ABSTRACT Use of three‐drug combinations is common and could increase the risk of adverse drug events (ADEs). Signals of ADEs from three‐drug combinations could be identified from real‐world data, and the risks of ADEs from single drug exposure to three‐drug combination exposure could be illustrated by a graphic model. We used a US nationwide insurance claim database. We explored the relationship between drug exposure (from single drugs to three‐drug combinations) and risks of ADEs (acute kidney injury, gastrointestinal bleeding, and opioid‐related ADE). We used a conditional logistic regression model to estimate the odds ratios (ORs), the posterior probability of null hypothesis to control false discovery rate (FDR), and a graphic model to illustrate the ORs from single drug exposure to three‐drug combination exposure. We derived approximately 1.7 million covariate‐matched ADE‐case–control pairs and investigated approximately 0.25 million three‐drug combinations. We identified 3412 signals of adverse three‐drug combinations (FDR < 0.01 and OR > 2). For the signals, we observed the medians and interquartile ranges (IQRs) of ORs increased from single drug exposure (median = 1.03, IQR: 0.95–1.15), through two‐drug combination exposure (median = 1.45, IQR: 1.23–1.75), to three‐drug combination exposure (median = 2.95, IQR: 2.43–3.91). We built a webpage‐based tool to illustrate the ORs from single drug exposure to three‐drug combination exposure. In conclusion, certain three‐drug combinations are associated with an increased risk of ADE, and the risks of ADE could increase from single drug exposure to three‐drug combination exposure.

Introduction The maintenance of a healthy epithelial-endothelial juxtaposition requires crosstalk within glomerular cellular niches. Here, we sought to understand the spatially anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. Methods From 132 human kidney samples, an integrated multiomics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor networks in glomerular capillary endothelial (EC-GC) and mesangial cells. Data were culled from single nucleus RNA, ATAC-sequencing and four orthogonal spatial transcriptomic technologies for correlation with histopathological and clinical trial data. Results We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a transcription factor network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the transcription factor network accelerated the transition from healthy EC-GCs toward a degenerative injured endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. Conclusions The MEF2C, MEF2A, and TRPS1 transcription factor network carefully balances the fate of the EC-GC in DKD. When the transcription factor network is “on” or over-expressed in DKD, EC-GCs may progress to a prEC state, while transcription factor suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

NAT2 encodes arylamine N-acetyltransferase 2, a key enzyme in the phase II metabolism of arylamines and arylhydrazines. NAT2 is highly polymorphic, resulting in variable distributions of rapid and poor metabolizers across global populations. Here, we detail the process undertaken by the Clinical Pharmacogenetics Implementation Consortium (CPIC) NAT2 Pharmacogene Curation Expert Panel (PCEP) to assign clinical function to NAT2 star (*) alleles using CPIC's standard terminology. Given the observed impact of NAT2 genetic variability on drug response, CPIC convened the NAT2-PCEP to standardize clinical allele function assignments. The NAT2-PCEP is comprised of multidisciplinary and international members, including researchers, clinicians, and implementers with expertise in pharmacogenomics and NAT2 molecular biology. Extensive in vitro and clinical literature was curated from PubMed and other sources to assess NAT2 genotype-to-phenotype concordance as well as the biochemical function of NAT2 star alleles. The NAT2-PCEP assigned allele clinical function using CPIC's standard terminology (increased, decreased, uncertain, and unknown function) to 59 star alleles cataloged by the Pharmacogene Variation Consortium (PharmVar). Two alleles, NAT2*1 and NAT2*4, were assigned increased function (historically known as rapid), 40 alleles were assigned decreased function (historically known as slow), 10 alleles were assigned uncertain function, and seven alleles were assigned unknown function. Rigorous evidence review and in-depth PCEP discussion were crucial in determining these function assignments. The findings reported here underscore the importance of standardized allele functional terms and diplotype-to-phenotype assignments to further the clinical implementation of NAT2 pharmacogenetic test results.

