (1.22– 2.11)* 1.59 (1.03– 2.45)* 0.74 (0.36– 1.54) Age at first birth 1.01 (0.98– 1.04) 1.03 (0.98– 1.07) 1.07 (1.01– 1.12)* Family his- tory BC (ref: no) Yes 1.18 (0.82– 1.69) 1.44 (0.83– 2.51) 1.11 (0.45– 2.73) 1.01 (0.66– 1.55) 1.28 (0.67– 2.42) 0.86 (0.29– 2.56) 1.29 (0.87– 1.92) 1.23 (0.65– 2.33) 1.34 (0.53– 3.39) Cancer Causes & Control 1 3 Discussion This study analyzes variation in breast cancer-risk factors according to method of detection and tumor phenotype. The results showed differences between the association of screen-detected and non-screen-detected breast cancer with the reproductive risk factors of age at menopause and nulliparity. In postmenopausal women, these factors were associated with a risk of screen-detected breast cancer. Meanwhile, age at menopause and nulliparity entailed a greater risk of tumors containing positive hormone recep- tors. Moreover, we found that screen-detected breast can- cers were less likely to be TN tumors. In addition, BMI was related to an increased risk of breast cancer, regardless of the detection method and tumor phenotype. There is little evidence available on the association of epidemiological risk factors with breast cancer accord- ing to method of detection. Unlike the present study, the study conducted by Sprague et al. [25]does not consider tumor phenotype in the analysis. This study reveals that the association of parity/nulliparity with screen-detected and non-screen-detected tumors varies, in line with our results. Nonetheless, they found no differences in the asso- ciation with age at menopause between screen-detected and non-screen-detected breast cancers. Previous analyses [29] found that the association of BMI and family history of breast cancer varied between interval BC and screen-detected BC. Although our study does not specifically look at interval breast cancers, we can assume that they are included in non-screen-detected BC, as defined in this paper. Our results show no variation in the association of family history with SD and NSD breast cancers, with no significant risk association in either case. Meanwhile, BMI is a known risk factor for cancer in general, and particularly for breast cancer in postmeno- pausal women [30], possibly due to increased levels of circulating endogenous estrogen [31]. Accordingly, our analysis reveals a risk association, though slight, between breast cancer and BMI. However, we found no variation in the association of BMI with SD and NSD breast can- cers. A prior study comparing SD and interval BC shows that overweight women are more likely to have SD breast cancer [29], although weight-related barriers to mam- mography screening have been described previously [32]. Further analysis is needed to ascertain the associations between screen-detected and non-screen-detected breast cancers and BMI as well as family history of breast cancer. Previous evidence has demonstrated a risk associa- tion between tumors expressing hormone receptors and reproductive factors [3–5, 33]. In the present study, simi- lar results were observed for nulliparity (in postmeno- pausal women) and age at menopause, although we found no further associations with other reproductive variables that are also known risk factors for hormone-dependent tumors. Considering the results according to detection method and phenotype, similarities can be observed in the asso- ciation between screen-detected breast cancers and HR+ tumors with reproductive factors, specifically for nullipar- ity and age at menopause. This fact suggests that varia- tion in breast cancer-risk factor associations by method of detection (screen-detected vs. non-screen-detected) could be explained by underlying prevailing phenotypes. Thus, the risk association of screen-detected tumors with these reproductive factors could be explained by the fact that they are predominantly hormone-sensitive tumors, which are in turn related to reproductive factors that modify endogenous hormone exposure [21, 34–36]. In line with these obser- vations, several studies [23, 24, 37, 38] contribute further evidence that TN tumors are less frequent in breast cancers detected by screening mammography. Due to their rapid progression and shorter asymptomatic phase, these tumors often appear in the time lapse between two screening mam- mograms (2 years). In this regard, prevalence bias may be a limitation in this study, since HR+ tumors grow slowly and are thus more likely to be detected by screening than other tumor phenotypes. Our results do not conclusively confirm associations with other known lifestyle-related risk factors [3, 4], such as tobacco use [39–43] and alcohol consumption [6, 44–47]; this could be due to the limitations of the study, which are outlined later in this section. With regard to self-referred data (e.g., detection method), there