Abstract
Background
Carriers of germline pathogenic variants (PVs) in the BRCA1 and BRCA2 genes are at higher risk of developing breast and ovarian cancer than the general population. It is unclear if these PVs influence other breast or ovarian cancer risk factors, including age at menopause (ANM), age at menarche (AAM), menstrual cycle length, BMI or height. There is a biological rationale for associations between BRCA1 and BRCA2 PVs and reproductive traits, for example involving DNA damage and repair mechanisms. The evidence for or against such associations is limited.
Methods
We used data on 3,046 BRCA1 and 3,264 BRCA2 PV carriers, and 2,857 non-carrier female relatives of PV carriers from the Epidemiological Study of Familial Breast Cancer (EMBRACE). Associations between ANM and PV carrier status was evaluated using linear regression models allowing for censoring. AAM, menstrual cycle length, BMI, and height in carriers and non-carriers were compared using linear and multinomial logistic regression. Analyses were adjusted for potential confounders, and weighted analyses carried out to account for non-random sampling with respect to cancer status.
Results
No statistically significant difference in ANM between carriers and non-carriers was observed in analyses accounting for censoring. Linear regression effect sizes for ANM were -0.002 (95%CI: -0.401, 0.397) and -0.172 (95%CI: -0.531, 0.188), for BRCA1 and BRCA2 PV carriers respectively, compared with non-carrier women. The distributions of AAM, menstrual cycle length and BMI were similar between PV carriers and non-carriers, but BRCA1 PV carriers were slightly taller on average than non-carriers (0.5 cm difference, p = 0.003).
Conclusion
Information on the distribution of cancer risk factors in PV carriers is needed for incorporating these factors into multifactorial cancer risk prediction algorithms. Contrary to previous reports, we found no evidence that BRCA1 or BRCA2 PV are associated with hormonal or anthropometric factors, except for a weak association with height. We highlight methodological considerations and data limitations inherent in studies aiming to address this question.
Supplementary Information
The online version contains supplementary material available at 10.1186/s13058-025-02030-9.
Keywords: BRCA1, BRCA2, Menopause, Menarche, Height, Body mass index, Cancer
Introduction
Germline pathogenic variants in BRCA1 and BRCA2 confer high risks of breast and ovarian cancer [1]. Reproductive factors, including age at natural menopause (ANM) and age at menarche (AAM), and anthropomorphic traits including height and body mass index (BMI) are established breast and/or ovarian cancer risk factors in the general population [2]. There is evidence from observational studies and Mendelian randomisation analyses that some breast cancer risk factors in the general population are also associated with cancer risk in PV carriers [3–10]. Risk prediction algorithms, notably BOADICEA, incorporate the effects of both PVs and other risk factors to predict cancer risk [11, 12]. These algorithms depend on assumptions about the distribution of these traits in PV carriers, and well as the associated effect sizes. It is necessary therefore to evaluate empirically the underlying distribution of the relevant cancer risk factors for BRCA1 and BRCA2 PV carriers. Further, management of PV carriers may include recommendation on risk reducing bilateral salpingo-oophorectomy (RRSO) and the likely timing of menopause may be an important consideration for women contemplating surgery.
Age at natural menopause is normally distributed in the general population with an average age of ~ 50 years in European ancestry women. Menopause occurs between ages 40–60 years in 99% of women and before age 40 years in ~ 1% of women; women with age at menopause less than 40 years may be diagnosed with premature ovarian insufficiency, a largely monogenic trait. Certain environmental factors are associated with earlier ANM, including lower BMI, alcohol, smoking and low birth weight. Maternal obesogenic diet during pregnancy also decreases the ovarian reserve in offspring [13, 14]. ANM has a strong genetic basis, mediated by multiple genetic loci, many of which have been identified through genome-wide association analyses [14]. ANM associated SNPs are enriched for variants near genes involved in DNA damage response (DDR). As summarized in Ruth et al. [14], DDR is the primary biological pathway that regulates reproductive senescence. Declining or inefficient activity in DNA repair mechanisms leads to accelerated ovarian aging by accumulation of DNA damage, and the BRCA genes may play a role in DSB repair in ovarian aging in humans [14]. ANM associated variants include common coding variants in BRCA1: the alleles associated with earlier ANM are also associated with reduced BRCA1 expression in blood [15]. These common variants have not, however, been associated with cancer risk [16]. BRCA1 expression decreases in human ovaries with age [17], while reduced brca1 expression in mouse models leads to reduced ovarian reserve [17]. BRCA1 directly inhibits a functional interaction with oestrogen receptor α and thus BRCA1 variants could also affect ANM through altered oestrogen signalling [18]. The mechanistic rationale for investigating association between and ANM and BRCA PV status is thus also strong.
In exome-wide analysis in UK Biobank data, rare loss-of-function (LOF) variants in BRCA1 and BRCA2 were associated with earlier (2.63 and 1.53 years respectively) ANM compared with non-carriers, while LOF variants in CHEK2 were associated later ANM (3.49 years difference) [14]. Rare coding variants in other DNA damage repair genes have also been associated with ANM [19]. Earlier epidemiological studies have suggested that natural menopause occurs at a younger age in BRCA1 and BRCA2 PV carriers compared with women from the general population [20–22], and that BRCA1 PV carriers may have reduced ovarian reserve [23] and consequently a shortened reproductive lifespan. Other studies, however, have reported no statistically significant differences between ANM in BRCA1/2 carriers and the general population [24]. These analyses are, however, complicated by incompleteness of data on preventative surgeries, in particular RRSO, and potential reverse causation as a diagnosis of cancer and associated treatments may also be associated with onset of menopause. Age at menarche, weight and height are also highly heritable polygenic traits, with both rare variant and polygenic influences [25–29]. A study in 1989, Jernström et al. [30], noted that BRCA1 PV carrier patients are small for gestational age compared with their unaffected relatives.
A number of studies have investigated association between reproductive and anthropomorphic traits and cancer risk in BRCA1 and BRCA2 PV carriers [10, 31–33], but apart from studies of ANM and a small study of AAM comprising only 31 BRCA1 and 11 BRCA2 PV carriers [34], to our knowledge there are no epidemiological studies evaluating the distribution of these traits in comparable carrier and non-carrier populations. Here we used data from the Epidemiological Study of Familial Breast Cancer (EMBRACE), a large national study of PV carriers and non-carrier relatives, to evaluate differences in reproductive and anthropometric trait distributions among BRCA1 and BRCA2 PV carriers and non-carriers. Information on the distribution of these traits can ultimately be used to adapt risk prediction algorithms for PV carriers and may further inform our understanding of reproductive biology of female carriers of PVs in these susceptibility genes.
