Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study

BACKGROUND

Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition.

OBJECTIVE

The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD.

METHODS

Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score.

RESULTS

Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method's specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection.

CONCLUSIONS

This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.

How rural is All of Us? Comparing characteristics of rural participants in the National Institute of Health's All of Us Research Program to other national data sources

PURPOSE

The National Institute of Health's All of Us Research Program represents a national effort to develop a database to advance health research, especially among individuals historically underrepresented in research, including rural populations. The purpose of this study was to describe the rural populations identified in the All of Us Research Program using the only proxy measure currently available in the dataset.

METHODS

Currently, the All of Us Research Program provides a proxy measure of rurality that identifies participants who self-reported delaying care due to far travel distances associated with living in rural areas. Using the All of Us Controlled Tier Dataset v6, we compared sociodemographic and health characteristics of All of Us rural participants identified via this proxy to rural US residents from nationally representative data sources using chi-squared tests.

RESULTS

3.1% of 160,880 All of Us participants were rural, compared to 15%-20% of US residents based on commonly accepted rural definitions. Proportionally more rural All of Us participants reported fair or poor health status, history of cancer, and history of heart disease (P<.01).

CONCLUSIONS

The All of Us measure may capture a subset of underserved participants who live in rural areas and experience health care access barriers due to distance. Researchers who use this proxy measure to characterize rurality should interpret their findings with caution due to differences in population and health characteristics using this proxy measure rural compared to other commonly used rural definitions.

Racial/Ethnic Disparities: Discrimination's Impact on Health-Related Quality of Life-An All of Us Cancer Survivors' Cross-sectional Study

BACKGROUND

Discrimination is associated with worse mental and physical health outcomes. However, the associations among cancer survivors are limited.

OBJECTIVE

We examined whether discrimination is associated with HRQoL and whether adjusting for it reduces racial/ethnic disparities in HRQoL among cancer survivors.

METHODS

Cross-sectional data from adult cancer survivors who completed surveys on discrimination in the medical settings (DMS), everyday perceived discrimination (PD), and HRQoL in the "All of Us" Program from 2018 to 2022 were assessed. We created a binary indicator for fair-to-poor vs. good-to-excellent physical health and mental health. PD and DMS scores were a continuous measure with higher scores reflecting more discrimination. Multivariable logistic regression models tested whether DMS and PD are associated with HRQoL and whether they differently affect the association between race/ethnicity and HRQoL.

RESULTS

The sample (N = 16,664) of cancer survivors was predominantly White (86%) and female (59%), with a median age of 69. Every 5-unit increase in DMS and PD scores was associated with greater odds of fair-to-poor physical health (DMS: OR [95%CI] = 1.66 [1.55, 1.77], PD: 1.33 [1.27, 1.40]) and mental health (DMS: 1.57 [1.47, 1.69], PD: 1.33 [1.27, 1.39]). After adjusting for DMS or PD, Black and Hispanic survivors had a decreased likelihood of fair-to-poor physical health and mental health (decrease estimate range: - 6 to - 30%) compared to White survivors. This effect was greater for Black survivors when adjusting for PD, as the odds of fair-to-poor mental health compared to White survivors were no longer statistically significant (1.78 [1.32, 2.34] vs 1.22 [0.90, 1.64]).

CONCLUSION

Experiences of discrimination are associated with lower HRQoL and reducing it may mitigate racial/ethnic disparities in HRQoL.

Association between MCU Gene Polymorphisms with Obesity: Findings from the All of Us Research Program

Obesity is a public health crisis, and its prevalence disproportionately affects African Americans in the United States. Dysregulation of organelle calcium homeostasis is associated with obesity. The mitochondrial calcium uniporter (MCU) complex is primarily responsible for mitochondrial calcium homeostasis. Obesity is a multifactorial disease in which genetic underpinnings such as single-nucleotide polymorphisms (SNPs) may contribute to disease progression. The objective of this study was to identify genetic variations of MCU with anthropometric measurements and obesity in the All of Us Research Program.

