Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Exploration of differential responses to FODMAPs and gluten in people with irritable bowel syndrome- a double-blind randomized cross-over challenge study.

INTRODUCTION: There is large variation in response to diet in irritable bowel syndrome (IBS) and determinants for differential response are poorly understood. OBJECTIVES: Our aim was to investigate differential clinical and molecular responses to provocation with fermentable oligo-, di-, monosaccharides, and polyols (FODMAPs) and gluten in individuals with IBS. METHODS: Data were used from a crossover study with week-long interventions with either FODMAPs, gluten or placebo. The study also included a rapid provocation test. Molecular data consisted of fecal microbiota, short chain fatty acids, and untargeted plasma metabolomics. IBS symptoms were evaluated with the IBS severity scoring system. IBS symptoms were modelled against molecular and baseline questionnaire data, using Random Forest (RF; regression and clustering), Parallel Factor Analysis (PARAFAC), and univariate methods. RESULTS: Regression and classification RF models were in general of low predictive power (Q2 ≤ 0.22, classification rate < 0.73). Out of 864 clustering models, only 2 had significant associations to clusters (0.69 < CR < 0.73, p < 0.05), but with no associations to baseline clinical measures. Similarly, PARAFAC revealed no clear association between metabolome data and IBS symptoms. CONCLUSION: Differential IBS responses to FODMAPs or gluten exposures could not be explained from clinical and molecular data despite extensive exploration with different data analytical approaches. The trial is registered at www. CLINICALTRIALS: gov as NCT03653689 31/08/2018.

CYP eicosanoid pathway mediates colon cancer-promoting effects of dietary linoleic acid.

Human and animal studies support that consuming a high level of linoleic acid (LA, 18:2ω-6), an essential fatty acid and key component of the human diet, increases the risk of colon cancer. However, results from human studies have been inconsistent, making it challenging to establish dietary recommendations for optimal LA intake. Given the importance of LA in the human diet, it is crucial to better understand the molecular mechanisms underlying its potential colon cancer-promoting effects. Using LC-MS/MS-based targeted lipidomics, we find that the cytochrome P450 (CYP) monooxygenase pathway is a major pathway for LA metabolism in vivo. Furthermore, CYP monooxygenase is required for the colon cancer-promoting effects of LA, since the LA-rich diet fails to exacerbate colon cancer in CYP monooxygenase-deficient mice. Finally, CYP monooxygenase mediates the pro-cancer effects of LA by converting LA to epoxy octadecenoic acids (EpOMEs), which have potent effects on promoting colon tumorigenesis via gut microbiota-dependent mechanisms. Overall, these results support that CYP monooxygenase-mediated conversion of LA to EpOMEs plays a crucial role in the health effects of LA, establishing a unique mechanistic link between dietary fatty acid intake and cancer risk. These results could help in developing more effective dietary guidelines for optimal LA intake and identifying subpopulations that may be especially vulnerable to LA's negative effects.

Longitudinal Mapping of Personal Biotic and Abiotic Exposomes and Transcriptome in Underwater Confined Space Using Wearable Passive Samplers.

Silicone-based passive samplers, commonly paired with gas chromatography-mass spectrometry (GC-MS) analysis, are increasingly utilized for personal exposure assessments. However, its compatibility with the biotic exposome remains underexplored. In this study, we introduce the wearable silicone-based AirPie passive sampler, coupled with nontargeted liquid chromatography with high-resolution tandem mass spectrometry (LC-HRMS/MS), GC-HRMS, and metagenomic shotgun sequencing methods, offering a comprehensive view of personalized airborne biotic and abiotic exposomes. We applied the AirPie samplers to 19 participants in a unique deep underwater confined environment, annotating 4,390 chemical and 2,955 microbial exposures, integrated with corresponding transcriptomic data. We observed significant shifts in environmental exposure and gene expression upon entering this unique environment. We noted increased exposure to pollutants, such as benzenoids, polycyclic aromatic hydrocarbons (PAHs), opportunistic pathogens, and associated antibiotic-resistance genes (ARGs). Transcriptomic analyses revealed the activation of neurodegenerative disease-related pathways, mostly related to chemical exposure, and the repression of immune-related pathways, linked to both biological and chemical exposures. In summary, we provided a comprehensive, longitudinal exposome map of the unique environment and underscored the intricate linkages between external exposures and human health. We believe that the AirPie sampler and associated analytical methods will have broad applications in exposome and precision medicine.

