Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile health data

The menstrual cycle is a key indicator of overall health for women of reproductive age. Previously, menstruation was primarily studied through survey results; however, as menstrual tracking mobile apps become more widely adopted, they provide an increasingly large, content-rich source of menstrual health experiences and behaviors over time. By exploring a database of user-tracked observations from the Clue app by BioWink of over 378,000 users and 4.9 million natural cycles, we show that self-reported menstrual tracker data can reveal statistically significant relationships between per-person cycle length variability and self-reported qualitative symptoms. A concern for self-tracked data is that they reflect not only physiological behaviors, but also the engagement dynamics of app users. To mitigate such potential artifacts, we develop a procedure to exclude cycles lacking user engagement, thereby allowing us to better distinguish true menstrual patterns from tracking anomalies. We uncover that women located at different ends of the menstrual variability spectrum, based on the consistency of their cycle length statistics, exhibit statistically significant differences in their cycle characteristics and symptom tracking patterns. We also find that cycle and period length statistics are stationary over the app usage timeline across the variability spectrum. The symptoms that we identify as showing statistically significant association with timing data can be useful to clinicians and users for predicting cycle variability from symptoms or as potential health indicators for conditions like endometriosis. Our findings showcase the potential of longitudinal, high-resolution self-tracked data to improve understanding of menstruation and women's health as a whole.Comment: The Supplementary Information for this work, as well as the code required for data pre-processing and producing results is available in https://github.com/iurteaga/menstrual_cycle_analysi

Varastest embrüotest pärit ekstratsellulaarsed vesiikulid: potentsiaal embrüokvaliteedi markeritena ja roll embrüo-emaka suhtluses

Väitekirja elektrooniline versioon ei sisalda publikatsiooneViljatus on globaalne rahvatervise probleem, mis mõjutab miljoneid inimesi. Abistav reproduktiivtehnoloogia, sealhulgas in vitro viljastamine, on aidanud mitmeid viljatuid inimesi. Küll on sellel metoodikal üheks kitsaskohaks implantatsiooni ebaõnnestumine isegi morfoloogiliselt parimate embrüotega. Seetõttu toimuvad jätkuvalt uuringud tuvastamaks paremaid meetodeid, mis hindavad embrüo kvaliteeti ja ennustavad siirdamise edukust, olles peamiselt embrüokasvusöötme baasil. Rakuvälised ehk ekstratsellulaarsed vesiikulid (EV) on membraaniga ümbritsetud nanoosakesed, mida toodavad peaaegu kõik rakutüübid erinevates füsioloogilistes ja patoloogilistes konditsioonides. Nende kaudu toimub rakuvaheline suhtlus. Mitmed uuringud, eriti vähi korral, on uurinud EVde potentsiaali biomarkerina ja ravimkandursüsteemina. Antud doktoritöö uuris implantatsiooni-eelse perioodi embrüost vabanenud EVde potentsiaali embrüokvaliteedi markerina ja embrüo-emaka suhtluse vahendajana. Katsed viidi läbi kasutades veise-embrüoid ja inimrakukultuuride põhiseid eksperimentaalmudeleid. Esimene uuring tõestas, et individuaalselt kasvatatud implantatsiooni-eelse perioodi veise-embrüod eritavad EVsid kasvusöötmesse ning nende kontsentratsiooni- ja suurusprofiil sõltub embrüo kvaliteedist ja arengustaadiumist. Järgnevalt katsetati munajuharakkudel implantatsiooni-eelse perioodi embrüost pärit EVde funktsionaalsust. Katse käigus selgus, et EVd kõrge kvaliteediga embrüotest muutsid munajuharakkude geeniekspressiooni, mida aga ei teinud halva kvaliteediga embrüote EVd. Suurenenud ekspressiooniga geenide hulgas olid mitmed interferoon-τ raja interferooni stimuleerivad geenid. Interferoon-τ peetakse mäletsejaliste tiinuse tuvastusmolekuliks. See leid viitab, et munajuha tunneb ära kvaliteetse embrüo. Viimaseks uuriti embrüo EVde funktsionaalsuse spetsiifilisust. Leiti, et endomeetrium reageerib vaid embrüo päritolu EVdele. Uuringute käigus tuvastati embrüost vabanenud EVde potentsiaal ja spetsiifilisus embrüokvaliteedi biomarkerina.Infertility is a global public health problem that affects millions of people in their reproductive life. Assisted reproductive technologies (ARTs) such as in-vitro fertilization have enabled many patients to overcome this issue. However, a bottleneck in ART success is the implantation failure even after the transfer of morphologically best embryos. Hence, investigations continue to identify better or complementary methods of assessing embryo quality and predicting transfer success, mainly based on the embryo culture media. Extracellular vesicles (EVs) are membrane-bound nanoparticles released by almost all types of cells under different physiological and pathological conditions. They mediate intercellular communication. Many studies, especially related to cancer, have investigated EVs' potential as biomarkers and therapeutic drug delivery systems. This project investigated preimplantation embryo-derived extracellular vesicles as a potential embryo quality marker and a mediator of embryo-maternal communication. Experiments were performed using bovine embryos and human cell-culture based experimental models. The first study showed that individually cultured preimplantation bovine embryos release EVs to their culture media, and their concentration and size profile are dependent on the quality and development stage of embryos. Subsequently, the functionality of preimplantation embryo-derived EVs were tested in the oviduct. It was observed that EVs from good quality embryos, but not the EVs from embryos of low developmental potential quality, could alter the gene expression of the oviduct. Among the up-regulated genes, many were interferon-stimulated genes of the interferon-τ pathway. Interferon-τ is considered the pregnancy recognition molecule in ruminant pregnancy. This finding suggests that the oviduct can serve as a biosensor of embryo quality. Finally, the functional specificity of embryonic EVs were investigated. It was observed that endometrium only react to embryonic EVs but not to the non-embryonic EVs. All these studies support the potential and specificity of embryo-derived EVs as a biomarker of embryo quality.https://www.ester.ee/record=b548409

