Comparing Ancestry Standardization Approaches for a Transancestry Colorectal Cancer Polygenic Risk Score.

Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRSs) aim to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating standardization. We compared four post-hoc methods using the All of Us Research Program Whole Genome Sequence data for a transancestry CRC PRS. We contrasted results from linear models trained on A. the entire data or an ancestrally diverse subset AND B. covariates including principal components of ancestry or admixture. Standardization with the training subset also adjusted the variance. All methods performed similarly within ancestry, OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal. Training a model on ancestrally diverse participants, adjusting both the mean and variance using admixture as covariates, created standard Normal z-scores, which can be used to identify patients at high polygenic risk. These scores can be incorporated into comprehensive risk calculation including other known risk factors, allowing for more precise risk estimates.

Navigating electronic health record accuracy by examination of sex incongruent conditions.

OBJECTIVE: The increasing reliance on electronic health records (EHRs) for research and clinical care necessitates robust methods for assessing data quality and identifying inconsistencies. To address this need, we develop and apply the incongruence rate (IR) using sex-specific medical conditions. We also characterized participants with incongruent records to better understand the scope and nature of data discrepancies. MATERIALS AND METHODS: In this cross-sectional study, we used the All of Us Research Program's latest version 7 (v7) EHR data to identify prevalent sex-specific conditions and evaluated the occurrence of incongruent cases, quantified as IR. RESULTS: Among the 92 597 males and 152 551 females with condition occurrence data available from All of Us and sex-conformed gender, we identified 167 prevalent sex-specific conditions. Among the 37 537 biological males and 95 499 biological females with these sex-specific conditions, we detected an overall IR of 0.86%. Attempt to include non-cisgender participants result in inflated overall IR. Additionally, a significant proportion of participants with incongruent conditions also presented with conditions congruent to their biological sex, indicating a mix of accurate and erroneous records. These incongruences were not geographically or temporally isolated, suggesting systematic issues in EHR data integrity. DISCUSSION: Our findings call attention to the existence of systemic data incongruences in sex-specific conditions and the need for robust validation checks. Extending IR evaluation to non-cisgender participants or non-sex-based conditions remain a challenge. CONCLUSION: The sex condition-specific IR, when applied to adult populations, provides a valuable metric for data quality assessment in EHRs.

Racial and ethnic differences in the receipt of continuous positive airway pressure treatment for obstructive sleep apnea.

OBJECTIVE: To examine the pattern of health services access and utilization that may contribute to racial/ethnic disparities in receiving continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA). METHODS: This cross-sectional study used a national sample from the All of Us Research Program, which included over 80 % of participants from underrepresented populations in biomedical research. Study participants included adults aged 18 years and older diagnosed with OSA (N = 8518). Diagnosis of OSA and CPAP treatment were ascertained by diagnostic and procedural codes from the electronic health records. Sociodemographic characteristics and health service utilization factors were identified using self-reported survey data. RESULTS: With this national survey, the overall diagnosed prevalence of OSA was 8.8 %, with rates of 8.12 % in non-Hispanic (NH) Black adults, 5.99 % in Hispanic adults, and 10.35 % in NH White adults. When comparing to NH White adults, Hispanic adults were less likely to receive CPAP treatment for OSA after adjusting for socioeconomic and demographic characteristics, access to and utilization of health services, and comorbidities such as obesity and having multiple chronic conditions (OR = 0.73, 95 % CI = 0.59,0.90), p < 0.01. CONCLUSIONS: The rates of CPAP treatment among OSA patients are not consistent across racial and ethnic groups. Unequal access to health services based on residence may contribute to these differences. Interventions that target disparities in OSA diagnosis, access to treatment, and barriers in insurance coverage could potentially help reduce racial and ethnic differences in OSA diagnosis and management.

Cigarette smoking, e-cigarette use, and sociodemographic correlates of mental health and tobacco-related disease risk in the All of Us research program.