Compassion and equity-focused clinical genomics training for health professional learners.

There remains an urgent need for expanded genomics training in undergraduate medical education, especially as genetic and genomic assessments become increasingly important in primary care and routine clinical practice across specialties. Physician trainees continue to report feeling poorly prepared to provide effective consultation or interpretation of genomic test results. Here we report on the development, pilot implementation, and evaluation of an elective offering for pre-clinical medical students called the Sanford Precision Health Scholars Immersive Learning Experience (PHS), which was designed leveraging genetic counseling expertise as one means to address this need. This 9-week course, piloted in Fall 2021 at UC San Diego, afforded students the opportunity to build technical skills and competencies in clinical genomics while identifying, addressing, and engaging with pervasive health disparities in genomics. Interactive exercises focused students' learning on strategies for empathic and compassionate patient interactions while supporting the application of concepts and knowledge to future practice. Upon completion of the course, participants reported increases in confidence related to skills required for clinical genomics practice. Drawing on learnings from this pilot implementation, recommendations for refining the program include deepening pedagogical engagement with ethical issues, expanding the offering to trainees across health professions, including pharmacy students, and incorporating an optional experiential learning component. Educational offerings, like PHS, that are designed with the input of genetic counseling expertise may ease pressures on the genetic counseling profession by building a more genomic-literate healthcare workforce that can better support efforts to expand access for patients.

Understanding Heterogeneity in Individual Responses to Digital Lifestyle Intervention Through Self-Monitoring Adherence Trajectories in Adults With Overweight or Obesity: Secondary Analysis of a 6-Month Randomized Controlled Trial.

BACKGROUND: Achieving clinically significant weight loss through lifestyle interventions for obesity management is challenging for most individuals. Improving intervention effectiveness involves early identification of intervention nonresponders and providing them with timely, tailored interventions. Early and frequent self-monitoring (SM) adherence predicts later weight loss success, making it a potential indicator for identifying nonresponders in the initial phase. OBJECTIVE: This study aims to identify clinically meaningful participant subgroups based on longitudinal adherence to SM of diet, activity, and weight over 6 months as well as psychological predictors of participant subgroups from a self-determination theory (SDT) perspective. METHODS: This was a secondary data analysis of a 6-month digital lifestyle intervention for adults with overweight or obesity. The participants were instructed to perform daily SM on 3 targets: diet, activity, and weight. Data from 50 participants (mean age: 53.0, SD 12.6 y) were analyzed. Group-based multitrajectory modeling was performed to identify subgroups with distinct trajectories of SM adherence across the 3 SM targets. Differences between subgroups were examined for changes in clinical outcomes (ie, body weight, hemoglobin A1c) and SDT constructs (ie, eating-related autonomous motivation and perceived competence for diet) over 6 months using linear mixed models. RESULTS: Two distinct SM trajectory subgroups emerged: the Lower SM group (21/50, 42%), characterized by all-around low and rapidly declining SM, and the Higher SM group (29/50, 58%), characterized by moderate and declining diet and weight SM with high activity SM. Since week 2, participants in the Lower SM group exhibited significantly lower levels of diet (P=.003), activity (P=.002), and weight SM (P=.02) compared with the Higher SM group. In terms of clinical outcomes, the Higher SM group achieved a significant reduction in body weight (estimate: -6.06, SD 0.87 kg; P<.001) and hemoglobin A1c (estimate: -0.38, SD 0.11%; P=.02), whereas the Lower SM group exhibited no improvements. For SDT constructs, both groups maintained high levels of autonomous motivation for over 6 months. However, the Lower SM group experienced a significant decline in perceived competence (P=.005) compared with the Higher SM group, which maintained a high level of perceived competence throughout the intervention (P=.09). CONCLUSIONS: The presence of the Lower SM group highlights the value of using longitudinal SM adherence trajectories as an intervention response indicator. Future adaptive trials should identify nonresponders within the initial 2 weeks based on their SM adherence and integrate intervention strategies to enhance perceived competence in diet to benefit nonresponders. TRIAL REGISTRATION: ClinicalTrials.gov NCT05071287; https://clinicaltrials.gov/study/NCT05071287. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2022.106845.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.

