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Background: The perimenopause is associated with considerable biopsychosocial changes. The majority of women manage to adjust to these changes and cope well with the shift from reproductive to non-reproductive life. However, some women develop burdensome physical and psychological symptoms during the perimenopause. A strong link between menopausal complaints and depressed mood has been shown in this regard. To date, the decisive factors determining whether a woman will successfully achieve a healthy transition remain unclear. Thus, the purpose of this study is to investigate a range of theory-based markers related to health in perimenopausal women. Methods: The Swiss Perimenopause Study comprises a sample of 135 healthy perimenopausal women aged 40-56. A variety of health-related genetic, epigenetic, endocrinological, physiological, and psychosocial markers associated with the menopausal transition are investigated over a period of 13 months. Discussion: The Swiss Perimenopause Study will contribute to a better understanding of the biopsychosocial processes associated with the perimenopause, which should help to improve the clinical care of women undergoing the menopausal transition.
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S T U D Y P R O T O C O L Open Access
The Swiss Perimenopause Study study
protocol of a longitudinal prospective study
in perimenopausal women
Jasmine Willi
1,2
, Hannah Süss
1,2
and Ulrike Ehlert
1,2*
Abstract
Background: The perimenopause is associated with considerable biopsychosocial changes. The majority of women
manage to adjust to these changes and cope well with the shift from reproductive to non-reproductive life.
However, some women develop burdensome physical and psychological symptoms during the perimenopause. A
strong link between menopausal complaints and depressed mood has been shown in this regard. To date, the
decisive factors determining whether a woman will successfully achieve a healthy transition remain unclear. Thus,
the purpose of this study is to investigate a range of theory-based markers related to health in perimenopausal
women.
Methods: The Swiss Perimenopause Study comprises a sample of 135 healthy perimenopausal women aged 4056.
A variety of health-related genetic, epigenetic, endocrinological, physiological, and psychosocial markers associated
with the menopausal transition are investigated over a period of 13 months.
Discussion: The Swiss Perimenopause Study will contribute to a better understanding of the biopsychosocial
processes associated with the perimenopause, which should help to improve the clinical care of women
undergoing the menopausal transition.
Keywords: Perimenopause, Menopausal transition, Reproductive aging, Womens health, Resilience, Depression,
Depressed mood
Background
The perimenopause is related to substantial biological,
psychological and social changes [13]. It describes the
biological shift from reproductive to non-reproductive life,
which is associated with significant alterations in the fe-
male hormonal system. The perimenopause is considered
as a phase of strong fluctuations in sex hormones such as
estradiol and progesterone [4,5]. Endocrinological varia-
tions have been correlated with a higher risk of
burdensome physical and psychological symptoms [612],
subsumed under the term menopausal complaints.
These complaints are generally divided into psychological,
somato-vegetative, and urogenital symptoms. However, in
spite of the major biopsychosocial changes and challenges,
most women adjust well to the perimenopause, reporting
a good quality of life and an overall positive well-being
[13]. It is assumed that resilience might be a key factor in
determining whether or not a woman will be negatively af-
fected by menopausal symptoms [14]. Women with high
levels of resilience are believed to more effectively and
successfully adapt to the substantial changes in the peri-
menopause [15,16] compared to less resilient women. In
turn, women with lower levels of resilience are suggested
to be more affected by menopausal complaints like
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* Correspondence: u.ehlert@psychologie.uzh.ch
Jasmine Willi and Hannah Süss contributed equally to this work.
1
Clinical Psychology and Psychotherapy, University of Zurich,
Binzmühlestrasse 14, 8050 Zurich, Switzerland
2
URPP Dynamics of Healthy Aging Research Priority Program, University of
Zurich, Zurich, Switzerland
Willi et al. Women's Midlife Health (2020) 6:5
https://doi.org/10.1186/s40695-020-00052-1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
vasomotor symptoms or sleep disturbances. Such symp-
toms may then co-occur and have been shown to interact
with depressed mood [1719], suggesting the perimeno-
pause as a window of vulnerability for mood disturbances
[20]. Indeed, prevalence rates of depressed mood during
the perimenopause are particularly high [21]. Conse-
quently, the investigation of depression in the course of
the menopausal transition has gained importance over the
last decades.
The attention paid to womens health research has
gradually increased since the 1970s [22]. One of the first
longitudinal studies on the menopausal transition was
the Massachusetts Womens Health Study (MWHS)
[23], followed by the Seattle Midlife Womens Health
Study (SMWHS), which collected data over 23 years
[24]. In Norway, a large cohort study, the Nord-
Trøndelag Health Study (HUNT) [25], began in 1984
and is considered one of the largest health studies ever
performed. Further large studies on the menopausal
transition, such as the Study of Womens Health Across
the Nation (SWAN) [26], the Penn Ovarian Aging Study
(POAS) [4], or the Harvard Study of Moods and Cycles
[27], followed in the US. Extensive research on women
in midlife has also been conducted in Australia, within
the Australian Longitudinal Study on Womens Health
(ALSWH) [28] or the Melbourne Womens Midlife
Health Project (MWMHP) [29]. Depressed mood was
one of the focal points across these studies, and all of
them highlighted the increased risk for new or recurrent
depressed mood, emphasizing the prevalence peak dur-
ing the menopausal transition. This previous research
formed an important basis for the understanding of de-
pressed mood in middle-aged women and the associated
changes and challenges.
Previous studies frequently included women across all
three menopausal stages, i.e. the pre-, the peri-, and the
postmenopause. Such a selection procedure seems to be
highly expedient in terms of identifying critical time
points in the development of symptoms in the progres-
sion from reproductive to non-reproductive life. More-
over, this sample selection also provides the opportunity
to compare the participants across the different meno-
pausal stages. However, in terms of investigating the
perimenopause as a critical window of increased biopsy-
chosocial changes and associated complaints, and in
order to specify the involved processes, it is essential to
focus specifically on the perimenopausal phase.
Therefore, the Swiss Perimenopause Study employs a
longitudinal design examining women in the perimeno-
pause only and assessing a variety of genetic, epigenetic,
endocrinological, physiological and psychosocial factors.
As such, we aim to investigate psychosocial factors like
personality traits, self-esteem, self-compassion, perceived
stress, emotion regulation and coping, or social support.
Furthermore, physiological parameters like blood pres-
sure and pulse, heart rate variability, hand grip force, or
body composition are assessed as measures associated
with healthy aging. Body weight and body height are
captured in order to take into account participantsbody
mass index (BMI) when investigating biomarkers. Add-
itionally, we measure the length of the index finger and
the ring finger in order to calculate the second to fourth
digit ratio (2D:4D). Recent research showed that a higher
2D:4D is associated with a higher age at menopause,
while a lower 2D:4D (interpreted as suggesting a higher
androgen exposure during the prenatal phase) is associ-
ated with a lower age at menopause [30]. As our work-
group has shown that looking younger than ones actual
age can be related to a variety of health outcomes [31],
we take standardized facial photographs of all partici-
pants. The full list of biopsychosocial factors included in
this study is shown in Tables 1and 2. The comprehen-
sive investigation of possible predictors of physical and
psychological complaints appears particularly promising
regarding the detection of underlying processes such as
epigenetic variations or specific hormonal patterns.
Data concerning genetic and epigenetic markers asso-
ciated with the menopausal transition are still scarce.
However, valuable information might be gained by ana-
lyzing gene-environment interactions and the effect of
epigenetic modifications on the menopausal transition-
ing [66]. For this purpose, we aim to investigate the
estrogen receptor genes (ER1, ER2, GPER), the gluco-
corticoid receptor (NR3C1) as well as the serotonin
transporter (5-HTTLPR) and the respective relationships
with a resilient menopausal transition or the develop-
ment of depression in the perimenopause. For example,
a recent study by our workgroup found a positive rela-
tionship between the methylation of the ERαenhancer
and depressed mood in women around the menopause
[67]. Consequently, one goal is to investigate the auto-
regulation between estradiol and the estrogen receptor
genes with regard to methylation patterns and subse-
quent effects on the participantsmood.
