Lower age at menarche affects survival in older Australian women: results from the Australian Longitudinal Study of Ageing
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Abstract
BACKGROUND: While menarche indicates the beginning of a woman's reproductive life, relatively little is known about the association between age at menarche and subsequent morbidity and mortality. We aimed to examine the effect of lower age at menarche on all-cause mortality in older Australian women over 15 years of follow-up. METHODS: Data were drawn from the Australian Longitudinal Study of Ageing (n = 1,031 women aged 65-103 years). We estimated the hazard ratio (HR) associated with lower age at menarche using Cox proportional hazards models, and adjusted for a broad range of reproductive, demographic, health and lifestyle covariates. RESULTS: During the follow-up period, 673 women (65%) died (average 7.3 years (SD 4.1) of follow-up for decedents). Women with menses onset or= 12 years (89.3%; n = 883). However, when age at menarche was considered as a continuous variable, the adjusted HRs associated with the linear and quadratic terms for age at menarche were not statistically significant at a 5% level of significance (linear HR 0.76; 95%CI 0.56 - 1.04; quadratic HR 1.01; 95%CI 1.00-1.02). CONCLUSION: Women with lower age at menarche may have reduced survival into old age. These results lend support to the known associations between earlier menarche and risk of metabolic disease in early adulthood. Strategies to minimise earlier menarche, such as promoting healthy weights and minimising family dysfunction during childhood, may also have positive longer-term effects on survival in later life.
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Abstract
Background: While menarche indicates the beginning of a woman's reproductive life, relatively little is known about
the association between age at menarche and subsequent morbidity and mortality. We aimed to examine the effect of
lower age at menarche on all-cause mortality in older Australian women over 15 years of follow-up.
Methods
Data were drawn from the Australian Longitudinal Study of Ageing (n = 1,031 women aged 65-103 years).
We estimated the hazard ratio (HR) associated with lower age at menarche using Cox proportional hazards models, and
adjusted for a broad range of reproductive, demographic, health and lifestyle covariates.
Results
During the follow-up period, 673 women (65%) died (average 7.3 years (SD 4.1) of follow-up for decedents).
Women with menses onset < 12 years of age (10.7%; n = 106) had an increased hazard of death over the follow-up
period (adjusted HR 1.28; 95%CI 0.99-1.65) compared with women who began menstruating aged ≥ 12 years (89.3%; n
= 883). However, when age at menarche was considered as a continuous variable, the adjusted HRs associated with the
linear and quadratic terms for age at menarche were not statistically significant at a 5% level of significance (linear HR
0.76; 95%CI 0.56 - 1.04; quadratic HR 1.01; 95%CI 1.00-1.02).
Conclusion
Women with lower age at menarche may have reduced survival into old age. These results lend support
to the known associations between earlier menarche and risk of metabolic disease in early adulthood. Strategies to
minimise earlier menarche, such as promoting healthy weights and minimising family dysfunction during childhood,
may also have positive longer-term effects on survival in later life.
Background
The timing and development of the reproductive system
can be viewed as a continuum across the lifespan [1] in
which there is an intimate association with underlying
metabolic processes, reproductive function and, poten-
tially, chronic disease risk. The onset of menarche is an
important milestone in a woman's reproductive career,
and appears to be meaningfully related to a range of
emergent chronic disease risk factors, and subsequent
morbidity and mortality in later-life.
An association between lower age at menarche - that is,
< 12 years [2] - and an increased risk of uterine cancer [3]
and breast cancer [4,5] is well established. One explana-
tion for the latter is that earlier menarche alters patterns
of adiposity, which in turn appear to be related to an
increased risk of breast cancer [6]. There is also a rela-
tionship between earlier age at menarche and cardiovas-
cular disease in adolescence [2]. Earlier age of menarche
has been commonly associated with increased body mass
index (BMI) in childhood and adolescence [7], which
appears to partially reflect restricted growth in very early
l i f e [ 8 ] f o l l o w e d b y r a p i d p o s t - n a t a l g r o w t h [ 9 ] . T h e s e
adverse changes also track and amplify over time into
adulthood, such that earlier menarche is associated with
increased risk of developing the metabolic syndrome in
adulthood [10]. There is conflicting evidence concerning
the effects of earlier age at menarche on cardiovascular
mortality [11-13].
