Thèse les Menstruations Comme Signe Vital quels sont les Facteurs de Risque et les Conséquences Psychosociales des Dysménorrhées H/F

Université Paris-Saclay GS Santé publique

Les douleurs menstruelles (ou dysménorrhées) sont fréquentes et peuvent significativement impacter les autres aspects de la santé et la qualité de vie des femmes, aboutissant à un fardeau à la fois individuel, sur les systèmes de santé et sur la société. Malgré leur forte prévalence, les dysménorrhées sont peu étudiées et souvent banalisées, menant à une prise en charge inadaptée et des résultats faibles en matière de santé. Etudier ces symptômes s'aligne avec les récentes stratégies gouvernementales pour l'amélioration de la santé des femmes et la réduction des inégalités de genre en santé.

Ce projet doctoral vise à apporter des connaissances permettant d'améliorer la prédiction, la prévention et la prise en charge des dysménorrhées et de réduire ses liens avec la santé mentale et le bien-être psychique au cours de la vie.

Spécifiquement, ses objectifs sont de :
1) Décrire la prévalence des dysménorrhées
2) Identifier les facteurs de risque des dysménorrhées
3) Explorer les liens entre dysménorrhées et bien-être psychosocial, en investiguant de potentiels effets causaux et facteurs confondants

Ce projet portera sur le projet multicentrique MenstruLife (UK, Australie, USA, Norvège) ainsi que sur la cohorte CONSTANCES (France). Il s'inscrit dans le cadre d'un projet de thèse en cotutelle internationale entre l'Université d'Exeter, UK (Department of Psychology, Faculty of Health and Life Sciences), ou le/a doctorant-e passera 2 ans, et l'Université Paris-Saclay, France (Epidemiology of Gynaecological Health Team, CESP/Inserm U1018), où il/elle passera 1 an.

Menstrual pain (MP, or dysmenorrhea) is common and can significantly affect other aspects of health and quality of life, placing burdens on individuals, healthcare systems, and society. Despite the high prevalence of this symptom, it is under-researched and often normalized, or dismissed, leading to mismanagement and poor health outcomes. Addressing these conditions aligns with recent government strategies (including in the UK and France) to improve women's health and reduce gender inequalities.

Painful menstrual periods pose a burden to individuals, healthcare systems, and society

There is growing public interest in menstrual health, but conditions that disproportionately affect women remain under-researched. Severe dysmenorrhea (menstrual pain) affects over a quarter of women during reproductive life, most often in the earlier reproductive years. Dysmenorrhea is defined subjectively as painful cramping, usually in the lower abdomen, occurring shortly before and/or during menstruation. Dysmenorrhea may occur in the absence of any identifiable pathology, or due to conditions such as endometriosis or adenomyosis.

Regardless of the cause, dysmenorrhea can impose significant burdens on individuals, healthcare systems and society. For example, dysmenorrhea can heighten pain sensitivity and exacerbate non-cyclical chronic pain conditions. In a nationwide survey of over 42,000 women in the Netherlands, 1 in 3 had to curtail their regular activities due to menstrual symptoms, and the adverse impact on quality of life is well-characterised. There are high individual financial costs and subsequent inequalities associated with self-management of menstrual symptoms, and the cost to the healthcare system is substantial. For example, a study in Japan found that healthcare costs were up to 2.9 times higher for women with MP respectively, compared to matched controls. Women in their reproductive years are often in education or contributing to the workforce, but problematic menstrual symptoms are associated with up to 23 days decreased productivity per year, potentially limiting career growth and economic empowerment. These avoidable health inequalities have been prioritised by the UK and Scottish Government in the development of recent strategies to improve women's health.

Painful menstrual periods could affect mental health and psychosocial wellbeing, and vice versa

Common mental health disorders like depressive disorders are diagnosed twice as commonly in women compared to men, with the greatest risk occurring during reproductive life. People with dysmenorrhea are at elevated risk of depressive symptoms and depressive disorders. For example, rates of depression in adolescents with dysmenorrhea are 156% higher compared to controls. It is unclear whether this association reflects a causal effect, and whether the effect is largely from dysmenorrhea to poor mental health/psychosocial wellbeing or vice versa, but there are plausible mechanisms for both. For example, sleep disturbance due to pain could cause cycles of fatigue and functional impairment that affect mood and avoidant behaviours, impacting on mental health, work/school engagement and social activities, academic/career performance, and limited life potential. Alternatively, or additionally, poor mental health and psychosocial wellbeing could cause dysmenorrhea via well-established interactions between stress, the hypothalamic-pituitary -adrenal (HPA) and -gonadal axis, and uterine physiology. These associations may be compounded by stigma around menstruation limiting sharing experiences and seeking social support, and by the common normalisation and dismissal of problematic menstrual experiences. Further difficulties for those lacking access to affordable menstrual products, pain relief, and healthcare could contribute to inequalities in both menstrual health and psychosocial wellbeing.