Modern machine learning approaches have led to performant diagnostic models for a variety of health conditions. Several machine learning approaches, such as decision trees and deep neural networks, can, in principle, approximate any function. However, this power can be considered to be both a gift and a curse, as the propensity toward overfitting is magnified when the input data are heterogeneous and high dimensional and the output class is highly nonlinear. This issue can especially plague diagnostic systems that predict behavioral and psychiatric conditions that are diagnosed with subjective criteria. An emerging solution to this issue is crowdsourcing, where crowd workers are paid to annotate complex behavioral features in return for monetary compensation or a gamified experience. These labels can then be used to derive a diagnosis, either directly or by using the labels as inputs to a diagnostic machine learning model. This viewpoint describes existing work in this emerging field and discusses ongoing challenges and opportunities with crowd-powered diagnostic systems, a nascent field of study. With the correct considerations, the addition of crowdsourcing to human-in-the-loop machine learning workflows for the prediction of complex and nuanced health conditions can accelerate screening, diagnostics, and ultimately access to care.

Ethical and Equitable Digital Health Research: Ensuring Self-Determination in Data Governance for Racialized Communities.

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies.

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

Longitudinal self-learning of individualized treatment rules in a nutrient supplementation trial with missing data.

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self-learning (LS-learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS-learning method relies on a temporally weighted self-learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0-3 years should this ITR be implemented to the entire study population of pregnant women.

Precision health equity for racialized communities.

In the last three decades, a cohort of genomicists have intentionally sought to include more racially diverse people in their research in human genomics and precision medicine. How such efforts to be inclusive in human genomic research and precision medicine are modeled and enacted, specifically if the terms of inclusion are equitable for these communities remains to be explored. In this commentary, we review the historical context in which issues of racial inclusion arose with early genome and genetics projects. We then discuss attempts to include racialized peoples in more recent human genomics research. In conclusion, we raise critical issues to consider in the future of equitable human genomics and precision medicine research involving racialized communities, particularly as it concerns working towards what we call Precision Health Equity (PHE). Specifically, we examine issues of genetic data governance and the terms of participation in inclusive human genomics and precision health research. We do so by drawing on insights and protocols developed by researchers investigating Indigenous Data Sovereignty and propose exploring their application and adaptation to precision health research involving racialized communities.

WITHDRAWN: Update in Sleep 2021.

Ahead of Print article withdrawn by publisher.

The ethics of precision health.

In this paper, I defend an account of the ethics of precision medicine that can explain both its possibilities and limits. Creating a new conceptual and normative model of the ethics of precision health can ensure that good medicine is also excellent and that excellent medicine is also good by providing a resource to scientists and clinicians. First, I propose a new conceptual analysis of precision health. I argue that precision health is defined primarily by targeted medical interventions and not by stratification, as others have asserted. Next, I argue that failure to be adequately responsive to this conceptual analysis explains common ethical abuses in the field. Third, I argue that this conceptual analysis can also pave the way for future research heretofore overlooked. Thus, we can limit abuses in precision health research and care while at the same time opening new avenues to help historically oppressed communities.

Physical activity interventions for cancer-related fatigue: A scoping review of randomized controlled trials from a Nursing Science Precision Health Model perspective.