METHODS

We used an additive genetic model to assess the association between obesity traits (body mass index (BMI), waist and hip circumference) and selected MCU SNPs in 19,325 participants (3221 normal weight and 16,104 obese). Eleven common MCU SNPs with a minor allele frequency ≥ 5% were used for analysis.

RESULTS

We observed three MCU SNPs in self-reported Black/African American (B/AA) men, and six MCU SNPs in B/AA women associated with increased risk of obesity, whereas six MCU SNPs in White men, and nine MCU SNPs in White women were protective against obesity development.

CONCLUSIONS

This study found associations of MCU SNPs with obesity, providing evidence of a potential predictor of obesity susceptibility in B/AA adults.

Functional Analysis of G6PD Variants Associated With Low G6PD Activity in the All of Us Research Program

Glucose-6-phosphate dehydrogenase (G6PD) protects red blood cells against oxidative damage through regeneration of NADPH. Individuals with G6PD polymorphisms (variants) that produce an impaired G6PD enzyme are usually asymptomatic, but at risk of hemolytic anemia from oxidative stressors, including certain drugs and foods. Prevention of G6PD deficiency-related hemolytic anemia is achievable through G6PD genetic testing or whole-genome sequencing (WGS) to identify affected individuals who should avoid hemolytic triggers. However, accurately predicting the clinical consequence of G6PD variants is limited by over 800 G6PD variants which remain of uncertain significance. There also remains significant variability in which deficiency-causing variants are included in pharmacogenomic testing arrays across institutions: many panels only include c.202G>A, even though dozens of other variants can also cause G6PD deficiency. Here, we seek to improve G6PD genotype interpretation using data available in the All of Us Research Program and using a yeast functional assay. We confirm that G6PD coding variants are the main contributor to decreased G6PD activity, and that 13% of individuals in the All of Us data with deficiency-causing variants would be missed if only the c.202G>A variant were tested for. We expand clinical interpretation for G6PD variants of uncertain significance; reporting that c.595A>G, known as G6PD Dagua or G6PD Açores, and the newly identified variant c.430C>G, reduce activity sufficiently to lead to G6PD deficiency. We also provide evidence that five missense variants of uncertain significance are unlikely to lead to G6PD deficiency, since they were seen in hemi- or homozygous individuals without a reduction in G6PD activity. We also applied the new WHO guidelines and were able to classify two synonymous variants as WHO class C. We anticipate these results will improve the accuracy, and prompt increased use, of G6PD genetic tests through a more complete clinical interpretation of G6PD variants. As the All of Us data increases from 245,000 to 1 million participants, and additional functional assays are carried out, we expect this research to serve as a template to enable complete characterization of G6PD deficiency genotypes. With an increased number of interpreted variants, genetic testing of G6PD will be more informative for preemptively identifying individuals at risk for drug- or food-induced hemolytic anemia.

Glucagon-like Peptide-1 Receptor Agonists Associated Gastrointestinal Adverse Events: A Cross-Sectional Analysis of the National Institutes of Health All of Us Cohort

BACKGROUND

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are commonly used diabetes and obesity medications but have been associated with gastrointestinal (GI) adverse events. However, real-world evidence on comparative GI adverse reaction profiles is limited.

OBJECTIVES

This study aimed to evaluate GI adverse events among GLP-1 RA users and compare semaglutide, dulaglutide, liraglutide, and exenatide safety regarding the GI adverse reaction profile.

METHODS

This retrospective cross-sectional analysis utilized real-world data on 10,328 adults with diabetes/obesity in the National Institutes of Health All of Us cohort. New GLP-1 RA users were identified, and GI adverse events were examined. Logistic regression determined factors associated with GI adverse events.

RESULTS

The mean age of the study population was 61.4 ± 12.6 years, 65.7% were female, 51.3% were White, and they had a high comorbidity burden. Abdominal pain (57.6%) was the most common GI adverse event, followed by constipation (30.4%), diarrhea (32.7%), nausea and vomiting (23.4%), GI bleeding (15.9%), gastroparesis (5.1%), and pancreatitis (3.4%). Dulaglutide and liraglutide had higher rates of abdominal pain, constipation, diarrhea, and nausea and vomiting than semaglutide and exenatide. Liraglutide and exenatide had the highest pancreatitis (4.0% and 3.8%, respectively). Compared to semaglutide, dulaglutide and liraglutide had higher odds of abdominal pain, and nausea and vomiting. They also had higher odds of gastroparesis than semaglutide. No significant differences existed in GI bleeding or pancreatitis risks between the GLP-1 RAs.