Learning predictive models from menstrual cycle data

Despite being a physiological phenomenon that impacts billions of womxn worldwide, menstruation has long been understudied. In this dissertation, we first explore the menstrual characteristics of nearly 380,000 womxn, as collected via a self-tracking mobile health (mHealth) app, Clue. We examine how variation in menstrual cycle length is related to volatility in other experienced symptoms, helping to debunk the idea that menstrual cycles should be 'regular.' We then develop predictive models for menstruation utilizing this dataset, demonstrating first how a fully generative model that explicitly accounts for the possibility that self-tracked data may be flawed in terms of reliability can both outperform baselines and aid in the detection of self-tracking artifacts (i.e., instances where a user supposedly did not experience a period event, but in reality forgot or otherwise neglected to track it). Finally, we explore a hierarchical, deep generative model for symptom tracking, where we utilize a deep neural network to learn per-user parameters for tracking and retain a mechanism for modeling per-user likelihood of adherence. We find that leveraging symptom data at the time series level allows us to predict occurrence of next bleeding and non-bleeding tracking events with high accuracy. This work demonstrates the great potential that large-scale mHealth data holds to better understanding menstruation as a whole, as well as the importance of treating such data carefully

Mechanisms responsible for ‘scarless’ tissue repair in the endometrium

The endometrium is the inner lining of the uterine cavity, composed of distinct epithelial and stromal cell compartments with the latter containing fibroblasts, a vascular compartment and fluctuating populations of immune cells. The endometrium is a highly dynamic tissue that undergoes cycles of proliferation and stromal cell differentiation (decidualisation) followed by tissue shedding (menstruation) and rapid repair/remodelling, all under control of fluctuating concentrations of steroid hormones secreted from the ovaries. This is known as the menstrual cycle. In response to the ‘injury’ inflicted as a consequence of decidual breakdown and shedding, the endometrium exhibits a unique capacity to restore tissue architecture by rapid tissue repair. This repair process is tightly regulated to ensure that the endometrium heals consistently every month throughout a woman’s reproductive lifespan, without the accumulation of fibrotic scar tissue which could have a negative impact on fertility. The initiation of menstruation is triggered by the withdrawal of progesterone as a consequence of the demise of the corpus luteum within the ovaries which precipitates an increase in production of inflammatory mediators, focal hypoxia and activation of matrix metalloproteinase enzymes culminating in endometrial shedding. In contrast the cellular and molecular mechanisms responsible for the rapid and scar-free tissue repair of endometrium remain poorly understood. Parallels can be drawn between the repair process of the endometrium and that of the foetal skin and oral mucosa which also exhibit ‘scarless’ healing, including rapid reepithelialisation, widespread cellular proliferation and migration and a short time to wound closure. However endometrial repair also shares key features of the wound healing experienced by adult tissues that exhibit scarring including extensive angiogenesis and a substantial inflammatory response. The endometrium appears to be unique, fitting in a gap between tissues that typically undergo ‘scarless’ or ‘scarring’ tissue repair. In women, endometrial shedding is considered an inflammatory event and the culmination of a cascade of inflammatory signals result in the accumulation of a diverse population of immune cells within the tissue. Whilst we believe immune cells play a key role in regulating spatial and temporal tissue breakdown and shedding their role in repair and restoration of tissue homeostasis remains poorly understood. One process essential for endometrial repair is restoration of the luminal epithelial cell layer (re-epithelialisation) and imaging studies have demonstrated that this appears not only to be rapid but also to occur synchronously with tissue degeneration and shedding. Re-epithelialisation was previously thought to be governed by proliferation and migration of glandular epithelial cells in the basal (unshed) tissue compartment, however new data suggest a role for trans-differentiation of stromal cells into epithelial cells which merits further investigation. In addition to role(s) for immune and stromal cells in regulation of endometrial tissue function, a role for somatic stem/progenitor cells capable of differentiating into mature endometrial cells to regenerate the tissue has also been claimed. In summary, whilst progress has been made in understanding the processes governing endometrial decidualisation, breakdown and shedding the regulation and roles of the different cell types that participate in scar-free repair of the tissue remain poorly defined. The studies in this thesis set out to address this gap by addressing three key aims: Aim 1. To investigate the phenotype and location of immune cell populations during scarless tissue repair. Aim 2. To identify and characterise a putative population of mesenchymal stem/progenitor cells in endometrium. Aim 3. To investigate the contribution of putative mesenchymal stem/progenitor cells to endometrial tissue repair. The aims were addressed using a recently refined and extensively characterised mouse model in which endometrial shedding (‘menstruation’) and repair occurs over a 48 hour period following removal of a progesterone stimulus. Importantly