Somewhat surprisingly, few authors have examined the
relationship between age at menarche and all-cause mor-
tality. Previous studies were limited by inadequate adjust-
* Correspondence: [email protected]
1 Life course and Intergenerational Health Research Group, Robinson Institute,
The University of Adelaide, Adelaide South Australia 5005, Australia
Full list of author information is available at the end of the article
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ment for confounding factors [14] or through samples
that were not representative of the wider population [12].
Lakshman et al.'s recent large study [13] partly addresses
these limitations, but did not include some potentially
important covariates such as age at menopause.
Therefore, the aim of the present study was to examine
the effect of earlier age at menarche on all-cause mortal-
ity over 15 years of follow-up in a sample of older women
drawn from the general Australian population. We were
also able to adjust for a broader range of reproductive,
demographic, health, and lifestyle factors than in previ-
ous studies in this area.
Methods
W e drew data from the Australian Longitudinal Study of
Ageing (ALSA) that began in 1992 in Adelaide, South
A u s t r a l i a . A L S A ' s m a j o r o b j e c t i v e s w e r e t o a s s e s s t h e
effects of social, biomedical, behavioural, economic, and
environmental factors upon age-related changes in the
health and wellbeing of older persons. The study has been
described in detail elsewhere [15,16]. Briefly, the primary
sample was randomly selected from the South Australian
Electoral Roll, and stratified by local government area,
gender and age group (70 - 74, 75 - 79, 80 - 84 and > 85
years). Older males were over-sampled to ensure suffi-
cient numbers for longitudinal follow-up. Persons were
eligible for the study if they were resident in the Adelaide
S t a t i s t i c a l D i v i s i o n a n d a g e d 7 0 y e a r s o r m o r e o n 3 1
December 1992 (the mid-date of the study's Wave 1).
Spouses aged 65 years or more and other, non-spousal
household members aged at least 70 were also invited to
take part. A total of 1,031 of the 2,087 Wave 1 partici-
pants were female.
Ethical approval for the study was granted by the
Flinders Committee for Clinical Investigation, and each
participant gave written informed consent.
Menstrual and reproductive variables
Wave 1 questions concerning menstrual and reproduc-
tive histories were used to derive variables reflecting
these histories. Age at menarche (classified as < 12 or ≥
12 years, after Remsberg et al. [2]), age at menopause (≤
44, 45 - 49, 50 - 54, ≥ 55 years, or surgical), number of
reproductive years (≤ 30, 31 - 39, and ≥ 40) and the num-
ber of live births (0, 1 - 2, 3 - 4, ≥ 5) were derived from the
women's self-reported histories.
Demographic, health and lifestyle variables
The analyses were adjusted for the effects of demo-
graphic, health, and lifestyle variables at study baseline
demonstrated in previous studies to be associated with
age at menarche or survival [12-14]. Demographic vari-
ables included age group and place of residence (commu-
nity or residential care). Self-rated health was classified as
excellent/very good, good, and fair/poor. The number of
chronic conditions was derived from self-reported infor-
mation on whether each participant had ever suffered
from 10 common conditions, including diabetes, cancer
and cardiovascular disease [13,17]. Cognitive function
was dichotomised as impaired or intact based on results
from a subset of items from the Mini-Mental State Exam-
ination [18,19]. Participants were classed as current, for-
mer, or never smokers from their responses to questions
concerning smoking. Participants were classified as
active or sedentary based on questions about the exercise
undertaken in the fortnight prior to Wave 1 interview
[16]. BMI was derived from height and weight measured
at clinical assessment, and categorised as 25 kg/m
2.
Statistical analyses
The bivariate associations between age group at menar-
che and the reproductive, demographic, health and life-
style covariates were investigated through chi-square
tests of association for categorical covariates and Mann-
Whitney U-tests for covariates with a continuous distri-
bution.