Importance Apolipoprotein L1 locus ( APOL1 ) high-risk alleles are associated with incidence of chronic kidney disease (CKD) among people with African ancestry. Few studies have examined the effect of genetic return of results on blood pressure (BP) management and control. Objective To determine whether providing APOL1 high-risk genotype results to people with hypertension and their clinicians would reduce systolic BP (SBP) and improve CKD screening and diagnosis. Design, Setting, and Participants From July 1, 2020, to September 30, 2023, adults aged 18 to 70 years with hypertension and self-reported African ancestry were enrolled at 14 institutions and 54 clinical sites across the US. Eligible patients either (1) lacked diagnoses of diabetes and CKD or (2) had a diagnosis of CKD with or without diabetes. Interventions Participants were randomized to receive APOL1 genotype results immediately (intervention) or 6 months after enrollment (control). Clinical decision support encouraged appropriate CKD screening, diagnosis, and antihypertensive therapy. Main Outcomes and Measures The primary outcome was change in SBP in individuals with APOL1 high-risk allelles at 3 months, assessed in a modified intention-to-treat analysis. Prespecified per-protocol subgroup analyses included those with uncontrolled BP (baseline SBP ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg), uncontrolled BP while receiving antihypertensive therapy, and CKD at enrollment. Secondary outcomes included urine microalbumin screening and new CKD diagnoses. Results Of 6754 individuals recruited (mean [SD] age, 55.3 [10.3] years; 4310 women [63.8%]), 954 (14.1%; mean [SD] age, 54.9 [10.0] years; 600 women [62.9%]) had 2 APOL1 risk alleles. At 3 months, there was no difference in SBP between the intervention and control groups (between-group difference, −0.3 mm Hg [95% CI, −2.7 to 2.1 mm Hg]). Among 377 individuals with uncontrolled BP, the mean SBP change was −4.1 mm Hg (95% CI, −7.7 to −0.5 mm Hg) more in the intervention group than the control group ( P = .004). SBP improvement was also observed for the intervention in the subgroup of patients with uncontrolled BP receiving antihypertensive therapy (SPB difference, −4.3 mm Hg [95% CI −8.0 to −0.5 mm Hg]; P = .004), but not the CKD subgroup (SPB difference, 0.8 mm Hg [95% CI, −3.0 to 4.5 mm Hg]). Provision of APOL1 genotype led to increased urine microalbumin screening (between-group difference, 17.3% [95% CI, 9.6%-24.9%]; P < .001) and CKD diagnoses (between-group difference, 5.7% [95% CI, 2.2%-9.3%]; P = .002) at 6 months. Conclusions and Relevance Provision of APOL1 genotype high-risk results to participants and clinicians was not associated with SBP reduction overall. Among the subset of patients with uncontrolled BP, the intervention group had a significant SBP reduction. APOL1 disclosure also increased the rate of CKD screening and diagnosis. Effects of reporting APOL1 genotype merit further investigation among those with uncontrolled BP. Trial Registration ClinicalTrials.gov Identifier: NCT04191824

Introduction:No widely adopted guidelines exist for collecting and reporting donor-level metadata in tissue-based research, limiting interpretability, reproducibility, and potentially introducing bias. This study aimed to inform ethical and appropriate metadata practices. Methods:Semi-structured interviews were conducted with 16 investigators from the Human BioMolecular Atlas Program. Thematic analysis using inductively derived codes identified metadata elements and perspectives on their collection and reporting. Results:Participants identified 80 metadata variables across six domains: demographic, sociodemographic, medical history, personally identifying information, cause of death, and tissue/organ data. Most supported routine collection and reporting of demographics and medical history, whereas views on cause of death and sociodemographic data were mixed. Conclusion:We recommend routinely collecting and reporting demographics and medical history, while restricting cause of death and sociodemographic variables to situations with explicit consent or justification. These findings provide initial evidence to inform ethical donor metadata guidelines, with further stakeholder engagement and consensus-building needed.