Methods
Study design and population
Participants were enrolled through an on-going nationwide study of individuals undergoing genetic testing in regional genomics centres in the United Kingdom and Ireland (EMBRACE) (https://ccge.medschl.cam.ac.uk/embrace/). EMBRACE recruits individuals who are carriers of pathogenic or likely pathogenic variants (PVs) in breast and/or ovarian cancer susceptibility genes, and their relatives. The analysis reported here included only women of self-reported White ethnicity. Women were eligible if they were at least 18 years of age at recruitment and had tested positive for a BRCA1 or BRCA2 PV or were non-carrier family members of PV carriers. PVs were defined according to ENIGMA/ClinGen guidelines (https://clinicalgenome.org/affiliation/50087/). The distribution of PV sub-classes (protein-truncating, missense, in-frame deletions) are shown in STable 1.
Data collection
All study participants were invited to complete a baseline questionnaire requesting detailed information on known or suspected risk factors for breast and ovarian cancer, including family history of cancer, height, weight at age 18, current weight, reproductive history and surgical interventions including risk-reducing mastectomy (RRM) or RRSO. The questionnaires also requested information on age at last menstruation, whether the woman had had any period in the past year, the number of years/months since last menstruation, and reason(s) for periods stopping. PV carriers also completed follow-up questionnaires: however, since these were not completed by non-carriers and the primary interest was the comparison of carriers and non-carriers, only information from the baseline questionnaire was used here.
Women were considered premenopausal if they indicated at baseline questionnaire that they had had a period in the past year, or that their periods had not stopped completely, or if the ‘reason for periods stopping’ was medication or oral contraceptive use (unless 40 years or older), pregnancy or breast-feeding, unless censored earlier due to cancer diagnosis, chemotherapy or radiotherapy, RRSO or hysterectomy. For N = 17 women there was no information on periods stopping or reason for menopause or age at which periods stopped, these women were considered premenopausal until age at interview. STable 2 outlines numbers of women with missing information for each variable. Age at menopause for those who indicated no period in the past year or periods had stopped completely was determined by adding 1 year to ‘age at last menstruation’. Women were considered as having experienced natural menopause if the reason for periods stopping was recorded as ‘natural menopause’ (and not for any other reason such as chemotherapy, childbirth, pregnancy, breast feeding, hysterectomy, or ‘other’ (unspecified) reason), and age at menopause preceded RRSO, any cancer diagnosis (apart from non-melanoma skin cancer), or interview. Women were also considered as menopausal at age 55 years. Women reporting RRSO or hysterectomy as the reason for periods stopping were considered premenopausal until the age at last period. Women reporting periods stopping (due to natural menopause, RRSO or hysterectomy) but with missing age at menopause or age at last period were excluded from the analyses (see STable 2).
Table 2.
Association between age at natural menopause and BRCA1 and BRCA2 PV carrier vs non-carrier status
| BRCA1 PV carriers | BRCA2 PV carriers | |||||||
|---|---|---|---|---|---|---|---|---|
| estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value | |
| Linear regression among pre and post-menopausal women using cens.normal function in VGAM | ||||||||
| + Birth cohort | −0.002 | −0.401 | 0.397 | 0.991 | −0.172 | −0.531 | 0.188 | 0.349 |
| + Parity + AFB + BMI + AAMa | −0.052 | −0.451 | 0.347 | 0.798 | −0.139 | −0.500 | 0.222 | 0.451 |
| + Parity + AFB + BMI18 + AAMa | 0.006 | −0.395 | 0.406 | 0.977 | −0.104 | −0.467 | 0.260 | 0.576 |
| In women with ANM or end of FUP > = 40 years | 0.077 | −0.302 | 0.457 | 0.69 | 0.002 | −0.334 | 0.339 | 0.989 |
| Linear regression among menopausal women only | ||||||||
| + Birth cohort | −0.700 | −1.241 | −0.159 | 0.011 | −0.215 | −0.686 | 0.255 | 0.369 |
| + Cens agegroup | −0.129 | −0.578 | 0.321 | 0.574 | −0.171 | −0.602 | 0.260 | 0.437 |
| + Cens agegroup + Parity + AFB + BMI18 + AAMa | −0.113 | −0.570 | 0.343 | 0.625 | −0.074 | −0.509 | 0.361 | 0.739 |
| + Cens agegroup + Parity + AFBb | −0.098 | −0.579 | 0.383 | 0.688 | −0.058 | −0.510 | 0.393 | 0.800 |
ANM age at natural menopause, AAM age at menarche, AFB age at first birth, BMI Body Mass Index at baseline, BMI18 BMI at age 18 years, ‘Cens agegroup’, refers to analyses adjusted for the last age at which menopause could be observed; FUP, follow-up
Analyses were adjusted for birth cohort (categorised as < 1940, 1940–1949,1950–1959, > = 1960) and using weights derived as described in the Methods
acarried out on data with no missing information on AAM, BMI or BMI at age 18, parity; AFB and parity were considered as categorical variables
bonly among parous women with information on AFB; AFB was considered as a continuous variable
RRSO and hysterectomy are initially collected by self-reported questionnaires. When a participant self-reports RRSO (with or without hysterectomy), the study team then confirms these reports with the hospital and/or clinic. The reasons for censoring by menopausal status, are summarised in STable 3. The numbers of women experiencing RRSO at censoring and the numbers of breast cancers diagnosed prior to or at interview by age at diagnosis are shown in Table 1.
Table 1.