Addressing research gaps will inform better ways to manage dysmenorrhea and reduce the impact on psychosocial wellbeing

Identifying the causes of, treating, and managing dysmenorrhea is a global health priority, with growing recognition amongst the media, policy makers and medical researchers that menstruation is a critical issue for women and society. Public consultation for the 2022 Women's Health Strategy for England reported that menstrual health and gynaecological conditions were the leading priority in women's health, identified by 47% of respondents. However, research and clinical practice are lagging behind public interest and awareness and there are substantial knowledge gaps. Urgent research is needed to fill these gaps in an under-recognised, under-resourced and under-researched area of clinical need. Until we do so, a substantial proportion of those with dysmenorrhea are likely to be mis-managed and suffer poor physical and mental health and quality of life.

RESEARCH GAP 1: What is the prevalence of dysmenorrhea?
Current prevalence estimates for dysmenorrhea are imprecise (e.g. 16-91%), partly because of methodological differences in defining and measuring the symptom, and partly because it is often under-reported, normalised, and dismissed. Factors contributing to this knowledge gap include social stigma and taboo around menstruation, and uncertainty amongst individuals and healthcare providers about what a normal menstrual experience should entail. HOW WE WILL ADDRESS IT: This project brings together some of the very few cohort studies globally that have collected data on dysmenorrhea. It leverages prospective and longitudinal data to describe the prevalence, severity and trajectories of dysmenorrhea, using general population data to minimise underreporting. WHAT THIS WILL ACHIEVE: Our findings will provide more precise estimates of the prevalence of dysmenorrhea based on self-report (i.e. in line with the subjective clinical definition), which will help raise public and clinical awareness of the importance of these symptoms.

RESEARCH GAP 2: What are the risk factors for developing dysmenorrhea across the life course?
The genetic, demographic, medical history, behavioural, social and environmental factors impacting on risk of dysmenorrhea are poorly understood. A review from 2014 found that work and general life stress were prospective risk factors, but limited data meant that uncertainty remained for socioeconomic position (SEP), body mass index (BMI) and modifiable health behaviours such as smoking and diet. We are not aware of any published studies that have taken a life course approach to understand which early-life/historical exposures increase risk, and how risk factors differ for dysmenorrhea experienced at different stages of the reproductive life course. HOW WE WILL ADDRESS IT: We will capitalise on rich, deeply phenotyped, longitudinal and genetic data to conduct a comprehensive exploration of potential genetic, behavioural, social, and environmental risk factors for developing dysmenorrhea across the life course in multiple cohorts. WHAT THIS WILL ACHIEVE: Our findings will help clinicians assess who is most at risk of dysmenorrhea, informing predictive tools. Identified risk factors will provide new insights into potential mechanisms underlying dysmenorrhea, which could be modifiable targets for more tailored intervention to improve or prevent this symptom.

RESEARCH GAP 3: How is dysmenorrhea related to psychosocial wellbeing?
Evidence demonstrating an association between dysmenorrhea and depressive symptoms is largely based on retrospective, cross-sectional data, and literature reviews have identified only a handful of small studies using prospective or longitudinal data. Consequently, it remains unclear whether individuals with dysmenorrhea are more at risk of developing poor mental health/psychosocial wellbeing at certain times of the life course, and whether these associations reflect potential causal effects (of dysmenorrhea on psychosocial wellbeing), confounding (e.g. by socioeconomic position, other health conditions, or treatment effects), or reverse causation (whereby people with poor psychosocial wellbeing are more likely to develop dysmenorrhea or report menstrual symptoms as problematic). In addition, the complex biopsychosocial mechanisms underlying relationships between dysmenorrhea and mental health outcomes have not been studied. HOW WE WILL ADDRESS IT: We will leverage prospective longitudinal and genetic data to infer (causal) relationships between dysmenorrhea and psychosocial wellbeing, in both directions. We will explore how these associations vary by patient characteristics such as age, race/ethnicity, and socioeconomic position. WHAT THIS WILL ACHIEVE: Understanding the extent, direction, timing, and mechanisms of the relationship between dysmenorrhea and psychosocial wellbeing is essential to inform prediction and clinical care. For example, if people with poor mental health are at elevated risk of dysmenorrhea, this could help clinicians identify those at risk and highlight psychosocial wellbeing as a potential target to reduce dysmenorrhea. If we find that dysmenorrhea impacts on psychosocial wellbeing, this will inform new policy or clinical interventions to reduce this impact, and provide opportunities for prevention/early detection/treatment.

