Faculty Bibliography

OBJECTIVE:To examine associations of inherited chromosomally integrated human herpesvirus 6 (iciHHV-6) with incident dementia and mortality, characterize proteomic and metabolomic correlates of carrier status, and evaluate whether these biomarkers relate to dementia and mortality risk. METHODS:≤ 15,416) data and ≈15 years of follow-up. Cox proportional hazards models adjusted for key confounding factors evaluated associations of iciHHV-6 with dementia and mortality. Multivariable linear models assessed iciHHV-6 associations with metabolomic and proteomic profiles. Mediation and interaction were evaluated using structural equation models and Cox models with biomarker interactions. Least absolute shrinkage and selection operator regression identified independent proteomic and metabolomic predictors using imputed biomarker data. RESULTS:iciHHV-6 was associated with higher dementia risk (hazard ratio [HR] = 1.32), particularly among women (HR = 1.66) and individuals with elevated Alzheimer's disease polygenic risk (HR = 1.49). Branched-chain amino acids (leucine and isoleucine) were elevated among carriers without mediating dementia risk. Proteomic analyses identified 126 nominally associated iciHHV-6-related proteins, many of which (Neurofilament Light Chain (NEFL), Glial Fibrillary Acidic Protein (GFAP), Yes-Associated Protein 1 (YAP1), Sialic Acid-binding Immunoglobulin-like Lectin 5 (SIGLEC5), Interleukin 19 (IL19), A Disintegrin And Metalloproteinase with Thrombospondin Motifs 16 (ADAMTS16), Sphingomyelin Phosphodiesterase 1 (SMPD1)) were also associated with dementia and/or mortality. Exploratory pathway analyses suggested enrichment of proteins related to immune regulation and post-translational modification. Predictive models identified NEFL, GFAP, Vascular Endothelial Growth Factor A (VEGF), Brevican (BCAN), remnant cholesterol, and polyunsaturated fatty acids as dementia predictors (area under the curve [AUC] = 0.83), whereas NEFL, Growth Differentiation Factor 15 (GDF15) Latent Transforming Growth Factor Beta Binding Protein 2 (LTBP2), Ectodysplasin A2 Receptor (EDA2R) and Advanced Glycosylation End-product Specific Receptor (AGER) predicted mortality (AUC = 0.70). DISCUSSION/CONCLUSIONS:iciHHV-6 was associated with increased dementia risk, particularly among women and genetically susceptible individuals, with neuroimmune and metabolic biomarker profiles potentially relevant to brain aging and mortality risk.

OBJECTIVE/UNASSIGNED:Timely use of sodium-glucose cotransporter-2 inhibitors (SGLT2is) is vital for managing type 2 diabetes (T2DM) and preventing cardiovascular and renal complications. However, socioeconomic barriers and prescribing inertia may disproportionately affect disadvantaged populations. We examined racial and ethnic differences in the initiation of SGLT2i among Medicaid patients with T2DM. METHODS/UNASSIGNED:Adult participants 18-64 with T2DM and prescribed with metformin were drawn from MarketScan Multistate Medicaid Database (2015-2022) for this retrospective cohort study. We used a Cox proportional hazards model, adjusted for age, sex, type of Medicaid coverage, and comorbidities, to assess time to SGLT2i initiation. RESULTS/UNASSIGNED:Among 13 744 Medicaid patients, non-Hispanic Black patients had an 18% lower rate of SGLT2i initiation compared with non-Hispanic White patients (hazard ratio [HR] = 0.82; 95%CI: 0.75-0.90). This disparity was most pronounced among patients without pre-existing atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease (HR = 0.79; 95%CI: 0.71-0.87). No significant racial/ethnic differences were observed among patients with these conditions. CONCLUSIONS/UNASSIGNED:Significant delays in SGLT2i initiation among Black Medicaid patients-particularly in early stage of diabetes-may increase their risk for long-term complications. Addressing structural barriers through targeted interventions is essential to promote equity in diabetes care.