Characteristics of BRCA1 and BRCA2 PV carriers and non-carriers in EMBRACE
| BRCA1 PV carriers | BRCA2 PV carriers | Non-carriers | |
|---|---|---|---|
| N = 3,046 | N = 3,264 | N = 2,857 | |
| Age at interview (years) | 46 (13); 45 (19) | 48 (13); 48 (20) | 46 (14); 45 (20) |
| Age at interview (years) categories | |||
| < 30 | 358 (12%) | 281 (8.6%) | 390 (14%) |
| 30–39 | 752 (25%) | 686 (21%) | 672 (24%) |
| 40–49 | 818 (27%) | 835 (26%) | 705 (25%) |
| 50–59 | 647 (21%) | 758 (23%) | 585 (20%) |
| 60 + | 471 (15%) | 704 (22%) | 505 (18%) |
| Birth cohort (year)a | |||
| < 1940 | 110 (3.6%) | 144 (4.4%) | 125 (4.4%) |
| 1940–1949 | 369 (12%) | 431 (13%) | 323 (11%) |
| 1950–1959 | 604 (20%) | 739 (23%) | 608 (21%) |
| 1960–1969 | 833 (27%) | 861 (26%) | 779 (27%) |
| 1970–1979 | 618 (20%) | 604 (19%) | 502 (18%) |
| > = 1980 | 512 (17%) | 485 (15%) | 520 (18%) |
| Age at menarche (years) | 12.97 (1.56);13.00 (2.00) | 12.90 (1.51); 13.00 (2.00) | 12.94 (1.53); 13.00 (2.00) |
| Unknown age at menarche (N) | 35 | 24 | 25 |
| Age at menarche (years) categories | |||
| < 12 years | 556 (18%) | 634 (20%) | 552 (19%) |
| 12–14 years | 1,996 (66%) | 2,151 (66%) | 1,859 (66%) |
| > = 15 years | 459 (15%) | 455 (14%) | 421 (15%) |
| Unknown | 35 | 24 | 25 |
| Height (m) | 1.641 (0.07);1.63 (0.08) | 1.639 (0.07); 1.63 (0.08) | 1.636 (0.07);1.63 (0.09) |
| Unknown height (N) | 6 | 7 | 6 |
| BMI at interview (kg) | 25.6 (5.3); 24.6 (6.4) | 25.8 (5.4); 24.6 (6.2) | 25.7 (5.4); 24.6 (6.1) |
| Unknown BMI (N) | 26 | 22 | 30 |
| BMI at age 18 (kg) | 21.49 (3.28); 20.99 (3.38) | 21.46 (3.20); 20.91 (3.37) | 21.55 (3.41); 20.99 (3.49) |
| Unknown BMI age 18 (N) | 109 | 101 | 101 |
| Menopausal status (y/n) | |||
| Premenopausal | 2,667 (88%) | 2,618 (80%) | 2,212 (77%) |
| Menopausal | 379 (12%) | 646 (20%) | 645 (23%) |
| Age at menopause (years) | 50.04 (4.36); 51 (5) | 50.55 (4.31); 51 (5) | 50.77 (3.85); 51 (5) |
| Age at RRSO | 46 (40, 52) | 48 (43, 56) | 45 (40, 50) |
| RRSO at censoring (N) | 310 | 228 | 157 |
| Interval between menarche and menopause or censoring (years) | |||
| mean, sd, median, iqr | 26.2 (8.5); 26.4 (13.1) | 28.3 (8.6); 28.8 (12.8) | 28.0 (9.4); 28.9 (15) |
| Interval between menarche and menopause in menopausal women (years) | |||
| mean, sd, median, iqr | 36.9 (4.5); 38 (5.5) | 37.6 (4.6); 38 (6) | 37.7 (4); 38 (5.5) |
| range | (13.5—45.0) | (16—45) | (23—45) |
| (0,30] | 35 (9%) | 55 (9%) | 36 (6%) |
| (30,35] | 80 (21%) | 117 (18%) | 122 (19%) |
| (35,40] | 179 (47%) | 276 (43%) | 309 (48%) |
| (40,45] | 83 (22%) | 194 (30%) | 175 (27%) |
| Menstrual cycle regularity | |||
| Always regular | 1612 (53%) | 1807 (56%) | 1521 (54%) |
| Usually regular | 1064 (35%) | 1125 (35%) | 966 (34%) |
| Never regular | 348 (12%) | 312 (10%) | 348 (12%) |
| Missing | 22 | 20 | 22 |
| Menstrual cycle length (categories) among women reporting “always regular” or “usually regular” cycles | |||
| < 26 days | 337 (13%) | 362 (13%) | 325 (14%) |
| 26—27 days | 231 (9%) | 263 (9%) | 225 (9%) |
| > = 28 days | 1988 (78%) | 2191 (78%) | 1839 (77%) |
| Missing cycle length (N) | 104 | 105 | 86 |
| Parity | |||
| Nulliparous | 672 (22%) | 665 (20%) | 696 (24%) |
| 1 live birth | 498 (16%) | 521 (16%) | 450 (16%) |
| 2 live births | 1,179 (39%) | 1,308 (40%) | 1,076 (38%) |
| 3 + live births | 697 (23%) | 770 (24%) | 635 (22%) |
| Age at first birth (years) categories | |||
| < 20 | 332 (14%) | 342 (13%) | 350 (16%) |
| 20–24 | 717 (30%) | 811 (31%) | 721 (33%) |
| 25–29 | 769 (32%) | 834 (32%) | 663 (31%) |
| 30–34 | 403 (17%) | 446 (17%) | 315 (15%) |
| > = 35 | 152 (6.4%) | 164 (6.3%) | 111 (5.1%) |
| Unknown Pregnancy Age | 1 | 2 | 1 |
| Breast cancers diagnosesb | 1346 | 1401 | 105 |
| % | 44% | 43% | 3.7% |
1Mean (SD); Median (IQR); n (%)
aFor the main analyses birth cohorts after 1960 were combined
bNumber of breast cancers diagnosed prior to or at interview
Age at menarche was coded as a continuous variable or categorised as age < 12, 12–14 and ≥ 15 years. The interval between menarche and the earliest of menopause and age at censoring (years) was treated as a continuous variable. Women were asked if occurrence of menstrual cycle was always regular, usually regular, or never regular. For women with always regular or usually regular cycles, menstrual cycle length information was categorised as < 26, 26–27 and ≥ 28 days. Parity at baseline was coded as nulliparous, one, two, or three or more live births. Age at first birth was categorised as a continuous or categorical variable (age < 20, 21–25, 26–30, 31–35, ≥ 35 years). Height (m) was treated as a continuous variable. BMI was calculated as weight (kg) divided by height (m) squared.
Statistical analyses
To explore whether age at natural menopause was influenced by PV carrier status, we carried out linear regression analyses allowing for a censored outcome, using the cens.normal function in the VGAM package in R (https://CRAN.R-project.org/package=VGAM and [35, 36]). Women were censored at the earliest of age at natural menopause, age at RRSO, any cancer diagnosis apart from non-melanoma skin cancer, death, age at interview or age 55 years. This analysis allowed pre-menopausal women (right censored at baseline) as well as post-menopausal women to be included but assumes that carrier status shifts the mean ANM (rather than the proportional hazards assumption made in a Cox regression). We also carried out standard linear regression, including only women experiencing natural menopause, adjusted for birth cohort (as described below), and adjusting for age-group at censoring (in two-year categories from < 40 to ≥ 54 years), the last age at which menopause could be observed.
These analyses were also used to evaluate the association between carrier status and the interval between ANM and AAM, and carrier status.
Linear regression models were used to test for associations between PV carrier status and AAM, menstrual cycle length, height, BMI at interview and BMI at age 18 years. Associations with categorical AAM and menstrual cycle length was also assessed using multinomial logistic regression.