SIGNIFICANCE: Research on the conditions under which electronic cigarette (EC) use produces a net reduction in the population harm attributable to combusted cigarette (CC) use requires the triangulation of information from cohort(s) of smokers, non-smokers, EC users, and dual-users of all varieties. MATERIALS AND METHODS: This project utilizes data from the All of Us Research Program to contrast a panel of wellness and disease-risk indicators across a range of self-reported tobacco-use profiles, including smokers, current, and former EC users. This article focuses on the tobacco use history and current tobacco use status among All of Us participants enrolled between May 2017 and February 2023 (Registered Controlled Tier Curated Data Repository [CDR] v7). RESULTS: The present analytic sample included an unweighted total of N = 412 211 individuals with information on ever-use of both CC and EC. Among them, 155 901 individuals have a history of CC use, with 65 206 identified as current smokers. EC usage is reported by 64 002 individuals, with 16 619 being current users. Model predicted analyses identified distinct patterns in CC and EC usage across demographic and socioeconomic variables, with younger ages favoring ECs. DISCUSSION: Age was observed to significantly affect EC usage, and gender differences reveal that males were significantly more likely to use CC and/or EC than females or African Americans of any gender. Higher educational achievement and income were associated with lower use of both CC and EC, while lower levels of mental health were observed to increase the likelihood of using CC and EC products. CONCLUSION: Findings suggest the potential for the All of Us Research Program for investigation of causal factors driving both behavioral use transitions and cessation outcomes.

Molluscum contagiosum is associated with atopic dermatitis and sexually transmitted infections in a matched case-control study using a national database.

BACKGROUND: Molluscum contagiosum (MC) is a poxvirus that manifests as firm, smooth, dome-shaped, umbilicated, flesh-colored papules. In adults, MC is commonly spread by sexual contact, and is self-limited in patients with intact immune systems but more widely distributed and difficult to treat in immunocompromised persons. We analyzed cases of adult MC for associations with immunosuppression, lifestyle risk factors, and sexually transmitted infections (STIs). METHODS: Using the All of Us Research Program database, adults with MC were identified and matched with controls 1:10 based on demographic factors. Comorbidities, lifestyle risk factors, and medication exposures were analyzed. Odds ratios were calculated using logistic regression. RESULTS: Our analysis included 146 cases of adults with MC and 1460 demographic-matched controls. Patients with MC were 48 years old on average, 59% female, and majority White (82.5%). Controls were similar for all demographic features. Adults with MC were more likely to have syphilis (odds ratio (OR) 16; 95% confidence interval (CI) 2.57-99.5), human immunodeficiency virus (HIV) (OR 9.54; 95% CI 3.95-23.0), chlamydia (OR 6.24; 95% CI 2.38-16.4), condyloma acuminata (OR 13.9; 95% CI 7.36-26.2), genital herpes (OR 4.13; 95% CI 1.87-9.15), or atopic dermatitis (AD) (OR 2.85; 95% CI 1.5-5.4) (all p < .01). There were no differences in prevalence of other comorbidities, lifestyle risk factors, nor medication exposures (all p > .05). CONCLUSIONS: We showed that adult MC is associated with AD and STIs, including HIV, chlamydia, condyloma acuminata, genital herpes, and syphilis. Sexually active adolescents and adults and those diagnosed with AD may be screened for MC and counseled on their potentially increased risk.

Communicating research findings as a return of value to All of Us Research Program participants: insights from staff at Federally Qualified Health Centers.

OBJECTIVES: Research participants value learning how their data contributions are advancing health research (ie, data stories). The All of Us Research Program gathered insights from program staff to learn what research topics they think are of interest to participants, what support staff need to communicate data stories, and how staff use data story dissemination tools. MATERIALS AND METHODS: Using an online 25-item assessment, we collected information from All of Us staff at 7 Federally Qualified Health Centers. RESULTS: Topics of greatest interest or relevance included income insecurity (83%), diabetes (78%), and mental health (78%). Respondents prioritized in-person outreach in the community (70%) as a preferred setting to share data stories. Familiarity with available dissemination tools varied. DISCUSSION: Responses support prioritizing materials for in-person outreach and training staff how to use dissemination tools. CONCLUSION: The findings will inform All of Us communication strategy, content, materials, and staff training resources to effectively deliver data stories as return of value to participants.

Balancing efficacy and computational burden: weighted mean, multiple imputation, and inverse probability weighting methods for item non-response in reliable scales.

