Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review: Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings: In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary: This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer researchThis work was supported in part by CLARIFY project, within European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 875160, Instituto de Fomento de la Región de Murcia (INFO) and the European Regional Development Fund (FEDER

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock

Purpose of Review: Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, inflammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may offer new options for increasing cancer treatment effectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings: In recent years, there has been a significant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very first time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary: This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the state-of-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.publishersversionpublishe

Chronodisruption and Ambulatory Circadian Monitoring in Cancer Patients: Beyond the Body Clock.

Purpose of Review Circadian rhythms impose daily rhythms a remarkable variety of metabolic and physiological functions, such as cell proliferation, infammation, and DNA damage response. Accumulating epidemiological and genetic evidence indicates that circadian rhythms’ disruption may be linked to cancer. The integration of circadian biology into cancer research may ofer new options for increasing cancer treatment efectiveness and would encompass the prevention, diagnosis, and treatment of this disease. Recent Findings In recent years, there has been a signifcant development and use of multi-modal sensors to monitor physical activity, sleep, and circadian rhythms, allowing, for the very frst time, scaling accurate sleep monitoring to epidemiological research linking sleep patterns to disease, and wellness applications providing new potential applications. Summary This review highlights the role of circadian clock in tumorigenesis, cancer hallmarks and introduces the stateof-the-art in sleep-monitoring technologies, discussing the eventual application of insights in clinical settings and cancer research.post-print1077 K

Using Machine Learning Techniques on Real-World Data to Understand the Characteristics of the Manchester, NH Health Care for the Homeless Patient Population for Risk Factor Identification and Intervention Improvement

This thesis aims to use health care domain knowledge, statistical techniques, and machine learning methods to conduct an exploratory real-world evidence study of the characteristics of the Health Care for the Homeless of Manchester, NH (HCHM) clinics’ patients in collaboration with academic and clinic partners and the public and community health stakeholders supporting their work. By constructing and analyzing a multivariate feature set created from a sample of anonymized patient data from January 1, 2018, through December 31, 2019, I hope to use machine learning methods to accurately represent 2,265 HCHM clinic patients experiencing homelessness or housing insecurity during the period. By regularly collaborating with analytics and clinical experts at HCHM, I hope to accurately describe the clinics’ service populations and aid staff in identifying care gaps, enabling the enrichment of future interventions for homeless people in the primary care setting. By engaging in strategic science (Bunnell, Ryan & Kent, 2021), I hope to reduce bias around the study of this vulnerable population. The study period pre-dates the COVID-19 pandemic and is designed to provide a baseline analysis that will allow for future comparisons of HCH patients’ sub-population characteristics and health care needs before, during, and after the pandemic. The introduction outlines the public health crisis of homelessness in our country, connects the goal of providing care for people experiencing homelessness with the ongoing work of ensuring health equity, introduces the National Health Care for the Homeless Council and its care paradigm, and describes care provided by the Manchester, NH clinics within the city context. The chapter on Data describes the data sources used to create the aggregated data set and the data safeguards put in place to protect the privacy and dignity of people whose medical records were used in the study. The Feature Development section details the dataset cleaning process and the development of the multivariate features, including local weather-based features and the creation of ICD-10 code-based condition categories specific to the challenges of persons experiencing homelessness. The Description chapter provides descriptive statistics related to the patient sample and outlines the health risks of clinic patients. The modeling goal was to utilize the full feature set, without removing outliers, to describe the variation in characteristics of clinic patients and group them into meaningful sub-populations by their utilization patterns. The Modeling section provides a detailed discussion of model evolution, and details about the dimension reduction and clustering algorithms applied to partition the data into service groups with specific characteristics, and how those characteristics were discoverable. The Service Groups chapter outlines the relationships between discovered clusters and patient service groups validated by HCH partners. The Discussion and Limitations chapter expands on and summarizes how the insights gleaned from this study may be helpful to the clinics, the community, the clients, and the health care system in providing future care to people experiencing homelessness and advancing health equity. It then discusses the limitations of the data, features, approach, and algorithms used in the study. It touches on study generalizability and ethics and bias considerations in research and algorithmic use and how these considerations were applied here. The thesis concludes with an endorsement of directions for building upon this work in the future

Individualised care for patients with breast or prostate cancer aided by an interactive app : a frame of process evaluation