OBJECTIVE:To examine associations of inherited chromosomally integrated human herpesvirus 6 (iciHHV-6) with incident dementia and mortality, characterize proteomic and metabolomic correlates of carrier status, and evaluate whether these biomarkers relate to dementia and mortality risk. METHODS:≤ 15,416) data and ≈15 years of follow-up. Cox proportional hazards models adjusted for key confounding factors evaluated associations of iciHHV-6 with dementia and mortality. Multivariable linear models assessed iciHHV-6 associations with metabolomic and proteomic profiles. Mediation and interaction were evaluated using structural equation models and Cox models with biomarker interactions. Least absolute shrinkage and selection operator regression identified independent proteomic and metabolomic predictors using imputed biomarker data. RESULTS:iciHHV-6 was associated with higher dementia risk (hazard ratio [HR] = 1.32), particularly among women (HR = 1.66) and individuals with elevated Alzheimer's disease polygenic risk (HR = 1.49). Branched-chain amino acids (leucine and isoleucine) were elevated among carriers without mediating dementia risk. Proteomic analyses identified 126 nominally associated iciHHV-6-related proteins, many of which (Neurofilament Light Chain (NEFL), Glial Fibrillary Acidic Protein (GFAP), Yes-Associated Protein 1 (YAP1), Sialic Acid-binding Immunoglobulin-like Lectin 5 (SIGLEC5), Interleukin 19 (IL19), A Disintegrin And Metalloproteinase with Thrombospondin Motifs 16 (ADAMTS16), Sphingomyelin Phosphodiesterase 1 (SMPD1)) were also associated with dementia and/or mortality. Exploratory pathway analyses suggested enrichment of proteins related to immune regulation and post-translational modification. Predictive models identified NEFL, GFAP, Vascular Endothelial Growth Factor A (VEGF), Brevican (BCAN), remnant cholesterol, and polyunsaturated fatty acids as dementia predictors (area under the curve [AUC] = 0.83), whereas NEFL, Growth Differentiation Factor 15 (GDF15) Latent Transforming Growth Factor Beta Binding Protein 2 (LTBP2), Ectodysplasin A2 Receptor (EDA2R) and Advanced Glycosylation End-product Specific Receptor (AGER) predicted mortality (AUC = 0.70). DISCUSSION/CONCLUSIONS:iciHHV-6 was associated with increased dementia risk, particularly among women and genetically susceptible individuals, with neuroimmune and metabolic biomarker profiles potentially relevant to brain aging and mortality risk.

OBJECTIVE/UNASSIGNED:This study examined decade-long trends and differences in sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) prescriptions among adults with type 2 diabetes using real-world data from Epic Cosmos. RESEARCH DESIGN AND METHODS/UNASSIGNED:We analyzed electronic health records of 1 517 594 adults with type 2 diabetes without end-stage renal disease from 2014 to 2024. Annual prescribing trends were evaluated by patient race and insurance type using negative binomial regression. Medication exposure was defined using active prescriptions/orders recorded in Epic Cosmos during the calendar year. In pooled descriptive analyses, we also characterized patients prescribed these medications by clinical characteristics, neighborhood-level social vulnerability, and prescriber specialty. RESULTS/UNASSIGNED:From 2014 to 2024, SGLT2i use rose from 0.5% to 12.1% and GLP-1 RA use increased from 0.9% to 15.9%. Black patients had consistently lower prescription rates than White patients across insurance groups. Primary care physicians prescribed about one-third of these medications. In pooled descriptive analyses, endocrinology was associated with higher observed prescribing rates than primary care or cardiology. Patients from neighborhoods with lower social vulnerability were more likely to receive these therapies. CONCLUSIONS/UNASSIGNED:Use of SGLT2i and GLP-1 RA increased substantially over the past decade, significant racial, socioeconomic, and insurance-related differences persist in prescribing these therapies.

Somatic mutations rewire the ubiquitin-proteasome system (UPS) to support tumor growth, but the proteome-wide consequences of cancer-driver alterations on UPS composition remain incompletely understood. Using harmonized proteogenomic data from up to 11 CPTAC cohorts, we performed an integrated pan-cancer analysis of UPS protein dysregulation, prognostic associations, and mutation-driven remodeling. We show that mRNA poorly predicts UPS protein abundance, that a defined set of E3 ligases is recurrently dysregulated across cancers, and that somatic mutations (most strikingly TP53 loss) produce coherent UPS protein-quantitative trait locus (pQTL) signatures. Two case studies (UBR5 and TRIM28) illustrate orthogonal modes of UPS rewiring: a mutation-driven axis in which TP53-mutant tumors elevate UBR5 to support replication stress tolerance, and a lineage-driven axis in which TRIM28 engages tissue-restricted regulatory networks with opposing prognostic effects in glioblastoma versus head and neck cancer. Each axis exposes context-specific therapeutic vulnerabilities, including sensitivity to DNA damage response inhibitors (UBR5-high) and lineage-specific drug responses (TRIM28-high). Together, these analyses define a mechanistic framework for how cancer-driver mutations reshape proteostasis through the UPS and nominate mutation- and lineage-defined dependencies for precision degrader therapy. The harmonized pan-tissue atlas and the UbiDash interactive resource that underpin parts of this analysis are reported in our companion paper [1].

