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Differential patterns of reproductive and lifestyle risk factors for breast cancer according to birth cohorts among women in China, Japan and Korea

Breast Cancer Research volume 26, Article number: 15 (2024) Cite this article

Abstract

Background

The birth cohort effect has been suggested to influence the rate of breast cancer incidence and the trends of associated reproductive and lifestyle factors. We conducted a cohort study to determine whether a differential pattern of associations exists between certain factors and breast cancer risk based on birth cohorts.

Methods

This was a cohort study using pooled data from 12 cohort studies. We analysed associations between reproductive (menarche age, menopause age, parity and age at first delivery) and lifestyle (smoking and alcohol consumption) factors and breast cancer risk. We obtained hazard ratios (HRs) with 95% confidence intervals (CIs) using the Cox proportional hazard regression analysis on the 1920s, 1930s, 1940s and 1950s birth cohorts.

Results

Parity was found to lower the risk of breast cancer in the older but not in the younger birth cohort, whereas lifestyle factors showed associations with breast cancer risk only among the participants born in the 1950s. In the younger birth cohort group, the effect size was lower for parous women compared to the other cohort groups (HR [95% CI] 0.86 [0.66–1.13] compared to 0.60 [0.49–0.73], 0.46 [0.38–0.56] and 0.62 [0.51–0.77]). Meanwhile, a higher effect size was found for smoking (1.45 [1.14–1.84] compared to 1.25 [0.99–1.58], 1.06 [0.85–1.32] and 0.86 [0.69–1.08]) and alcohol consumption (1.22 [1.01–1.48] compared to 1.10 [0.90–1.33], 1.15 [0.96–1.38], and 1.07 [0.91–1.26]).

Conclusion

We observed different associations of parity, smoking and alcohol consumption with breast cancer risk across various birth cohorts.

Key messages

  • There are associations between reproductive factors and breast cancer risk.

  • There are associations between smoking and alcohol consumption and increased breast cancer risk among Asian women.

  • There are differential patterns of parity, age at first delivery and smoking status across birth cohorts.

  • Reproductive risk factors were more apparent in the older birth cohorts, whereas smoking and alcohol consumption were only notable in the younger generation.

Breast cancer is reportedly the most commonly diagnosed type of cancer worldwide and the leading cause of cancer mortality among women [1, 2]. Although the incidence rate in Asia has been rather low historically, recent trends showed that it is rapidly increasing in several Asian countries, including Japan, China and Korea, with the rates now approaching 76.3, 39.1 and 64.2 (per 100,000 women), respectively, in these countries [3, 4]. The change in secular breast cancer trends in Asia has been attributed to birth cohort effects by some previous studies [5,6,7,8].

Reproductive factors, including menarche age, parity, age at first delivery and menopause age, and lifestyle factors, such as smoking, are widely known as important considerations in the assessment of breast cancer risk [9]. Similar to those of breast cancer, secular trends of reproductive and lifestyle factors have also been observed in previous studies, for example, a decrease in menarche age [10, 11], an increase in menopause age [12, 13], a decrease in parity number [12] and an increase in the proportion of women who smoke [10]. Given that both breast cancer incidence and its determinants have been suggested to show notable trends based on birth cohorts, the associations between certain factors and breast cancer risk may also differ according to the birth cohort.

Therefore, we conducted a cohort study to determine whether a difference exists in the associations between reproductive and lifestyle factors and breast cancer risk according to birth cohorts using a large sample of the Asian population from 12 prospective cohort studies from China, Japan and Korea.

Methods

Study design and population

This cohort study was conducted based on the Asia Cohort Consortium (ACC), an international collaborative project to combine existing cohorts across Asia that currently involves more than a million participants. A more detailed description of the ACC has been given in previous articles [14]. Among all the participating cohorts in the ACC, 12 cohorts that had agreed to participate in this study were included. These cohorts were from China, Japan and Korea, comprising the following studies: Shanghai Women’s Health Study (SWHS) [15], Japan Public Health Centre-based Prospective Study I and II (JPHC I and JPHC II) [16, 17], Japan Collaborative Cohort Study (JACC) [18], Life Span Study Cohort (LSS) [19], Miyagi Cohort Study (Miyagi) [20], Ohsaki National Health Insurance Cohort Study (Ohsaki) [20], Korean National Cancer Centre Cohort (KNCC) [21], Takayama Study [22], Three Prefecture Cohort Study Miyagi (3 Pref. Miyagi) [23], Korean Multi-center Cancer Cohort Study (KMCC) [24], and The Namwon Study (Namwon) [25].