CONCLUSIONS

In this real-world cohort, GI adverse events were common with GLP-1 RAs. Differences in GI safety profiles existed between agents, with exenatide appearing safer than other GLP-1 RAs, except for gastroparesis. These findings can inform GLP-1 RA selection considering GI risk factors. Further studies are needed to evaluate the causal relationship and GLP-1 RA safety with concomitant medication use.

Nativity differences in socioeconomic barriers and healthcare delays among cancer survivors in the All of Us cohort

PURPOSE

We aimed to assess whether nativity differences in socioeconomic (SES) barriers and health literacy were associated with healthcare delays among US cancer survivors.

METHODS

"All of Us" survey data were analyzed among adult participants ever diagnosed with cancer. A binary measure of healthcare delay (1+ delays versus no delays) was created. Health literacy was assessed using the Brief Health Literacy Screen. A composite measure of SES barriers (education, employment, housing, income, and insurance statuses) was created as 0, 1, 2, or 3+. Multivariable logistic regression model tested the associations of (1) SES barriers and health literacy with healthcare delays, and (2) whether nativity modified this relationship.

RESULTS

Median participant age was 64 years (n = 10,020), with 8% foreign-born and 18% ethnic minorities. Compared to survivors with no SES barriers, those with 3+ had higher likelihood of experiencing healthcare delays (OR 2.18, 95% CI 1.84, 2.58). For every additional barrier, the odds of healthcare delays were greater among foreign-born (1.72, 1.43, 2.08) than US-born (1.27, 1.21, 1.34). For every 1-unit increase in health literacy among US-born, the odds of healthcare delay decreased by 9% (0.91, 0.89, 0.94).

CONCLUSION

We found that SES barriers to healthcare delays have a greater impact among foreign-born than US-born cancer survivors. Higher health literacy may mitigate healthcare delays among US cancer survivors. Healthcare providers, systems and policymakers should assess and address social determinants of health and promote health literacy as a way to minimize healthcare delays among both foreign- and US-born cancer survivors.

Association Between a First-Degree Family History and Self-Reported Personal History of Obesity, Diabetes, and Heart and Blood Conditions: Results From the All of Us Research Program

Background Family history reflects the complex interplay of genetic susceptibility and shared environmental exposures and is an important risk factor for obesity, diabetes, and heart and blood conditions (ODHB). However, the overlap in family history associations between various ODHBs has not been quantified. Methods and Results We assessed the association between a self-reported family history of ODHBs and their risk in the adult population (age ≥20 years) of the AoU (All of Us) Research Program, a longitudinal cohort study of diverse participants across the United States. We conducted a family history-wide association study to systematically assess the association of a first-degree family history of 15 ODHBs in AoU. We performed stratified analyses based on racial and ethnic categories, education, household income and gender minority status, and quantified associations by type of affected relatives. Of 125 430 participants, 76.8% reported a first-degree family history of any ODHB, most commonly hypertension (n=64 982, 51.8%), high cholesterol (49 753, 39.7%), and heart attack (29 618, 23.6%). We use the FamWAS method to estimate 225 familial associations among 15 ODHBs. The results include overlapping associations between family history of different types of cardiometabolic conditions (such as type 2 diabetes and coronary artery disease), and their risk factors (obesity, hypertension), where adults with a family history of 1 ODHB exhibited 1.1 to 5.6 times (1.5, on average) the odds of having a different ODHB. Conclusions Our findings inform the utility of family history data as a risk assessment and screening tool for the prevention of ODHBs and to provide additional insights into shared risk factors and pathogenic mechanisms.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort

Background

Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the USA and has a greater observed prevalence among those who identify as Black or Hispanic.