S u rv i v a l s t a t u s a t t h e c e n s o r i n g d a t e o f 1 5 y e a r s a f t e r
the Wave 1 interview was ascertained. The Epidemiology
B r a n c h o f t h e D e p a rt m e n t o f H e a l t h i n So u t h A u s t r a l i a
conducted searches of official death certificates and
deaths were confirmed by the South Australian Births,
Deaths and Marriages bureau. Full name, date of birth
and last known address of ALSA participants were used
in the data linkage with the Deaths database. If no direct
match was made, the Electoral Roll was checked for
errors in birth dates, changes or errors in recorded name,
and changes or errors in recorded address. Informants
nominated by ALSA participants at Wave 1 were con-
tacted if participants could not be located at subsequent
interviews. The date of death supplied by informants was
used if a participant died outside of South Australia.
These methods of death ascertainment for ALSA partici-
pants have been validated previously [20,21].
The response variable was the number of days to death
from the date of the Wave 1 interview for decedents and
5,497 days for participants who survived 15 years after
their initial interview. Cox proportional hazards models
were fit to the data to ascertain the hazard ratio (HR) for
the effect of lower age at menarche on survival, control-
ling for the other demographic, health, lifestyle and
r e p r o d u c t i v e c h a r a c t e r i s t i c s . M o d e l s t h a t s e q u e n t i a l l y
adjusted for demographic, then health, then lifestyle, then
reproductive characteristics were fit to investigate how
these groups of variables modified the association, if any,
between time to death and age at menarche. The Efron
Method
was used to correct for ties in the time to death
[22,23].
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The fit of models was assessed using graphical methods
based on martingale residual s [24,25]. The assumption of
proportional hazards was assessed by regressing the
scaled Schoenfeld residuals against the log of time and
testing for zero slope [26]. Stata version 10.0 (College Sta-
tion, Texas) was used in all analyses [27].
To assess the sensitivity of results to missing values for
age at menarche and age at menopause, multiple imputa-
tion methods were used to estimate missing data for
these two variables. Among other factors, nutrition, eth-
nicity and family composition are known to influence
menses onset [28]. Therefore, in the multiple imputation
analysis age at menarche (missing n = 42; 4%) was
imputed 10 times according to the dependence of menar-
cheal age on number of sisters, BMI (adult BMI as a
marker for childhood BMI), year of birth, and country of
birth (Australia, England, other European country, other
non-European country). Age at menopause (missing n =
77; 7%) was imputed 10 times according to its depen-
dence on type of menopause (natural or otherwise), BMI,
parity, age at first pregnancy, age at last pregnancy, num-
ber of breast-fed children, number of sisters, smoking sta-
tus at time of menopause, age left full-time education,
and country of birth. The 'ice' and 'micombine' packages
in Stata were used in the imputations [29]. Complete case
and imputed case analyses were run for all models and
Results
are reported for both types of analyses.
Results
The 1,031 women included in our analyses were aged
between 65 and 103 at study baseline, with an average age
of 77.3 years (SD 7.1). The mean age at menarche in these
women was 13.6 years (SD 1.7), while the median was 14
years (interquartile range (IQR) 12 - 15 years). A total of
10.7% of the women experienced menarche aged less
than 12 years, while 12.5% of the women were aged 16
years or more when their menses commenced. The aver-
age age of menopause among the women in our study was
48.1 years (SD 5.8; median 50 years, IQR 45 - 52). More
than one fifth of the sample experienced menopause
before 45 years of age, while 10.5% were aged 55 years or
more when they experienced menopause. Three quarters
of the women underwent natural menopause. The
women in the study had an average of 34.5 reproductive
years (SD 6.0; median 35; IQR 31 - 38). A total of 31
women (3%) reported they were using hormone therapy
at the time of the Wave 1 interview.