Background: Patients receiving outpatient cancer treatment often experience distressing symptoms and unmet needs. Collecting patient-reported outcomes via apps (ePROs) facilitates patient-clinician communication regarding symptoms and is recommended in clinical guidelines. Previous studies of an interactive app (Interaktor) for individualised symptom management show reduced symptom burden for patients undergoing breast and prostate cancer treatment. Aim: To contribute to the knowledge of the value of implementing ePRO in clinical practice by studies framed as a process evaluation of an intervention for individualised symptom management assisted by Interaktor. Methods: Following the Medical Research Council framework for process evaluation of complex interventions, qualitative and quantitative data were collected along two randomised controlled trials (RCTs). Patients receiving neoadjuvant chemotherapy for breast cancer (N=149), and radiotherapy for prostate cancer (N=150) were randomised to standard care with or without intervention. Intervention group patients reported symptoms and concerns daily by questionnaire and free text. The app included selfcare advice and symptom history graphs. Oncology nurses responded to alerts triggered by severe symptoms. Study I investigates which and how patients engaged, by analysing adherence and usage predictors from logged data and telephone interviews with patients. Study II analyses the effects on patients' perceptions of individualised care and health literacy by questionnaires. Study III assesses if the intervention is cost-effective according to the Swedish National Board of Health and Welfare. Cost-effectiveness analyses (CEA) estimate gains in Quality-adjusted lifeyears (QALYs), intervention costs, and the patient's healthcare utilisation as obtained from the Stockholm Council database. Acute healthcare use is also explored. Results: Study I shows that adherence to daily symptom reporting was 83 %; most patients used the self-care advice and free text. Patients regarded the app easy to use and helpful for self-management. Marital status, age, education level, and comorbidity were associated with usage variations. Study II shows no between-group differences in individualised care or health literacy among patients with breast cancer. Intervention group patients with prostate cancer rated their support for decision control as more individualised than their control group, and their ability to seek, understand and communicate health information improved. Study III shows the intervention produced significantly more QALYs, although the effect was small. The weekly intervention cost per patient was low. The cost-effectiveness depended on the type of healthcare costs studied. The intervention was cost-effective for patients with breast cancer if non-acute healthcare costs were excluded, and for patients with prostate cancer, considering all healthcare costs. Healthcare costs varied greatly. Patients with breast cancer who used the app had more acute visits for fever. Patients with prostate cancer who used the app had fewer acute visits for urinary problems. Conclusions: Patients used and valued Interaktor as promoting assurance and participation in care. Using the app can positively affect care individualisation and health literacy for patients with prostate cancer during radiotherapy. It may be beneficial to increase the individualisation of features and settings for patients with breast cancer. The intervention may be cost-effective, but to show if healthcare savings can be achieved requires a larger study

Epidemiological studies on frailty and its associations with mortality, dementia, and polypharmacy

Frailty describes the status of decreased physiological reserves and increased vulnerability to adverse outcomes. As the aging population increases, frailty has become an important public health concern. However, longitudinal studies disclosing the associations of frailty with adverse outcomes over the life course are limited. In the thesis, we aimed at investigating the associations of frailty with mortality, dementia, and polypharmacy using three Swedish longitudinal studies of aging and comparing the characteristics of frailty between young and old adults using Swedish and UK data. Frailty was measured using the frailty index (FI). In Study Ⅰ, we assessed how frailty trajectories look by age at death and compared the predictive values of the level of frailty and the changes of frailty on mortality. We found that individuals who died before the age of 70 years had a steadily increasing trajectory, whereas in those individuals who died at older ages, frailty only increased after 75 years. The level of FI was a stronger predictor of mortality than the rate of change in FI in a longitudinal setting. In Study Ⅱ, we examined the association between baseline FI and the risk of subsequent dementia using a multivariate Cox model. Familial effects on frailty-dementia association were analyzed using a within-pair analysis. The age-varying effects of FI on dementia were also assessed. We found that the FI was associated with an increased risk of dementia independent of the Apolipoprotein E (APOE) ɛ4 carrier status. After adjusting for familial factors, no attenuation was found in dizygotic (DZ) and monozygotic (MZ) twins, indicating that shared environmental and genetic factors had no influence on the frailty-dementia association. The effect of the FI on dementia was constant after age 50. In Study Ⅲ, we investigated the differences in the prevalence, characteristics, and risk factors of early-life (aged <65) and late-life (aged ≥65) frailty using data from Sweden and UK. Comparison of the characteristics of early-life and late-life frailty was performed by collating the FI items (deficits) into domains and comparing the domain scores. We found that frailty is prevalent also in younger age groups, with pooled prevalence rates of 10.3% and 14.4% in individuals aged ≤ 55 and 55-65 years, respectively. Younger frail adults had higher scores in immunological, mental wellbeing, and pain-related domains, whereas older frail adults had higher scores in cardiometabolic, cancer, musculoskeletal, and sensory-related domains. Higher age, female sex, smoking, lower alcohol consumption, lower education, obesity, overweight, low income, and maternal smoking were similarly associated with the risk of early-life and late-life frailty. In Study Ⅳ, we focused on visualizing FI trajectories by polypharmacy and assessing the longitudinal associations between frailty and polypharmacy using a linear mixed model. We found that the long-term polypharmacy group had a higher FI trajectory than the transient and non-polypharmacy group. Polypharmacy was significantly associated with a higher risk of frailty, and the risk of being frail conferred by polypharmacy increased with age. In conclusion, frailty is a strong and independent predictor of adverse outcomes. Monitoring frailty and frailty progression is of great importance in middle-aged and older adults. Also, appropriate prescribing should be considered for middle-aged and old adults to prevent later frailty