Methods

This study aimed to assess T2D racial and ethnic disparities using the All of Us Research Program data and to measure associations between genetic ancestry (GA), socioeconomic deprivation, and T2D. We used the All of Us Researcher Workbench to analyze T2D prevalence and model its associations with GA, individual-level (iSDI), and zip code-based (zSDI) socioeconomic deprivation indices among participant self-identified race and ethnicity (SIRE) groups.

Results

The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n = 2311), Black (n = 16,282), Hispanic (n = 16,966), and White (n = 50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest levels of socioeconomic deprivation, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation, both iSDI and zSDI, are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with iSDI and zSDI on T2D. Higher levels of iSDI and zSDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of iSDI and zSDI are negatively associated with T2D at high levels of African and Native American ancestry.

Conclusions

Socioeconomic deprivation is associated with a higher prevalence of T2D in Black and Hispanic minority groups, compared to the majority White group. Nonetheless, socioeconomic deprivation is associated with reduced T2D risk within the Black and Hispanic groups. These results are paradoxical and have not been reported elsewhere, with possible explanations related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

Computational phenotyping with the All of Us Research Program: identifying underrepresented people with HIV or at risk of HIV

Objective

This study aims to identify the people living with HIV (PWH) and pre-exposure prophylaxis (PrEP) users in the All of Us (AoU) database by integrating information from both electronic health record (EHR)- and self-reported survey data.

Methods

We identified PWH and PrEP users if they met the inclusion criterion by conditions, lab measurements, or medications related to HIV in EHR data or confirmed questions in the Survey data.

Results

We evaluated the latest data release through July 1, 2022 in AoU. Through computational phenotyping, we identified 4575 confirmed and 3092 probable adult PWH and 564 PrEP users. PWH was most identified by a combination of medications and conditions (3324, 43.4%) and drug exposure alone (2191, 28.6%), then less commonly by survey data alone (608, 7.9%) and lab alone (81, 1.1%).

Discussion and conclusion

Our methods serve as an overall framework for other researchers using AoU data for conducting HIV-related research.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort

Background

Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the United States and has greater observed prevalence among those who identify as Black or Hispanic.

Methods

The aims of this study were to determine whether T2D racial and ethnic disparities can be observed in data from the All of Us Research Program and to measure associations of genetic ancestry (GA) and socioeconomic deprivation with T2D. The All of Us Researcher Workbench was used to calculate T2D prevalence and to model T2D associations with GA, individual-level (iSDI) and zip code-based (zSDI) socioeconomic deprivation indices within and between participant self-identified race and ethnicity (SIRE) groups.

Results

The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n=2,311), Black (n=16,282), Hispanic (n=16,966), and White (n=50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest median SDI values, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with SDI on T2D. Higher levels of SDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of SDI are negatively associated with T2D at high levels of African and Native American ancestry.

Conclusion

Socioeconomic deprivation is positively associated with the SIRE group T2D disparities observed here but negatively associated with T2D within the Black and Hispanic groups that show the highest T2D prevalence. These results are paradoxical and have not been reported elsewhere. We discuss possible explanations for this paradox related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

Evaluating Discrepancies in Self-Reported Glaucoma and Electronic Health Records in the National Institutes of Health All of Us Database

PURPOSE

Patient understanding of glaucoma diagnosis is critical for disease management and it is unclear if there are racial/ethnic differences in this regard. The purpose of this study was to determine the level of agreement between glaucoma diagnosis by self-report and electronic health record (EHR) data using the National Institute of Health's "All of Us (AoU)" database and to examine the association between race/ethnicity and discordance of glaucoma diagnosis between self-report and EHR data.

DESIGN

Cross-sectional study.

PARTICIPANTS

Individuals in AoU who answered a survey question about glaucoma diagnosis and had EHR data availability.

METHODS

The agreement between self-reported glaucoma and EHR data was estimated using Cohen's κ coefficient. Multivariable logistic regression was performed, adjusting for age, sex, education level, income, and health care literacy, to examine the associations between race/ethnicity and discordance between self-reported glaucoma and EHR diagnosis.

MAIN OUTCOME MEASURES

Agreement between self-reported glaucoma and EHR diagnosis.