Table 1 shows the reproductive, demographic, health
and lifestyle characteristics and 15 year survival status of
the female participants in Wave 1 of ALSA classified
according to age at menarche. Of the 1,031 women who
took part in Wave 1, 673 (65%) died within 15 years of
their Wave 1 interview; the average length of follow-up
for decedents was 7.3 (SD 4.1) years. Among the women
with a reported age at menarche (n = 989), there were sta-
tistically significant associations between age at menar-
che and attained age group (X
2 = 9.73 on 4 df; P = 0.045),
number of morbid conditions (Mann-Whitney U-test z =
2.51; P = 0.012), cognitive function (X2 = 4.67 on 1 df; P =
0.031), BMI category (X 2 = 16.58 on 3 df; P = 0.001), and
number of reproductive years (X 2 = 8.39 on 2 df; P =
0.015).
Younger age group, living in the community, better self-
rated health, fewer co-morbid conditions, better cogni-
tive function, never smoking and exercise were jointly
significant predictors of longer survival and subsequent
analyses adjusted for these variables. We also controlled
for BMI category, parity > 0, age at menopause, and num-
ber of reproductive years in the analyses, based on the
significant effects of these variables on all-cause mortality
reported previously [12,13]. The adjusted hazard ratios
associated with each of these variables are presented in
Table 2. The assumption of proportional hazards was ten-
able in the age-adjusted (global test X
2 = 3.26 on 5 df, P =
0.660) and all covariate adjusted (global test X2 = 25.73 on
23 df; P = 0.314) analyses.
As shown in Table 3, the effect of lower age at menar-
che overall was associated with approximately a one third
increase in the hazard of death over a 15 year follow-up
period (age adjusted HR 1.35; 95%CI 1.05 - 1.73). The
effect persisted when adjusted for other covariates,
although the hazard reduced to marginal statistical signif-
icance when other reproductive covariates were included
in addition to health, lifestyle and demographic covari-
ates (adjusted HR 1.28; 95%CI 0.99 - 1.65). Results did not
differ substantively in the analyses based on the multiply
imputed data (Table 3).
We repeated the analyses with age at menarche as a
continuous variable and included a quadratic term. The
Results
showed the adjusted HR corresponding to the lin-
ear term for age at menarche was 0.76 (95%CI 0.56 - 1.04;
P = 0.091) and the adjusted HR corresponding to the qua-
dratic term was 1.01 (95%CI 1.00-1.02: P = 0.083).
Discussion
Younger age at menarche (i.e. < 12 years) appears to be
associated with roughly a one third increased hazard of
death over a 15 year period among Australian women
aged 65 years or more, after adjustment for a broad range
of reproductive, health and lifestyle variables. Sensitivity
analyses using multiple imputation of missing data did
not change the substantive conclusions. However, when
considered as a continuous variable, age at menarche was
not associated with an increased hazard of death at a con-