the Saunders’ group have already demonstrated that this model recapitulates the key features of human menses including overt vaginal bleeding, immune cell influx, tissue necrosis, transient hypoxia, re-epithelialisation and most importantly simultaneous breakdown and repair. Uterine tissues recovered 12, 24, and 48hrs after removal of progesterone and were investigated using immunohistochemistry (spatial organisation), flow cytometry (quantitation of cell subpopulations), FACS sorting (isolation of subpopulations) and molecular profiling (qPCR, RNAseq and single cell sequencing) with additional insights from bioinformatic analysis. To address Aim 1 endometrial shedding and repair was studied in Macgreen® mice: in this transgenic line all the cells of the mononuclear phagocyte lineage (monocytes, monocyte-derived macrophages) express green fluorescent protein. Immunohistochemistry revealed striking spatio-temporal changes in both numbers and location of GFP+ cells during endometrial breakdown and repair, the most prominent changes occurring 24hrs after removal of progesterone. Flow Cytometry quantified several immune cell populations with a significant increase in GFP+ cells during repair, the majority of which were GR1+F4/80- (inflammatory monocytes). These novel data provided compelling evidence to support a role for inflammatory monocytes in endometrial repair and provide the platform for future studies on the role of these cells in scarless healing. To address Aims 2 and 3 Pdgfrβ-BAC-eGFP® transgenic mice in which GFP was expressed under control of promoter elements of the Pdgfrβ gene was used to identify putative mesenchymal progenitor cells and investigate their role in endometrial repair. GFP+ cells were located exclusively within the endometrial stromal compartment and examination of tissue sections revealed that two subpopulations could be distinguished based on the both the intensity of GFP and expression of CD146 (Mcam). Characterisation by immunohistochemistry, flow cytometry and qPCR identified a GFPbright subpopulation located adjacent to CD31+ endothelial cells that were classified as pericytes based on location and phenotype (NG2+, CD146+, CD31-). When menstruation was stimulated in Pdgfrβ-BAC-eGFP® mice detailed analysis using flow cytometry revealed an increase in the perivascular pericyte subpopulation during active healing (24hrs) and also identified a new previously unidentified subpopulation of GFP+ cells which had a unique phenotype during repair. Evidence that GFP+ cells contribute to restoration of epithelial repair was obtained with an increase in expression of the epithelial cell marker EpCAM and GFP+ cells in the renewed epithelial cell layer. RNAseq and single cell sequencing combined with bioinformatics complemented these findings by identifying novel changes in gene expression in both endometrial fibroblasts and pericyte populations consistent with induction of novel pathways and trans-differentiation of stromal cells by a mesenchymal-to-epithelial transition (MET). In conclusion, using a mouse model of endometrial breakdown and repair a heterogeneous population of myeloid cells and a putative population of endometrial progenitors (pericytes) have been characterised, quantified and novel changes in gene expression identified. Adaptation of these cell types to the insult of endometrial shedding appears to play a key role in temporal and spatial regulation of rapid, scar-free endometrial tissue repair. These novel findings may inform the development of new approaches to treating gynaecological disorders associated with aberrant endometrial repair such as heavy menstrual bleeding, Asherman’s syndrome and endometriosis as well as other disorders associated with excessive fibrosis and scar formation

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

Enabling Automated, Conversational Health Coaching with Human-Centered Artificial Intelligence

Health coaching is a promising approach to support self-management of chronic conditions like type 2 diabetes; however, there aren’t enough coaching practitioners to support those in need. Advances in Artificial Intelligence (AI) and Machine Learning (ML) have the potential to enable innovative, automated health coaching interventions, but important gaps remain in applying AI and ML to coaching interventions. This thesis aims to identify computational approaches and interactive technologies that enable automated health coaching systems. First, I utilized computational approaches that leverage individuals’ self-tracking and health data and used an expert system to translate ML inferences into personalized nutrition goal recommendations. The system, GlucoGoalie, was evaluated in multiple studies including a 4-week deployment study which demonstrated the feasibility of the approach. Second, I compared human-powered and automated/chatbot approaches to health coaching in a 3-week study which found that t2.coach — a scripted, theoretically-grounded chatbot designed through an iterative, user-centered process — cultivated a coach-like experience that had many similarities to the experience of messaging with actual health coaches, and outlined directions for automated, conversational coaching interventions. Third, I examined multiple AI approaches to enable micro-coaching dialogs — brief coaching conversations related to specific meals, to support achievement of nutrition goals — including a knowledge-based system for natural language understanding, and a data-driven, reinforcement learning approach for dialog management. Together, the results of these studies contribute methods and insights that take steps towards more intelligent conversational coaching systems, with resonance to research in informatics, human-computer interaction, and health coaching