IMPORTANCE: Scales often arise from multi-item questionnaires, yet commonly face item non-response. Traditional solutions use weighted mean (WMean) from available responses, but potentially overlook missing data intricacies. Advanced methods like multiple imputation (MI) address broader missing data, but demand increased computational resources. Researchers frequently use survey data in the All of Us Research Program (All of Us), and it is imperative to determine if the increased computational burden of employing MI to handle non-response is justifiable. OBJECTIVES: Using the 5-item Physical Activity Neighborhood Environment Scale (PANES) in All of Us, this study assessed the tradeoff between efficacy and computational demands of WMean, MI, and inverse probability weighting (IPW) when dealing with item non-response. MATERIALS AND METHODS: Synthetic missingness, allowing 1 or more item non-response, was introduced into PANES across 3 missing mechanisms and various missing percentages (10%-50%). Each scenario compared WMean of complete questions, MI, and IPW on bias, variability, coverage probability, and computation time. RESULTS: All methods showed minimal biases (all <5.5%) for good internal consistency, with WMean suffered most with poor consistency. IPW showed considerable variability with increasing missing percentage. MI required significantly more computational resources, taking >8000 and >100 times longer than WMean and IPW in full data analysis, respectively. DISCUSSION AND CONCLUSION: The marginal performance advantages of MI for item non-response in highly reliable scales do not warrant its escalated cloud computational burden in All of Us, particularly when coupled with computationally demanding post-imputation analyses. Researchers using survey scales with low missingness could utilize WMean to reduce computing burden.

Sounding out solutions: using SONAR to connect participants with relevant healthcare resources.

OBJECTIVE: This article outlines a scalable system developed by the All of Us Research Program's Genetic Counseling Resource to vet a large database of healthcare resources for supporting participants with health-related DNA results. MATERIALS AND METHODS: After a literature review of established evaluation frameworks for health resources, we created SONAR, a 10-item framework and grading scale for health-related participant-facing resources. SONAR was used to review clinical resources that could be shared with participants during genetic counseling. RESULTS: Application of SONAR shortened resource approval time from 7 days to 1 day. About 256 resources were approved and 8 rejected through SONAR review. Most approved resources were relevant to participants nationwide (60.0%). The most common resource types were related to support groups (20%), cancer care (30.6%), and general educational resources (12.4%). All of Us genetic counselors provided 1161 approved resources during 3005 (38.6%) consults, mainly to local genetic counselors (29.9%), support groups (21.9%), and educational resources (21.0%). DISCUSSION: SONAR's systematic method simplifies resource vetting for healthcare providers, easing the burden of identifying and evaluating credible resources. Compiling these resources into a user-friendly database allows providers to share these resources efficiently, better equipping participants to complete follow up actions from health-related DNA results. CONCLUSION: The All of Us Genetic Counseling Resource connects participants receiving health-related DNA results with relevant follow-up resources on a high-volume, national level. This has been made possible by the creation of a novel resource database and validation system.

The AORTA Gene score for detection and risk stratification of ascending aortic dilation.

BACKGROUND AND AIMS: This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone. METHODS: Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants. RESULTS: AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049). CONCLUSIONS: A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.

Characterizing apparent treatment resistant hypertension in the United States: insights from the All of Us Research Program.

BACKGROUND: Hypertension (HTN) remains a significant public health concern and the primary modifiable risk factor for cardiovascular disease, which is the leading cause of death in the United States. We applied our validated HTN computable phenotypes within the All of Us Research Program to uncover prevalence and characteristics of HTN and apparent treatment-resistant hypertension (aTRH) in United States. METHODS: Within the All of Us Researcher Workbench, we built a retrospective cohort (January 1, 2008-July 1, 2023), identifying all adults with available age data, at least one blood pressure (BP) measurement, prescribed at least one antihypertensive medication, and with at least one SNOMED "Essential hypertension" diagnosis code. RESULTS: We identified 99 461 participants with HTN who met the eligibility criteria. Following the application of our computable phenotypes, an overall population of 81 462 were further categorized to aTRH (14.4%), stable-controlled HTN (SCH) (39.5%), and Other HTN (46.1%). Compared to participants with SCH, participants with aTRH were older, more likely to be of Black or African American race, had higher levels of social deprivation, and a heightened prevalence of comorbidities such as hyperlipidemia and diabetes. Heart failure, chronic kidney disease, and diabetes were the comorbidities most strongly associated with aTRH. ß-blockers were the most prescribed antihypertensive medication. At index date, the overall BP control rate was 62%. DISCUSSION AND CONCLUSION: All of Us provides a unique opportunity to characterize HTN in the United States. Consistent findings from this study with our prior research highlight the interoperability of our computable phenotypes.

Applying Engagement Marketing And Human-Centered Design to Cocreate a Digital Decision Support Tool for Research Participation with LGBTQIA+ Community Members.