BACKGROUND: The Nursing Science Precision Health (NSPH) Model has the potential to guide research on the development, testing, and targeting of interventions. PURPOSE: This scoping review examines the relationship between physical activity (PA) and cancer-related fatigue (CRF) within the context of the NSPH Model. METHODS: The Joanna Briggs Institute scoping review methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guided this review. We included randomized controlled trials in people with cancer that investigated PA interventions and measured change in CRF as an outcome. DISCUSSION: A total of 181 studies met the eligibility criteria. Over 20 different instruments were used to measure CRF. The most common PA interventions were strength training (48%), walking (36%), cycling (26%), and yoga (15%). A limited number of studies reported phenotypic characteristics (32/181, 17%) or biomarkers (31/181, 17%) associated with CRF. CONCLUSION: This scoping review identified the body of existing research exploring CRF and PA from a precision health perspective.

[Precision Health in Nursing Practice].

Precision health is a new trend in medical care that follows in the footsteps of precision medicine. While precision medicine focuses on treating diseases after occurrence, precision health places greater emphasis on preventative healthcare and health empowerment to prevent and predict disease. Precision health aims to assess the social, economic, cultural, and environmental factors of individuals based on their unique biology, genomics, and other factors and to provide personalized healthcare, preventive medicine, and health promotion through disease prediction to empower people to lead the best possible healthy life. Precision healthcare is the focus of development in advanced countries. Disease diagnosis, treatment, and the successful implementation of precision health needs are optimized using technology such as genomic testing in combination with individual clinical and health information. Precision health focuses on the early identification of risks and prevention. Nursing staff should integrate evidence-based precision health and provide the best medical services and personalized care to each individual to achieve the best quality of life.

Early Lessons from the Implementation of Genomic Medicine Programs.

Massively parallel sequencing is emerging from research settings into clinical practice, helping the vision of precision medicine to become a reality. The most successful applications are using the tools of implementation science within the framework of the learning health-care system. This article examines the application of massively parallel sequencing to four clinical scenarios: pharmacogenomics, diagnostic testing, somatic testing for molecular tumor characterization, and population screening. For each application, it highlights an exemplar program to illustrate the enablers and challenges of implementation. International examples are also presented. These early lessons will allow other programs to account for these factors, helping to accelerate the implementation of precision medicine and health.

Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.

BACKGROUND: The context in which a behavioral intervention is delivered is an important source of variability and systematic approaches are needed to identify and quantify contextual factors that may influence intervention efficacy. Machine learning-based phenotyping methods can contribute to a new precision health paradigm by informing personalized behavior interventions. Two primary goals of precision health, identifying population subgroups and highlighting behavioral intervention targets, can be addressed with psychosocial-behavioral phenotypes. We propose a method for psychosocial-behavioral phenotyping that models social determinants of health in addition to individual-level psychological and behavioral factors. PURPOSE: To demonstrate a novel application of machine learning for psychosocial-behavioral phenotyping, the identification of subgroups with similar combinations of psychosocial characteristics. METHODS: In this secondary analysis of psychosocial and behavioral data from a community cohort (n = 5,883), we optimized a multichannel mixed membership model (MC3M) using Bayesian inference to identify psychosocial-behavioral phenotypes and used logistic regression to determine which phenotypes were associated with elevated weight status (BMI ≥ 25kg/m2). RESULTS: We identified 20 psychosocial-behavioral phenotypes. Phenotypes were conceptually consistent as well as discriminative; most participants had only one active phenotype. Two phenotypes were significantly positively associated with elevated weight status; four phenotypes were significantly negatively associated. Each phenotype suggested different contextual considerations for intervention design. CONCLUSIONS: By depicting the complexity of psychological and social determinants of health while also providing actionable insight about similarities and differences among members of the same community, psychosocial-behavioral phenotypes can identify potential intervention targets in context.

Precision health in behaviour change interventions: A scoping review.