Objective:Predicting health outcomes from electronic health records (EHRs) is challenging because traditional models rely on structured data and often ignore external medical knowledge. We propose an approach that integrates structured EHR with text‑based clinical evidence to improve prediction and interpretability. Methods:We introduce PHO-Agents, a multi-agent system powered by large language models (LLMs) for health outcome prediction. Structured EHR sequences are encoded to produce attention-based representations and initial logits, which are converted into patient summaries by a data agent. A retrieval agent gathers relevant clinical guidelines. Research and practical doctor agents independently assess the patient, and a leader agent synthesizes their analyses. Outputs from the EHR-based model and the LLM agents are fused to generate final predictions and explanation reports. PHO-Agents was evaluated on three real-world cohorts: acute kidney injury (AKI) patients (in-hospital mortality), chronic kidney disease patients (AKI onset within two years), and cancer patients receiving immune checkpoint inhibitors (immune-related adverse events within one year). Results:PHO-Agents outperformed single-agent and multi-agent LLM baselines across all cohorts. In the AKI mortality task, it achieved a PR-AUC of 90.20 ± 2.07, compared with 56.46 ± 2.98 for the best single-agent baseline. Similar gains were observed in the ICI and CKD cohorts. Ablation studies showed that both multi-agent reasoning and logit-level fusion contributed to performance improvements, and case analyses demonstrated clinically consistent explanations. Conclusion:PHO-Agents integrates longitudinal EHR modeling with collaborative LLM reasoning, improving predictive performance, interpretability, and robustness across diverse clinical tasks. This hybrid approach offers a trustworthy strategy for real-world clinical decision support.

Immunoglobulin A nephropathy (IgAN) is the most common glomerular disease worldwide and is a leading cause of chronic kidney disease. The current paradigm of IgAN risk stratification, the International IgAN Prediction Tool (IIgAN-PT), relies on clinical and histologic data collected at the time of biopsy. Clinical data includes variables such as estimated glomerular filtration rate (eGFR), proteinuria, and blood pressure. Histologic data from the diagnostic kidney biopsy are assessed by the Oxford classification system; however, its reliability can be significantly affected by interobserver variability. The emergence of "pathomics," in which morphometric features are extracted from histologic objects, aims to address this issue and provides a more objective and reproducible assessment. In this study, we created a dataset of clinical variables and averaged pathomic features from non-globally sclerotic glomeruli, arteries/arterioles, and tubules. We used this data to train and test logistic regressions (LGs) to predict whether patients would experience kidney function decline (i.e., eGFR decline > 50% from baseline or dialysis initiation within 5 years post-biopsy). LGs on only glomerular pathomic features and clinical variables did not perform better than IIgAN-PT predictions, but there was some improvement in accuracy, balanced accuracy, precision, and recall when adding other histologic objects (i.e. tubules and arteries/arterioles) with the elastic net penalty. The greatest improvement in performance was derived from including pathomic features from non-glomerular histologic objects and removing MEST-C variables. This improved model accuracy, balanced accuracy, precision, and recall for LG with the ridge penalty from 0.714 to 0.929, 0.455 to 0.833, 0 to 1.000, and 0 to 0.667.

Background:Tubulitis is a defining histologic feature of T cell-mediated rejection (TCMR), while glomerulitis is often characteristic of antibody mediated rejection (AMR). Histologic quantification of tubulitis and glomerulitis using Banff criteria is subject to interobserver variability. Bulk transcriptomic assays (e.g., MMDx) have introduced molecular correlations of tubulitis with TCMR and glomerulitis with AMR, but lack spatial resolution. Methods:We applied a web-based platform, FUSION (Functional Unit State Identification in Whole Slide Images), to a cohort of 8 cases (n=2 per condition) with kidney allograft biopsy samples acute TCMR, active AMR, chronic active AMR, and no rejection (control). The machine-learning (ML) platform enabled integrated visualization and analysis of spatial transcriptomics (10x Genomics Visium v2) together with high-resolution whole-slide histology. Results:Transcriptomics-derived immune cell proportions within AI-segmented tubular and glomerular regions were used to generate spatial Banff t- and g-scores. Derived t-scores showed full concordance with pathologist scores in both acute TCMR cases; g-scores showed concordance in 2 of 4 AMR cases, with discordant cases characterized by low absolute immune signal near the classification boundary. Conclusions:We demonstrate the feasibility of using AI-based FTU segmentation integrated with spatial transcriptomics-derived immune cell proportions to generate spatially informed t- and g-scores aligned with Banff criteria, with full concordance in severe rejection and partial concordance in mild rejection. This approach lays the foundation for validated, spatial transcriptomics-augmented t -scores and g -scores that enhance diagnostic precision, reduces inter-observer variability among renal pathologists, and support potential clinical adoption.