All data harmonisation was performed centrally by the ACC coordinating centre. The centre sent the details of the data processing method to each cohort so that the data could be processed following the same method and then sent back to the coordinating centre. After verifying the data content, the ACC coordinating centre organised and pooled all the data. After signing an agreement form, investigators could access and analyse the data using remote access to a virtual private network or the computer at the National Cancer Center in Tokyo, Japan. A working group was created to process and derive the necessary reproductive variables to ensure that investigators had standardised variables and used the data appropriately. Detailed information on the cleaning of the reproductive variables by the working group has been given in another article [26].

Of the 583,334 participants from the 12 cohorts, men (n = 229,465) and participants with missing information on sex (n = 6) were excluded; thus, 353,863 women remained. Further exclusions were made for participants with missing information on age (n = 2416), pregnancy status or number of deliveries or parity (n = 37 305), follow-up duration (n = 1465), as well as those with a follow-up duration of less than 0 days (n = 53). Finally, 311,955 participants were included in this study, including 4581 breast cancer cases. A flow chart of participant selection is presented in Fig. 1, and the details of participant selection from each cohort study are provided in Additional file 1: Table S1. Although a large number of participants were excluded because of missing information, most of the basic characteristics did not show large differences between excluded and included participants in the evaluation of the standardised differences (absolute value < 0.5), except for the number of children (Additional file 1: Table S2) [27].

Fig. 1
figure 1

Participants selection

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The pooled analysis of the ACC cohorts was approved by the ethics committee of the National Cancer Centre Japan (number 2014-041), and each participating study was approved by the respective overseeing ethics committee. Informed consent was previously collected from the participants before each cohort study. All methods included in the present study were performed in accordance with relevant guidelines and regulations.

Exposure and outcome

The exposure in this study consisted of reproductive factors (menarche age, parity, age at first delivery and menopause age) and lifestyle factors (smoking and alcohol consumption), which were collected by self-report questionnaire in each cohort study. Menarche age was grouped into < 13, 13–14, 15–16 and ≥ 17 years; parity was grouped into nulliparous and parous; age at first delivery was grouped into ≤ 20, 21–25, 26–30, > 30 years and nulliparous; and menopause age was grouped into < 45, 45–49, 50–54 and ≥ 55 years. Smoking status was defined as never and ever smokers and alcohol consumption was defined as non-current drinkers and current drinkers, with non-current drinkers comprising both participants who never drank and past drinkers.

The reproductive working group in the ACC was responsible for harmonising and cleaning the reproductive variables. Missing data for menopausal status was inputted as post-menopause if there was information on the age of menopause or if the baseline age was more than 54 years, as pre-menopause if the baseline age was less than 44 years, and as missing or unknown if the baseline age was 45–53 years. Furthermore, the plausible range of age at menarche was defined as 10–23 years, menopause age as 20 years or more, and age at first delivery as 10–49 years. Responses outside these ranges were considered implausible and thus treated as missing.

Breast cancer cases were defined with reference to the International Classification of Diseases for Oncology as codes C50.0–50.9 by each cohort study through linkages with the cancer registries. Most of the cancer registries from the participating countries meet the inclusion criteria of high-quality cancer registries and have been published in the Cancer Incidence in Five Continents by The International Agency for Research on Cancer [28]. The follow-up duration was defined as the interval between the entry date and the breast cancer incidence date for cases or the last follow-up date for non-cases.

Statistical analysis

Cox proportional hazard regression analysis was performed to obtain the hazard ratio (HR) with a 95% confidence interval (CI) for the overall breast cancer risk from the pooled data by adjusting for baseline age and age at first delivery in model 1 and baseline age, age at first delivery and cohort studies in model 2. Considering the potential heterogeneity and effects of each study’s quality and size, a meta-analysis of the dataset was performed to assess whether the results from both analyses were comparable. Heterogeneity was evaluated using the Cochran Q and Higgins I2 tests; P < 0.1 or I2 < 50% indicated a significant difference [29].