may be a recall bias. Nonetheless, the impact was less- ened thanks to the intervention of professional interviewers and the thorough design of the epidemiological question- naire. Another limitation in this analysis may be the limited number of cases including information on phenotype, which makes stratification difficult. The strengths of our study include its population-based design and the fact that it includes both opportunistic and organized screening, unlike most studies, which refer exclu- sively to the latter. As far as we know, this is one of the few studies conducted in a European context that analyzes breast cancer-risk factors according to detection method and tumor phenotype. Taking into account these results it would be advisable to consider detection method in future epidemio- logical studies on breast cancer. Moreover, further research on the differences between SD and NSD tumors, regarding prognostic and predictive tumor characteristics, could pro- vide relevant information for patient management. In conclusion, we found that screen-detected breast cancers are essentially associated with reproductive risk factors, and also with tumors including positive hormone Cancer Causes & Control 1 3 receptors. Our results seem to indicate that the differences found in breast cancer-risk factor associations according to detection method can be attributed to predominating phenotypes, given that screening mammography mostly detects hormone-sensitive tumors which are susceptible to changes caused by reproductive factors. Supplementary Information The online version contains supplemen- tary material available at https:// doi. org/ 10. 1007/ s10552- 021- 01511-4. Acknowledgments The authors thank all patients and families for their participation in the MCC-Spain study, as well as all study centers and collaborators. Author contributions AM-B, MP, MK, and DS conceptualized and designed the study. MH-G, AM-B, MV-E, and DS developed the meth- odology. BP, NA, PA, JMA, GC-V, MS, ME, VM, IG-A, CV, AT, RM-G, MP, MK, and DS participated in the acquisition of data. MH-G, AM-B, MV-E, ÓZ, BP, NA, PA, JMA, GC-V, MS, ME, VM, IG-A, CV, AT, RM-G, MP, MK, and DS analyzed and interpreted the data. MH-G, AM-B, MV-E, ÓZ, BP, NA, PA, JMA, GC-V, MS, ME, VM, IG-A, CV, AT, RM-G, MP, MK, and DS wrote and/or revised the manuscript. DS, AM-B, and ÓZ supervised the study. Funding This research was supported by Acción Transversal del Cáncer, approved by the Spanish Council of Ministeres on the 11th October 2007, by the Carlos III Health Institute-FEDER (PI08/1770, PI08/0533, PI08/1359, PS09/00773, PS09/01286, PS09/01903, PS09/02078, PS09/ 01662, PI11/01403, PI11/01889-FEDER, PI11/00226, PI11/01810, PI11/02213, PI12/00488, PI12/00265, PI12/01270, PI12/00715, PI12/00150, PI14/01219, PI14/0613, PI15/00069, PI15/00914, PI15/01032, PI11/01810, PI14/01219, PI11/02213, PIE16/00049, PI17/01179, PI17-00092), by the Fun- dación Marqués de Valdecilla (API 10/09), by the ICGC International Cancer Genome Consortium CLL (the ICGC CLL-Genome Project is funded by the Spanish Ministry of Economy and Competitiveness through the Carlos III Health Institute (ISCIII)), by the ISCIII Red Temática de Investigación del Cáncer (RTICC) (RD12/0036/0036), by the Regional Government of Castile and León (LE22A10-2), by the Regional Health Ministry of Andalusia (PI-0571–2009, PI-0306– 2011, salud201200057018tra), by the Regional Health Ministry of Valencia (AP_061/10), by Recercaixa (2010ACUP00310), by the Regional Government of the Basque Country, by the Regional Health Ministry of Murcia, by the European Commission (grants FOOD- CT-2006–036224-HIWATE), by the Spanish Association Against Cancer (AECC) Scientific Foundation (GCTRA18022MORE), by the Catalan Regional Government Agency for Management of University and Research Grants (AGAUR) (2014SGR647, 2014SGR850, and 2017SGR723), by the Fundación Caja de Ahorros de Asturias and by the University of Oviedo. ISGlobal is a member of the CERCA Programme, Regional Government of Catalonia. Sample collection was supported by the Carlos III Health Institute- FEDER: Parc de Salut MAR Biobank (MARBiobanc) (RD09/0076/00036), the La Fe Biobank (PT17/0015/0043RD 09 0076/00021), and the FISABIO Biobank (PT17/0015/0017) RD09 0076/00058); the Public Health Laboratory of Gipuzkoa, the Basque Biobank, ICOBIOBANC (spon- sored by the Catalan Institute of Oncology), the IUOPA Biobank from the University of Oviedo and ISCIII Biobank. The Spanish National Genotyping Centre (CEGEN-ISCIII) provided SNPs genotyping ser- vices. We would like to thank all those who participated in the study and all MCC-Spain study groups and collaborators. Availability of data and material The database was registered with the Spanish data protection Agency, under Number 210267217118. Data are available