Endocrinological parameters are commonly assessed
in studies on the menopausal transition. Previous re-
search in this regard showed a significant relationship
between sex steroids and menopausal complaints [68
70]. Several studies by our workgroup showed that corti-
sol or alpha amylase play a key role in the reactivity of
physiological stress systems [7173], and inflammatory
markers associated with various psychopathologies
change over the menopausal transition [74]. Therefore,
their assessment is included in this study. To maximize
the informative value and comparability of the forthcom-
ing results, the Swiss Perimenopause Study is designed
to conduct a close endocrinological monitoring of all
participants, enabling progression analyses of individual
Willi et al. Women's Midlife Health (2020) 6:5 Page 2 of 13
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Table 1 Psychosocial measures included in the Swiss Perimenopause Study
Ref. Construct Assessment instrument Authors Items
[32] Body image Body Image Questionnaire (Fragebogen zum Körperbild; FKB-20) Clement & Löwe,
1996
20
[33] Chronic stress Trier Inventory for Chronic Stress, short version (TICS-K) Schulz et al., 2004 30
[34] General health General Health Questionnaire (GHQ-12) Schmitz et al., 1999 12
[35] Coping COPE Inventory, short version Knoll et al., 2005 28
[36] Depressive symptoms German Version of the Center of Epidemiological Studies Depression Scale (CES-
D; Allgemeine Depressionsskala; ADS-L)
Hautzinger & Bailer,
1993
20
[37] Emotional intelligence Emotional Intelligence Questionnaire, German version (TEIQue) Freudenthaler
et al., 2008
30
[38] Emotion regulation Emotion Regulation Questionnaire, German version (ERQ) Abler & Kessler,
2009
10
[39] Eating behavior Eating Disorder Examination Questionnaire (EDE-Q) Hilbert & Caffier,
2006
28
[40] Explicit motives Life Goal Questionnaire (GOALS) Pöhlmann &
Brunstein, 1997
24
[41] Intrasexual competition German version of the Scale for Intrasexual Competition (ICS) Fiacco et al., 2018 12
[42] Life events German version of the Life Experience Survey (LES) Pluess et al., 2012 57
[43] Life satisfaction Satisfaction with Life Scale (SWLS) Glaesmer et al.,
2011
5
[44] Mastery Pearlin Mastery Scale (PM) Pearlin et al., 1981 7
[45] Menopausal symptoms Menopause Rating Scale (MRS II) Hauser et al., 1999 11
[46] Physical and mental symptoms Brief Symptom Inventory (BSI-18) Spitzer, 2011 18
[47] Pessimism/Pessimism Life Orientation Tests (LOT-R) Glaesmer et al.,
2008
10
[48] Parental bonding German version of the Parental Bonding Instrument (PBI; Fragebogen zur
elterlichen Bindung; FEB)
Lutz et al., 1995 25
[49] Personality Short version of the Big Five Inventory (BFI-K) Rammstedt & John,
2005
21
[50] Perceived stress German version of the Perceived Stress Scale (PSS-10) Klein, 2016 10
[51] Premenstrual symptoms PMS Inventory Ditzen et al., 2011 30
[52] Promiscuity Sociosexual Orientation Index (SOI-S) Penke & Asendorpf,
2008
9
[53] Relationship experiences Experiences in Close Relationships (ECR-RD 12) Ehrenthal, 2009 12
[54] Relationship quality German version of the Relationship Assessment Scale (RAS) Sander & Böcker,
1993
7
[55] Resilience Resilience Scale 11 (RS-11) Schumacher et al.,
2005
11
[56] Retrospectively assessed
relationship experiences
Sequence data analysis Abbott, 1995 10
[57] Self-compassion German Version of the Self-Compassion Scale (SCS-D) Hupfeld & Ruffieux,
2011
26
[58] Self-esteem Rosenberg Self-esteem Scale (RSE) von Collani &
Herzberg, 2003
10
[59] Sense of coherence Revised Sense of Coherence Scale (SOC-R) Bachem &
Maercker, 2016
13
[60] Sexual desire German version of the Decreased Sexual Desire Screener (DSDS) Clayton et al., 2009 5
[61] Sexual function Female Sexual Function Index (FSFI / FSFI-LL) Berner et al., 2004 19
[62] Sleep Pittsburgh Sleep Quality Index (PSQI) Backhaus, 2002 19
[63] Social support Berlin Social Support Scales (BSSS) Schulz &
Schwarzer, 2003
17
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hormone profiles. Standardized methods of analysis are
used, as described in the methods section.
By repeatedly assessing physiological data during the
night, it may be possible to address questions about the
relationship of sleep disturbances and vasomotor symp-
toms in women suffering from hot flushes and night
sweats. To the best of our knowledge, the Swiss Peri-
menopause Study represents the currently largest study
investigating such a wide range of health-related biopsy-
chosocial markers in perimenopausal women.
The purpose of the Swiss Perimenopause Study is to
gain a deeper understanding of the biopsychosocial
Table 1 Psychosocial measures included in the Swiss Perimenopause Study (Continued)
Ref. Construct Assessment instrument Authors Items
[64] Spirituality German version of the Expressions of Spirituality Inventory-Revised (ESI-R) Proyer & Laub,
2017
32
[65] Traumatic experiences Childhood Trauma Scale Short Form (CTQ-SF) Klinitzke et al., 2011 28
Table 2 Biological parameters included in the Swiss Perimenopause Study
Source Label Parameter
Saliva (SaliCaps) Endocrine parameters Cortisol
Alpha amylase
Testosterone (T)
Dehydroepiandrosterone sulfate (DHEA-S)
Estradiol (E2)
Progesterone (P)
Blood (DBS) Endocrine and inflammatory parameters C-reactive protein (CRP)
Interleukin-6 (IL-6)
Sex hormone-binding globulin (SHBG)
Luteinizing hormone (LH)
Follicle-stimulating hormone (FSH)
Genetic and epigenetic parameters Estrogen receptor genes (ER1, ER2, GPER)
Glucocorticoid receptor (NR3C1)
Serotonin transporter (5-HTTLPR)
Blood pressure monitor Peripheral physiological measures Blood pressure (BP)
Pulse
Ava bracelet Peripheral physiological nighttime measures Skin temperature
Ambient temperature
Electrodermal activity (EDA)
Heart rate (HR)
Heart rate variability (HRV)
Blood circulation of the skin
Body acceleration
Breathing rate
Sleep phases
Body scale Anthropometric measures Body weight
Scale Height
BIA Body composition
Vernier caliper Finger lengths
Hand dynamometer Grip force
Camera Standardized facial photograph
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factors and processes associated with 1) resilience and
health in the perimenopause, and 2) the development of
new or recurrent depression. One key goal of this study
is to investigate biopsychosocial markers, determinants,
and processes related to a resilient menopausal transi-
tioning. In this regard, we aim to examine psychosocial
variables such as optimism, emotional stability, or self-
esteem as well as biological markers such as female sex
steroids and gonadotropins, stress hormones or inflam-
matory parameters and their relationship with well-
being and health. We assume that women with higher
psychosocial resilience (e.g. higher optimism, higher
emotional stability, higher self-esteem) and higher levels
of female sex steroids will be more successful in their
adaptation to changes and challenges associated with the
menopausal transition. A second key goal is to investi-
gate the biopsychosocial determinants and associations
of perimenopausal depression. To achieve this, we aim
to investigate specific risk factors for depressive symp-
toms in the perimenopause and to examine the effect of
endocrinological alterations and psychosocial changes
and their effect on the womens mood. We assume that
a history of depression and fluctuations of sex steroids
are major risk factors for perimenopausal depression. As
such, it is our endeavor to expand and develop the exist-
ing knowledge on healthy menopausal transitioning. To
our knowledge, this is the first study to investigate both
depression and resilience within the perimenopause.
Methods
Design
The Swiss Perimenopause Study is an exploratory,
longitudinal, single-center, national study examining
different facets of female menopausal development
over 13 months. Data are being collected between
June 2018 and January 2021. The study is divided
into a screening phase and a study phase. After the
application of the strict inclusion criteria, interested
women were screened for about 2 months for our
study purposes. Subsequently, the actual study phase
began, which will last for 11 months. Relevant genetic,
epigenetic, endocrinological, physiological, and psy-
chological aspects of the menopausal transition are
investigated by means of different measurement
methods. The participation includes two visits to our
laboratory at the Institute of Psychology, University of
Zurich. The standardized lab visits include blood
sampling and the assessment of different peripheral
physiological (blood pressure, pulse) and anthropo-
metric parameters (body weight, body height, bioelec-
trical impedance analysis (BIA), second to fourth digit
ratio, grip strength, standardized facial photograph).