A stratified analysis according to the birth cohort from the pooled data was performed by categorising the participants’ birth year into four groups: 1920s or earlier, 1930s, 1940s and 1950s or later. The HR for breast cancer risk in each birth year group was adjusted for baseline age, age at first delivery and cohort. The birth cohort groups in the participating cohort studies were not equally distributed; thus, the pooled analysis results might be weighted toward particular cohort studies. Therefore, as for the overall analysis, a meta-analysis was performed to determine whether the obtained risks in each birth cohort were consistent with the results of the stratified analysis of the pooled data. Additionally, the standardised difference between the included and excluded participants was assessed and stratified analyses by menopausal status and country were performed. The analysis of alcohol consumption only included 11 cohorts and excluded one cohort (Takayama Study) due to data unavailability. All analyses were performed using Stata version 16.0 software (Stata Corporation, College Station, TX).

Results

Overall pooled analysis

Among 311,955 participants (mean [standard deviation] age 54.2 [10.7] years), 4581 breast cancer cases were confirmed during a mean of 16.5 years of follow-up, and the mean age at diagnosis was 61.9 years (Table 1). Details of the distributions of baseline characteristics in each cohort are presented in Additional file 1: Table S3. Table 2 shows that there was an increased risk of breast cancer among participants with a younger menarche age (HR [95% CI] 1.17 [1.06–1.29] for 15–16 years, 1.34 [1.22–1.48] for 13–14 years and 1.41 [1.23–1.61] for < 13 years compared to those > 17 years in model 2). Compared to the group aged 50–54 years, a younger menopause age was associated with a decreased risk of breast cancer (0.84 [0.76–0.93] for 45–49 years and 0.82 [0.72–0.94] for < 45 years). Furthermore, breast cancer risk was lower among parous women than nulliparous women (0.61 [0.55–0.68]). Compared to the group aged 21–25 years at first delivery, the younger age group showed a lower risk of breast cancer (0.79 [0.69–0.90]); meanwhile, the older groups and nulliparous group showed a higher risk of breast cancer (1.24 [1.15–1.33] for 26–30 years, 1.52 [1.37–1.70] for > 30 years and 1.76 [1.58–1.97] for nulliparous). Participants who were ever smokers (1.13 [1.01–1.26]) and those who were current alcohol drinkers at baseline (1.15 [1.05–1.26]) also showed a higher risk of breast cancer.

Table 1 Baseline characteristics of participants based on birth cohort group
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Table 2 Associations of reproductive factors, smoking status and alcohol consumption with breast cancer risk
Full size table

The results of the meta-analysis of the association between each variable and breast cancer risk showed no notable heterogeneity except for the analysis of parity (P value of Q test = 0.02 and I2 = 52%). However, the random effects model result (0.58 [0.50–0.67]) was comparable to the pooled analysis result (Additional file 1: Figures S1–S5).

Breast cancer risk according to birth cohorts

The results of the stratified analysis by birth cohort showed associations between reproductive factors and breast cancer risk among participants who were born in the 1920s or earlier, 1930s (except for menopause age) and 1940s, with the same directions as the overall analysis. However, no association was identified between smoking and alcohol consumption and breast cancer risk among these birth cohorts. Among the participants born in the 1950s or later, associations between smoking (1.45 [1.14–1.84]) and alcohol consumption (1.22 [1.01–1.48]) and a higher risk of breast cancer were observed. Furthermore, in this younger birth cohort, a menarche age < 13 years (1.30 [1.01–1.68]) and age at first delivery > 30 years (1.31 [1.08–1.60]) were also observed to increase the risk of breast cancer.

For some factors, the observed effect sizes for breast cancer risk varied across the birth cohorts. For a younger menarche age, the HRs for breast cancer varied between 1.24 and 1.55 (< 13 years), and between 1.12 and 1.47 (13–14 years). HRs for breast cancer appeared to be between 0.65 and 0.98 for a menopause age < 45 years, 0.75 and 1.24 for a menopause age of 45–49 years, and 0.79 and 2.66 for a menopause age ≥ 55 years. Furthermore, the HRs for breast cancer among parous women fell between 0.46 and 0.86. Regarding older age at first delivery, the HRs varied between 1.07 and 1.44 for those aged 26–30 years and 1.31 and 1.77 for ages > 30 years. Among ever smokers, the HRs were between 0.89 and 1.46.

The results of the meta-analysis of the association between each variable and breast cancer risk in the youngest and oldest birth cohort groups showed no notable heterogeneity. Furthermore, the result of the fixed effect model appeared to be comparable to the main results. (Additional file 1: Figures S6–S10).