under request. Code availability Not Applicable. Declarations Conflict of interest The authors declare that they have no conflict of interests related to this study. They state that they have no affiliations with and are not involved in any organization or entity with financial (such as honoraria; educational grants; participation in speakers’ bu- reaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrange- ments), or non-financial interests (such as personal or professional re- lationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript. Ethical approval The MCC-Spain study followed national and inter- national directives on ethics and data protection (the Declaration of Helsinki and Spanish law on data confidentiality, Organic Law 15/1999 of 13 December on the Protection of Personal Data—LOPD) and meets the requirements of current legislation. The MCC-Spain study protocol was approved by the ethics committees of participating institutions. Consent to participate All participants provided informed consent. Consent for publication Not applicable. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. 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Liu Y, Nguyen N, Colditz GA (2015) Links between Alcohol Consumption and Breast Cancer: A Look at the Evidence. Women Health (Lond Engl) 11(1):65–77. https:// doi. org/ 10. 2217/ WHE. 14. 62 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Authors and Affiliations Marta Hernández‑García1 · Ana Molina‑Barceló1  · Mercedes Vanaclocha‑Espi1 · Óscar Zurriaga2,3,4,5 · Beatriz Pérez‑Gómez5,6 · Nuria Aragonés5,7 · Pilar Amiano5,8,9 · Jone M. Altzibar8,10 · Gemma Castaño‑Vinyals5,10,11,12 · María Sala11,13 · María Ederra5,14,15 · Vicente Martín5,16 · Inés Gómez‑Acebo5,17 · Carmen Vidal5,18,19 · Adonina Tardón5,20 · Rafael Marcos‑Gragera5,21,22 · Marina Pollán5,23 · Manolis Kogevinas5,10,11,12 · Dolores Salas1,4,5 1 Cancer and Public Health Area, Foundation for the Promotion of Health and Biomedical Research of Valencia Region(FISABIO), Avda. Catalunya 21, 46020 Valencia, Spain 2 Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, University of Valencia, Avda. Vicent Andrés Estellés, s/n, 46100 Burjassot, Valencia, Spain 3 Joint Research Unit on Rare Diseases, FISABIO-UVEG, Avda. Catalunya 21, 46020 Valencia, Spain 4 Directorate General of Public Health, Avda. Catalunya 21, 46020 Valencia, Spain 5 Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain 6 Department of Epidemiology of Chronic Diseases, National Center of Epidemiology, Carlos III Health Institute (ISCIII), Avda. Monforte de Lemos 5, 28029 Madrid, Spain 7 Epidemiology Section, Public Health Division, Department of Health of Madrid, C/San Martín de Porres, 6, 28035 Madrid, Spain 8 Epidemiology of Chronic and Communicable Diseases Group, Biodonostia Health Research Institute, Doctor Begiristain, s/n, 20014 San Sebastián, Spain 9 Sub Directorate for Public Health and Addictions of Gipuzkoa, Ministry of Health of the Basque Government, 2013, San Sebastian, Spain 10 ISGlobal, Barcelona Institute for Global Health, Doctor Aiguader 88, 08003 Barcelona, Spain 11 IMIM (Hospital del Mar Medical Research Institute), Doctor Aiguader 88, 08003 Barcelona, Spain 12 Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002 Barcelona, Spain 13 Research Network on Health Services in Chronic Diseases (REDISSEC), Barcelona, Spain 14 Navarra Public Health Institute, C/ Leyre, 15, 31003 Navarra, Spain 15 IdiSNA, Navarra Institute for Health Research, C/Irunlarrea, 3, 31008 Pamplona, Spain 16 The Research Group in Gene - Environment and Health Interactions (GIIGAS), Biomedicine Institute (IBIOMED), Cancer Causes & Control 1 3 University of León, Vegazana Campus, s/n, 24071 León, Spain 17 Cantabria University– IDIVAL, C/Cardenal Herrera Oria, s/n, Santander, 39011 Cantabria, Spain 18 Cancer Screening Unit, Catalan Institute of Oncology, Duran I Reynals Hospital, Avda. de La Gran Via de L′Hospitalet, 199-203, L′Hospitalet de Llobregat, 08908 Barcelona, Spain 19 Early Detection of Cancer Research Group, EPIBELL Program, Bellvitge Biomedical Research Institute, Avda. de La Granvia de L′Hospitalet, 199, L′Hospitalet de Llobregat, 08908 Barcelona, Spain 20 Oncology Institute (IUOPA), University of Oviedo, Edificio Santiago Gascón, Campus El Cristo B, 33006 Oviedo, Spain 21 Epidemiology Unit and Girona Cancer Registry, Oncology Coordination Plan, Department of Health, Autonomous Government of Catalonia, Catalan Institute of Oncology. Sant Ponç, Avda de França, 0, 17007 Girona, Spain 22 Descriptive Epidemiology, Genetics and Cancer Prevention Group, [Girona Biomedical Research Institute] IDIBGI, C/ del Dr. Castany, s/n, Salt, 17190 Girona, Spain 23 National Center of Epidemiology Directorate, Carlos III Health Institute (ISCIII), Avda. Monforte de Lemos 5, 28029 Madrid, Spain