Ethical complianceWe have complied with all ethical regulations related to this study. All studies included received approval from their respective institutional review boards (IRB) at each participating center which included the Mass General Brigham IRB (BKBC study), the University of North Carolina at Chapel Hill IRB (ARIC Study), the Johns Hopkins University IRB (ARIC Study), the University of Minnesota IRB (ARIC Study), the University of Mississippi Medical Center IRB (ARIC Study), and the University of Washington IRB (KPMP and CHROME studies). Informed consent was obtained from participants of the BKBC, ARIC, and KPMP studies. In the CHROME study, participants were enrolled under an IRB-approved waiver of informed consent. All studies were conducted in accordance with the principles of the Declaration of Helsinki.Study populationsThe BKBC is a prospective, observational cohort study of patients who underwent native kidney biopsy at three tertiary care hospitals in Boston, Massachusetts, including Brigham and Women’s Hospital, Massachusetts General Hospital, and Beth Israel Deaconess Medical Center. The study includes adults ≥18 years of age who underwent a clinically indicated kidney biopsy between September 2006 and October 2018. Exclusion criteria were the inability to provide written consent, severe anemia, pregnancy, and enrollment in competing studies. Details of the study design have been previously described60. Patients provided blood samples on the day of kidney biopsy. For this study, we evaluated 434 participants with available plasma samples. The KPMP is a multicenter prospective cohort study of people with CKD or AKI who undergo a protocol kidney biopsy at study entry as part of the KPMP consortium (https://KPMP.org)61. For plasma proteomics analyses, we evaluated protein measurements of 44 participants (26 with AKI and 18 who provided healthy reference tissue). The ARIC study is a prospective cohort study of individuals recruited from four US communities62. Participants were enrolled between 1987 and 1989, with subsequent visits in 1990–1992 (visit 2), 1993–1995 (visit 3), 1996–1998 (visit 4), 2011–2013 (visit 5), 2016–2017 (visit 6), and 2018–2019 (visit 7), visit 8 (2020), visit 9 (2021–2022), and visit 10 (2023). In this study, we included 4,610 participants from visit 5 who had available plasma proteomic profiling and non-missing covariates at baseline. The CHROME study is a prospective cohort study of critically ill patients admitted to three hospitals affiliated with the University of Washington in Seattle, WA between March 2020 and May 2021. Details of the study design have been previously described63. In brief, patients were eligible if admitted to a medical intensive care unit (ICU) with signs or symptoms of acute respiratory illness. Exclusion criteria included being under 18 years of age, incarcerated, pregnant, or undergoing chronic maintenance hemodialysis. In this study, we included 268 individuals with available SOMAScan plasma proteomic profiling. All studies included in this manuscript received approval from their respective institutional review boards at each participating center.Sample collection and proteomics assaysProteomic profiling was performed on blood samples from the baseline visit of the BKBC, KPMP, and CHROME cohort, as well as visit 5 of the ARIC study. After collection, blood samples were aliquoted and stored at −80 °C. The SOMAscan assay was utilized for proteomic measurements64. This assay employs SOMAmers (selective single-stranded deoxyoligonucleotides) for protein binding and quantifies proteins based on fluorescence intensity, indicative of relative protein concentrations. The ARIC and CHROME plasma samples were analyzed using the SOMAscan 5k platform (approximately 5000 proteins), while the BKBC and KPMP samples were assessed using the SOMAscan 7k platform (approximately 7000 proteins). In the BKBC, 6592 aptamers passed quality control metrics and were included in subsequent analyses; the mean coefficient of variation (CV) on 8 blind duplicate pairs was 4.7%. In ARIC, KPMP, and the CHROME cohort, we evaluated only those proteins that were significantly associated with ATI in the BKBC (156 unique proteins). For ARIC visit 5, the mean Bland Altman coefficient of variation was 6.6% from 26 samples in blind triplicate. In KPMP, the mean CVs on 2 sets of 4 blind duplicates was 4.9%. For all studies, protein aptamers were log2-transformed and winsorized at mean±5×SD and adaptive normalization was performed by maximum likelihood as previously described64.Histopathologic outcomeIn the BKBC, kidney biopsy specimens were adjudicated under light microscopy by two experienced kidney pathologists who provided semiquantitative scores of ATI scored from 0 to 3 reflecting none, mild, moderate, and severe lesion severity. Methods to evaluate and score histopathologic lesion severity were previously described in detail60. The weighted kappa statistic (95% CI) from 26 randomly selected biopsies for repeat review months after the initial scoring for ATI was 0.67 (0.45–0.89)60. All participants’ charts were reviewed alongside histopathologic evaluations to provide the final primary clinicopathologic diagnosis.Outcome of acute kidney injuryIn KPMP, patients eligible for percutaneous kidney biopsy for AKI must have elevated serum creatinine that is either sustained or accompanied by evidence of parenchymal injury. Detailed inclusion criteria for AKI biopsies were previously described in detail61. In ARIC, individuals included in this study were free of AKI at baseline and followed prospectively for incident AKI. Incident cases of AKI were identified by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes 584.5 to 584.9 and Tenth Revision, Clinical Modification (ICD-10-CM) codes N17.0 to N17.9 or a 50% increase from outpatient serum creatinine during hospitalization. ICD codes were retrieved from hospital discharge billing and death certificates65. In the CHROME Cohort, the primary outcome was severe AKI within 7 days, defined by either initiation of kidney replacement therapy (KRT) or a doubling of serum creatinine from ICU admission63. In the ARIC and CHROME studies, blood samples were collected prior to the diagnosis of AKI or severe AKI, respectively.CovariatesIn the BKBC, detailed patient information was collected at the biopsy visit, including demographics, medical history, medication