Additional analyses

The results of the stratified analysis by menopausal status are presented in Additional file 1: Table S4. Associations between reproductive factors and breast cancer risk among both pre- and post-menopausal women were similar to the overall analysis. Furthermore, regarding lifestyle factors, the results had the same direction as the overall analysis among both pre- and post-menopausal women.

The results of the stratified analysis by country indicated substantial differences in breast cancer risk across the country (Additional file 1: Table S5). Therefore, we further performed an additional analysis of the association between reproductive factors, lifestyle factors, and breast cancer risk by including the country as the adjusting variable (Additional file 1: Table S6). In general, the findings of this model were comparable to the results of the main analysis in terms of significance and direction, except for the group with a younger age at first delivery in the 1920s cohort, which appeared disparately.

Discussion

In this study, we aimed to determine whether there is a differential pattern of reproductive and lifestyle factors for breast cancer risk according to birth cohorts. The study findings showed associations between reproductive factors and breast cancer risk in all birth cohorts; however, the associations of lifestyle factors were only notable in the later birth cohorts. The risk of breast cancer was observed to differ substantially across birth cohorts among parous women, those who were 26–30 years old at first delivery and those who had ever smoked.

In the overall pooled analysis, we observed associations between all reproductive factors and breast cancer risk, which was an expected result and is consistent with results from previous studies [30,31,32,33]. However, evidence of lifestyle factors, such as smoking and alcohol consumption, and their associations with breast cancer risk were inconsistent in Asia. Results from previous studies, which used data from the cohort studies that were included in this study, showed no association between active smoking and breast cancer risk [34, 35], similar to a meta-analysis among the Chinese [36]. However, other, more recent meta-analyses that involved more articles [37, 38] and a study with a larger sample size [39] reported contradictory findings. The contribution of alcohol drinking and the dose–response association to an elevated risk of breast cancer has been widely suggested [40,41,42,43]. However, this association was not apparent in the present study, which included only Asians [44,45,46]. A meta-analysis by Sun et al. [43] found an association between alcohol consumption and breast cancer in the overall analysis; however, it was not notable among the Asian population in their subgroup analysis, and articles from Asia had the lowest attributable percentages compared to those of North America and Europe. In this study, we confirmed positive associations between ever smoking and current drinking and an increased risk of breast cancer.

Many previous studies suggested that the birth cohort effect shifted the trends of reproductive and lifestyle factors, which subsequently contributed to the increasing rate of breast cancer in each corresponding country [5, 7, 30]. A previous study even suggested differences in breast cancer risk according to birth year [30]. In this study, we added evidence of a differential pattern of breast cancer risk, which was notable for some of the factors. Parous women were observed to have a lower breast cancer risk in the older generations and not the younger generation (1950s-born participants), with a higher magnitude observed among the 1930s birth cohort. Furthermore, this study’s analyses of lifestyle factors resulted in disparate associations across the birth cohorts. The associations between smoking and breast cancer only appeared in the younger generation group and not the older generation groups. Similarly, alcohol consumption appeared to have the same pattern as smoking.

The differing risk of breast cancer that appeared in the younger birth cohort in our study might be caused by the changes in prevalence of some factors which corresponds to the report by previous studies. A previous study reported a decreased number of parity [12], which may explain our finding that the association between parity and breast cancer risk was attenuated in the younger birth cohort. Furthermore, the increased number of women who smoke may explain the prominent association, with a higher effect size, between smoking and breast cancer risk in the younger birth cohort in the present study. Moreover, the difference between the ≥ 1950s birth cohort and the other older birth cohorts is supported by a previous study that concluded the presence of birth cohort effects based on the timing of westernisation in several Asian countries [47]. According to the study, Japan and Korea were assumed to have begun westernisation in the mid-1940s, whereas the timing in China varied according to the region [47]. This may explain the difference in breast cancer risk between participants born earlier (the 1920s to 1940s) and later.