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Table 1: Summary statistics for 1,031 female participants in wave 1 of ALSA classified according to age at menarche
Age at menarche
Characteristic Overall < 12 ≥ 12 Missing P-valuea
n = 1,031 n = 106 n = 883 n = 42
Age group
65 - 69 123
(12%)
14
(13%)
106
(12%)
3
(7%)
0.045
70 - 74 283
(27%)
41
(39%)
238
(27%)
4
(10%)
75 - 79 241
(23%)
26
(25%)
211
(24%)
4
(10%)
80 - 84 194
(19%)
14
(13%)
170
(19%)
10
(24%)
≥ 85 190
(18%)
11
(10%)
158
(18%)
21
(50%)
Place of residence
Community 948
(92%)
100
(94%)
821
(93%)
27
(64%)
0.601
Residential 83
(8%)
6
(6%)
62
(7%)
15
(36%)
Self rated health
Excellent/very good 401
(39%)
32
(30%)
357
(40%)
12
(29%)
0.114
Good 324
(31%)
36
(34%)
269
(31%)
19
(45%)
Fair/poor 306
(30%)
38
(36%)
257
(29%)
11
(26%)
Number of morbid conditions
Median (IQR) 3
(2 - 4)
3
(2 - 5)
3
(2 - 4)
2
(0 - 3)
0.012d
Cognitive function
Good 888
(86%)
99
(93%)
758
(86%)
31
(74%)
0.031
Poor 143
(14%)
7
(7%)
125
(14%)
11
(26%)
Smoking status
Never smoker 718
(69%)
66
(62%)
618
(70%)
35
(83%)
0.266
Former smoker 229
(23%)
29
(28%)
193
(22%)
7
(16%)
Current smoker 84
(8%)
11
(10%)
72
(8%)
1
(2%)
Sedentary
No 541
(52%)
54
(50%)
461
(52%)
26
(62%)
0.805
Yes 490
(48%)
52
(50%)
422
(48%)
16
(38%)
Body Mass Index
< 20 kg/m2 41
(4%)
2
(2%)
39
(4%)
0
(0%)
0.001
20 - 25 kg/m2 280
(27%)
18
(17%)
254
(29%)
8
(19%)
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> 25 kg/m2 428
(42%)
64
(60%)
355
(40%)
9
(21%)
Missing 282
(27%)
22
(21%)
235
(27%)
25
(59%)
Natural menopause
No 273
(26%)
32
(30%)
219
(25%)
22
(52%)
0.229
Yes 758
(74%)
74
(70%)
664
(75%)
20
(48%)
Age at menopause
≤ 44 b 104
(10%)
15
(14%)
89
(10%)
0
-
0.123
45 - 49 192
(19%)
19
(18%)
172
(19%)
1
(4%)
50 - 54 316
(31%)
33
(31%)
276
(31%)
7
(27%)
≥ 55 148
(15%)
7
(7%)
129
(15%)
12
(46%)
Surgical 254
(25%)
32
(30%)
216
(24%)
6
(23%)
Reproductive years
≤ 30 c 218
(23%)
20
(19%)
198
(24%)
-0 . 0 1 5
31 - 40 608
(65%)
63
(60%)
545
(65%)
-
≥ 40 116
(12%)
22
(21%)
94
(11%)
-
Parity
0 136
(13%)
14
(13%)
113
(13%)
9
(21%)
0.527
1 - 2 439
(43%)
44
(42%)
372
(42%)
23
(55%)
3 - 4 351
(34%)
41
(39%)
302
(34%)
8
(19%)
≥ 5 105
(10%)
7
(7%)
96
(11%)
2
(5%)
Age first pregnancy
Median (IQR) 25
(22 - 28)
24
(21 - 28)
25
(22 - 28)
26
(22 - 35)
0.149d
Age last pregnancy
Median (IQR) 33
(29-37)
33
(29 - 36)
33
(29 - 37)
31
(29 - 36)
0.425d
Dead at 15 years follow-up
No 358
(35%)
34
(32%)
319
(36%)
5
(12%)
0.411
Yes 673
(65%)
72
(68%)
564
(64%)
37
(88%)
Shown are n (%) unless otherwise noted.
a: P-value excludes missing category in calculation
b: n = 954 available age at menopause
c: n = 942 available reproductive years
d: P-value based on Mann-Whitney U-test; all other shown P-values based on chi-square tests of association
Table 1: Summary statistics for 1,031 female participants in wave 1 of ALSA classified according to age at menarche
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ventional 5% level of significance. This suggests that any
relationship between menarcheal age and mortality is not
of a simple functional form.