Participants in EMBRACE were recruited from a population undergoing genetic testing. Affected individuals are therefore more likely to be sampled than unaffected individuals. Additionally, there is a higher probability of sampling younger affected individuals. To account for this bias, a weighted cohort method in which affected and unaffected women are assigned different weights in all analyses according to their age at diagnosis, or age at censoring, was used so that the weighted cohort mimicked a true cohort [37, 38]. This method has been shown to provide estimates of relative risk which are close to unbiased [37, 38]. An individual was considered a case if they had had a breast cancer diagnosis prior to or at age at interview, regardless of menopausal status, and otherwise a control. For calculation of weights the person-years for unaffected women were calculated from birth to the first of age at interview or RRM, while the person-years for affected women were from birth to age at breast cancer diagnosis, regardless of menopausal status. Individuals were weighted such that the observed breast cancer incidence rates were consistent with established age-specific incidence rates and relative risk estimates for BRCA1 and BRCA2 PV carriers [12, 39, 40] (STables 4 and 5). Non-carriers were not weighted (weight = 1) as the proportion of non-carriers that were affected was small [41].
Table 4.
Association between menstrual cycle length and BRCA1 and BRCA2 PV carrier vs non-carrier status
| BRCA1 PV carriers | BRCA2 PV carriers | |||||||
|---|---|---|---|---|---|---|---|---|
| Analyses among women with no missing information on AAM, and with always regular or usually regular periods | ||||||||
| Linear Regression (+ Birth cohort) | ||||||||
| estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value | |
| Menstrual cycle length (days) | 0.139 | −0.017 | 0.295 | 0.081 | 0.030 | −0.115 | 0.175 | 0.688 |
| Multinomial regression (+ Birth cohort) | ||||||||
| OR | L95 CI | U95 CI | p-value | OR | L95 CI | U95 CI | p-value | |
| < 26 years | 1.000 | 1.000 | ||||||
| 26–27 | 1.006 | 0.793 | 1.278 | 0.958 | 1.131 | 0.897 | 1.426 | 0.297 |
| > = 28 | 1.041 | 0.883 | 1.229 | 0.630 | 1.045 | 0.887 | 1.231 | 0.597 |
| Analyses among women with no missing information on AAM, BMI, height and parity and always regular or usually regular periods | ||||||||
| Linear Regression (+ Birth cohort) | ||||||||
| estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value | |
| Menstrual cycle length (days) | 0.143 | −0.012 | 0.299 | 0.071 | 0.029 | −0.117 | 0.174 | 0.699 |
| + AAM + height + BMI + Parity + AFBa | 0.128 | −0.027 | 0.283 | 0.106 | 0.018 | −0.127 | 0.163 | 0.808 |
| + AAM + height + BMI18 + Parity + AFBa | 0.116 | −0.040 | 0.273 | 0.146 | 0.007 | −0.140 | 0.154 | 0.923 |
AAM, Age at menarche; BMI, Body Mass Index at baseline, BMI18, BMI at age 18 years; AFB, Age at first birth
Analyses adjusted for birth cohort (< 1940, 1940–1949,1950–1959, 1960–1969, 1970–1979, > = 1980); and using weights derived as described in the Methods; analyses were also adjusted for age at interview
a only among parous women with information on AFB; AFB was considered as a categorical variable
Analyses of ANM were adjusted by birth cohort (year of birth < 1940, 1940–1949,1950–1959 and ≥ 1960); by parity, with the number of full-term pregnancies categorised as 0, 1, 2, and 3 or more; and by age at the start of first full-term pregnancy, categorised as < 20, 20–24, 25–29, 30–34 and ≥ 35 years. Analyses were carried out clustering for family membership, and robust variance-adjusted confidence intervals reported.
For analyses of AAM, menstrual cycle length, height, BMI at interview and BMI at age 18 years, models were adjusted using a finer categorisation of birth cohort (i.e. splitting the final category into 1960–1969 and ≥ 1970 groups). For AAM, analyses were also adjusted for BMI at age 18 years. When evaluating menstrual cycle length, analyses were also adjusted for age at interview.
All statistical analyses were conducted using R version 4.3.1 and associated packages.
Results
Study participants.
A total of 3,046 BRCA1 PV carriers, 3,264 BRCA2 PV carriers and 2,857 non-carriers from EMBRACE were included in the analyses. Cohort characteristics and distribution of reproductive risk factors, height and BMI are shown in Table 1. The distribution of age at interview was similar between carriers and non-carriers. Approximately 44% of carriers had been diagnosed with breast cancer at interview, compared with ~ 3.6% of non-carriers.
Distribution of age at natural menopause among BRCA1 and BRCA2 carriers and non-carriers
Among women included in the analysis, 379 (12%) of BRCA1 PV carriers, 646 (20%) of BRCA2 PV carriers and 645 (23%) of non-carriers experienced natural menopause prior to RRSO, a cancer diagnosis (apart from non-melanoma skin cancer) or interview (Table 1).
There was no effect of carrier status on ANM in linear regression analyses allowing for a censored outcome, which included data from both pre- and post-menopausal women (ANM difference = −0.002 (95%CI: −0.401, 0.397), Table 2). The mean ANM was lower among BRCA1 carriers than non-carriers (50.0 vs 50.8 years respectively) (Table 1), and this difference was statistically significant in linear regression analyses unadjusted for age at censoring (p = 0.01, Table 2), including only menopausal women. However, in line with the primary analyses described above, this difference was no longer apparent when analyses were adjusted for the age at censoring, the last age at which menopause could have been observed (ANM carrier vs. non-carrier difference −0.129 years, (95%CI: −0.578, 0.321)) (Table 2).
Similarly, there was no difference in distribution of age at menopause between BRCA2 carriers and non-carriers (mean age at menopause among BRCA2 PV carriers = 50.6 years; linear regression coefficient = −0.172 (95%CI: −0.531, 0.188) (Table 2). Adjustment for BMI, parity and age at first birth did not materially alter the estimates. Results were similar for sensitivity analyses restricting the definition of PV carriers to those carrying only protein truncating variants (PTVs) (STable 6).
Distribution of age at menarche, menstrual cycle length and reproductive lifespan among BRCA1 and BRCA2 carriers and non-carriers
Mean age at menarche was 12.97 and 12.90 years for BRCA1 and BRCA2 carriers respectively, and 12.94 years among non-carriers. There was no statistically significant difference in age at menarche either alone (as a continuous or categorical variable) or after adjusting for BMI (Table 3). There were no statistically significant differences between carriers and non-carriers in menstrual cycle length (in women with always regular or usually regular cycles) (Table 4). The interval between menarche and age at menopause was also similar between carriers and non-carriers in regression analyses allowing for censoring (Table 5).
Table 3.