Statistical significance is dependent on the sample size, and thus, significance might be affected by a large study population [48]. Therefore, interpreting the results using effect sizes rather than solely assessing significance may be justified because effect size is independent of sample size [48]. In our study, apart from the different associations found across birth cohorts, the effect sizes of several factors on the risk of breast cancer tended to differ according to birth cohort. Compared to the older birth cohorts, in the ≥ 1950s birth cohort, the effect size was lower for parous women (HR = 0.86 compared to 0.60, 0.46 and 0.62 in the ≤ 1920s, 1930s and 1940s respectively), higher for smoking (HR = 1.45 compared to 1.25, 1.06 and 0.89 in the ≤ 1920s, 1930s and 1940s respectively), and slightly higher for alcohol consumption (HR = 1.22 compared to 1.10, 1.15 and 1.07 in the ≤ 1920s, 1930s and 1940s respectively).

This study had some limitations. First, the information was collected by self-report questionnaires for most variables. Second, the number of participants who were ex-drinkers was small, and some cohorts did not provide this information; thus, we could not perform an analysis of past drinking habits and had to merge the never drinkers and past drinkers into the same category. Third, detailed information on breast cancer type was unavailable, which limited the analysis to the overall breast cancer cases only, whereas the risk pattern might also differ according to the breast cancer type. Fourth, we did not include other factors that could possibly influence the risk of breast cancer, such as different dietary patterns in each country and cohort study; therefore, further study is suggested to examine their influence. Fifth, we were unable to include all cohort studies from the Asia Cohort Consortium in this study. Only 12 cohort studies from three countries, which provided information on the variable of interest and agreed to participate, were included. This suggests that expanded research on Asians is needed. Finally, in the pooled data, only a few studies provided information regarding the number of cigarettes and the amount of alcohol consumed by the participants. Therefore, we could not perform a dose–response analysis in this study. Further study is recommended to assess the differential pattern of breast cancer risk factors by including the breast cancer subtype. Nevertheless, this study has the strength of the use of a large-scale pooled analysis of prospective studies in several Asian countries to study the differential pattern of reproductive and lifestyle risk factors of breast cancer. Moreover, to the best of our knowledge, the present study is by far the largest to assess the association between smoking and breast cancer risk among Asian women.

Conclusions

In conclusion, in this study of a large sample of Asian women, positive associations between reproductive factors and breast cancer risk were observed, consistent with previous reports. We also added evidence regarding the associations of smoking and alcohol consumption with the increased risk of breast cancer among Asian women. Furthermore, we observed differential patterns of parity, smoking and alcohol consumption across birth cohorts. Reproductive risk factors were more apparent in the older birth cohorts, whereas smoking and alcohol use were only notable in the younger generation.

Availability of data and materials

The data that support the findings of this study were obtained from the Asia Cohort Consortium upon request. The data are not publicly available to protect the privacy of the individuals who participated in the study and can be accessed on request from the National Cancer Center Japan.

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Acknowledgements

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Funding

Shanghai Women’s Health Study, US National Cancer Institute (R37 CA070867 and UM1 CA182910); Japan Public Health Center-Based Prospective Study (1 and 2), National Cancer Center Research and Development Fund (23-A-31[toku], 26-A-4 and 2020-A-4, since 2011) and a Grant-in-Aid for Cancer Research from the Ministry of Health, Labour and Welfare of Japan (from 1989 to 2010); Japan Collaborative Cohort Study, National Cancer Center Research and Development Fund (a grant-in-aid for cancer research), grant for health services and grant for comprehensive research on cardiovascular and life-style related diseases from Ministry of Health, Labour and Welfare, Japan, and grant for the scientific research from Ministry of Education, Culture, Sports, Science and Technology, Japan; Life Span Study Cohort–Radiation Effects Research Foundation, The Japanese Ministry of Health, Labour and Welfare and the US Department of Energy; Miyagi Cohort Study, National Cancer Center Research and Development Fund; Ohsaki National Health Insurance Cohort Study, National Cancer Center Research and Development Fund; Takayama Study, National Cancer Center Research and Development Fund; Miyagi 3-prefecture Miyagi, National Cancer Center Research and Development Fund; Korea National Cancer Center Cohort, National Cancer Center Research Grant (1510040, 1810090, and 1910330); Korea Multi-Center Cancer Cohort Study, National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (2016R1A2B4014552); The Namwon Study, Chonnam National University Hwasun Hospital Research grant (HCRI21019, HCRI18007-1, HCRI16911-1); Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2022R1A2B5B01002471).