During the months between the two lab visits, partici-
pants independently collect saliva samples at home,
measure various physiological parameters with the
Ava bracelet (Ava AG, Zürich, Switzerland), complete
a menstrual cycle and mood diary, and answer a set
of different psychosocial questionnaires. An overview
of the study design can be found in Fig. 1. To date,
the screening phase and t
1
have been completed.
Sample
The population-based sample of 135 perimenopausal
women aged 4056 were recruited through mailing lists,
social media, context-specific online forums, flyers as
well as newspaper and magazine articles. For the sample
size estimation of the planned project, we performed an
a priori sample size estimation analysis using G*Power
3.1 for different statistical analyses such as correlation
analysis, linear regression analysis, linear multiple regres-
sion analysis, or Wilcoxon signed-rank test with an α-
level < .05 [75].
From June 2018 to December 2019, a total of 1121
women showed interest in participating in the Swiss
Perimenopause Study by completing an online screening
questionnaire. The eligibility for study participation was
checked by following inclusion and exclusion criteria
(Table 3).
Major reasons for exclusion included pre- or postmen-
opausal status (n= 717), poor subjective health (n= 97),
age below 40 or above 60 years (n= 58), hormone ther-
apy in the last 6 months (n= 34), acute or chronic som-
atic or mental illness (n= 22), and psychiatric or
psychotropic drug use (n= 11). The sample inclusion
and elimination process is displayed in Fig. 2. Participants
eligible for the study were granted access to the online
menstrual cycle and mood diary for the two-month
screening phase. Within this phase, the perimenopausal
status was ensured. The perimenopausal status was de-
fined according to the Stages of Reproductive Aging
Workshop +10(STRAW) criteria, representing the most
frequently used classification system [76]:
1. Early menopausal transition: increased variability in
menstrual cycle length, defined as a persistent
difference of 7 days in the length of 10 consecutive
cycles.
2. Late menopausal transition: the occurrence of
amenorrhea of 60 days or longer.
In addition to the exclusion criteria, the screening
questionnaire also recorded whether the currently sub-
jectively healthy participants show a history of depres-
sion. In this regard, prior depression must either have
been previously diagnosed by an expert, or as self-
report, in accordance with the criteria for major depres-
sion according to the Diagnostic and Statistical Manual
of Mental Disorders, Fifth Edition (DSM-5).
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Retention efforts
Numerous efforts have been undertaken to retain the
eligible participants throughout the study. These include
personal and consistent contact from the research staff,
daily accessibility to the research staff, annual Christmas
cards, reminders about the data collection by text mes-
sage or telephone calls, and regular menopause-specific
information on our homepage and social media profiles.
Furthermore, participants were given an individual
health profile after the evaluation phase and are given
various incentives throughout the different time points
during the study (e.g. vouchers for massage, wellness
and spa treatments, free gym subscription, cosmetics,
food vouchers).
Data collection
During the entire screening and study phase, partici-
pants are asked to mark bleeding days in their individual
paper-and-pencil cycle calendar. This provides an easily
accessible method to remember the days of bleeding for
the biweekly online mood and cycle diary. Every 2
weeks, participants are reminded to complete the online
cycle and mood diary, which assesses specific cycle con-
ditions, menopausal complaints, and mood disturbances
over the last 2 weeks. At the beginning of the study
phase (t
1
), further demographic data (e.g. marital status,
occupation) and a variety of psychosocial factors were
assessed via online questionnaires. Psychosocial mea-
sures include psychological traits, physical and mental
health outcomes, behavioral aspects, and interpersonal
factors. The validated self-report questionnaires assessed
in the course of the study can be found in Table 1,
which lists the measured constructs, the name of the as-
sessment instrument, the authors, and the number of
items of the instrument (Table 1). The questionnaires
are completed at the beginning and at the end of the
Fig. 1 Project Design
Table 3 Inclusion and exclusion criteria
Inclusion criteria
Female sex
Age 40 to 60 years
Perimenopausal status
Good knowledge of the German language
Good to excellent self-reported health condition
Exclusion criteria
Acute or chronic somatic disease
Acute or chronic mental disorder
Psychiatric drug use
Psychotropic drug use in the last 2 months
More than two standard units of alcoholic beverages a day
Pregnancy in the last 6 months
Current use of oral contraceptives or hormone therapy in the last 6
months
Postmenopausal status (no menstrual period in the last 12 months)
Premenopausal status (regular menstrual cycle)
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study (t
1
and t
3
). Participants receive the respective links
by e-mail. All questionnaires need to be completed
consecutively.
Two standardized laboratory visits to the Institute of
Psychology, University of Zurich are planned within the
study phase, at t
1
and t3. Women are instructed a) not
to drink caffeinated drinks (e.g. coffee, black or green
tea, energy drinks) or alcohol for at least 48 h (2 days)
prior to the visit, b) not to engage in intensive physical
activity for at least 24 h (1 day) prior to the visit, and c)
not to eat, drink or engage in physical effort (e.g., cyc-
ling) on the morning of the examination. The laboratory
sessions are standardized. Participants arrive at the la-
boratory at 7:45 am. At the first laboratory visit (t
1
), a
well-instructed psychologist first conducted the German
Version of the Structured Clinical Interview for DSM-IV
[77] to ensure that all the participants were mentally
healthy at the beginning of the study. Otherwise, partici-
pants were excluded.
At both laboratory visits (t
1
and t
3
), small capillary
blood samples are drawn from the participants finger.
Blood pressure and pulse (Medisana MTV upper arm
blood pressure instrument) are measured and body
height and weight are assessed. In addition, the body
composition is determined using bioelectrical impedance
analysis (BIA, Biocorpus RX4000, Idiag AG, Fehraltdorf,
Switzerland). This instrument is a fully digital, phase-
sensitive, four-channel impedance-measuring device.
Each channel applies a 50 kHz alternating current to
measure resistance, reactance and phase angle. To meas-
ure the second to fourth digit ratio (2D:4D), the length
of the index finger and the ring finger is measured
(Digital Caliper Micrometer Vernier Gauge Tool).
Accordingly, the grip strength is assessed (Baseline
Hydraulic Hand Dynamometer). The laboratory sessions
also include a standardized facial photograph. These
photos are used for self-rated attractiveness as well as
externally rated attractiveness. For the latter, independ-
ent and external examiners (N= 10) rate the standard-
ized facial photographs of the participants according to
the perceived age, attractiveness and health on a scale
from 0 to 100. The assessments are conducted via pres-
entation of the photographs on a computer screen. The
examiners have signed a binding declaration of
confidentiality.
Within all laboratory sessions, the investigator also
asks a short, standardized set of questions about factors
and activities potentially biasing the current hormone
concentrations. Subjects are instructed to autonomously
collect saliva samples in a non-invasive manner with sal-
iva sampling tubes (SaliCaps, IBL International GmbH,
Hamburg, Germany). Furthermore, the participants
Fig. 2 Sample inclusion and elimination process
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receive written instructions for each step of the saliva
sampling. The participants have been provided with 100
SaliCaps (IBL International GmbH, Hamburg, Germany)
to collect one daily morning saliva sample for 3 months
in total (i.e. 28 daily samples per month during t
1
,t
2
and
t
3
) under standardized conditions. To investigate the
cortisol awakening response (CAR) as well as fluctua-
tions throughout the day, four additional saliva samples
are collected on the first 2 days of the month of t
2
and
t
3
. The additional saliva samples are timed 30 and 60
min after the first saliva sample, at 12:00 pm and before
going to bed. The exact sampling times as well as spe-
cific information on caffeine or alcohol consumption,
sports or current gum bleeding are recorded in a proto-
col. Participants are reminded to start the saliva sam-
pling at the beginning of t
1,
t
2
, and t
3
.
During the first lab visit, the participants were also in-
troduced to the Ava bracelet (Ava AG, Zürich,
Switzerland), which they were asked to wear at night for
a total of 6 months. The Ava bracelet assesses raw data
of nine different nighttime peripheral physiological pa-
rameters: skin temperature, ambient temperature, elec-
trodermal activity, heart rate, heart rate variability, blood
circulation of the skin, body acceleration, breathing rate,
and sleep phases.