Impact
This project will address a leading priority for women's health by generating new and robust knowledge about the prevalence, severity, risk factors, and mental health impact of a common menstrual symptom. It will inform ways to predict/prevent dysmenorrhea and predict/prevent/treat adverse effects on psychosocial wellbeing. This information can guide GPs, clinical psychologists, gynaecologists and other clinical care providers to stratify patients by risk and develop interventions promoting menstrual and mental health. Ultimately, this will help improve the health, wellbeing, and social, educational and workplace participation of people who menstruate, and reduce gender-based health and social inequalities.

Aim 1: To describe the prevalence of dysmenorrhea
Aim 2: To identify risk factors for developing dysmenorrhea
Aim 3: To explore the relationships between dysmenorrhea and psychosocial wellbeing, including potential causal effects and confounding factors

Study populations

This project will expand the MenstruLife study (UK, Australia, USA, Norway) by including data from the CONSTANCES cohort (France).

MenstruLife

The multicohort MenstruLife study (www.menstrulife.com) harnesses existing data across multiple longitudinal cohort studies to describe the prevalence of and risk factors for problematic menstrual symptoms, and potential causal relationships between these symptoms and mental health.

These cohorts are:
-Adolescent Brain Cognitive Development (ABCD) [USA, aged 9 at recruitment with annual follow-ups, dysmenorrhea collected data from age 11];
-Avon Longitudinal Study of Parents and Children (ALSPAC) [UK; relevant cohorts are the mothers (G0), who have provided data from 1991/2 to present and are now aged ~48 to 76 (mode=59), and the children (G1), who are now ~32 years];
-Australian Longitudinal Study of Women's Health (ALSWH) [three cohorts with data on dysmenorrhea: those born in 1946-51, 1973-78, and 1989-95];
-Norwegian Mother, Father and Child Cohort Study (MoBa) [dysmenorrhea data are currently being collected for the index offspring at age 18];
-The Raine Cohort [Australia; recruited at birth in 1989-92, now aged ~30, data on dysmenorrhea from adolescence];
-The UK Biobank [mean aged 56 at recruitment, but with linkage to electronic health record data from earlier ages dating back to the 1940s].

In all cohorts, data on dysmenorrhea are self-reported, except for UK Biobank where the information comes from linked electronic health records (primary & secondary care) and is defined using ICD10 code N94. Although this means we can only capture clinically-recorded dysmenorrhea in UK Biobank, the inclusion of this cohort greatly increases power for genetic analysis.

Data on mental health and psychosocial wellbeing (e.g. subjective happiness, wellbeing, educational attainment, life satisfaction) are measured using validated questionnaires. Measures will be harmonised and standardised across cohorts where possible. In addition, most cohorts have self-reported or linked information on whether mental health conditions have ever been diagnosed or treated by a doctor.

CONSTANCES

CONSTANCES is a French prospective cohort study involving 220,000 adult volunteers (116,000 of whom are women) affiliated with the general health scheme of the national social security system. Participants were aged 18-69 years when recruited between 2012 and 2021 and have been followed-up annually since inclusion.

Data on dysmenorrhea were collected at inclusion, and those on mental health and psychosocial wellbeing were collected both at inclusion and at each annual follow-up (e.g. Centre for Epidemiologic Study-Depression scale, CES-D). Participants have also self-reported whether mental health conditions have ever been diagnosed or treated by a doctor at inclusion and throughout follow-up.