The development of psychiatric disorders and adverse behaviors : from context to prediction

Psychiatric disorders by definition cause significant impairment in an individual’s daily functioning. Certain disorders, such as borderline personality disorder (BPD) and eating disorders, have worse prognosis and high mortality rates compared to other psychiatric disorders. Similarly, adverse behaviors such as self-harm, suicide, and crime are often present in individuals with psychiatric disorders. It is of interest to further understand the etiology and associations of BPD and eating disorders to uncover potential avenues and opportunities for intervention. Moreover, prediction modeling has recently come of interest to psychiatric epidemiologists with the rise of large data sets. Prediction modeling may provide valuable information about the nature of risk factors and eventually aid clinical diagnostics and prognostics. Thus, the studies included in this thesis seek to examine the etiology, associations, and prediction approaches of psychiatric disorders and adverse behaviors. Study I examined the individual and familial association between type 1 diabetes (T1D) and eating disorder diagnoses. We used national health care records from Denmark (n = 1,825,920) and Sweden (n = 2,517,277) to calculate the association within individuals, full siblings, half siblings, full cousins, and half cousins. Individuals with T1D had twice the hazard rate ratio of being diagnosed with an eating disorder compared to the general population. There was conflicting evidence for the risk of an eating disorder in full siblings of T1D patients. However, there was no evidence to support a further familial relationship between the two conditions. Study II aimed to illuminate the nature of the correlates for BPD across time, sex, and for their full siblings. We examined 87 variables across psychiatric disorders, somatic illnesses, trauma, and adverse behaviors (such as self-harm). In a sample of 1,969,839 Swedes with 12,175 individuals diagnosed with BPD, we found that BPD was associated with nearly all of the examined variables. The associations were largely consistent across time and between the sexes. Finally, we found that having a sibling diagnosed with BPD was associated with psychiatric disorders, trauma, and adverse behaviors but not somatic illnesses. Study III created a prediction model that could predict who would have high or low psychiatric symptoms at age 15 based on data from parental reports and national health care registers collected at age 9 or 12. Additionally, we compared multiple types of machine learning algorithms to assess predictive performance. The sample included 7,638 twins from the Child and Adolescent Twin Study in Sweden (CATSS). Our model was able to predict the outcome with reasonable performance but is not suitable for use in clinics. Each model performed similarly indicating that researchers with similar data and research questions do not need to forgo standard logistic regression. Study IV aimed to determine if an individual will exhibit suicidal behaviour (self-harm or suicidal thoughts), aggressive behaviour, both, or neither before adulthood with prediction modeling. Through variable importance scores we examined the usefulness of genetic variables within the model. A total of 5,974 participants from CATSS and 2,702 participants from the Netherlands Twin Register (NTR) were included in the study. The model had adequate performance in both the CATSS and NTR datasets for all classes except for the suicidal behaviors class in the NTR, which did not perform better than chance. The included genetic data had higher variable importance scores than questionnaire data completed at age 9 or 12, indicating that genetic biomarkers can be useful when combined with other data types. In conclusion, the development of psychiatric disorders and symptoms are associated with many factors across somatic illnesses, other psychiatric disorders, trauma, and harmful behaviors. The results of this thesis demonstrates the limitations of prediction modeling in psychiatric clinics but highlights their use in research and on the path forward towards personalized medicine