The results from this study are also in broad agreement
with the two recent studies by Jacobsen and colleagues
[12,14], although there are some important differences
between their work and the present study. In an earlier
study, Jacobsen and colleagues had established a modest
effect of age at menopause on all-cause mortality in the
subset of naturally menopausal women within their Nor-
wegian cohort [30]. However, they do not appear to have
adjusted for age at menopause in their final analyses con-
cerning the effect of age at menarche on all-cause mortal-
ity in the same cohort [14]. This may be a minor concern,
as our findings concerning lower age at menarche did not
differ substantively when age at menopause was excluded
from the fitted model. More than one third of the women
in the Californian cohort were pre-menopausal while
almost half of the post-menopausal woman had a surgi-
cally-induced menopause [12]. In contrast, all of the
women in the ALSA study had undergone menopause at
least a decade prior to their Wave 1 interview, and less
than one quarter of the women in our study had a surgical
menopause. Our adjusted HR of 1.28 (95%CI 0.99 - 1.65)
is also broadly consistent with that reported by Lakshman
et al. (adjusted HR 1.22; 1.07-1.39). While the latter study
was larger, with close to 16,000 women, the women in
Lakshman et al.'s study were younger at baseline (range
40-79 years) and some remained pre-menopausal at fol-
low-up. In contrast, women had to have reached age 65 to
be eligible for inclusion in our study, so it is possible that
left-censoring is in operation. Women with earlier
menarche who died before the age of 65 could not enter
our study, but this will not alter the effect of age at menar-
che on longevity among women who have already sur-
vived to older age. Thus we argue that our results more
accurately represent the effect of age at menarche on
mortality in a heterogeneous, post-menopausal sample of
older women.
Menstrual onset follows from a cascade of endocrine
changes that include increases in the secretion of gonado-
tropin-releasing hormone, growth hormone and insulin
[6]. The activation of menarche is modulated by the
hypothalamic-pituitary-gonadal system, particularly
endogenous estradiol and lower sex hormone binding
globulin [31,32]. While a broad range of genetic and envi-
ronmental influences have been proposed to affect age at
menarche [28], the pathways through which these influ-
ences operate remain poorly understood [33]. Genetic
inheritance through the mother, growth and weight dur-
ing infancy and early childhood [34,35], socioeconomic
factors [36], and the quality of father-daughter relation-
ships in early childhood [37] have all been highlighted as
important factors that may serve to trigger menstrual
Table 2: Adjusted hazard ratios for effect of covariates on
15-year survival
Variable HRa 95% CIb
Age group
65 - 69 1.00
70 - 74 1.62 1.10 - 2.38
75 - 79 3.43 2.35 - 5.00
80 - 84 5.73 3.86 - 8.50
≥ 85 9.08 6.02 - 13.70
Dwelling
Community 1.00
Residential aged care 1.91 1.41 - 2.57
Self rated health
Excellent/very good 1.00
Good 1.40 1.14 - 1.72
Fair/poor 1.42 1.15 - 1.77
Number of morbid conditions 1.06 1.01 - 1.11
Cognitive impairment
No 1.00
Yes 1.58 1.25 - 1.99
Smoking status
Never 1.00
Former 1.25 1.02 - 1.54
Current 2.02 1.50 - 2.71
Sedentary
No 1.00
Yes 1.31 1.10 - 1.56
Body Mass Index (kg/m
2)
20 - 25 1.00
25 0.93 0.75 - 1.14
Missing 1.09 0.87 - 1.36
Parity
Nulliparous 1.00
> 0 0.87 0.69 - 1.09
Age at menopause
≤ 44 1.01 0.72 - 1.43
45 - 49 1.10 0.83 - 1.40
50 - 54 1.00
≥ 55 0.74 0.53 - 1.04
Surgical 0.98 0.76 - 1.27
Reproductive years
≤ 30 1.00
31-39 0.99 0.72 - 1.38
≥40 0.99 0.69 - 1.42
a: Hazard Ratios adjusted for other covariates
b: 95% confidence interval
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o n set. I t is pos si b l e t h a t t h e be n e fi ts fr o m i n t e rv e n t i o n s
that target menarche triggers will continue to accrue in
older age, and further research effort in this area appears
necessary.
There are several pathways posited through which age
a t m e n a r c h e m a y i m p a c t o n s u b s e q u e n t s u r v i v a l . E a r l y
menarche is associated with increased body fatness in
adult women [10,38], which may in turn be associated
with cardiovascular disease. Recent work has suggested
that early menarche may not in itself be a determinant of
an unfavourable cardiovascular profile, but may reflect
negative metabolic imprinting during pre-pubescence
[38]. Low birth weight and greater weight gain up to the
age of 8 years have recently been demonstrated to predict
younger age at menarche [39], so it may be that the effect
of lower age at menarche on survival is reflecting events
much earlier in the life course.