An overview of all biological parameters assessed in
the course of the study can be found in Table 2, which
lists the sources of the parameters, the label, and the
name of the specific parameter (Table 2).
Data handling
A link for the online screening was provided on the
study homepage. Women interested in participation
were led to the online platform Unipark, where they
were asked to provide initial, brief information followed
by a declaration of consent. All questionnaires at base-
line, t
1
and t
3
(www.unipark.com), the data analysis of
retrospectively assessed relationship experiences (www.
qualtrics.com), as well as the mood and cycle diary
(www.findmind.ch), are completed online. The partici-
pants were explicitly informed about the study design,
the aims, the procedure and the expected duration of
each part of the assessment. Data security is ensured at
all times. All calendars and protocols are paper-and-
pencil-based. The online platform Unipark is reliably
protected from any external access. The BSI-certified
(Bundesamt für Sicherheit in der Informationstechnik
[Federal Office for Information Security]) data center is
subject to requirements of high data protection and
safety in conformity with ISO (International
Organization for Standardization) 27,001 based on the
IT basic protection. Qualtrics is hosted by trusted data
centers that are independently audited using the indus-
try standard Statement on Standards for Attestation
Engagements no. 16 (SSAE 16) method. HITECH-up-
dated (Health Information Technology for Economic
and Clinical Health Act) HIPAA (Health Insurance Port-
ability and Accountability Act) rules are applied to en-
sure data protection and security of all customer data.
The menstrual cycle and mood diary is recorded via the
online platform Findmind (www.findmind.ch). Findmind
treats data as confidential information, strives to ensure
the privacy of the data and does not share information
with third parties. It is operated exclusively on servers in
Switzerland and stores all user data. For the dried blood
spots (DBS), a sterile disposable lancet (Accu-Chek®
Safe-T-Pro Plus) is used to collect up to five drops of
blood (about 50 μL per drop), which are spotted onto
standardized filter paper (No. 903 Whatman®, DBS Pro-
tein Saver Card). The samples are dried and subse-
quently stored in the laboratory freezer of the Institute
of Psychology, University of Zurich at 20 °C. DBS sam-
ples are analyzed in the biochemical laboratory of the
Department of Clinical Psychology and Psychotherapy,
University of Zurich, Switzerland (genetic and epigenetic
analyses) and the Cytolab laboratory in Regensdorf,
Switzerland (FSH and LH).
Saliva samples are collected with SaliCap sampling
tubes of 2 mL capacity (IBL International GmbH, Ham-
burg, Germany). Participants are instructed to drool into
the tube using a polypropylene straw (passive drool
method). After collecting a full month of saliva samples,
these can be returned directly to the Institute of Psych-
ology during the study phase or stored at home in the
freezer until the second laboratory appointment. The
saliva samples are then thawed and centrifuged prior to
biochemical analysis, using IBL Saliva Immunoassays
(IBL International GmbH, Hamburg, Germany). All sal-
iva samples are analyzed at the Cytolab laboratory in
Regensdorf, Switzerland.
The Ava bracelet and its accompanying software is a
CE-certified, Class 1 medical device approved by the
Food and Drug Administration. It is worn at night on
the wrist like a regular watch. The synchronization takes
place every morning after waking up, with the corre-
sponding application (Ava fertility tracker). The software
runs on Apple and Android phones. The login for the
Ava fertility tracker app corresponds to the subjects
study ID number (personal code). Participants without a
smartphone have been provided with an Android phone
during the study phase by the University Research Prior-
ity Program Dynamics of Healthy Aging (funding
source). The data recorded by the Ava bracelet are
treated confidentially and in accordance with the privacy
policy. Members of the Ava AG have signed a binding
declaration of confidentiality. All data handling is subject
to the data protection provisions approved by the Swiss
Ethics Committee.
Willi et al. Women's Midlife Health (2020) 6:5 Page 8 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Biochemical data analyses
Our biochemical laboratory at the Institute of Psych-
ology, University of Zurich, performs the salivary and
DBS analyses. As listed in Table 2, most endocrine pa-
rameters are assessed in saliva. Thawed saliva samples
are centrifuged and analyzed using enzyme-linked im-
munosorbent assay (ELISA; IBL International GmbH,
Hamburg, Germany). FSH and LH levels are determined
in DBS samples (Table 2) using the MILLIPLEX MAP
Human Pituitary Magnetic Bead Panel 1 (Merck, Darm-
stadt, Germany). Intra-assay variation for this kit for
FSH and LH is less than 10% and inter-assay variation
less than 15%. Sensitivity is 6.40 pg/mL for FSH and
1.34 pg/mL for LH. Sex hormone-binding globulin
(SHBG) can be measured in DBS using Human SHBG
ELISA Kit (DRG International, Marburg Germany) with
intra- and inter-assay coefficients of variability of less
than 10% [78]. In terms of immune markers, the Quanti-
kine HS ELISA Human IL-6 (HS600B, R&D Systems,
Minneapolis, MN) is recommended for analyzing inter-
leukin 6 (IL- 6; 37) and the sandwich ELISA (BioCheck,
Inc., Foster City, CA) for investigating C-reactive protein
[79]. Intra- and inter-assay variation for these kits for IL-
6 and CRP are less than 15%. Detection rates of these as-
says are 0.67 pg/ml for IL-6 and 3.00 mg/L for CRP.
The DNA extraction is assessed from DBS samples, as
described in Table 2. The Qiagen QIAamp DNA Investi-
gator Kit (Qiagen, Hombrechtikon, Switzerland) is used to
extract genomic DNA from three (each 3 mm in diameter)
punches of blood-soaked filter paper. These punches are
then eluted in 30 μl of RNase-free water, and the DNA
concentration is assessed using the Qubit Fluorometer
(Thermo Fischer Scientific, Reinach, Switzerland). For epi-
genetic analyses, we perform a bisulfite conversion of
DNA using the EZ 96-DNA Methylation-Gold Kit (Zymo
Research, Luzern, Switzerland). The procedure for the
next-generation sequencing (NGS) library preparation is
described elsewhere by our research group [80].
Statistical data analyses
A variety of statistical analytic strategies are planned for
the different measures. Variables will be tested for nor-
mal distribution (Kolmogorov-Smirnov test) and homo-
geneity (Levenes test). Skewed biological data will be
logarithmically transformed [81]. Effect sizes will be esti-
mated using eta-squared [82]. When investigating asso-
ciations, correlation or partial correlation analyses will
be conducted. If assumptions are violated, non-
parametric methods will be applied. Regression analyses
will be conducted to investigate the strength and form of
associations between the various biopsychosocial
markers and determinants and resilience- or depression-
related factors. Analyses of variance, t-tests or Mann-
Whitney U tests, depending on the level of
measurement, are planned for the investigation of group
differences between participants with and without a his-
tory of depression regarding different outcome measures
associated with the menopausal transition. Furthermore,
factor analyses will be used [81,83] to examine which of
the assessed variables related to resilience can be
assigned to a common factor. For the longitudinal data
evaluation of specific predictors of perimenopausal de-
pression, multilevel analyses will be conducted [81]. In
addition to inference methods, unsupervised methods
(e.g. time series clustering methods [84,85], machine
learning algorithms [86]) will be necessary to analyze the
massive volume of big data generated by the Ava
bracelet.
The level of significance will be two-sided with p< .05,
if not stated otherwise. The analysis will be run via the
statistical software SPSS 25.0 (IBM, Armonk, NY, USA)
and the open source software R (foundation for Statis-
tical Computing, Vienna, Austria).
Results
Of the 1121 women interested in participating in the
Swiss Perimenopause Study, a total of 265 (23.64%) were
found to be eligible for the study after applying the in-
clusion and exclusion criteria. Of these, 177 declared
their consent to participate. Up until December 2019, 42
women (23.73% of the participants) were reported as
dropouts. Excessive effort for study participation was
stated as a major cause for dropping out. Hence, the
total sample consisted of 135 perimenopausal women.
The sociodemographic data of the participants are
shown in Table 4. The participantsage at the point of
study enrollment ranged from 40 to 56 years, with a
mean of 48.60 years. Most of the women were Swiss
(85.2%) and married (56.3%). With regard to highest
educational attainment, 43.0% had completed university
education and 37.0% had completed secondary school.