Association between age at menarche and BRCA1 and BRCA2 PV carrier vs non-carrier status
| BRCA1 PV carriers | BRCA2 PV carriers | |||||||
|---|---|---|---|---|---|---|---|---|
| Linear Regression | ||||||||
| estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value | |
| AAM as continuous variable + Birth cohort | 0.068 | −0.014 | 0.150 | 0.106 | −0.071 | −0.152 | 0.010 | 0.084 |
| AAM as categorical variable—trend + Birth cohort | 0.027 | −0.004 | 0.058 | 0.085 | −0.021 | −0.052 | 0.010 | 0.187 |
| Analyses among women with no missing information on AAM, BMI, and height | ||||||||
| + Birth cohort | 0.064 | −0.019 | 0.147 | 0.130 | −0.076 | −0.158 | 0.005 | 0.067 |
| + Birth cohort + BMI18 | 0.064 | −0.018 | 0.146 | 0.127 | −0.071 | −0.152 | 0.010 | 0.085 |
| + Birth cohort + BMI18 + height | 0.056 | −0.027 | 0.138 | 0.186 | −0.075 | −0.156 | 0.006 | 0.069 |
| Multinomial regression (+ Birth cohort) | ||||||||
| OR | L95 CI | U95 CI | p-value | OR | L95 CI | U95 CI | p-value | |
| < 12 years | 1.000 | 1.000 | ||||||
| 12–14 years | 1.115 | 0.974 | 1.276 | 0.115 | 0.940 | 0.826 | 1.069 | 0.344 |
| > = 15 years | 1.171 | 0.980 | 1.400 | 0.082 | 0.884 | 0.743 | 1.053 | 0.167 |
AAM, Age at menarche; BMI, Body Mass Index at baseline; BMI18, BMI at age 18 years
Analyses were adjusted for birth cohort (categorised as < 1940, 1940–1949,1950–1959, 1960–1969, 1970–1979, > = 1980); and using weights derived as described in the Methods
Table 5.
Interval between menopause and menarche and BRCA1 and BRCA2 PV carrier vs non-carrier status
| BRCA1 PV carriers | BRCA2 PV carriers | |||||||
|---|---|---|---|---|---|---|---|---|
| estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value | |
| Birth cohort | −0.064 | −0.484 | 0.356 | 0.765 | −0.036 | −0.419 | 0.347 | 0.853 |
| + Parity + AFB + BMIa | −0.079 | −0.498 | 0.339 | 0.710 | −0.020 | −0.403 | 0.362 | 0.917 |
| + Parity + AFB + BMI18a | −0.025 | −0.445 | 0.394 | 0.905 | 0.001 | −0.384 | 0.387 | 0.994 |
AFB, age at first birth; BMI, Body Mass Index at baseline, BMI18, BMI at age 18 years
Linear regression analyses of interval between menopause and menarche in BRCA1 and BRCA2 PV carrier vs non-carriers were carried out using the norm.cens regression (VGAM); and using weights derived as described in the Methods
a only among women with no missing information on AFB, parity or BMI, Parity and AFB treated as categorical variables
Distribution of height and BMI among BRCA1 and BRCA2 carriers and non-carriers
BRCA1 PV carriers were slightly taller than non-carriers (mean difference 0.005 m, p = 0.003); for BRCA2 PV carriers the difference was 0.002 m p = 0.2 (Table 6). In unweighted analyses, the effect was also statistically significant for BRCA2 (p < 0.05) (STable 7). The effect estimate was similar after adjusting for covariates BMI or BMI at age 18, AAM, height, parity, and age at first birth. There was no difference in BMI at age at interview or at age 18 years between carriers and non-carriers.
Table 6.
Association between height, and BMI and BRCA1 and BRCA2 PV carrier vs non-carrier status
| BRCA1 PV carriers | BRCA2 PV carriers | |||||||
|---|---|---|---|---|---|---|---|---|
| Trait | estimate | L95 CI | U95 CI | p-value | estimate | L95 CI | U95 CI | p-value |
| height (m) | ||||||||
| + Birth cohort (finer) | 0.005 | 0.002 | 0.009 | 0.003 | 0.002 | −0.001 | 0.006 | 0.212 |
| + Birth cohort (finer) + AAM + Parity + AFBa | 0.005 | 0.001 | 0.008 | 0.011 | 0.002 | −0.001 | 0.006 | 0.242 |
| + Birth cohort (finer) + BMI18 + AAM + Parity + AFBa | 0.004 | 0.001 | 0.008 | 0.018 | 0.002 | −0.002 | 0.006 | 0.294 |
| BMI (kg) | ||||||||
| + Birth cohort (finer) | −0.116 | −0.407 | 0.175 | 0.434 | 0.235 | −0.070 | 0.539 | 0.131 |
| + Birth cohort (finer) + AAM + height + Parity + AFBa | 0.011 | −0.274 | 0.295 | 0.940 | 0.272 | −0.025 | 0.569 | 0.072 |
| BMI at age 18 (kg) | ||||||||
| + Birth cohort (finer) | −0.009 | −0.196 | 0.179 | 0.928 | 0.087 | −0.098 | 0.272 | 0.357 |
| + Birth cohort (finer) + AAM + height | 0.044 | −0.139 | 0.227 | 0.637 | 0.066 | −0.114 | 0.246 | 0.471 |
AAM Age at menarche, BMI, BMI, Body Mass Index at baseline, BMI18, BMI at age 18 years; AFB Age at first birth
all analyses adjusted for birth cohort (categorised as < 1940, 1940–1949,1950–1959, 1960–1969, 1970–1979, > = 1980); and using weights derived as described in the Methods
aOnly women with no missing information on age at menarche, BMI, height and parity were included in the analyses; Parity and AFB treated as categorical variable
Discussion
We compared the distributions of breast cancer risk factors including ANM, AAM, the interval between ANM and AAM, menstrual cycle length, height and BMI in a cohort of BRCA1 and BRCA2 PV carriers, and non-carriers, from a large national study.
We found no statistically significant differences in the distributions of any of these traits, apart from height. In unadjusted analyses among women reporting natural menopause, we observed a lower mean ANM in BRCA1 carriers compared with non-carriers. However, in naïve analyses not accounting for age at censoring, ANM will inevitably be lower in PV carriers, as natural menopause can only be observed if it takes place prior to RRSO. Analyses adjusting for age at censoring (the last age at which menopause could be observed) or allowing for censoring using the ‘norm.cens’ function in R corrected for this phenomenon, and we found no statistically significant difference in ANM when these analytical strategies were applied. The larger correction for BRCA1 PV carriers is consistent with the higher cancer risk and more frequent and earlier uptake of RRSO.
