Browsing by Author "Moledina, Dennis G."

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    Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data
    (Oxford University Press, 2022) Moledina, Dennis G.; Eadon, Michael T.; Calderon, Frida; Yamamoto, Yu; Shaw, Melissa; Perazella, Mark A.; Simonov, Michael; Luciano, Randy; Schwantes-An, Tae-Hwi; Moeckel, Gilbert; Kashgarian, Michael; Kuperman, Michael; Obeid, Wassim; Cantley, Lloyd G.; Parikh, Chirag R.; Wilson, F. Perry; Medicine, School of Medicine
    Background: Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. Methods: In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of >150 variables with AIN, including demographics, comorbidities, vital signs and laboratory tests (training set 70%). We used least absolute shrinkage and selection operator methodology to select prebiopsy features associated with AIN. We performed area under the receiver operating characteristics curve (AUC) analysis with internal (held-out test set 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested the change in model performance after the addition of urine biomarkers in the Yale AIN study. Results: We included 393 patients (AIN 22%) in the training set, 158 patients (AIN 27%) in the test set, 1118 patients (AIN 11%) in the validation set and 265 patients (AIN 11%) in the Yale AIN study. Variables in the selected model included serum creatinine {adjusted odds ratio [aOR] 2.31 [95% confidence interval (CI) 1.42-3.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20-0.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91-0.99)] and protein [aOR 0.39 (95% CI 0.23-0.68)]. This model showed an AUC of 0.73 (95% CI 0.64-0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69-0.79)]. The AUC improved to 0.84 (95% CI 0.76-0.91) upon the addition of urine interleukin-9 and tumor necrosis factor-α. Conclusions: We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon the addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN.
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    Plasma proteomics of acute tubular injury
    (Springer Nature, 2024-08-27) Schmidt, Insa M.; Surapaneni, Aditya L.; Zhao, Runqi; Upadhyay, Dhairya; Yeo, Wan-Jin; Schlosser, Pascal; Huynh, Courtney; Srivastava, Anand; Palsson, Ragnar; Kim, Taesoo; Stillman, Isaac E.; Barwinska, Daria; Barasch, Jonathan; Eadon, Michael T.; El-Achkar, Tarek M.; Henderson, Joel; Moledina, Dennis G.; Rosas, Sylvia E.; Claudel, Sophie E.; Verma, Ashish; Wen, Yumeng; Lindenmayer, Maja; Huber, Tobias B.; Parikh, Samir V.; Shapiro, John P.; Rovin, Brad H.; Stanaway, Ian B.; Sathe, Neha A.; Bhatraju, Pavan K.; Coresh, Josef; Kidney Precision Medicine Project; Rhee, Eugene P.; Grams, Morgan E.; Waikar, Sushrut S.; Medicine, School of Medicine
    The kidney tubules constitute two-thirds of the cells of the kidney and account for the majority of the organ’s metabolic energy expenditure. Acute tubular injury (ATI) is observed across various types of kidney diseases and may significantly contribute to progression to kidney failure. Non-invasive biomarkers of ATI may allow for early detection and drug development. Using the SomaScan proteomics platform on 434 patients with biopsy-confirmed kidney disease, we here identify plasma biomarkers associated with ATI severity. We employ regional transcriptomics and proteomics, single-cell RNA sequencing, and pathway analysis to explore biomarker protein and gene expression and enriched biological pathways. Additionally, we examine ATI biomarker associations with acute kidney injury (AKI) in the Kidney Precision Medicine Project (KPMP) (n = 44), the Atherosclerosis Risk in Communities (ARIC) study (n = 4610), and the COVID-19 Host Response and Clinical Outcomes (CHROME) study (n = 268). Our findings indicate 156 plasma proteins significantly linked to ATI with osteopontin, macrophage mannose receptor 1, and tenascin C showing the strongest associations. Pathway analysis highlight immune regulation and organelle stress responses in ATI pathogenesis.