BACKGROUND:Tacrolimus is primarily metabolized by Cytochrome P450 (CYP)3A4/5. The Clinical Pharmacogenetics Implementation Consortium recommends increasing the initial dose 1.5- to 2-fold in CYP3A5 expressers to enhance transplant outcomes. Our objective was to investigate the impact of CYP3A5 expresser status on tacrolimus dosing requirements and attainment of target trough concentrations in heart transplant recipients. METHODS:We performed a retrospective cohort analysis of tacrolimus dose, concentration, demographics, CYP3A4/5 genotype, concomitant medications, and biochemical data in heart transplant recipients from December 2020 to August 2023. The primary outcome was the time to first therapeutic trough concentration, compared by CYP3A5 expression status. Secondary outcomes included the tacrolimus dose at target trough and dose-adjusted tacrolimus trough concentration (C0/D). Stepwise multiple regression was performed to account for potential covariates. Moreover, clinical outcomes were assessed at 1-year post-transplantation and compared based on CYP3A5 expression status. RESULTS:Among 33 patients, CYP3A5 expressers (27.3%) required longer to achieve therapeutic trough concentrations (median [Q1, Q3]: expressers: 14 [9.5, 16] days vs. nonexpressers 7.5 [6.0, 11] days; p = 0.0073) and required nearly double the tacrolimus dose to reach target concentrations (10 [5.5, 13] mg/day for expressers vs. 5 [3.3, 5.9] mg/day for nonexpressers; p = 0.0019). Conversely, the C0/D was nearly 2-fold higher in nonexpressers 2.0 [1.6, 3.4] ng/(mL*mg) than expressers (1.1 [0.83, 1.7] ng/(mL*mg); p = 0.0015). Stepwise regression identified route of administration (sublingual vs. oral) at therapeutic trough and initial dose as covariates for all outcomes. All clinical outcomes showed no significant differences based on CYP3A5 expression status, with the exception that poor metabolizers demonstrated higher serum creatinine elevation at 1-week post-transplantation (p = 0.047). CONCLUSION:Our findings highlight the impact of CYP3A5 expresser status on the time needed and dosing requirements to attain tacrolimus therapeutic concentrations in heart transplant recipients, suggesting CYP3A5-guided dosing strategies may improve rapid attainment of therapeutic tacrolimus concentrations.

Background: The Kidney Precision Medicine Project (KPMP) consortium aims to redefine chronic kidney disease (CKD) by integrating clinical, pathological, and molecular tissue data from kidney biopsies. Here, we demonstrate how biopsy data in CKD can clarify disease etiology and contribute to understandings of disease pathophysiology and clinical prognosis. Methods: The KPMP is obtaining research kidney biopsies from individuals with CKD (defined as an estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73m2 and/or albuminuria >30 mg/g creatinine) and diabetes (enrolled as diabetes and CKD or DKD) or hypertension (enrolled as hypertension and CKD or HCKD). A team of kidney pathologists and nephrologists adjudicated the primary clinico-pathological diagnosis for 258 participants with CKD. We compared pathological features and kidney transcriptional signatures between participants with a primary adjudicated diagnosis of diabetic nephropathy and those with other causes of CKD. We developed a model using clinical and biomarker data that predicted the probability of diabetic nephropathy and tested associations of the signature with CKD progression among Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes (n=229). Results: Among 183 participants enrolled as DKD, 102 (56%) had a primary adjudicated clinico-pathologic diagnosis of diabetic nephropathy. Among 75 participants enrolled as HCKD, 42 (56%) had a primary diagnosis of hypertension-associated kidney disease. Those with diabetic nephropathy, compared with other diagnoses, had more severe interstitial fibrosis, tubular atrophy, tubular injury, segmental sclerosis, and severe arteriolar hyalinosis, and single-nucleus and single-cell transcriptional analyses revealed upregulation of immune and inflammatory pathways and downregulation of oxidative phosphorylation. A combination of age, hemoglobin A1c, urine albumin-creatinine ratio, and serum KIM-1 and sTNFR1 predicted a clinico-pathologic diagnosis of diabetic nephropathy in the KPMP (AUC 0.82, 95% CI 0.75-0.89) and was associated with an increased risk of CKD progression among patients with diabetes enrolled in CRIC (HR 1.48 [95% CI 1.27-1.73] per 10% higher predicted probability of diabetic nephropathy). Conclusion: In common presentations of CKD, kidney biopsies may alter a priori impressions, reveal a diversity of diagnosis, structure, and function that is associated with clinical outcomes and can impact therapeutic decisions. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Protocols ### Funding Statement The Kidney Precision Medicine Project (KPMP) is supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through the following grants: U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, and UH3DK114937. Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028,U01DK060980, U01DK060963, U01DK060902 and U24DK060990). In addition, this work was supported in part by: the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award NIH/NCATS UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the National Center for Advancing Translational Sciences (NCATS) component of the National Institutes of Health and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago CTSA UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/NCRR UCSF-CTSI UL1 RR-024131, Department of Internal Medicine, University of New Mexico School of Medicine Albuquerque, NM R01DK119199. We gratefully acknowledge the essential contributions of our patient participants and the support of the American public through their tax dollars. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the University of Washington Institutional Review Board (IRB 20190213). Written informed consent was received from all participants prior to study participation. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Clinical and histopathological data used in the study are available upon request to the KPMP via kpmp.org. Biomarker and molecular data used in the study are available online at kpmp.org.