We used engagement marketing and human-centered design principles to cocreate a digital decision support tool for research participation with LGBTQIA+ community members to help them make an informed decision about joining the All of Us Research Program. Building on results from the research phase, we conducted eight problem validation and solutioning workshops with 48 LGBTQIA+ community members. Community members validated barriers to engagement with All of Us and brainstormed 47 potential digital solutions. We developed potential solutions into 27 concepts (descriptive text and visual storyboards) and assessed acceptability, appropriateness, feasibility, and engagement in a set of 10 concept testing workshops with 57 community members. We developed one of the highest rated concepts, the "Decide Later Tool," into a prototype and tested it with 45 LGBTQIA+ community members and 14 community advisory group members to assess acceptability, appropriateness, feasibility, usability, and engagement. Prototype testing participants indicated that the tool provides information to help with decision making, provides a clear value or benefit to them, was designed for someone like them, provides the right amount of information, and is easy to use; they also offered constructive feedback to improve it. Across the design and development phases, community members indicated that the process of engaging them demonstrated integrity, competence, dependability, trust, and collaboration; fostered a sense of connection to All of Us; and will enhance future engagement with All of Us. Our next steps are to develop the prototype into a fully functioning web tool and pilot test it in community and health care settings.

Associations between Statin Use and Glaucoma in the All of Us Research Program.

PURPOSE: To investigate associations between statin use and glaucoma in the 2017 to 2022 All of Us (AoU) Research Program. DESIGN: Cross-sectional, population-based. PARTICIPANTS: 79 742 adult participants aged ≥40 years with hyperlipidemia and with electronic health record (EHR) data in the AoU database. METHODS: Hyperlipidemia, glaucoma status, and statin use were defined by diagnoses and medication information in EHR data collected by AoU. Logistic regression analysis was performed to evaluate the association between statin use and glaucoma likelihood. Logistic regression modeling was used to examine associations between glaucoma and all covariates included in adjusted analysis. Serum low-density lipoprotein cholesterol (LDL-C) was used to assess hyperlipidemia severity. Analyses stratified by LDL-C level and age were performed. MAIN OUTCOME MEASURES: Any glaucoma as defined by International Classification of Diseases codes found in EHR data. RESULTS: Of 79 742 individuals with hyperlipidemia in AoU, there were 6365 (8.0%) statin users. Statin use was associated with increased glaucoma prevalence when compared with statin nonuse (adjusted odds ratio [aOR]: 1.13, 95% confidence interval [CI]: 1.01-1.26). Higher serum levels of LDL-C were associated with increased odds of glaucoma (aOR: 1.003, 95% CI: 1.003, 1.004). Statin users had significantly higher LDL-C levels compared to nonusers (144.9 mg/dL versus 136.3 mg/dL, P value < 0.001). Analysis stratified by LDL-C identified positive associations between statin use and prevalence of glaucoma among those with optimal (aOR = 1.39, 95% CI = 1.05-1.82) and high (aOR = 1.37, 95% CI = 1.09-1.70) LDL-C levels. Age-stratified analysis showed a positive association between statin use and prevalence of glaucoma in individuals aged 60 to 69 years (aOR = 1.28, 95% CI = 1.05-1.56). CONCLUSIONS: Statin use was associated with increased glaucoma likelihood in the overall adult AoU population with hyperlipidemia, in individuals with optimal or high LDL-C levels, and in individuals 60 to 69 years old. Findings suggest that statin use may be an independent risk factor for glaucoma, which may furthermore be affected by one's lipid profile and age. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Factors associated with pityriasis versicolor in a large national database.