In accordance with the STRAW criteria [76], a total of
43.7% were in the early phase of the menopausal transi-
tion, while 56.3% were in the late phase at the point of
screening. Overall, 60.0% of the women showed no prior
depression and 40.0% of the participants reported a his-
tory of depression.
Discussion
The Swiss Perimenopause Study was established to in-
vestigate specific health-related markers associated with
the menopausal transition. The aim of this large longitu-
dinal study is to address the limited scientific under-
standing of the factors determining whether a woman
undergoing the menopausal transition develops clinically
relevant symptoms, such as those of a depressive dis-
order, or whether she manages to successfully adapt to
the predominant biopsychosocial changes. Successful
Willi et al. Women's Midlife Health (2020) 6:5 Page 9 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
adaptation can be defined on a continuum of, for ex-
ample, higher life satisfaction, lower psychological dis-
tress, better general psychological health, and milder
menopausal complaints [14]. Thus, the study focuses on
the perimenopause as the phase of strong hormonal
fluctuations and the prevalence peak of burdensome
symptoms.
Strengths of the Swiss Perimenopause Study include
the longitudinal design and the in-depth investigation
of the perimenopausal stage. Strict inclusion and ex-
clusion criteria are deployed. All participants reported
good to excellent subjective health at baseline. The
gold-standard STRAW criteria were used to identify
perimenopausal women on the basis of bleeding pat-
terns. Individual menstrual cycle data are collected
throughout the entire screening and study phase and
matched with the assessment of mood changes. An
extensive symptom assessment is carried out. Genetic
and epigenetic analyses provide the opportunity to in-
vestigate inherited physical characteristics as well as
gene-environment interactions. Repeated physiological
data assessment enables the examination of patterns
related to sleep disturbances and vasomotor symp-
toms. Furthermore, a comprehensive inspection of
endocrine processes is planned, to complement the
various psychosocial questionnaires applied in our
study. Participants were randomly identified from the
general population and represent an eligible sample
for this longitudinal study on health-related factors of
the menopausal transition.
There are a few limitations to consider: The study in-
cludes a predominantly well-educated sample, despite
efforts to recruit women with different socioeconomic
backgrounds. Additionally, the sample mostly consists
of Caucasian women, although this corresponds to the
distribution of the Swiss population. The initial goal to
recruit a balanced sample size of women with and with-
out a history of depression, with a sample size of 80
participants in each group, had to be discarded due to
difficulties in enrolling eligible participants with prior
depression. A total of 54 (40.0%) women with and 81
(60.0%) women without a history of depression were in-
cluded in the final sample. Compared to the SWAN or
the SMWHS studies, only a relatively small sample size
and shorter study duration could be accomplished.
However, a sufficiently large number of women has
been achieved for all planned analyses, with adequate
statistical power. Furthermore, the self-reported nature
of all validated psychosocial questionnaires used might
lead to a response bias and socially desirable answers.
Thus, the evaluation of self-reported data using object-
ive measures, where feasible, will be essential. More-
over, the strict inclusion and exclusion criteria applied
in the Swiss Perimenopause Study offers the chance to
investigate the actual changes and challenges associated
with the perimenopause. However, as this is a very spe-
cific sample, the results cannot be applied to other sub-
groups of the general population such as younger
women or postmenopausal women. Additionally, a se-
lection bias has to be considered when interpreting the
results. Women agreeing to participate in such a com-
prehensive examination about the menopausal transi-
tion might suffer more from menopausal symptoms,
and women with a higher socioeconomic status might
show a higher interest in participating in a scientific
project.
There has been an increased scientific effort to ad-
dress questions on the development of menopausal
symptoms and depressed mood within the meno-
pausal transition. Longitudinal data on specific factors
associated with the menopausal transition are essen-
tial to foster our understanding of changes and symp-
toms experienced by middle-aged women. The Swiss
Perimenopause Study, as one of the large studies in-
vestigating the menopausal transition, aims to con-
tribute to increased scientific information about the
biopsychosocial processes associated with this vulner-
able time frame in a womans life and thus improve
the clinical care of midlife women.
Table 4 Baseline sample characteristics
N (%) M (SD)
Age 48.60 (3.87)
Nationality
Swiss 115 (85.2%)
German 17 (12.6%)
Other 3 (2.1%)
Marital status
Single 34 (25.2%)
Married 76 (56.3%)
Registered partnership 4 (3%)
Divorced 18 (13.3%)
Widowed 3 (2.2%)
Highest educational attainment
Secondary school 50 (37.0%)
Grammar school 27 (20.0%)
University 58 (43.0%)
Menopausal stage
Early MT 59 (43.7%)
Late MT 76 (56.3%)
History of depression
No 81 (60.0%)
Yes 54 (40.0%)
Note. N sample size, Mmean, SD standard deviation, MT menopausal transition
Willi et al. Women's Midlife Health (2020) 6:5 Page 10 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Abbreviations
2D:4D: Second to Fourth Digit Ratio; ALSWH: Australian Longitudinal Study
on Womens Health; App: Application; BIA: Bioelectrical Impedance Analysis;
BMI: Body Mass Index; BSI: Federal Office for Information Security (Bundesamt
für Sicherheit in der Informationstechnik); CAR: Cortisol Awakening Response;
DBS: Dried Blood Spots; DSM: Diagnostic and Statistical Manual of Mental
Disorders; ELISA: Enzyme-Linked Immunosorbent Assay; HIPAA: Health
Insurance Portability and Accountability Act; HITECH: Health Information
Technology for Economic and Clinical Health Act; HUNT: Nord-Trøndelag
Health Study; IBL: Immuno Biological Laboratories; ISO: International
Organization for Standardization; kHz: Kilohertz; LH: Luteinizing Hormone
(LH); MWMHP: Melbourne Womens Midlife Health Project;
MWHS: Massachusetts Womens Health Study; NGS: Next-Generation
Sequencing; POAS: Penn Ovarian Aging Study; SHBG: Sex hormone-binding
globulin; SMWHS: Seattle Midlife Womens Health Study; SSAE: Statement on
Standards for Attestation Engagements; STRAW: Stages of Reproductive
Aging Workshop; SWAN: Study of Womens Health Across the Nation
Acknowledgements
We acknowledge the contribution of the participants who have provided
data for the Swiss Perimenopause Study. We wish to thank Jessica Grub and
our research team for their great support in recruiting the participants for
the study. Finally, we warmly thank Sarah Mannion for proof reading the
article.
Authorscontributions
Conception: UE, JW / HS. Acquisition of data: JW / HS. Drafting the
manuscript: JW / HS. Critical revision: UE. Jasmine Willi and Hannah Süss are
shared first authors. The author(s) read and approved the final manuscript.
Authorsinformation
UE: Project leader of the Swiss Perimenopause Study.
JW and HS: Principal investigators of the Swiss Perimenopause Study.
Funding
This research was funded by the University Research Priority Program
Dynamics of Healthy Aging of the University of Zurich in Switzerland.
Availability of data and materials
Not available.
Ethics approval and consent to participate
Ethical approval for the study protocol of the Swiss Perimenopause Study
was provided by the Swiss Ethics Committee (KEK-ZH-Nr. 201800555).
Approved informed consent forms were obtained from each participant of
the study before the screening phase (online) and before the study phase
(paper and pencil). All participants were informed about the detailed study
procedure, the estimated duration of the individual steps, data protection,
and the aims of the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interest.
Received: 17 January 2020 Accepted: 13 July 2020
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... Our reliance solely on age-based criteria to define a menopausal/postmenopausal cohort might exclude some women who attain natural menopause at younger ages; however, limiting the lower age range to 50 years rather than using the lower limit of the WHO average menopausal age range (1) enables exclusion of more women who might be perimenopausal rather than menopausal/postmenopausal in our analyses. This is important since oestrogen levels, which impact pain sensitivity and intensity (39), greatly differ in these women (40)(41)(42). Finally, our study aggregated opioids for trend analyses and did not focus on trends based on potency or doses of prescribed opioids; this would be useful in future studies to further understand risks within the population studied. ...