Importance:Apolipoprotein L1 locus (APOL1) high-risk alleles are associated with incidence of chronic kidney disease (CKD) among people with African ancestry. Few studies have examined the effect of genetic return of results on blood pressure (BP) management and control. Objective:To determine whether providing APOL1 high-risk genotype results to people with hypertension and their clinicians would reduce systolic BP (SBP) and improve CKD screening and diagnosis. Design, Setting, and Participants:From July 1, 2020, to September 30, 2023, adults aged 18 to 70 years with hypertension and self-reported African ancestry were enrolled at 14 institutions and 54 clinical sites across the US. Eligible patients either (1) lacked diagnoses of diabetes and CKD or (2) had a diagnosis of CKD with or without diabetes. Interventions:Participants were randomized to receive APOL1 genotype results immediately (intervention) or 6 months after enrollment (control). Clinical decision support encouraged appropriate CKD screening, diagnosis, and antihypertensive therapy. Main Outcomes and Measures:The primary outcome was change in SBP in individuals with APOL1 high-risk allelles at 3 months, assessed in a modified intention-to-treat analysis. Prespecified per-protocol subgroup analyses included those with uncontrolled BP (baseline SBP ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg), uncontrolled BP while receiving antihypertensive therapy, and CKD at enrollment. Secondary outcomes included urine microalbumin screening and new CKD diagnoses. Results:Of 6754 individuals recruited (mean [SD] age, 55.3 [10.3] years; 4310 women [63.8%]), 954 (14.1%; mean [SD] age, 54.9 [10.0] years; 600 women [62.9%]) had 2 APOL1 risk alleles. At 3 months, there was no difference in SBP between the intervention and control groups (between-group difference, -0.3 mm Hg [95% CI, -2.7 to 2.1 mm Hg]). Among 377 individuals with uncontrolled BP, the mean SBP change was -4.1 mm Hg (95% CI, -7.7 to -0.5 mm Hg) more in the intervention group than the control group (P = .004). SBP improvement was also observed for the intervention in the subgroup of patients with uncontrolled BP receiving antihypertensive therapy (SPB difference, -4.3 mm Hg [95% CI -8.0 to -0.5 mm Hg]; P = .004), but not the CKD subgroup (SPB difference, 0.8 mm Hg [95% CI, -3.0 to 4.5 mm Hg]). Provision of APOL1 genotype led to increased urine microalbumin screening (between-group difference, 17.3% [95% CI, 9.6%-24.9%]; P < .001) and CKD diagnoses (between-group difference, 5.7% [95% CI, 2.2%-9.3%]; P = .002) at 6 months. Conclusions and Relevance:Provision of APOL1 genotype high-risk results to participants and clinicians was not associated with SBP reduction overall. Among the subset of patients with uncontrolled BP, the intervention group had a significant SBP reduction. APOL1 disclosure also increased the rate of CKD screening and diagnosis. Effects of reporting APOL1 genotype merit further investigation among those with uncontrolled BP. Trial Registration:ClinicalTrials.gov Identifier: NCT04191824.