DNA damage and repair mechanisms are central in the biology of menopause and BRCA1 and BRCA2 proteins play a crucial role in the process of DNA double strand break repair through regulation of homologous recombination. It is therefore biologically plausible that these processes interact to influence ANM in carriers. Case–control analysis in UK Biobank data have reported earlier natural menopause in women harbouring PTVs in BRCA1 or BRCA2 [14]. However, the number of carriers in that study were limited (N = 32 BRCA1 and N = 143 BRCA2 carriers). In addition, the effects were smaller in Ward et al. [19], after removing women known to have undergone gynaecological surgeries. The same study [14], however, also reported an earlier ANM in carriers of PALB2 PTVs, an association that was replicated in data from the BRIDGES study (mean ANM difference 1.78 years) [42]. Given the functional similarity between BRCA2 and PALB2, a similar effect on ANM might be expected, so this discrepancy is perplexing.
While only 14% and 21% of carriers experienced natural menopause in EMBRACE, our study included many more PV carriers than Ruth et al. [14] and should be sufficiently powered to detect differences at least of the magnitude estimated using UK Biobank data. For example, the 95%CI for the effect size in the linear regression would exclude a half year earlier (or later) mean ANM in both BRCA1 and BRCA2 PV carriers.
Our results highlight that methodological considerations are important in studies to evaluate risk factors in PV carriers, particularly when evaluating the distribution of age at natural menopause. Interventions, including RRSO in PV carriers, complicate interpretation and results may be sensitive to measurement error. Menopause occurs over a period of time and the recording of both the timing and reason for menopause may be inaccurate. The analyses were based only on data gathered at baseline questionnaire, hence the number of women where the information is completely missing is small. Menopausal status at censoring was inferred/‘imputed’ from answers to multiple different questions. However, potential inaccuracies in the reasons given for menopause, and inaccuracies in ages that periods stopped and other events, could lead to misclassification of menopausal status and a regression to the null. Recording of RRSO and cancer diagnoses may also be incomplete or inaccurate and flagging of cancer could be incomplete. A decision to undergo RRSO may be related to family history of ANM or cancer, as has been previously documented. Furthermore, RRSO may have been scheduled close to anticipated menopause.
There are also limitations in the methodology used to assess associations with ANM. As linear regression ignores data on pre-menopausal women, information is lost. It is also possible that recruitment might be influenced by menopausal status, although this seems unlikely since recruitment is largely determined by family history of cancer. Modelling using the cens.norm function was used as the primary analysis as this method overcomes some of these issues, allowing for censoring whilst using all available data. Due to unbalanced sampling due to recruitment through genetics clinics, analyses with differential weighting of cases and controls were carried out. Another limitation is that non-carriers were only followed up until age at interview, and for this reason only information obtained via the baseline questionnaire was used for both carriers and non-carriers.
Future studies providing accurate record linkage to surgeries and medication use, additional confounders including lifestyle factors related to ovarian aging, and more frequent follow-up to identify when women when first experience menopausal symptoms in relation to other life events, will be valuable.
Menarche, on the other hand, takes place well before the development of cancer, RRSO or genetic testing. We found no association between AAM and carrier status, though age at menarche may be inaccurately reported and could be susceptible to recall bias. BMI at baseline is likely to be accurately reported, and we found no difference in the distributions of BMI between carriers and non-carriers. We did, however, find a small but statistically significant difference in height between carriers and non-carriers, BRCA1 PV carriers being ~ 0.5 cm taller than non-carriers. Measurement of height is likely to be accurate and unbiased. Height is an established risk factor for breast cancer, and many of the biological pathways underlying growth are also relevant to cancer, but to our knowledge this has not so far implicated BRCA-related mechanisms. This observation could be a chance finding. Alternatively, other unmeasured confounding factors (such physical activity or adolescent smoking) might contribute to this association. Of note, the effect for BRCA2 PV carriers differs between the weighted and unweighted analyses. If replicated it would be interesting to investigate the mechanisms underlying differences in height between carriers and non-carriers, and implications for cancer risk.
A major strength of this study is comparability between carriers and non-carriers, many of whom are family members of carriers. On the other hand, it is possible that non-carriers are not entirely representative of the general population. Known and unknown factors relevant to membership of a PV carrier family, for example higher levels of screening, or healthy volunteer bias could be relevant.
In addition to the intrinsic biological interest, the results of these analyses have practical implications. The BOADICEA model assumes that the baseline distributions of risk factors are independent of genotype. If that were not the case, the model would need to be adapted to allow for genotype-specific distributions. While the results of our study suggest that any association between PV status and ANM is likely to be weak, and we report no association between PV status and AAM or BMI, these results should be evaluated in the context of limitations outlined above inherent in evaluating these questions in PV carriers. Under the assumption of risk-factor/genotype independence, it would be possible to evaluate the interactions between risk factors in population-based studies using case-only analyses, which are more powerful than case–control analyses, particularly for rare exposures such as PV status. Currently, in the BOADICEA model lifestyle/hormonal risk factors are assumed to be associated with the same relative risk in PV carriers as non-carriers. It has proved difficult to obtain sufficient prospective data to evaluate this directly, and such case-only analyses may provide a more powerful basis to evaluate these interactions. This, in turn, should provide a reliable basis for counselling and management of PV carriers.
Supplementary Information
Acknowledgements
We thank all patients and clinicians participating in this study. A full list of EMBRACE Trusts and Principal investigators follows: University Hospitals Bristol NHS Foundation Trust, Clinical Genetics, St Michael’s Hospital: Alan Donaldson; All Wales Medical Genomics Service, Cardiff: Alex Murray; London North West Healthcare NHS Trust, North West Regional Genetics Service: Angela Brady; Nottingham University Hospitals NHS Trust, Clinical Genetics Service, City Hospital Campus: Claire Searle; Trinity St Jame's Cancer Institute, Cancer Genetics Service: David Gallagher; Birmingham Women’s and Children’s NHS Foundation Trust, West Midlands Regional Genetics Service: Farah Kanani; Manchester University NHS Foundation Trust, Manchester Centre for Genomic Medicine, St Mary's Hospital: Gareth Evans, Fiona Lalloo; The Leeds Teaching Hospital NHS Trust, Leeds Genomic Medicine Service: Hannah Musgrave; Royal Devon University Healthcare NHS Foundation Trust, Clinical Genetics Dept: Harriet Copeland; All Wales Medical Genomics Service, Cardiff: Hector Conti; Sheffield Children's NHS Foundation Trust, Sheffield Clinical Genetics Service: Jackie Cook; The Lothian University Hospitals NHS Trust, South East of Scotland Clinical Genetic Service: Jennie Murray; University Hospitals of Leicester NHS Trust, Leicestershire, Northamptonshire and Rutland Clinical Genetics Service: Julian Barwell; South West Thames Regional Genetics Service, St Georges University of London: Katie Snape; Oxford University Hospitals NHS Foundation Trust, Oxford Regional Genetics Service: Lisa Walker; Guy's and St Thomas' NHS Foundation Trust, Genetics Research Team, Guys Hospital: Louise Izatt, Vishakha Tripathi; University Hospital Southampton NHS Foundation Trust, Wessex Clinical Genetics Service: Lucy Side; Cambridge University Hospitals NHS Foundation Trust, East Anglian Medical Genetics Service, Addenbrookes Hospital: Marc Tischkowitz; Great Ormond Street NHS Hospital for Children NHS Foundation Trust, North East Thames Regional Clinical Genetics Service: Munaza Ahmed; Belfast Health and Social Care Trust, Clinical Genetics Service: Patrick Morrison; The Newcastle upon Tyne Hospitals NHS Foundation Trust, Northern Genetics Service: Paul Brennan; Liverpool Women's NHS Foundation Trust, Liverpool Centre for Genomic Medicine: Rachel Hart; NHS Greater Glasgow and Clyde, West of Scotland Genetics Services: Rosemarie Davidson; The Royal Marsden NHS Foundation Trust, Royal Marsden Clinical Genetics Unit: Zoe Kemp; NHS Grampian, North of Scotland Regional Genetics Service: Zosia Miedzybrodzka.