Early menarche has also been associated with an
increased risk of breast cancer [4,5], and it is thought that
the early exposure to the 'hormonal milieu' associated
with regular menstrual cycles may be important in the
aetiology of the disease. It is also possible that a lower age
at menarche leads to a greater number of reproductive
years, given that age at menarche and age at natural
menopause are not highly correlated [40]. In turn, a
greater number of reproductive years has been linked to a
reduced lifespan [41]. An alternative view is that women
who are biologically older than their chronological age
(i.e. those women with a younger age at menarche) also
die at a younger age than those women who experienced
menarche aged 12 years or more.
The findings from the present study must be inter-
p r e t e d w i t h s e v e r a l c a v e a t s . A l t h o u g h w e a d j u s t e d f o r
many reproductive, demographic, health and lifestyle
Table 3: Summary of effect of lower age at menarche on 15-year survival
Complete case analyses (n = 942) Mult iple imputation analyses (n = 1,031)
Model HR 95% CI P-value HR 95% CI P-value
1: Age
adjusted
Menarche
≥ 12y
1.00 1.00
Menarche
< 12y
1.35 1.05 - 1.73 0.018 1.30 1.02 - 1.65 0.032
2: Age + Health adjusteda
Menarche
≥ 12y
1.00 1.00
Menarche
< 12y
1.31 1.02 - 1.68 0.037 1.26 0.98 - 1.61 0.070
3: Age + Health + Lifestyle adjustedb
Menarche
≥ 12y
1.00 1.00
Menarche
< 12y
1.31 1.02 - 1.69 0.035 1.29 1.01 - 1.64 0.045
4: Age + Health + Lifestyle + Reproductive adjustedc
Menarche
≥ 12y
1.00 1.00
Menarche
< 12y
1.28 0.99 - 1.65 0.062 1.25 0.98 - 1.60 0.074
a: Overall analyses adjusted for age group, place of residence, and health variables (self-rated health, cognitive function, number of morbid
conditions).
b: Overall analyses adjusted for age group, place of residence, health variables, and lifestyle variables (smoking status, exercise status, BMI
category).
c: Overall analyses adjusted for age group, place of residence, health variables, lifestyle variables, and reproductive variables (parity, age at
menopause, number of reproductive years)
Giles et al. BMC Public Health 2010, 10:341
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Page 8 of 10
covariates, complete data were unavailable for some
potentially important factors such as use of oral contra-
ceptives and lifetime use of hormone replacement ther-
a p y . H o w e v e r , o r a l c o n t r a c e p t i v e s w e r e n o t w i d e l y
available during the cohort's reproductive lifetime, given
that the youngest women in the study were aged 65 years
in 1992 (and thus aged 34 years in 1961 when oral contra-
ceptives first became available in Australia). Perhaps
more serious is our lack of data concerning lifetime use of
hormone replacement therapy. At the time of the baseline
interview, three per cent of the women reported current
hormone therapy use, but we do not know how many of
the women in our study had previously used hormone
therapy. However, the youngest women in our cohort
were perimenopausal in the early 1980s, preceding the
widespread use of hormone therapy. Therefore, we
believe that the proportion of women in our cohort who
had used hormone therapy in the past would be small and
have minimal impact on our findings.
Another limitation in this study was that self-reported
age at menarche was recalled a minimum of five decades
after menses onset. The reliability of menarcheal age
recalled in women aged 65 years or more does not appear
to have been reported in the extant literature. Studies that
have examined the reliability of age at menarche recalled
in middle age have had mixed findings, with two studies
reporting strong agreement [42,43]. A third study
reported only moderate agreement of age at menarche
collected in adolescence and again in middle age,
although the authors suggested that categorising menar-
cheal age may improve the reliability of the measure [44].
Opportunities that may exist to examine the reliability of
r e c a l l e d a g e a t m e n a r c h e i n c o h o r t s o f o l d e r w o m e n
should be explored.