Identifying mechanisms of kidney disease commonly involves comparing diseased samples with healthy reference tissues; however, the effects of variability in tissue procurement, storage, and donor characteristics remain underexplored. In this study, we systematically evaluated 3 reference tissue types — tumor nephrectomy (TN), pretransplant biopsies from living donors (LD), and percutaneous biopsies from healthy control volunteers (HC) — to determine their impact on differential gene expression across 3 diabetic kidney disease states. We observed distinct injury markers, cell state proportions, and gene signatures associated with procurement method, sex, and donor age. Adjustment for these confounding factors significantly influenced pathway analysis results. Specifically, correcting for age and sex eliminated significant enrichment of IFN-γ response when comparing the diabetes mellitus–resilient group and HC group. Processes related to biological aging were enriched in older reference tissues, potentially confounding disease-specific interpretations. Importantly, TNF signaling via NF-κB remained enriched in LD and TN samples relative to HC, even after accounting for confounders. These results underscore the critical importance of selecting appropriate control tissues and rigorously adjusting for confounding variables to reliably discern the molecular mechanisms underlying kidney diseases.

Introduction: Clinical research informatics (CRI) platforms support biomedical discovery by integrating advanced computational tools into research workflows. Emerging technologies such as spatial omics and AI-enabled imaging expand research capabilities but introduce complex interfaces that increase cognitive burden and alter established analytical processes. Traditional usability frameworks identify general usability issues but often miss challenges specific to high-dimensional biomedical data. Methods: We conducted two complementary studies involving 39 participants to evaluate conventional usability heuristics and identify CRI-specific criteria. Study 1 included 19 undergraduates completing interactive tasks, and Study 2 involved 20 clinical professionals completing an asynchronous hierarchical task framework. Observational and interview data were analyzed using deductive coding based on standard usability heuristics and emerging CRI-specific themes. Results: Simultaneous presentation of complex data overlays and analytical tools overwhelmed users, particularly those with limited spatial-omics experience. Participants relied on trial-and-error exploration and struggled with unlabeled tools in data-rich environments. Feedback indicated that users benefit from phased onboarding, contextual guidance, and progressive feature introduction rather than immediate access to all functionality. Discussion: High-dimensional research platforms require domain-specific usability criteria beyond traditional frameworks. We propose three specialized heuristics: Active Parameter Transparency, Point-of-Use Guidance, and Phased Feature Disclosure. These heuristics help developers manage complexity, provide contextual support, and improve accessibility for multidisciplinary research teams.

Epigenetic aging is a hallmark of chronic diseases, arising from sustained injuries and unresolved repairs. To investigate cell-type-specific epigenetic alterations, we built a cross-species single-cell multi-omics atlas of DNA methylomes, chromatin accessibilities, and transcriptomes on healthy, injured (human) and aging (mouse) kidneys. We identified accelerated epigenetic aging dominated by tubular epithelia in diseased kidneys. The pathological state mirrors transcriptional trajectories observed in normal aging, driven by the preferential dysregulation of lineage-specific genes lacking CpG islands. Spatially, these epigenetic changes mapped to pathological niches of failed repair. Co-profiling of single-cell DNA methylation and 3D genome architecture revealed that epithelial repair states in disease undergo significant higher-order genome reorganizations, activating genes associated with renal decline. Our findings demonstrate that epithelial aging is driven by a collapse of 3D chromatin structure and local methylome integrity, which silences cell identity and promotes a non-resolving repair state.

Systemic lupus erythematosus (SLE) shows marked clinical and molecular heterogeneity, yet patient stratification often relies on gene expression signatures lacking multicellular context. Here we construct a transcriptional patient map of SLE by analyzing 1,167 total samples (783 SLE, 384 healthy controls) across different resolutions, including single-cell and bulk blood as well as spatially resolved kidney tissue transcriptomes. Using an unsupervised approach we inferred patient-level transcriptomic immune programs from two independent single-cell RNA sequencing cohorts of peripheral blood mononuclear cells (PBMCs), capturing both differences between SLE and health as well as within-SLE heterogeneity. Specifically, we identified four conserved programs comprising two multicellular inflammatory programs driven by interferon and TNF/NFkB activity across immune cells, and two cell type-specific programs reflecting CD8 T cell cytotoxicity and a CD4 T cell naive-to-effector state. Functional analysis of these programs revealed a rewiring of both cell-to-cell interactions and task allocation across cell types during disease activation. In addition, mapping these programs onto an external longitudinal blood transcriptomic cohort predicted flare risk and identified candidate blood protein biomarkers detectable by proteomics. Finally, we showed that these blood programs were enriched in immune-infiltrated glomerular regions from kidney biopsies of individuals with lupus nephritis using spatially resolved transcriptomic data, thereby linking systemic immune programs to local tissue pathology.