Abbreviations
- PV
Pathogenic variant
- ANM
Age at natural menopause
- AAM
Age at menarche
- BMI
Body mass index
- RRSO
Risk reducing salpingo-oophorectomy
- EMBRACE
Epidemiological Study of Familial Breast Cancer
- LOF
Loss-of-function
- ER
Oestrogen receptor
- CI
Confidence intervals
- p
P-value
Authors’ contributions
Writing Group: NM, ACA, DFE; Study design: NM, ACA, DFE; Data management: DF, EZ; Statistical Analysis: NM, DFE, DRB; Funding: ACA, DFE, MT, DGE, SA; Provided data: MA, JB, AFB, PB, HCon, JC, HCop, RD, AD, ED, DG, RH, LI, ZK, FL, ZM, PJM, JM, AM, HM, CS, LS, KS, VT, LW, SA, DGE, MT, ACA, DFE. All authors read and approved the final version of the manuscript.
Funding
EMBRACE was supported by Cancer Research UK grants PRCPJT-Nov21\100004 and A26886. This analysis was supported by Cancer Research UK grant: PPRPGM-Nov20\100002. MT was supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312). DGE was supported by the Manchester National Institute for Health Research Manchester Biomedical Research Centre (NIHR203308).
Data availability
The datasets generated and/or analysed during the current study are not publicly available, as they potentially include personal data. However, they can be accessed upon reasonable request made to the EMBRACE study Data Access Coordination Committee (embrace@medschl.cam.ac.uk) and the completion of a data sharing agreement.
Declarations
Ethics approval and consent to participate
The EMBRACE study was approved by the East of England – Cambridge South Ethics Committee (ref 98/5/026, IRAS 20971). All participants gave written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Kuchenbaecker KB, Hopper JL, Barnes DR, Phillips KA, Mooij TM, Roos-Blom MJ, et al. Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers. JAMA. 2017;317(23):2402–16. [DOI] [PubMed] [Google Scholar]
- 2.Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies. Lancet Oncol. 2012;13(11):1141–51. [DOI] [PMC free article] [PubMed]
- 3.Qian F, Wang S, Mitchell J, McGuffog L, Barrowdale D, Leslie G, et al. Height and Body Mass Index as Modifiers of Breast Cancer Risk in BRCA1/2 Mutation Carriers: A Mendelian Randomization Study. J Natl Cancer Inst. 2019;111(4):350–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Manders P, Pijpe A, Hooning MJ, Kluijt I, Vasen HF, Hoogerbrugge N, et al. Body weight and risk of breast cancer in BRCA1/2 mutation carriers. Breast Cancer Res Treat. 2011;126(1):193–202. [DOI] [PubMed] [Google Scholar]
- 5.Kast K, John EM, Hopper JL, Andrieu N, Noguès C, Mouret-Fourme E, et al. Associations of height, body mass index, and weight gain with breast cancer risk in carriers of a pathogenic variant in BRCA1 or BRCA2: the BRCA1 and BRCA2 Cohort Consortium. Breast Cancer Res. 2023;25(1):72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Park B, Hopper JL, Win AK, Dowty JG, Sung HK, Ahn C, et al. Reproductive factors as risk modifiers of breast cancer in BRCA mutation carriers and high-risk non-carriers. Oncotarget. 2017;8(60):102110–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kim SJ, Huzarski T, Gronwald J, Singer CF, Møller P, Lynch HT, et al. Prospective evaluation of body size and breast cancer risk among BRCA1 and BRCA2 mutation carriers. Int J Epidemiol. 2018;47(3):987–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Cohen SY, Stoll CR, Anandarajah A, Doering M, Colditz GA. Modifiable risk factors in women at high risk of breast cancer: a systematic review. Breast Cancer Res. 2023;25(1):45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kim SJ, Lubinski J, Huzarski T, Møller P, Armel S, Karlan BY, et al. Weight Gain and the Risk of Ovarian Cancer in BRCA1 and BRCA2 Mutation Carriers. Cancer Epidemiol Biomarkers Prev. 2021;30(11):2038–43. [DOI] [PubMed] [Google Scholar]
- 10.Bhardwaj P, Iyengar NM, Zahid H, Carter KM, Byun DJ, Choi MH, et al. Obesity promotes breast epithelium DNA damage in women carrying a germline mutation in BRCA1 or BRCA2. Sci Transl Med. 2023;15(684):eade1857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lee AJ, Cunningham AP, Kuchenbaecker KB, Mavaddat N, Easton DF, Antoniou AC. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer. 2014;110(2):535–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Lee A, Mavaddat N, Wilcox AN, Cunningham AP, Carver T, Hartley S, et al. BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genet Med. 2019;21(8):1708–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Aiken CE, Tarry-Adkins JL, Penfold NC, Dearden L, Ozanne SE. Decreased ovarian reserve, dysregulation of mitochondrial biogenesis, and increased lipid peroxidation in female mouse offspring exposed to an obesogenic maternal diet. FASEB J. 2016;30(4):1548–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ruth KS, Day FR, Hussain J, Martinez-Marchal A, Aiken CE, Azad A, et al. Genetic insights into biological mechanisms governing human ovarian ageing. Nature. 2021;596(7872):393–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Day FR, Ruth KS, Thompson DJ, Lunetta KL, Pervjakova N, Chasman DI, et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat Genet. 2015;47(11):1294–303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Baynes C, Healey CS, Pooley KA, Scollen S, Luben RN, Thompson DJ, et al. Common variants in the ATM, BRCA1, BRCA2, CHEK2 and TP53 cancer susceptibility genes are unlikely to increase breast cancer risk. Breast Cancer Res. 2007;9(2):R27. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Titus S, Li F, Stobezki R, Akula K, Unsal E, Jeong K, et al. Impairment of BRCA1-related DNA double-strand break repair leads to ovarian aging in mice and humans. Sci Transl Med. 2013;5(172):172ra21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Fan S, Wang J, Yuan R, Ma Y, Meng Q, Erdos MR, et al. BRCA1 inhibition of estrogen receptor signaling in transfected cells. Science. 1999;284(5418):1354–6. [DOI] [PubMed] [Google Scholar]