lists, and pertinent laboratory data and stored using REDCap electronic data capture tools hosted at Partners Health Care. We obtained serum creatinine (SCr) from the electronic medical record (EMR) on the day of biopsy. In participants for whom this was unavailable, we measured SCr in available blood samples collected on the day of the biopsy. We obtained spot urine protein-to-creatinine ratio (UPCR) or urine albumin-to-creatinine ratio (UACR) from the date of kidney biopsy to 3 months before biopsy from the EMR. If both were available, the UACR was used. If a participant did not have any of these values, we measured urine albumin-to-creatinine ratio from urine collected on the day of the kidney biopsy. SCr and urine creatinine were measured using a Jaffe-based method and urine albumin was measured by an immunoturbidometric method. The creatinine-based Chronic Kidney Disease Epidemiology Collaboration 2021 equation was used to calculate the eGFR66. In ARIC, covariates included age, sex, self-reported race, systolic blood pressure (SBP), UACR, smoking status, diabetes (fasting glucose of ≥126 mg/dL or non-fasting glucose level of ≥200 mg/dL, self-reported history of diabetes diagnosed by a physician, or use of medications for diabetes), hypertension (SBP ≥ 140 mm Hg and DBP ≥ 90 mm Hg, or use of medication for high BP), and eGFR (calculated using the CKD-EPI 2021 equation, which takes both serum creatinine and cystatin C into account)66. Serum creatinine was measured using a Roche enzymatic method (visit 5) and serum cystatin C was measured using the Roche Cobas 6000 chemistry analyzer. The UACR was calculated using urine albumin and creatinine (measured using an immunoturbidometric method on the ProSpec nephelometric analyzer and the Roche enzymatic method, respectively). In CHROME, covariates including age, sex, and COVID-19 status were extracted from the EMR.Regional proteomicsWe investigated the expression of ATI biomarker proteins in regional tissue proteomics from KPMP kidney biopsy samples (https://atlas.kpmp.org/explorer/regionalpro; access date: April 20, 2024). Detailed protocols and dataset information are available at https://www.kpmp.org/help-docs/technologies. In brief, kidney tissue was laser-microdissected to isolate glomerular and tubulointerstitial compartments. Following protein extraction from targeted tissue sections, proteins were analyzed using high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) for comprehensive identification and quantification. The study included tissue samples from 31 individuals (12 with AKI, 14 with CKD, and 5 healthy controls). We analyzed and compared the expression profiles of ATI biomarkers between the tubulointerstitial and glomerular compartments across all participants, and separately, evaluated differences in tubulointerstitial protein expression between healthy controls and those with AKI.Kidney gene expression analysisTo map biomarker proteins to gene expression data, we investigated the expression of ATI biomarker-corresponding genes using regional transcriptomics and scRNA-seq data from KPMP (https://atlas.kpmp.org/explorer; access date: August 1, 2023). Tissue samples used for regional transcriptomics were drawn from living donor biopsy participants and used to compare expression (Fold Change) of our ATI biomarker genes of interest in the tubulointerstitium (n = 36; 9 healthy reference, 22 CKD, 5 AKI). Tissue samples analyzed using scRNA-seq were drawn from 47 participants (12 with AKI, 15 with CKD, and 20 healthy controls) and used to test for differential gene expression in kidney cell types. Details on these datasets have been described previously67,68.Pathway analysisTo obtain basic functional information on biomarker proteins that were significantly associated with ATI severity and to investigate potentially relevant biological pathways, we applied Pathway Enrichment Analysis using gene sets obtained from publicly available databases including Gene Ontology, KEGG, and Reactome69,70. We ranked proteins based on their strength of association with ATI. We then calculated normalized enrichment scores to identify pathways with significant overrepresentation of ATI biomarkers. The Benjamini–Hochberg approach was employed to account for multiple testings and used to rank pathways based on the obtained p-value. Analyses were performed using the R package fgsea.Statistical analysisWe summarized descriptive statistics as count with percentages for categorical variables and mean ± standard deviation or median with interquartile range for continuous variables. For skewed data distributions, we performed logarithmic transformation as appropriate. In the BKBC, multivariable linear regression models were used to assess associations of each plasma biomarker protein with ATI severity. In these models, the ATI severity score was used as the independent variable and each log2-transformed biomarker as the dependent variable. The adjusted model included the covariates age, race, sex, and eGFR. A prespecified α level of 7.58×10−6 set by Bonferroni correction (0.05/6592 proteins) was used to determine statistical significance. In ARIC, we used Cox proportional hazards models to test associations between the ATI biomarkers identified in the BKBC and the outcome of incident AKI. Models were adjusted for age, sex, self-reported race, SBP, smoking status, diabetes, hypertension UACR, and eGFR. In KPMP, we compared plasma levels of the ATI biomarkers in individuals with AKI and healthy controls using Analysis of Variance (ANOVA). In the CHROME cohort, we explored associations between the ATI biomarkers and severe AKI within 7 days of ICU admission using logistic regression models adjusted for age, sex, and COVID-19 status. In KPMP, we used a Bonferroni-corrected significance threshold of p < 3.21 × 10−4 (0.05/156 proteins). In ARIC and CHROME, we adjusted this threshold to p < 4.1 × 10−4 (0.05/122 proteins) and p < 3.79 × 10−4 (0.05/132 proteins), respectively, reflecting that only measurements of 122 (ARIC) and 132 (CHROME) of the 156 ATI biomarkers were available. In other analyses, we considered a two-sided p-value < 0.05 statistically significant. Statistical analyses were performed using R Version 3.6.1 (R Foundation for Statistical Computing, Vienna, Austria) and STATA 18.0 (STATACorp, College Station, TX).Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
