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OBJECTIVE: To investigate associations between statin use and glaucoma in the 2017-2022 All of Us (AoU) Research Program.
METHODS: Cross-sectional, population-based.
METHODS: 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.
METHODS: Any glaucoma as defined by International Classification of Diseases (ICD) codes found in EHR data.
RESULTS: Of 79,742 individuals with hyperlipidemia in AoU, there were 6,365 (8.0%) statin users. Statin use was associated with increased glaucoma prevalence when compared with statin non-use (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 60-69 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 60-69 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.

OBJECTIVE: 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.
METHODS: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.

UNASSIGNED: The All of Us Research Program aims to collect longitudinal health-related data from a million individuals in the United States. An inherent challenge of a non-probability sampling strategy through voluntary participation in All of Us is that findings may not be nationally representative for addressing health and health care at the population level. We generated survey weights for the All of Us data that can be used to address the challenge.
UNASSIGNED: We developed raked weights using demographic, health, and socioeconomic variables available in both the 2020 National Health Interview Survey (NHIS) and All of Us. We then compared the unweighted and weighted prevalence of a set of health-related variables (health behaviors, health conditions, and health insurance coverage) estimated from All of Us data with the weighted prevalence estimates obtained from NHIS data.
UNASSIGNED: The sample included 100,391 All of Us participants 18 years of age and older with complete data collected between May 2017 and January 2022 across the United States.
UNASSIGNED: Final variables in the raking procedure included age, sex, race/ethnicity, region of residence, annual household income, and home ownership. The mean percentage difference between known proportions obtained from the NHIS and All of Us was reduced by 18.89% for health-related variables after applying the raked weights.
UNASSIGNED: Raking improved the comparability of prevalence estimates obtained from All of Us to known national prevalence estimates. Refining the process of variable selection for raking may further improve the comparability between All of Us and nationally representative data.

Computational variant effect predictors offer a scalable and increasingly reliable means of interpreting human genetic variation, but concerns of circularity and bias have limited previous methods for evaluating and comparing predictors. Population-level cohorts of genotyped and phenotyped participants that have not been used in predictor training can facilitate an unbiased benchmarking of available methods. Using a curated set of human gene-trait associations with a reported rare-variant burden association, we evaluate the correlations of 24 computational variant effect predictors with associated human traits in the UK Biobank and All of Us cohorts.
AlphaMissense outperformed all other predictors in inferring human traits based on rare missense variants in UK Biobank and All of Us participants. The overall rankings of computational variant effect predictors in these two cohorts showed a significant positive correlation.
We describe a method to assess computational variant effect predictors that sidesteps the limitations of previous evaluations. This approach is generalizable to future predictors and could continue to inform predictor choice for personal and clinical genetics.

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