Cannabidiol (CBD) use has increased in America due to its widespread availability. Cannabidiol is metabolized by multiple polymorphic enzymes including CYP3A, CYP2C9, and CYP2C19. We sought to evaluate the genotype-specific adverse events and pharmacokinetic profiles of cannabidiol, 7-OH cannabidiol (an active metabolite), and 7-COOH cannabidiol. We completed a secondary analysis of an open-label, fixed-sequence, single-center study of cannabidiol in 33 healthy subjects. Patients first received a single dose of cannabidiol 5 mg/kg orally with serial plasma concentrations measured. Later, patients were titrated to 5 mg/kg twice daily for 14 days to reach steady state with serial plasma concentrations measured. CYP3A, CYP2C9, and CYP2C19 genotypes were assessed. Pharmacokinetic parameters were calculated by noncompartmental analysis. Diarrhea was observed more frequently in individuals with both CYP3A5 poor metabolism and CYP2C19 intermediate/normal metabolism (39%) compared to individuals with other genotypes (7%, p = 0.0463). Individuals with both CYP3A5 poor metabolism and CYP2C19 intermediate/normal metabolism had increased 7-OH cannabidiol and 7-COOH cannabidiol exposure at steady state. Cannabidiol parent drug exposure varied by CYP2C19 metabolizer status, with lower cannabidiol exposure and parent to metabolite ratios in intermediate metabolizers after single dose (p = 0.014) and at steady state (p = 0.0033). Similar CYP2C19 genotype-specific exposure was observed in an external validation cohort. Minor differences in exposure of cannabidiol and its metabolites were observed between CYP3A5 and CYP2C9 genotype groups. Significant changes in pharmacokinetics were observed between CYP2C9, CYP2C19, and CYP3A5 genotype groups. Future studies should assess whether pharmacogenomics can predict intestinal concentrations of CBD, its metabolites, and diarrhea.

IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide. Clinicians rely on kidney histology and clinical data, such as estimated GFR (eGFR), to obtain a patient's prognosis1. Current clinical tools, including a histologically obtained MEST-C score (histological object- level features) and the International IgAN Prediction Tool (IIgAN PT), estimate the patient's odds of end-stage kidney disease (ESKD). The histopathologic evaluation of the kidney biopsies relies on inter-observer reproducibility and qualitative interpretation. Pathomics, which uses computational image analysis to extract quantitative features, offers an objective alternative to visual scoring and can uncover histologic signatures with prognostic significance. Cluster-Aware enseMblE Learning with pathOMIC featureS (CAMELOMICS) uses non-sclerotic glomeruli pathomic data and clinical data from IgAN patients at a single institution to estimate 5-year eGFR. Our pipeline clusters the glomeruli and trains high-dimensional regression models on the cluster-level information that may better capture objects ' heterogeneity in disease presentation. CAMELOMICS, using biopsy-time data to predict the patients 5-year eGFR, had a R-2 of 0.922 and MSE of 127.282 ml2/min2. The selected features included varying pathomic features between different clusters. For example, Cluster 3 combined luminal space pathomics with hypertension status, aligning with hypertension's effect on glomerular morphology2. Our pipeline shows promise in identifying clinically-relevant clusters and informative pathomic biomarkers of IgAN. Identified pathomics describe glomerular luminal space and mesangial area, guiding further study for their association with eGFR in IgAN patients. CAMELOMICS serves as a proof-of-concept that warrants testing with a larger dataset and could inform a future framework for a predictive model.

BACKGROUND Acute interstitial nephritis (AIN) is a common cause of acute kidney injury (AKI), but the diagnosis may be missed as kidney biopsies are rarely obtained when acute tubular injury (ATI) is suspected.METHODS The Kidney Precision Medicine Project is a cohort study that obtains kidney biopsies from individuals with AKI, which undergo pathologic and molecular interrogation. We compared ATI and AIN cases among the first 60 AKI participants.RESULTS On clinicopathologic adjudication, 30 patients (50%) had a primary adjudicated diagnosis of ATI, 13 (22%) patients had AIN, 9 (15%) had diabetic nephropathy, and 3 (5%) had other conditions. There were increased interstitial white blood cells and tubulitis (P < 0.05 for both) in AIN compared with ATI. Prior to biopsy, the treating clinician suspected ATI in 83% of the cases with adjudicated ATI, while the treating clinician suspected AIN in 54% of the cases with AIN. Tissue transcriptomic signatures showed enrichment of proinflammatory signaling and increased expression of CXCL9, a chemokine induced by IFN-γ, in myeloid cells of participants with AIN. CXCL9 localized to inflammatory infiltration in spatial transcriptomic data.CONCLUSION Adjudication of kidney biopsies revealed distinct pathologic and molecular profiles between ATI and AIN. Kidney biopsy should be considered more frequently in AKI, as AIN is clinically underrecognized.TRIAL REGISTRATION ClinicalTrials.gov NCT04334707.FUNDING National Institute of Diabetes and Digestive and Kidney Diseases grants U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, and UH3DK114937.