Author information

Authors and Affiliations

  1. Department of Biomedical Sciences, Seoul National University Graduate School, 103 Daehak-ro, Jongno-gu, 03080, Seoul, Republic of Korea

    Salma Nabila & Ji-Yeob Choi

  2. BK21plus Biomedical Science Project, Seoul National University College of Medicine, Seoul, Republic of Korea

    Salma Nabila & Ji-Yeob Choi

  3. Institute of Health Policy and Management, Seoul National University Medical Research Center, Seoul, Republic of Korea

    Ji-Yeob Choi

  4. Cancer Research Institute, Seoul National University, Seoul, Republic of Korea

    Ji-Yeob Choi, Aesun Shin & Daehee Kang

  5. Division of Prevention, National Cancer Center Institute for Cancer Control, Tokyo, Japan

    Sarah Krull Abe, Md Rashedul Islam, Md Shafiur Rahman & Manami Inoue

  6. Hitotsubashi Institute for Advanced Study, Hitotsubashi University, Tokyo, Japan

    Md Rashedul Islam

  7. Research Center for Child Mental Development, Hamamatsu University School of Medicine, Hamamatsu, Japan

    Md Shafiur Rahman

  8. National Center for Global Health and Medicine, Institute for Global Health Policy Research, Tokyo, Japan

    Eiko Saito

  9. Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea

    Aesun Shin, Sue K. Park & Daehee Kang

  10. The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, Australia

    Melissa A. Merritt

  11. Division of Cohort Research, National Cancer Center Institute for Cancer Control, Tokyo, Japan

    Ryoko Katagiri, Norie Sawada & Shoichiro Tsugane

  12. National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, Japan

    Ryoko Katagiri & Shoichiro Tsugane

  13. Division of Epidemiology, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA

    Xiao-Ou Shu, Hui Cai & Wei Zheng

  14. Department of Public Health, Faculty of Medicine, Hokkaido University, Sapporo, Japan

    Akiko Tamakoshi & Takashi Kimura

  15. Radiation Effects Research Foundation, Hiroshima, Japan

    Ritsu Sakata

  16. Tohoku University Graduate School of Medicine, Sendai, Miyagi Prefecture, Japan

    Atsushi Hozawa, Seiki Kanemura & Yumi Sugawara

  17. Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea

    Jeongseon Kim

  18. Department of Epidemiology and Preventive Medicine, Gifu University Graduate School of Medicine, Gifu, Japan

    Chisato Nagata & Keiko Wada

  19. Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea

    Sun-Seog Kweon & Min-Ho Shin

  20. Department of Public Health Sciences, University of Chicago, Chicago, IL, USA

    Habibul Ahsan

  21. Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA

    Paolo Boffetta

  22. Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

    Paolo Boffetta

  23. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Kee Seng Chia

  24. Division Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan

    Keitaro Matsuo

  25. Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Keitaro Matsuo

  26. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    You-Lin Qiao

  27. Division of Cancer Epidemiology and Genetics, Occupational and Environmental Epidemiology Branch, National Cancer Institute, Bethesda, MD, USA

    Nathaniel Rothman

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  1. Salma Nabila
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  2. Ji-Yeob Choi
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  32. Wei Zheng
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  34. Daehee Kang
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Contributions

SN performed the analyses, discussed the interpretation, and made the manuscript. JYC designed the present study, performed the analyses, discussed the interpretation, and supervised the study. SKA, ES, MSR, and MRI coordinated the ACC (administrative, technical, and material support). AS, MM, RK cleaned the data and contributed to the discussion. XOS, NS, AT, RS, AH, CN, JK, SKP, SSK, HC, ST, TK, SK, YS, KW, MHS, HA, KSC, KM, NR, PB, YLQ, WZ, MI, and DK contributed to the data collection and discussion. All authors contributed to the manuscript editing and approved the final manuscript.

Corresponding author

Correspondence to Ji-Yeob Choi.

Ethics declarations

Ethics approval and consent to participate

The pooled analysis of the ACC cohorts was approved by the ethics committee of the National Cancer Centre Japan (number 2014-041), and each participating study was approved by the respective overseeing ethics committees. Informed consent was previously collected from the participants before each cohort study. All methods included in the present study were performed in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Additional file 1

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Nabila, S., Choi, JY., Abe, S.K. et al. Differential patterns of reproductive and lifestyle risk factors for breast cancer according to birth cohorts among women in China, Japan and Korea. Breast Cancer Res 26, 15 (2024). https://doi.org/10.1186/s13058-024-01766-0

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Keywords

Breast Cancer Research

ISSN: 1465-542X

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