... Given the age-based criteria used to define our study cohort, it may be that a considerable proportion of women aged 50-54 are perimenopausal rather than menopausal/postmenopausal. A sensitivity analysis extending the lower age to 45 showed similar trends, supporting this assumption (Supplemental Figures 4-7). The widely fluctuating oestrogen levels observed in perimenopausal women particularly predispose them to development of musculoskeletal pain which can be sudden and intense in nature, albeit often transient (3,41,42). The incidence and discontinuation trends observed in women aged 50-54, and 45-49, compared to women aged 55+ are unsurprising, given menopausal/postmenopausal women are known to have steady, low oestrogen levels (40) which underpin age-related pain increases; these steady, age-related pain increases result in the observed steady rates of prescribing and longer duration of overall use compared with younger women, consistent with prior general population studies in patients 65+ (10,12,43). ...
Article
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Background Opioid use for chronic noncancer pain (CNCP) is consistently higher in menopausal/postmenopausal women than in younger women or men, elevating their risk of opioid-related adverse health outcomes. Since pain severity increases with hormonal changes accompanying menopause, these women should be a focus of opioid stewardship efforts. Aim To examine opioid prescribing trends for CNCP in menopausal/postmenopausal women diagnosed with a musculoskeletal condition. Design & setting Population-based drug utilisation study using IQVIA Medical Research Data UK. Method Annual opioid prescribing incidence, prevalence, and average duration of use were calculated for a cohort of women aged 50-79 with musculoskeletal conditions newly diagnosed between 2010-2021. Specific results were stratified by age, pain indication, and Townsend score. Results From 2010 to 2021, incident prescribing rates of opioids increased in women aged 50-54 (161.4 [95% CI 149.7-174.0] per 1000 PYAR in 2010 to 239.6 [95% CI 211.7-271.2] per 1000 PYAR in 2021); these women discontinued opioid use faster (~1 year) than older age groups (~2 years). Overall, opioid prescribing prevalence decreased from 23% in 2010 to 14% in 2021, and average opioid use duration decreased from 3 years to 1 year (2010 - post-2017) in women aged 50-79. Conclusion The overall observed decrease in prevalence and average duration of opioid use is encouraging. Incident prescriptions are rising in women aged 50-54 and those with fibromyalgia while remaining steady in women aged 55-79. Understanding the impact of menopause/post-menopause on opioid use trends is important for effective opioid stewardship.
... In the second stage, amenorrhea episodes can continue up to 12 months and are characterized by increased menstrual irregularity lasting more than 60 days. ( Delamater & Santoro,2018;Willi & et al.,2020;Harper & et al., 2022). The perimenopause, often known as the menopausal transition, is linked to significant hormonal and reproductive changes. ...
... Sexuality is one of the fundamental components that determine the quality of life and the physical, psychological, and social well-being of women, according to the World Health Organization (WHO) (21). Moreover, sexuality is a fundamental part of women's health (22); however, it is not considered as often as it should be by health professionals, which favors the appearance of associated psychological and physical disorders with the consequences that this entails, such as a reduction in quality of life (23)(24)(25)(26). Women may experience problems with sexual dysfunction related to lubrication, libido, orgasm, and general satisfaction with sexual activity, as well as problems with anticipatory anxiety and depression (27)(28)(29)(30). ...
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Objective To determine the relationship between sexual dysfunction and sleep disorders. Methods Observational study was conducted in 2021 and 2022 including 975 Spanish women over 18 years of age. The Women’s Sexual Function Questionnaire (FSM-12) was used as a source of information, and the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. A bivariate and multivariate analysis was performed using binary logistic regression, adjusting for confounding variables. Crude (OR) and adjusted (aOR) odds ratios were estimated with their respective 95% confidence intervals (CI). Results Around 29.2% (285) of the women presented some type of sexual dysfunction, and 73.4% (716) showed sleep disturbance with scores ≥5 on the PSQI scale. The mean score on the PSQI was 8.23 points (SD = 3.93). All the dimensions of the sexual function scale were statistically related to sleep disturbance ( p ≤ 0.05), except for sexual activity and the reasons for sexual activity not having penetration. In the multivariable analysis, women with sexual dysfunction presented an aOR of sleep disturbance of 1.88 (95% CI: 1.29–2.76) compared to women without dysfunction. Conclusion Global sexual dysfunction and almost all the dimensions that make up sexual function are related to changes in sleep quality.
Article
Perimenopausal symptoms adversely impacting women’s sexual health are often unacknowledged or even dismissed by healthcare professionals (HCP). Using semi-structured interviews with 23 women experiencing perimenopause, we aimed to understand how HCPs address women’s sexual health needs and concerns during this transition. Thematic data analysis was employed with the reduction of data into themes. Several major themes were identified concerning participants’ experiences with a HCP and sexual health, including insufficient sexual health information and preventative education; the lack of open discussion about sexual health; the need for enhanced support and treatment options; feeling dismissed by a healthcare professional; and sexual pleasure not addressed by healthcare professionals. Additionally, women expressed concerns regarding medical education and training in healthcare professionals on approaching sexual health conversations. There appears to be a need for more formal training, both within medical school and continuing medical education (CME), to address these important aspects of women’s sexual and reproductive health.
Chapter
Perimenopausal transition is a natural phase in every woman's life that is often marked by a range of physical and psychological symptoms that can remarkably impact the quality of life. The gradual decline in oestrogen levels leads to a wide array of symptoms, including mood swings, hot flashes, sleep disturbances, and irregular menstrual cycles. Managing perimenopausal symptoms effectively requires accurate detection and timely intervention to alleviate discomfort and optimise quality of life. AI-powered early identification and management of these symptoms are crucial for women's health and well-being. Machine learning techniques with the use of large datasets, self-reported symptoms, and clinical records offer a powerful tool for analysing complex patterns to achieve high accuracy and reliability in symptom recognition. AI-driven devices can aid in symptom tracking, personalised applications, remote monitoring, predictive analytics, and treatment efficacy assessment, thus improving clinical decision-making, patient outcomes, and the overall quality of women's health.
Article
Background: Women are more likely to experience depressive symptoms and poor quality of life (QoL) during perimenopause. The effectiveness of physical activity (PA) in perimenopause on mental well-being and health outcomes has been frequently reported. This study aimed to investigate the mediating effect of PA on the relationship between depression and QoL in Chinese perimenopausal women. Methods: A cross-sectional study was performed, and participants were recruited through a multistage, stratified, probability proportional to size sampling method. Depression, PA, and QoL were measured by Zung Self-rating Depression Scale, Physical Activity Rating Scale-3, and World Health Organization Quality of Life Questionnaire. The direct and indirect effects of PA on QoL were analyzed by PA in a mediation framework. Results: 1100 perimenopausal women participated in the study. PA mainly plays partial mediating roles in the relationship between depression and physical (ab = -0.493, 95 % CI: -0.582 to -0.407; ab = -0.449, 95 % CI: -0.553 to -0.343) and psychological (ab = -0.710, 95 % CI: -0.849 to -0.578; ab = -0.721, 95 % CI: -0.853 to -0.589; ab = -0.670, 95 % CI: -0.821 to -0.508) domains of QoL. Additionally, intensity (ab = -0.496, 95 % CI: -0.602 to -0.396; ab = -0.355, 95 % CI: -0.498 to -0.212) and duration (ab = -0.201, 95 % CI: -0.298 to -0.119; ab = -0.134, 95 % CI: -0.237 to -0.047) intermediated the relationship between moderate-to-severe depression and physical domain; frequency (ab = -0.130, 95 % CI: -0.207 to -0.066) only had a mediation influence between moderate depression and physical domain; intensity (ab = -0.583, 95 % CI: -0.712 to -0.460; ab = -0.709, 95 % CI: -0.854 to -0.561; ab = -0.520, 95 % CI: -0.719 to -0.315), duration (ab = -0.433, 95 % CI: -0.559 to -0.311; ab = -0.389, 95 % CI: -0.547 to -0.228; ab = -0.258, 95 % CI: -0.461 to -0.085), and frequency (ab = -0.365, 95 % CI: -0.493 to -0.247; ab = -0.270, 95 % CI: -0.414 to -0.144) all interceded between the psychological domain and all levels of depression, except for the frequency between severe depression and psychological domain; in terms of social relationship and environment domains, intensity (ab = -0.458, 95 % CI: -0.593 to -0.338; ab = -0.582, 95 % CI: -0.724 to -0.445), duration (ab = -0.397, 95 % CI: -0.526 to -0.282; ab = -0.412, 95 % CI: -0.548 to -0.293), and frequency (ab = -0.231, 95 % CI: -0.353 to -0.123; ab = -0.398, 95 % CI: -0.533 to -0.279) were mediators only on mild depression. Limitations: The cross-sectional study and self-reported data are major limiting factors. Conclusion: PA and its components partially mediated the association between depression and QoL. Suitable prevention methods and interventions for PA may improve the QoL for perimenopausal women.