RESULTS

Of all 87 782 individuals, 1985 (2.26%) had both self-reported and EHR glaucoma, 81 781 (92.16%) had no glaucoma, 2022 (2.31%) individuals had EHR-only glaucoma, and 1994 (2.27%) had self-report-only glaucoma (Cohen's κ = 0.47). In the multivariable regression, Black or African American (adjusted odds ratio [aOR], 1.67; 95% confidence interval [CI], 1.40-1.98), Asian (aOR, 2.63; 95% CI, 1.97-3.44), and Hispanic or Latino (aOR, 1.63; 95% CI, 1.33-1.99) individuals were more likely to have EHR-only glaucoma than White individuals. Additionally, Black or African American (aOR, 2.30; 95% CI, 1.97-2.67) and Hispanic or Latino individuals (aOR,1.47; 95% CI, 1.21-1.79) were more likely to have self-report-only glaucoma compared with White individuals.

CONCLUSIONS

In the AoU database, we found that Black or African American and Hispanic or Latino individuals had higher odds of discordance between glaucoma diagnosis by self-report and EHR. Future studies are needed to examine the issues leading to this discordance, such as a lack of patient understanding regarding their diagnosis or a lack of culturally appropriate physician explanation/teaching.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm

Background

Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm?

Methods

Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto. Aortic diameter prediction models were built with the polygenic score ("AORTA Gene") and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us.

Results

In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us. AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us.

Conclusions

Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.

All of Us and the Promise of Precision Medicine: Achieving Equitable Access for Federally Qualified Health Center Patients

The United States National Institutes of Health's (NIH) All of Us (AoU) initiative recruits participants from diverse backgrounds to improve the makeup of biobanks, considering nearly all biospecimens used in research come from people of European ancestry. Participants who join AoU consent to provide samples of blood, urine, and/or saliva and to submit their electronic health record to the program. In addition to diversifying precision medicine research studies, AoU will return genetic results back to many participants, which may require further follow-up care (i.e., more frequent cancer screening or mastectomy after a BRCA result). To help achieve its goals, AoU has partnered with Federally Qualified Health Centers (FQHCs), which is a type of community health center whose patient base is comprised largely of people who are uninsured, underinsured, or on Medicaid. Our NIH-funded study convened FQHC providers involved in AoU to better understand precision medicine in community health settings. Drawing from our findings, we present barriers community health patients and their providers face when accessing diagnostics and specialty care after genetic results necessitate medical follow-up care. We also propose several policy and financial recommendations to help overcome the challenges discussed, stemming from a commitment to equitable access to precision medicine advances.

Risk Factors for Multiple Sclerosis Development After Optic Neuritis Diagnosis Using a Nationwide Health Records Database

Multiple sclerosis (MS) is an autoimmune demyelinating disease that often initially presents with optic neuritis (ON). Little is known about the demographic factors and familial histories that may be associated with the development of MS after a diagnosis of ON. We utilised a nationwide database to characterise specific potential drivers of MS following ON as well as analyse barriers to healthcare access and utilisation. The All of Us database was queried for all patients who were diagnosed with ON and for all patients diagnosed with MS after an initial diagnosis of ON. Demographic factors, family histories, and survey data were analysed. Multivariable logistic regression was performed to analyse the potential association between these variables of interest with the development of MS following a diagnosis of ON. Out of 369,297 self-enrolled patients, 1,152 were identified to have a diagnosis of ON, while 152 of these patients were diagnosed with MS after ON. ON patients with a family history of obesity were more likely to develop MS (obesity odd ratio: 2.46; p < .01). Over 60% of racial minority ON patients reported concern about affording healthcare compared with 45% of White ON patients (p < .01). We have identified a possible risk factor of developing MS after an initial diagnosis of ON as well as alarming discrepancies in healthcare access and utilisation for minority patients. These findings bring attention to clinical and socioeconomic risk factors for patients that could enable earlier diagnosis and treatment of MS to improve outcomes, particularly in racial minorities.

Novel Association between Opioid Use and Increased Risk of Retinal Vein Occlusion Using the National Institutes of Health All of Us Research Program

Purpose

To assess for risk factors for retinal vein occlusion (RVO) among participants in the National Institutes of Health All of Us database, particularly social risk factors that have not been well studied, including substance use.

