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Using computer-assisted morphometrics of 5-year biopsies to identify biomarkers of late renal allograft loss.

ABSTRACT: The current Banff scoring system was not developed to predict graft loss and may not be ideal for use in clinical trials aimed at improving allograft survival. We hypothesized that scoring histologic features of digitized renal allograft biopsies using a continuous, more objective, computer-assisted morphometric (CAM) system might be more predictive of graft loss. We performed a nested case-control study in kidney transplant recipients with a surveillance biopsy obtained 5 years after transplantation. Patients that developed death-censored graft loss (n = 67) were 2:1 matched on age, gender, and follow-up time to controls with surviving grafts (n = 134). The risk of graft loss was compared between CAM-based models vs a model based on Banff scores. Both Banff and CAM identified chronic lesions associated with graft loss (chronic glomerulopathy, arteriolar hyalinosis, and mesangial expansion). However, the CAM-based models predicted graft loss better than the Banff-based model, both overall (c-statistic 0.754 vs 0.705, P < .001), and in biopsies without chronic glomerulopathy (c-statistic 0.738 vs 0.661, P < .001) where it identified more features predictive of graft loss (% luminal stenosis and % mesangial expansion). Using 5-year renal allograft surveillance biopsies, CAM-based models predict graft loss better than Banff models and might be developed into biomarkers for future clinical trials.

SUBMITTER: Denic A

PROVIDER: S-EPMC8214914 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The survival of transplant kidneys using deceased donors (DD) is inferior to living donors (LD). In this study, we conducted a whole-transcriptome expression analysis of 24 human kidney biopsies paired at 30 minutes and 3 months post-transplantation using DD and LD. The transcriptome profile was found significantly different between two time points regardless of donor types. There were 446 differentially expressed genes (DEGs) between DD and LD at 30 minutes and 146 DEGs at 3 months, with 25 genes common to both time points. These DEGs reflected donor injury and acute immune responses associated with inflammation and cell death as early as at 30 minutes, which could be a precious window of potential intervention. DEGs at 3 months mainly represented the changes of adaptive immunity, immunosuppressive treatment, remodeling or fibrosis via different networks and signaling pathways. The expression levels of 20 highly DEGs involved in kidney diseases and 10 genes dysregulated at 30 minutes were found correlated with renal function and histology at 12 months, suggesting they could be potential biomarkers. These genes were further validated by quantitative polymerase chain reaction (qPCR) in 24 samples analysed by microarray, as well as in a validation cohort of 33 time point unpaired allograft biopsies. This analysis revealed that SERPINA3, SLPI and CBF were up-regulated at 30 minutes in DD compared to LD, while FTCD and TASPN7 were up-regulated at both time points. At 3 months, SERPINA3 was up-regulated in LD, but down-regulated in DD, with increased VCAN and TIMP1, and decreased FOS, in both donors. Taken together, divergent transcriptomic signatures between DD and LD, and changed by the time post-transplantation, might contribute to different allograft survival of two type kidney donors. Some DEGs including FTCD and TASPN7 could be novel biomarkers not only for timely diagnosis, but also for early precise genetic intervention at donor preservation, implantation and post-transplantation, in particular to effectively improve the quality and survival of DD.

Project description:IntroductionThe diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.Methods and analysisThe protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.Ethics and disseminationEthical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.Prospero registration numberCRD42022292261.

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Using computer-assisted morphometrics of 5-year biopsies to identify biomarkers of late renal allograft loss.

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