Article
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Objective: Vitamin D deficiency is a significant problem that affects the population living in most countries. This issue is independent by place of residence, sex, age or skin color. It is mainly influenced by the environment we live in and by an unhealthy lifestyle, including bad eating habits. The aim of this study was to evaluate lipid profile, glucose levels, and vitamin D levels, considering sociodemographic variables, smoking and alcohol consumption in perimenopausal women. Depressive mood was also assessed considering sociodemographic variables and vitamin D levels. Patients and methods: The study was conducted on a group of 191 women and performed in two stages. The first of them was carried out using a diagnostic survey with the use of a technique questionnaire. The applied research instruments were the author's questionnaire (concerning sociodemographic and selected medical data), and the Beck Depression Inventory. The second stage of the study involved the collection of peripheral blood from each respondent, in order to determine lipid profile, glycemia and serum vitamin D levels. Results: The age of the female respondents ranged from 45 to 65 years, mean age was 53.1 ± 5.37 years, median 53 years. Vitamin D levels were below normal in 78%; 77% had elevated total cholesterol levels; 91.6% of the respondents had high density lipoprotein (HDL) cholesterol levels within the normal range; 64.4% was characterized by too high (low-density lipoprotein) LDL cholesterol, and 84.8% of the women showed normal triglyceride levels. Among the respondents, 91.1% had normal glycemic levels. Analysis of the collected data showed a weak negative correlation between serum vitamin D levels and the levels of total cholesterol (rho=-0.14; p=0.05), LDL cholesterol (rho=-0.16; p=0.026), and triglycerides (rho=-0.22; p=0.002). Only in the case of HDL cholesterol (p=0.067), there was no statistically significant correlation. There were also no statistically significant correlations between serum vitamin D levels and glycemia or severity of depression. Conclusions: 1. The majority of the women did not manifest depressive disorders. Of all factors analyzed, only education was associated with the severity of depressiveness. 2. Smoking adversely affected serum vitamin D levels in the studied women. 3. The cessation of menstruation affected carbohydrate metabolism and vitamin D levels. Blood glucose levels increased with the age of the studied women. 4. Relationships were found between the levels of vitamin D and the levels of total cholesterol, LDL cholesterol, and triglycerides. Therefore, it is important to maintain normal vitamin D levels.
Article
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Purpose: To determine the prevalence of depression, review some variables that are believed to be associated and assess the relationships between depression and sexual quality of life in postmenopausal women. Design and methods: This is a cross-sectional study conducted on postmenopausal women in Ankara, Turkey from February to June 2020. The study group consisted of 242 postmenopausal women. The Beck Depression Inventory and Sexual Quality of Life Questionnaire were used to assess the level of depression and sex life, respectively. Online questionnaire forms (Google Form) prepared by using the literature in line with the study objective were completed by the women online. Independent t-test, analysis of variance, Tukey's test, and Pearson's correlation analysis were used for statistical analyses. Statistical significance was accepted as p ≤ 0.05. Findings: The of women was found to be 52.64 (±6.245) years and the average menopause age was found to be 47.81 (±4.039) years in this study. The mean score obtained by the women from the Beck Depression Inventory was 13.04 (±7.82). It was determined that the women showed "mild depressive symptoms" mostly. As for the women's sexual quality of life, the mean score obtained from the Sexual Quality of Life Questionnaire was 61.32 (±14.70). A statistically significant and moderate negative correlation was detected between the mean scores obtained by the women from the Beck Depression Inventory and the Sexual Quality of Life Questionnaire (r = -0.305; p < 0.01). Practice implications: It was determined in the study that sexual quality of life is affected by menopause negatively and the women had mild depressive symptoms. Depression among postmenopausal women is an important women's health problem that should be addressed. A negative correlation was found between depression and sexual quality of life. Early diagnosis and treatment of menopause as well as activities for raising awareness among postmenopausal women will be effective in improving quality of life.
Article
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Background Estrogen receptor α (ERα) contributes to maintaining biological processes preserving health during aging. DNA methylation changes of ERα gene (ESR1) were established as playing a direct role in the regulation of ERα levels. In this study, we hypothesized decreased DNA methylation of ESR1 associated with postmenopause, lower estradiol (E2) levels, and increased age among healthy middle-aged and older women. Methods We assessed DNA methylation of ESR1 promoter region from dried blood spots (DBSs) and E2 from saliva samples in 130 healthy women aged 40–73 years. Results We found that postmenopause and lower E2 levels were associated with lower DNA methylation of a distal regulatory region, but not with DNA methylation of proximal promoters. Conclusion Our results indicate that decreased methylation of ESR1 cytosine-phosphate-guanine island (CpGI) shore may be associated with conditions of lower E2 in older healthy women.
Article
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Objectives: Intrauterine environmental conditions may affect the number of primordial follicles and in this way the timing of menopause. The aim of the present study was to investigate association patterns between right hand digit ratio, that is, 2D:4D - as an indicator of prenatal androgen and estrogen exposure, and age at menopause. Methods: One hundred sixty-nine women, who had experienced natural menopause, were enrolled in the study. Length of second and fourth finger were measured directly from the palmar side and digit ratios of both hands were calculated. For further analyses the digit ratio of the right hand was used only. Additionally, smoking habits, body weight and body height, body mass index and the number of children were determined. Multiple regression analyses were used to test association patterns between digit ratio and age at menopause, body height, BMI, nicotine consumption as well as number of births and age at menopause. Results: Age at menopause correlated significantly positively with the digit ratio. A more feminine digit ratio is associated with a higher age at menopause, while a low digit ratio, interpreted as a hint of a higher androgen exposure during prenatal phase was associated with a lower age at menopause. Conclusions: Low digit ratio is associated with an earlier onset of natural menopause.
Article
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Background: Adversity in early development seems to increase the risk of stress-related somatic disorders later in life. Physiologically, functioning of the hypothalamic–pituitary–adrenal and hypothalamic–pituitary–gonadal axes is often discussed as long-term mediators of risk. In particular, DNA methylation in the glucocorticoid receptor gene promoter (NR3C1) has been associated with type and strength of early life adversity and subsequent effects on HPA axis signaling in humans. Animal studies, moreover, suggest changes in DNA methylation in the estrogen receptor gene (ERα) upon early life adversity. We investigated the association of type and severity of childhood adversity with methylation in NR3C1 and ERα and additionally considered associations between methylation and steroid hormone secretion. Methods: The percentage of methylation within the NR3C1 promoter and the ERα shore was investigated using dried blood spot samples of 103 healthy women aged 40–73 years. Childhood adversity was examined with the Childhood Trauma Questionnaire. Linear regression analyses were performed with methylation as dependent variable and the experience of emotional abuse and neglect, physical abuse and neglect, and sexual abuse (compared to non-experience) as independent variables. All analyses were controlled for age, BMI, annual household income, and smoking status and were adjusted for multiple testing. Results: Overall, over 70% of the sample reported having experienced any kind of abuse or neglect of at least low intensity. There were no significant associations between childhood adversity and methylation in the NR3C1 promoter (all p > .10). Participants reporting emotional abuse showed significantly higher methylation in the ERα shore than those who did not (p = .001). Additionally, higher levels of adversity were associated with higher levels of ERα shore methylation (p = .001). Conclusion: In healthy women, early life adversity does not seem to result in NR3C1 promoter hypermethylation in midlife and older age. This is the first study in humans to suggest that childhood adversity might, however, epigenetically modify the ERα shore. Further studies are needed to gain a better understanding of why some individuals remain healthy and others develop psychopathologies in the face of childhood adversity.