Analytical approach
Aim 1: To describe the prevalence of dysmenorrhea
-Aim 1.1: to understand how many people are affected by dysmenorrhea, the prevalence of dysmenorrhea will be calculated in CONSTANCES and compared to the prevalences that will have already been calculated for MenstruLife. Of note, in pilot analyses in ALSPAC G0, the lifetime prevalence of dysmenorrhea over 8 timepoints was 20%.
-Aim 1.2 to understand when individuals are most at risk of dysmenorrhea, the prevalence in MenstruLife and CONSTANCES will be compared by age. E.g., in ALSPAC G0, prevalence of dysmenorrhea decreases with age.
Aim 2: To identify risk factors for developing dysmenorrhea
-Aim 2.1: to assess associations between dysmenorrhea and potential risk factors across the life course, we will capitalise on the rich longitudinal data available in the MenstruLife and CONSTANCES cohorts to derive (harmonised as far as possible) variables for each potential risk factor. These analyses will already have been conducted for MenstruLife; the current PhD will attempt to replicate these findings in CONSTANCES. Multivariable regression models will be used to assess associations between each potential risk factor and dysmenorrhea. Models will be adjusted for potential confounders, as identified by Directed Acyclic Graphs (DAGs). Where more than two cohorts contribute comparable results, a meta-analysis will be conducted to estimate the average effect and increase precision. More generally, results will be compared between MenstruLife and CONSTANCES by plotting and comparing the direction and size of effect estimates. Potential heterogeneity arising from differences between cohort methodologies or populations (e.g. different confounding structures) will be considered.
Aim 3: To understand the relationship between dysmenorrhea and psychosocial wellbeing
-Aim 3.1: to understand the extent to which dysmenorrhea is associated with psychosocial wellbeing, we will first conduct a scoping review of the literature and develop a conceptual framework to describe potential mechanisms. Then multivariable logistic regression models will be used in both MenstruLife and CONSTANCES. Analyses will be conducted with and without adjustment for potential confounders. In ALSPAC G0, preliminary analysis using logistic mixed effects regression showed higher odds of having a clinically high depressive symptom score among women experiencing dysmenorrhea (odds ratio 4.0, 95% CI 1.7 to 9.5, P=0.002). We will build on this work by expanding the list of outcomes and comparing across cohorts.
-Aim 3.2: to understand whether dysmenorrhea causes poor psychosocial wellbeing, or vice versa, we will triangulate evidence from two approaches: (1) we will capitalise on repeated measures data to provide insights about the temporal order of events (i.e., what comes first, dysmenorrhea or poor psychosocial wellbeing?), to help infer the direction of effect. To do this, we will visualise trajectories of depressive symptoms by dysmenorrhea status and use cross-lagged structural equation models. Fixed effects models will also be used where data on psychosocial wellbeing were collected pre- and post- onset or resolution of dysmenorrhea, for example ABCD, ALSPAC G1, MoBa and Raine have depressive symptoms data pre- and post-menarche. The approach enables evaluation of whether the onset/resolution of dysmenorrhea led to a change in depressive symptoms. Preliminary analyses in ABCD suggest the direction of causation is primarily from dysmenorrhea to depression: adolescents who experienced dysmenorrhea had higher scores for depressive symptoms post-menarche, but not necessarily pre-menarche, compared to adolescents without dysmenorrhea. (2) we will leverage genetic data to conduct bidirectional Mendelian Randomization (MR)25, which will use SNPs as a proxy for dysmenorrhea to interrogate the causal effect of these symptoms on psychosocial wellbeing, and vice versa.

The PhD student will contribute to the project by:

-Systematically reviewing the literature on the relationship between dysmenorrhea and psychosocial wellbeing, including developing a conceptual framework to describe potential mechanisms.
-Preparing and analysing longitudinal cohort data from the MenstruLife cohorts, particularly around Aim 3 (data for Aims 1 and 2 will have already been prepared and analysed by post-docs working on the MenstruLife project).
-Preparing and analysing data from the French CONSTANCES cohort, thereby expanding the pool of cohorts that can be included in MenstruLife meta-analyses, and enabling comparison of MenstruLife results with a large cohort that is more ethnically and socially diverse.
-Expanding the focus of MenstruLife (which largely focused on the impact of dysmenorrhea on depressive symptoms) to include a wider range of psychosocial outcomes.
-Hosting workshops with diverse stakeholders to co-produce methods to disseminate project findings in an accessible manner to facilitate impact. This will involve designing, delivering, and evaluating (via a feedback survey) a public engagement event, with support from the project team.