BACKGROUND: Pityriasis versicolor (PV), a cutaneous fungal infection, most commonly affects adolescents and young adults and is associated with hyperhidrosis and humid weather. Understanding other factors associated with PV might help improve diagnostic and treatment practices. OBJECTIVES: PV's associations with patient demographics, comorbidities and medication exposures were assessed using the All of Us Database, a large, diverse, national database from the United States. METHODS: A case-control study with multivariable analysis was performed. RESULTS: We identified 456 PV case-patients and 1368 control-patients. PV case-patients (vs. control-patients) were younger (median age [years] (standard deviation): 48.7 (15.4) vs. 61.9 (15.5); OR: 0.95, CI: 0.94-0.96) and more likely to be men versus women (42.8% vs. 33.9%, OR: 1.45, CI: 1.16-1.79) and Black (19.5% vs. 15.8%, OR: 1.35, 95% CI: 1.02-1.80) or Asian (4.6% vs. 2.7%, OR: 1.86, CI: 1.07-3.24) versus White. PV case-patients more frequently had acne (5.3% vs. ≤1.5%, OR: 5.37, CI: 2.76-10.48) and less frequently had type 2 diabetes mellitus (T2DM) (14.7% vs. 24.7%, OR: 0.52, CI: 0.39-0.70) and hypothyroidism (OR: 10.3% vs. 16.4%, OR: 0.59, CI: 0.42-0.82). In multivariable analysis, PV odds were significantly higher in those with acne and lower in those with T2DM, older age and female sex. CONCLUSIONS: Our results may be used as a basis for future studies evaluating whether acne treatment may decrease PV risk. Physicians could educate patients with acne about PV, including strategies to control modifiable PV risk factors, such as avoidance of hot and humid environments and avoidance of use of topical skin oils.

Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain. DESIGN: This study was a retrospective, observational study. METHODS: We used demographic, diagnosis, and social survey data from the NIH 'All of Us' program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model. RESULTS: The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance. CONCLUSION: Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes. CLINICAL RELEVANCE: Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.

Multi-modality risk prediction of cardiovascular diseases for breast cancer cohort in the All of Us Research Program.

OBJECTIVE: This study leverages the rich diversity of the All of Us Research Program (All of Us)'s dataset to devise a predictive model for cardiovascular disease (CVD) in breast cancer (BC) survivors. Central to this endeavor is the creation of a robust data integration pipeline that synthesizes electronic health records (EHRs), patient surveys, and genomic data, while upholding fairness across demographic variables. MATERIALS AND METHODS: We have developed a universal data wrangling pipeline to process and merge heterogeneous data sources of the All of Us dataset, address missingness and variance in data, and align disparate data modalities into a coherent framework for analysis. Utilizing a composite feature set including EHR, lifestyle, and social determinants of health (SDoH) data, we then employed Adaptive Lasso and Random Forest regression models to predict 6 CVD outcomes. The models were evaluated using the c-index and time-dependent Area Under the Receiver Operating Characteristic Curve over a 10-year period. RESULTS: The Adaptive Lasso model showed consistent performance across most CVD outcomes, while the Random Forest model excelled particularly in predicting outcomes like transient ischemic attack when incorporating the full multi-model feature set. Feature importance analysis revealed age and previous coronary events as dominant predictors across CVD outcomes, with SDoH clustering labels highlighting the nuanced impact of social factors. DISCUSSION: The development of both Cox-based predictive model and Random Forest Regression model represents the extensive application of the All of Us, in integrating EHR and patient surveys to enhance precision medicine. And the inclusion of SDoH clustering labels revealed the significant impact of sociobehavioral factors on patient outcomes, emphasizing the importance of comprehensive health determinants in predictive models. Despite these advancements, limitations include the exclusion of genetic data, broad categorization of CVD conditions, and the need for fairness analyses to ensure equitable model performance across diverse populations. Future work should refine clinical and social variable measurements, incorporate advanced imputation techniques, and explore additional predictive algorithms to enhance model precision and fairness. CONCLUSION: This study demonstrates the liability of the All of Us's diverse dataset in developing a multi-modality predictive model for CVD in BC survivors risk stratification in oncological survivorship. The data integration pipeline and subsequent predictive models establish a methodological foundation for future research into personalized healthcare.

An evaluation of the All of Us Research Program database to examine cumulative stress.

OBJECTIVES: To evaluate the NIH All of Us Research Program database as a potential data source for studying allostatic load and stress among adults in the United States (US). MATERIALS AND METHODS: We evaluated the All of Us database to determine sample size significance for original-10 allostatic load biomarkers, Allostatic Load Index-5 (ALI-5), Allostatic Load Five, and Cohen's Perceived Stress Scale (PSS). We conducted a priori, post hoc, and sensitivity power analyses to determine sample sizes for conducting null hypothesis significance tests. RESULTS: The maximum number of responses available for each measure is 21 participants for the original-10 allostatic load biomarkers, 150 for the ALI-5, 22 476 for Allostatic Load Five, and n = 90 583 for the PSS. DISCUSSION: The NIH All of Us Research Program is well-suited for studying allostatic load using the Allostatic Load Five and psychological stress using PSS. CONCLUSION: Improving biomarker data collection in All of Us will facilitate more nuanced examinations of allostatic load among US adults.