Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from assessing single individuals' multi-omics time series, and using individual responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multi-omics profiles. We applied our method to individual profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results showed that our method identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which was associated to measures of their diabetic status.

Best practices for recruitment of adolescents for biobanking and precision health research: a retrospective analysis comparing juvenile idiopathic arthritis cases with healthy controls

Background Precision health in adolescents relies on the successful collection of data and biospecimens from an adequately sized sample of cases and comparison group(s), often healthy controls, to answer the research question. This research report describes the recruitment strategy, enrollment rates, and approach utilized in a successful biobehavioral research study. The study was designed to examine key health indicators in adolescents (13-17 years of age) with juvenile idiopathic arthritis (JIA) compared to a control group of healthy adolescents. The purpose of this analysis is to establish best practices and identify strategies to overcome barriers to recruitment of older adolescents, an age group that tends to be underrepresented in research studies. Methods A retrospective secondary analysis of data from a parent study about JIA with high consent rates was employed to explore factors affecting enrollment into the biobehavioral study. Results Of the 113 subjects who were recruited to the study, 74 met the eligibility criteria and reviewed the consent form. The consented group (n=40) represents 54% of those who were eligible upon initial screening. The rate of project enrollment was 2.7 participants per month. The pediatric rheumatologists referred 85% of the JIA group, and the study’s principal investigator, a nurse scientist, referred 95% of the control group. Typical recruitment strategies, such as posting on social media, distributing flyers, and cold-calling potential participants from the clinic schedule were ineffective for both cases and controls. Barriers to enrollment included scheduling and fear of venipuncture. There were no demographic characteristics that significantly explained enrollment, differentiating between those who agreed to participate compared to those who refused. Successful strategies for enrollment of adolescents into this biobehavioral research study included scheduling study visits on weekends and school holidays; an informed consent and assent process that addressed adolescent fears of venipuncture; including a JIA patient on the study team; and utilizing existing relationships to maximize enrollment efforts. Conclusions Effective recruitment and enrollment practices were relationship-specific and patient-centered. Researchers should utilize best practices to ensure that precision health for adolescents is advanced.

Discriminant COVID-19 Outcomes Based on Safety Adherence in an Active Lifestyle Retirement Community

Compliance with preventive behaviors recommended by public health officials plays a critical role in the control and prevention of COVID-19. Data were collected from those living in The Villages, FL, and surrounding communities via The Villages Health COVID-19 Rapid Testing Program in partnership with The UFHealth Precision Health Research Center. A descriptive ecological study was conducted to model COVID-19 positivity result variations by age, sex and adherence to CDC safety recommendations using chi-square tests. 9,993 tests were performed using Abbott's BinaxNOW™ COVID-19 Ag Card, and 931 (9.30%) positive cases were confirmed between 10/19/2020-2/26/2021. Median age was 69 years (range:12-103), and 5,578 (55.8%) individuals were female. No significant differences were found in positive test status (≥65=9.8%,<65=8.8%) amongst those over 65 (n=6567) and under 65 (n=3180) years old [X2 (1, N=9847)=2.49,p=.114]; however, positive test result differed by sex with males (10.6%) testing positive at higher rates than females [8.3%, X2(1, N =9993)=14.888, p< .001)]. A significant relationship between preventative behaviors and positive test status was also found. Not engaging in regular handwashing (p< .001) and failing to stay 6 feet or more away from others outdoors (p< .001) was significantly associated with positive test status. Further, not wearing a face mask in businesses/shops (p<.001) or indoors around 6+ people, (p<.001) was significantly associated with positive test status. In light of debate around the efficacy of mask wearing, these findings signal the importance of following CDC recommended public health behaviors for all ages across the lifespan to reduce the spread of COVID-19 infection.

Diversity and molecular network patterns of symptom phenotypes

Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein–protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.