Hypoxia drives diabetic kidney disease (DKD) progression through Hypoxia Inducible Factor (HIF) signaling. The kidney’s cellular heterogeneity and complex architecture pose challenges for directly assessing the pharmacologic effects on kidney oxygenation and hypoxia-responsive pathways in vivo, such as treatment with SGLT2 inhibitors (SGLT2i), presumed to impact kidney oxygenation. Using single-cell transcriptional profiling of kidney tissue from youth with type 2 diabetes (T2D) who showed minimal clinical evidence of DKD, we identified cell type enrichment of HIF-regulated genes, findings that replicated in people with later-stage DKD in the Kidney Precision Medicine Project (KPMP). Using conserved transcription factor (TF) binding motifs, higher-order promoter regulatory structures identified potential cooperating TFs that explained the cell type enrichment pattern. From these promoter elements, 7 interconnected regulatory pathways were identified, comprising a network of 237 genes. Analysis of multiome data from reference tissue in KPMP demonstrated that 80% of the network genes resided in accessible chromatin. Expression of network genes increased significantly in the late compared to the early stage DKD and was validated in a hypoxic human organoid model system. Kidney tissue from individuals with T2D treated with SGLT2i demonstrated reversal of the accumulated changes in the HIF network compared to those not treated with SGLT2i. Most high-confidence genes showed concordant differential expression in spatial transcriptomics from individuals with T2D. Hypoxic kidney organoids treated with SGLT2i confirmed these protective effects. Our promoter-anchored HIF regulatory network provides a multi-component read-out that captures disease progression and quantifies therapeutic response to SGLT2i.

Key PointsUsed multiplex protein imaging to elucidate the intratubular cast components and the associated tubular alterations.Identified (Prominin-1/CD133), a dedifferentiation marker, as a major constituent of intratubular casts.Protein components within casts were associated with the injury of the surrounding tubular epithelium.BackgroundKidney intratubular casts are frequently observed in the distal nephron segments of the kidney and have long been regarded as a sign of kidney disease. However, the composition and pathologic significance of intratubular casts have remained understudied.MethodsWe leveraged Hematoxylin and Eosin (H&E) staining to identify intratubular casts along with concurrent codetection by indexing multiplexed spatial protein imaging on human kidney biopsy sections from the Kidney Precision Medicine Project. We also conducted immunoblotting of Prominin-1 (PROM1/CD133) in urine and assessed its levels from publicly available urinary proteomics datasets of the Kidney Precision Medicine Project consortium.ResultsWe analyzed 493 intratubular casts across 42 individuals with kidney disease or healthy controls. We identified PROM1 and insulin-like growth factor binding protein 7 as major constituents of casts (positive staining in 89.0% and 39.1%, respectively). Staining for uromodulin, an established cast component, was present in 86.6%. These components showed variable patterns across disease states. Intratubular casts were predominantly detected in the distal nephron segments, and their presence was associated with a marked loss of sodium-chloride cotransporter and aquaporin-2 expression in the cast-containing tubular epithelium, suggesting underlying injury. The loss of these transporters correlated with protein components within casts, and the presence of intracast PROM1 showed the strongest association, with an odds ratio of 26.7 (95% confidence interval, 13.1 to 54.7). Urinary PROM1 secretion was confirmed by immunoblotting and was greater in patients with AKI compared with healthy controls (P = 0.01).ConclusionsWe identified PROM1, a dedifferentiation and injury marker expressed in epithelial cells, as a novel major constituent of intratubular casts. Our studies suggest that protein composition signature within casts varies with disease state and is associated with tubular injury in distal nephron segments. Our study also suggests that urinary PROM1 may have potential as a biomarker for AKI.