Precision health seeks to optimise behavioural interventions by delivering personalised support to those in need, when and where they need it. Conceptualised a decade ago, progress toward this vision of personally relevant and effective population-wide interventions continues to evolve. This scoping review aimed to map the state of precision health behaviour change intervention research. This review included studies from a broader precision health review. Six databases were searched for studies published between January 2010 and June 2020, using the terms 'precision health' or its synonyms, and including an intervention targeting modifiable health behaviour(s) that was evaluated experimentally. Thirty-one studies were included, 12 being RCTs (39%), and 17 with weak study design (55%). Most interventions targeted physical activity (27/31, 87%) and/or diet (24/31, 77%), with 74% (23/31) targeting two to four health behaviours. Interventions were personalised via human interaction in 55% (17/31) and digitally in 35% (11/31). Data used for personalising interventions was largely self-reported, by survey or diary (14/31, 45%), or digitally (14/31, 45%). Data was mostly behavioural or lifestyle (20/31, 65%), and physiologic, biochemical or clinical (15/31, 48%), with no studies utilising genetic/genomic data. This review demonstrated that precision health behaviour change interventions remain dependent on human-led, low-tech personalisation, and have not fully considered the interaction between behaviour and the social and environmental contexts of individuals. Further research is needed to understand the relationship between personalisation and intervention effectiveness, working toward the development of sophisticated and scalable behaviour change interventions that have tangible public health impact.

Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department.

BACKGROUND: Early screening and accurately identifying Acute Appendicitis (AA) among patients with undifferentiated symptoms associated with appendicitis during their emergency visit will improve patient safety and health care quality. The aim of the study was to compare models that predict AA among patients with undifferentiated symptoms at emergency visits using both structured data and free-text data from a national survey. METHODS: We performed a secondary data analysis on the 2005-2017 United States National Hospital Ambulatory Medical Care Survey (NHAMCS) data to estimate the association between emergency department (ED) patients with the diagnosis of AA, and the demographic and clinical factors present at ED visits during a patient's ED stay. We used binary logistic regression (LR) and random forest (RF) models incorporating natural language processing (NLP) to predict AA diagnosis among patients with undifferentiated symptoms. RESULTS: Among the 40,441 ED patients with assigned International Classification of Diseases (ICD) codes of AA and appendicitis-related symptoms between 2005 and 2017, 655 adults (2.3%) and 256 children (2.2%) had AA. For the LR model identifying AA diagnosis among adult ED patients, the c-statistic was 0.72 (95% CI: 0.69-0.75) for structured variables only, 0.72 (95% CI: 0.69-0.75) for unstructured variables only, and 0.78 (95% CI: 0.76-0.80) when including both structured and unstructured variables. For the LR model identifying AA diagnosis among pediatric ED patients, the c-statistic was 0.84 (95% CI: 0.79-0.89) for including structured variables only, 0.78 (95% CI: 0.72-0.84) for unstructured variables, and 0.87 (95% CI: 0.83-0.91) when including both structured and unstructured variables. The RF method showed similar c-statistic to the corresponding LR model. CONCLUSIONS: We developed predictive models that can predict the AA diagnosis for adult and pediatric ED patients, and the predictive accuracy was improved with the inclusion of NLP elements and approaches.

Individual reference intervals for personalised interpretation of clinical and metabolomics measurements.

The Population Reference Interval (PRI) refers to the range of outcomes that are expected in a healthy population for a clinical or a diagnostic measurement. It is widely used in daily clinical practice and is essential for assisting clinical decision-making in diagnostics and treatment. In this manuscript, we start from the observation that each healthy individual has its own range for a given variable, depending on personal biological traits. This Individual Reference Interval (IRI) can be calculated and be utilised in clinical practice, in combination with the PRI for improved decision making. Nonparametric estimation of IRIs would require quite long time series. To circumvent this problem, we propose methods based on quantile models in combination with penalised parameter estimation methods that allow for information-sharing among the subjects. Our approach considers the calculation of an IRI as a prediction problem rather than an estimation problem. We perform a simulation study designed to benchmark the methods under different assumptions. From the simulation study we conclude that the new methods are robust and provide empirical coverages close to the nominal level. Finally, we evaluate the methods on real-life data consisting of eleven clinical tests and metabolomics measurements from the VITO IAM Frontier study.