- 19.Ward LD, Parker MM, Deaton AM, Tu HC, Flynn-Carroll AO, Hinkle G, et al. Rare coding variants in DNA damage repair genes associated with timing of natural menopause. HGG Adv. 2022;3(2):100079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Oktay K, Kim JY, Barad D, Babayev SN. Association of BRCA1 mutations with occult primary ovarian insufficiency: a possible explanation for the link between infertility and breast/ovarian cancer risks. J Clin Oncol. 2010;28(2):240–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Oktay K, Turan V, Titus S, Stobezki R, Liu L. BRCA Mutations, DNA Repair Deficiency, and Ovarian Aging. Biol Reprod. 2015;93(3):67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Finch A, Valentini A, Greenblatt E, Lynch HT, Ghadirian P, Armel S, et al. Frequency of premature menopause in women who carry a BRCA1 or BRCA2 mutation. Fertil Steril. 2013;99(6):1724–8. [DOI] [PubMed] [Google Scholar]
- 23.Phillips KA, Collins IM, Milne RL, McLachlan SA, Friedlander M, Hickey M, et al. Anti-Mullerian hormone serum concentrations of women with germline BRCA1 or BRCA2 mutations. Hum Reprod. 2016;31(5):1126–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Van Tilborg TC, Broekmans FJ, Pijpe A, Schrijver LH, Mooij TM, Oosterwijk JC, et al. Do BRCA1/2 mutation carriers have an earlier onset of natural menopause?Response. Response Menopause. 2016;23(8):903–10. [DOI] [PubMed] [Google Scholar]
- 25.Loos RJF, Yeo GSH. The genetics of obesity: from discovery to biology. Nat Rev Genet. 2022;23(2):120–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27(20):3641–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Marouli E, Graff M, Medina-Gomez C, Lo KS, Wood AR, Kjaer TR, et al. Rare and low-frequency coding variants alter human adult height. Nature. 2017;542(7640):186–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Day FR, Thompson DJ, Helgason H, Chasman DI, Finucane H, Sulem P, et al. Genomic analyses identify hundreds of variants associated with age at menarche and support a role for puberty timing in cancer risk. Nat Genet. 2017;49(6):834–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Kentistou KA, Kaisinger LR, Stankovic S, Vaudel M, Mendes de Oliveira E, Messina A, et al. Understanding the genetic complexity of puberty timing across the allele frequency spectrum. Nat Genet. 2024;56(7):1397–411. [DOI] [PMC free article] [PubMed]
- 30.Jernström H, Johannsson O, Borg A, Ivarsson H, Olsson H. BRCA1-positive patients are small for gestational age compared with their unaffected relatives. Eur J Cancer. 1998;34(3):368–71. [DOI] [PubMed] [Google Scholar]
- 31.Kim JY, Moon HG, Kang YJ, Han W, Noh WC, Jung Y, et al. The Effect of Reproductive Factors on Breast Cancer Presentation in Women Who Are BRCA Mutation Carrier. J Breast Cancer. 2017;20(3):279–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Toss A, Grandi G, Cagnacci A, Marcheselli L, Pavesi S, De Matteis E, et al. The impact of reproductive life on breast cancer risk in women with family history or BRCA mutation. Oncotarget. 2017;8(6):9144–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Geczik AM, Ferris JS, Terry MB, Andrulis IL, Buys SS, Daly MB, et al. Adherence to the 2020 American Cancer Society Guideline for Cancer Prevention and risk of breast cancer for women at increased familial and genetic risk in the Breast Cancer Family Registry: an evaluation of the weight, physical activity, and alcohol consumption recommendations. Breast Cancer Res Treat. 2022;194(3):673–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Jernström HC, Johannsson OT, Loman N, Borg A, Olsson H. Reproductive factors in hereditary breast cancer. Breast Cancer Res Treat. 1999;58(3):295–301. [DOI] [PubMed] [Google Scholar]
- 35.Yee TW, Wild CJ. Vector Generalized Additive Models. J Roy Stat Soc: Ser B (Methodol). 2018;58(3):481–93. [Google Scholar]
- 36.Yee TW. Vector Generalized Linear and Additive Models with an Implementation in R. New York: Springer; 2015.
- 37.Barnes DR, Lee A, Easton DF, Antoniou AC. Evaluation of association methods for analysing modifiers of disease risk in carriers of high-risk mutations. Genet Epidemiol. 2012;36(3):274–91. [DOI] [PubMed] [Google Scholar]
- 38.Antoniou AC, Goldgar DE, Andrieu N, Chang-Claude J, Brohet R, Rookus MA, et al. A weighted cohort approach for analysing factors modifying disease risks in carriers of high-risk susceptibility genes. Genet Epidemiol. 2005;29(1):1–11. [DOI] [PubMed] [Google Scholar]
- 39.Lee A, Mavaddat N, Cunningham A, Carver T, Ficorella L, Archer S, et al. Enhancing the BOADICEA cancer risk prediction model to incorporate new data on RAD51C, RAD51D, BARD1 updates to tumour pathology and cancer incidence. J Med Genet. 2022;59(12):1206–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Ficorella L, Yang X, Easton DF, Antoniou AC. BOADICEA model: updates to the BRCA2 breast cancer risks for ages 60 years and older. BJC Reports. 2024;2(1):53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Girardi F, Barnes DR, Barrowdale D, Frost D, Brady AF, Miller C, et al. Risks of breast or ovarian cancer in BRCA1 or BRCA2 predictive test negatives: findings from the EMBRACE study. Genet Med. 2018;20(12):1575–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Stankovic S, Shekari S, Huang QQ, Gardner EJ, Ivarsdottir EV, Owens NDL, et al. Genetic links between ovarian ageing, cancer risk and de novo mutation rates. Nature. 2024;633(8030):608–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available, as they potentially include personal data. However, they can be accessed upon reasonable request made to the EMBRACE study Data Access Coordination Committee (embrace@medschl.cam.ac.uk) and the completion of a data sharing agreement.