Design

Retrospective, case-control study.

Participants

Data were extracted for 380 adult participants with branch retinal vein occlusion (BRVO), 311 adult participants with central retinal vein occlusion (CRVO), and 1520 controls sampled among 311 640 adult participants in the All of Us database.

Methods

Data were extracted regarding demographics, comorbidities, income, housing, insurance, and substance use. Opioid use was defined by relevant diagnosis and prescription codes, with prescription use > 30 days. Controls were sampled at a 4:1 control to case ratio from a pool of individuals aged > 18 years without a diagnosis of RVO and proportionally matched to the demographic distribution of the 2019 U.S. census. Multivariable logistic regression identified medical and social determinants significantly associated with BRVO or CRVO. Statistical significance was defined as P < 0.05.

Main Outcome Measure

Development of BRVO or CRVO based on diagnosis codes.

Results

Among patients with BRVO, the mean (standard deviation) age was 70.1 (10.5) years. The majority (53.7%) were female. Cases were diverse; 23.7% identified as Black, and 18.4% identified as Hispanic or Latino. Medical risk factors including glaucoma (odds ratio [OR], 3.29; 95% confidence interval [CI], 2.22-4.90; P < 0.001), hypertension (OR, 2.15; 95% CI, 1.49-3.11; P < 0.001), and diabetes mellitus (OR, 1.68; 95% CI, 1.18-2.38; P = 0.004) were re-demonstrated to be associated with BRVO. Black race (OR, 2.64; 95% CI, 1.22-6.05; P = 0.017) was found to be associated with increased risk of BRVO. Past marijuana use (OR, 0.68; 95% CI, 0.50-0.92; P = 0.013) was associated with decreased risk of BRVO; however, opioid use (OR, 1.98; 95% CI, 1.41-2.78; P < 0.001) was associated with a significantly increased risk of BRVO. Similar associations were found for CRVO.

Conclusions

Understanding RVO risk factors is important for primary prevention and improvement in visual outcomes. This study capitalizes on the diversity and scale of a novel nationwide database to elucidate a previously uncharacterized association between RVO and opioid use.

Cardiovascular Health Disparities in Racial and Other Underrepresented Groups: Initial Results From the All of Us Research Program

Background

All of Us is a novel research program that aims to accelerate research in populations traditionally underrepresented in biomedical research. Our objective was to evaluate the burden of cardiovascular disease (CVD) in broadly defined underrepresented groups.

Methods and Results

We evaluated the latest data release of All of Us. We conducted a cross‐sectional analysis combining survey and electronic health record data to estimate the prevalence of CVD upon enrollment in underrepresented groups defined by race, ethnicity, age (>75 years), disability (not able to carry out everyday physical activities), sexual orientation and gender identity lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA+), income (annual household income <$35 000 US dollars) and education (less than a high school degree). We used multivariate logistic regression to estimate the adjusted odds ratio (OR) and product terms to test for interaction. The latest All of Us data release includes 315 297 participants. Of these, 230 577 (73%) had information on CVD and 17 958 had CVD (overall prevalence, 7.8%; 95% CI, 7.7–7.9). Multivariate analyses adjusted by hypertension, hyperlipidemia, type 2 diabetes mellitus, body mass index, and smoking indicated that, compared with White participants, Black participants had a higher adjusted odds of CVD (OR, 1.21; 95% CI, 1.16–1.27). Higher adjusted odds of CVD were also observed in underrepresented groups defined by other factors, including age >75 years (OR, 1.90; 95% CI, 1.81–1.99), disability (OR, 1.60; 95% CI, 1.53–1.68), and income <$35 000 US dollars (OR, 1.22; 95% CI, 1.17–1.27). Sex significantly modified the odds of CVD in several of the evaluated groups.

Conclusions

Among participants enrolled in All of Us, underrepresented groups defined based on race, ethnicity and other factors have a disproportionately high burden of CVD. The All of Us research program constitutes a powerful platform to accelerate research focused on individuals in underrepresented groups.