OBJECTIVE:Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC. MATERIALS AND METHODS/METHODS:The study included adult participants with linked FHIR-sourced EHR data. We characterized EHR diagnoses, procedures, medications, lab tests, and vital sign features associated with LC. A computable phenotyping algorithm was trained and validated against patient-reported symptoms. MAIN OUTCOME AND MEASURES/METHODS:We assessed model discrimination and calibration in a held-out test set. We describe important model features and evaluate model discrimination and calibration. RESULTS:The study included 1,501 RECOVER-Adult cohort participants with linked EHR data. 376 (25%) met criteria for highly symptomatic LC based on the RECOVER Long COVID Research Index (LCRI). EHR features associated with LC included clinician diagnosis of shortness of breath, malaise and fatigue, and cardiac dysrhythmias; documented treatment with albuterol, gabapentin, or duloxetine; or elevated heart rate. The algorithm identifying patients with highly symptomatic LC had an AUROC of 0.80 (95% confidence interval (CI) 0.74-0.85), and AUPRC of 0.58 (95% CI, 0.47-0.69). CONCLUSION AND RELEVANCE/CONCLUSIONS:These findings demonstrate that, using EHR data, a machine-learning model can accurately select patients with sets of self-reported LC symptoms. The model could help identify patients within a health system with the highest probability of the condition and facilitate screening, recruitment for clinical trials, and etiologic studies.

OBJECTIVE:Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC. MATERIALS AND METHODS/METHODS:The study included adult participants with linked FHIR-sourced EHR data. We characterized EHR diagnoses, procedures, medications, lab tests, and vital sign features associated with LC. A computable phenotyping algorithm was trained and validated against patient-reported symptoms. MAIN OUTCOME AND MEASURES/METHODS:We assessed model discrimination and calibration in a held-out test set. We describe important model features and evaluate model discrimination and calibration. RESULTS:The study included 1,501 RECOVER-Adult cohort participants with linked EHR data. 376 (25%) met criteria for highly symptomatic LC based on the RECOVER Long COVID Research Index (LCRI). EHR features associated with LC included clinician diagnosis of shortness of breath, malaise and fatigue, and cardiac dysrhythmias; documented treatment with albuterol, gabapentin, or duloxetine; or elevated heart rate. The algorithm identifying patients with highly symptomatic LC had an AUROC of 0.80 (95% confidence interval (CI) 0.74-0.85), and AUPRC of 0.58 (95% CI, 0.47-0.69). CONCLUSION AND RELEVANCE/CONCLUSIONS:These findings demonstrate that, using EHR data, a machine-learning model can accurately select patients with sets of self-reported LC symptoms. The model could help identify patients within a health system with the highest probability of the condition and facilitate screening, recruitment for clinical trials, and etiologic studies.

Not available.

OBJECTIVE/UNASSIGNED:This study examined decade-long trends and differences in sodium-glucose cotransporter 2 inhibitor (SGLT2i) and glucagon-like peptide-1 receptor agonist (GLP-1 RA) prescriptions among adults with type 2 diabetes using real-world data from Epic Cosmos. RESEARCH DESIGN AND METHODS/UNASSIGNED:We analyzed electronic health records of 1 517 594 adults with type 2 diabetes without end-stage renal disease from 2014 to 2024. Annual prescribing trends were evaluated by patient race and insurance type using negative binomial regression. Medication exposure was defined using active prescriptions/orders recorded in Epic Cosmos during the calendar year. In pooled descriptive analyses, we also characterized patients prescribed these medications by clinical characteristics, neighborhood-level social vulnerability, and prescriber specialty. RESULTS/UNASSIGNED:From 2014 to 2024, SGLT2i use rose from 0.5% to 12.1% and GLP-1 RA use increased from 0.9% to 15.9%. Black patients had consistently lower prescription rates than White patients across insurance groups. Primary care physicians prescribed about one-third of these medications. In pooled descriptive analyses, endocrinology was associated with higher observed prescribing rates than primary care or cardiology. Patients from neighborhoods with lower social vulnerability were more likely to receive these therapies. CONCLUSIONS/UNASSIGNED:Use of SGLT2i and GLP-1 RA increased substantially over the past decade, significant racial, socioeconomic, and insurance-related differences persist in prescribing these therapies.