The average menarcheal age of 13.6 years reported in
our study of older women was similar to the average age
at menarche of Australian schoolgirls reported in 1932
(13.1 years; [45]) and 1948 ( 13.7 years; [46]). Given that
the women in our study were born over a 38 year period
between 1889 and 1927, it is possible that there was a sec-
ular trend towards a lower menarcheal age in the younger
cohort members [47]. However, the association between
age group and age at menarche was only weakly statisti-
cally significant in our study. This suggests that if such a
trend existed in our cohort of women, its impact on the
Results
is likely to be small. More broadly, our findings
suggest that decreasing age at menarche could lead to
shorter life spans. However, the societal secular trend of
decreasing age at menarche has occurred concurrently
with major advances in medical treatments and technolo-
gies that can lengthen life. Thus the apparent contradic-
tion between decreasing age at menarche and increased
longevity is plausible given the broader environment in
which any change in age at menarche between subse-
quent generations has taken place.
Cause of death data are not yet available for the ALSA
cohort at the 15 year follow-up, but future work is
planned to examine the effect of age at menarche on car-
diovascular mortality and deaths from breast cancer. It is
possible that the relatively small sample size may lead to
analyses with inadequate statistical power when separate
causes of death are examined. Also noteworthy is that
ALSA was not explicitly designed to examine the effects
of reproductive factors on mortality, and the analyses
reported here are based on self-reported data and adjust
for covariates measured at baseline. However, these latter
Limitations
are true of the majority of studies that have
considered reproductive factors and survival. We believe
these restrictions are balanced by ALSA's strengths,
which include the richness of the baseline data, the Aus-
tralian setting, and the inclusion of residents in aged care
facilities. ALSA included a more heterogeneous popula-
tion sample than many other longitudinal studies of age-
ing.
Conclusions
In summary, our findings suggest that over a 15 year fol-
low-up period, older women who began menstruating
aged less than 12 years had an increased risk of overall
mortality compared to women with a menarcheal age of
12 years or more.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
LCG participated in the design and conduct of the study, performed the statis-
tical analyses and drafted the manuscript. GFVG performed the statistical anal-
yses and helped to draft the manuscript. VMM and MJD participated in the
design of the study and helped to draft the manuscript. MAL conceived of the
ALSA study, and participated in its design and coordination and helped to draft
the manuscript. All authors read and approved the final manuscript.
Acknowledgements
We thank the participants in the Australian Longitudinal Study of Ageing, who
have given their time over many years and without whom this study would not
have been possible. We also thank Penny Edwards, Sabine Schreiber and Kath-
ryn Browne-Yung of the Centre for Ageing Studies, Flinders University and staff
in the Epidemiology Branch of the South Australian Department of Health for
their assistance with tracing participants and identifying deaths. Dr Jennifer
Marino and Professor David Phillips are also thanked for helpful discussions
concerning this work. The ALSA was initially funded by the South Australian
Health Commission, the Australian Rotary Health Research Fund and by a grant
from the US National Institute of Health (Grant No. AG 08523-02). Funding has
also been provided by the Australian Research Council (ARC-LP 0669272 and
ARC-DP 0879152), the National Health & Medical Research Council (NHMRC
229922 and NHMRC Strategic Awards 465437 and 465455), and the Centre for
Intergenerational Health (fellowship to L Giles).
Giles et al. BMC Public Health 2010, 10:341
http://www.biomedcentral.com/1471-2458/10/341
Page 9 of 10
Author Details
1Life course and Intergenerational Health Research Group, Robinson Institute,
The University of Adelaide, Adelaide South Australia 5005, Australia, 2Discipline
of Public Health, The University of Adelaide, Adelaide South Australia 5005,
Australia, 3Discipline of Statistics, The University of Adelaide, Adelaide South
Australia 5005, Australia, 4Discipline of Obstetrics and Gynaecology, Robinson
Institute, The University of Adelaide, Adelaide South Australia 5005, Australia
and 5School of Psychology and Flinders Centre for Ageing Studies, Flinders
University, Adelaide South Australia 5001, Australia
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Received: 6 January 2010 Accepted: 15 June 2010
Published: 15 June 2010
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