Article
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Background: Previous research examining physiological changes across the menstrual cycle has considered biological responses to shifting hormones in isolation. Clinical studies, for example, have shown that women's nightly basal body temperature increases from 0.28 to 0.56 ˚C following postovulation progesterone production. Women's resting pulse rate, respiratory rate, and heart rate variability (HRV) are similarly elevated in the luteal phase, whereas skin perfusion decreases significantly following the fertile window's closing. Past research probed only 1 or 2 of these physiological features in a given study, requiring participants to come to a laboratory or hospital clinic multiple times throughout their cycle. Although initially designed for recreational purposes, wearable technology could enable more ambulatory studies of physiological changes across the menstrual cycle. Early research suggests that wearables can detect phase-based shifts in pulse rate and wrist skin temperature (WST). To date, previous work has studied these features separately, with the ability of wearables to accurately pinpoint the fertile window using multiple physiological parameters simultaneously yet unknown. Objective: In this study, we probed what phase-based differences a wearable bracelet could detect in users' WST, heart rate, HRV, respiratory rate, and skin perfusion. Drawing on insight from artificial intelligence and machine learning, we then sought to develop an algorithm that could identify the fertile window in real time. Methods: We conducted a prospective longitudinal study, recruiting 237 conception-seeking Swiss women. Participants wore the Ava bracelet (Ava AG) nightly while sleeping for up to a year or until they became pregnant. In addition to syncing the device to the corresponding smartphone app daily, women also completed an electronic diary about their activities in the past 24 hours. Finally, women took a urinary luteinizing hormone test at several points in a given cycle to determine the close of the fertile window. We assessed phase-based changes in physiological parameters using cross-classified mixed-effects models with random intercepts and random slopes. We then trained a machine learning algorithm to recognize the fertile window. Results: We have demonstrated that wearable technology can detect significant, concurrent phase-based shifts in WST, heart rate, and respiratory rate (all P<.001). HRV and skin perfusion similarly varied across the menstrual cycle (all P<.05), although these effects only trended toward significance following a Bonferroni correction to maintain a family-wise alpha level. Our findings were robust to daily, individual, and cycle-level covariates. Furthermore, we developed a machine learning algorithm that can detect the fertile window with 90% accuracy (95% CI 0.89 to 0.92). Conclusions: Our contributions highlight the impact of artificial intelligence and machine learning's integration into health care. By monitoring numerous physiological parameters simultaneously, wearable technology uniquely improves upon retrospective methods for fertility awareness and enables the first real-time predictive model of ovulation.
Article
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Background: A variety of biological and psychosocial factors are associated with women’s sexual health in midlife and older age. Evidence suggests a decline in sexual functioning in the context of aging and the menopausal transition, including changes in sexual desire, arousal, lubrication, orgasm, pain, and/or contentment. However, not all women in midlife and older age experience such a decline, and it remains unclear how the endocrine environment and psychosocial aspects contribute to the maintenance of healthy sexual functioning. Therefore, the aim of this study was to examine psychobiological predictors of sexual functioning in healthy middle-aged and elderly females. Methods: A total of 93 healthy, sexually active women aged 40–73 years completed a battery of validated psychosocial questionnaires, including measures of sexual functioning (Female Sexual Function Index) and of protective psychological traits and interpersonal variables. The steroid hormones estrogen, testosterone, progesterone and dehydroepiandrosterone sulfate were determined in saliva samples, while follicle-stimulating hormone, luteinizing hormone and sex hormone-binding globulin were determined in dried blood spots. The findings were statistically adjusted for multiple testing. Results: Age and postmenopausal status were negatively associated with overall sexual functioning, arousal, and lubrication. Regression analyses revealed that relationship satisfaction, emotional support, self-esteem, optimism, and life satisfaction each significantly predicted overall sexual functioning or specific aspects of sexual functioning, including arousal, contentment, orgasm, and pain (all p < 0.029). For desire and lubrication, no associations were found with the tested psychosocial factors. In terms of steroid hormones, testosterone was positively linked to orgasm (p = 0.012). In this sample, 79.6% reported to have healthy sexual functioning according to the questionnaires’ cutoff. Younger age (OR = 0.911, 95% CI 0.854–0.970, p = 0.004) and a higher level of emotional support (OR = 1.376, 95% CI 1.033–1.833, p = 0.029) were associated with the presence of healthy sexual functioning. Discussion: Although aging and menopause negatively affected aspects of sexual functioning, the accompanying endocrine correlates were not predictive for sexual functioning in this healthy sample of middle-aged and older females. Instead, our findings suggest that sexual functioning is highly dependent on psychosocial aspects related to well-being. Accordingly, personality traits such as optimism, and interpersonal aspects such as emotional support and relationship satisfaction were identified as important predictors of sexual functioning.
Article
The menopausal transition is a critical phase for psychological disorders such as depression and anxiety, with prevalence rates of depression ranging up to 20% during the menopause. Nevertheless, the majority of women cope adequately with this reproductive transition phase and thus appear to be resilient. We assert that a variety of psychological factors influence the menopausal transition and result in an individual state on a continuum from successful adjustment to maladjustment. The purpose of this review is to offer a conceptual framework of resilience factors during the menopausal transition and to reveal which dimensions of resilience have already been verified for a healthy menopausal transition. We searched the databases PubMed and PsycINFO for studies investigating resilience factors during the menopausal transition which influence psychological and physical adjustment or maladjustment. A total of 23 articles were included. Altogether, we identified 15 different resilience factors, assessed with 23 different questionnaires. These factors can be grouped into six categories: core resilience, spirituality, control, optimism, emotion and self-related resilience. They are associated with a better adjustment to menopausal symptoms, milder physical symptoms, a better quality of and satisfaction with life, better well-being, less perceived stress and fewer depressive symptoms compared with women with lower levels of the respective resilience factors. Our conceptual framework includes resilience factors which have already been verified by empirical data. Further research is needed to determine whether these resilience factors can be assigned to a common factor and to incorporate biological resilience markers.
Article
BACKGROUND: Within the female life cycle, the perimenopause is considered as a critical period for the development of depression. Prevalence rates are particularly high during this phase. Perimenopausal depression is characterized by affective symptoms as well as menopause-specific somatic complaints. Currently, a variety of questionnaires are used to assess mood during the perimenopause. The aim of this review is to determine the instruments employed to assess perimenopausal depression. METHODS: We searched the databases PubMed, Cochrane Library and PsycINFO for human studies investigating perimenopausal depression, and subsequently screened for the assessment instruments used to measure mood and menopause. A total of 37 articles were included. RESULTS: Altogether, 14 different instruments were applied to assess mood during menopause. The CES-D was by far the most frequently used depression scale, appearing in 16 out of the 37 studies. The methods used to identify perimenopausal status and symptoms were inconsistent. LIMITATIONS: Due to lacking information about data and methodology, a selection bias is conceivable. Additionally, a publication bias is possible. Finally, there is inevitable subjectivity in the screening process of a systematic search. CONCLUSIONS: The assessment of depression in the menopausal transition is highly heterogeneous, reducing the overall comparability of study results. Furthermore, menopausal complaints are not sufficiently taken into account. Accordingly, the use of a menopause-specific depression scale is highly recommended in order to account for physical and mood-related symptoms in the menopausal transition.
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Nowadays, people spend a considerable amount of their lives as older adults, but this longer lifespan is often accompanied by an increase in chronic conditions and disease, resulting in reduced quality of life and unprecedented societal and economic burden. Healthy aging is therefore increasingly recognized as a healthcare priority. Physical and mental adaptations to changes over the life course, and the maintenance of well-being, represent pivotal challenges in healthy aging. To capture the complexity of healthy aging, we propose a specific phenotype based on body composition, cognition, mood, and sexual function as indicators of different dimensions of healthy aging. With increasing age, sex hormones as well as glucocorticoids undergo significant alterations, and different patterns emerge for women and men. This review describes age-related patterns of change for women and men, and sheds light on the underlying mechanisms. Furthermore, an overview is provided of the challenges for healthy aging resulting from these age-related steroid alterations. While clinical practice guidelines recommend hormonal treatment only in the case of consistently low hormone levels and symptoms of hormone deficiency, physical exercise and a healthy lifestyle emerge as preventive strategies which can counter age-related hormonal changes and at best prevent chronic conditions.