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. 2022 Nov 7;26(1):344.
doi: 10.1186/s13054-022-04193-9.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

Collaborators, Affiliations

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

Alexis Piedrafita et al. Crit Care. .

Erratum in

Abstract

Background: The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways.

Methods: In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores.

Results: A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (< 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p < 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p < 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p < 0.001).

Conclusions: An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

Keywords: Acute kidney injury; Cardiac surgery; Intensive care unit; Prediction; Urinary peptidomics.

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Conflict of interest statement

Justyna Siwy and Jochen Metzger are the employee and former employee at Mosaiques diagnostics GmbH (Hannover, Germany), respectively. Harald Mischak is the CEO of Mosaiques diagnostics GmbH. The other authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Patient flowchart for the identification and validation of a predictive AKI urinary peptide signature. Three independent cohorts were used: a derivation CBP surgery cohort (n = 509), a validation CBP surgery cohort (n = 661)—both recruited in the University Hospital of Toulouse (France), but during different time periods—and an external ICU cohort (external ICU validation multicenter cohort [25], n = 2087). Sixty-three patients from the derivation and 181 from the validation CBP surgery cohorts were excluded because of missing urine samples or failure of the urinary peptidome analysis pipeline. Five hundred eighteen patients from the external ICU validation cohort were excluded because of missing urine samples, failure of the urinary peptidome analysis pipeline, or missing information with respect to the development or presence of AKI. CBP surgery, cardiac bypass surgery; ICU, intensive care unit
Fig. 2
Fig. 2
AKI prediction in CBP surgery patients based on clinical pre- and per-operative features. A. ROC curves and corresponding AUROC [95% confidence interval] of published clinical scores for the prediction of AKI (KDIGO 1, 2, or 3) in the CBP surgery validation cohort. B. Parameters and associated coefficients of a local clinical model defined in the derivation cohort. The clinical score is calculated as follows: logit(p(AKI)) = Ca x Age (Years) + Ch x Hypertension (0/1) + Cg x eGFR (mL/min.1.73m2) + Ck x Kidney Graft Recipient (0/1) + Cs x Valvular Surgery (0/1) + Cc x CPB Duration (min) + I. C. Association of the local clinical score with the development of AKI in the validation cohort (all stages, left; according to KDIGO stages, right). * p < 0.05; ** p < 0.01; *** p < 0.001. D. ROC curves and corresponding AUROC [95% confidence interval] of the local clinical score compared to baseline eGFR for the prediction of all stages of AKI in the validation cohort. The AUROCs of the local clinical score and eGFR were significantly different (Delong test; p value = 0.007). ROC, receiver operating characteristic curve; AUROC, area under the receiver operating characteristic curve; CI, confidence interval; AKI, acute kidney injury; CBP, cardiac bypass; eGFR, estimated glomerular filtration rate
Fig. 3
Fig. 3
Urinary peptidome changes during CBP-surgery-induced AKI. A. Peptides with significantly different abundances in AKI patients. The volcano plot displays log10-transformed and adjusted univariate p values as a function of log2-transformed fold changes of urinary peptides amplitudes. Sequenced peptides with differential abundances (significant after Benjamini–Hochberg adjusted Wilcoxon univariate testing (p < 0.05)) are represented in color (more abundant: red; less abundant: blue). B. Peptides with significantly different abundances (in respect to distribution frequency and amplitude signal) derived from non-collagenic proteins (y position), log2 fold changes (x position), and –log10 adjusted p values (color scale). The brown dashed line represents log2 fold change = 0. Peptides are ranked according to their functional role during the AKI progression (inflammation, epithelium, blood component, other non-collagenic proteins). C. Peptides with significantly different abundances derived from collagenic proteins (y position), log2 fold changes (x position), and –log10 adjusted p values (color scale). The brown dashed line represents log2 fold change = 0. D. Collagenic and non-collagenic proteins-derived peptides proportions among top differential peptides according to the Benjamini–Hochberg adjusted p value ranking. The red line corresponds to non-collagen and the blue line to collagen-derived peptides
Fig. 4
Fig. 4
Independent validation of the predictive value of the urinary peptide signature for early AKI diagnosis after CBP surgery. A Association of the urinary peptide-based score with the development of AKI (all stages, left; according to KDIGO stages, right) in the CBP surgery validation cohort (n = 480). *** p < 0.001. B Spline plot of the association between the peptide-based score and the risk of developing AKI. A univariate logistic generalized additive model was used. The black line indicates the estimated risk of AKI with respective 95% confidence intervals. The spikes show the distribution of the peptide-based scores. C ROC curves with corresponding AUROC and 95% confidence intervals for the 204 peptides-based score, the local clinical score (and reference urinary AKI biomarkers NGAL and TIMP2*IGFBP7 for the prediction of AKI (all stages) in the validation cohort. D Odds ratios and corresponding 95% confidence intervals in the validation cohort using a multivariate logistic regression model including the local clinical score, reference urinary AKI biomarkers NGAL and TIMP2*IGFBP7, the 204 peptides-based score or a combination of the local clinical and peptide-based scores as a qualitative value according to the selected threshold (optimal Youden index in the derivation cohort). ROC, receiver operating characteristics curve; AUROC, area under the receiver operating characteristics curve; CI, confidence interval; Thr, threshold
Fig. 5
Fig. 5
External validation of the predictive value of the urinary peptide signature for AKI diagnosis in an intensive care unit (ICU) cohort of 1,569 patients. A Peptide-based score according to AKI status (all stages (left part); according to KDIGO stages, right part) in the external ICU validation cohort. *** p < 0.001. B Spline plot of the association between the peptide-based score and the risk of developing AKI in the external ICU cohort. A univariate logistic generalized additive model was used. The black line indicates the estimated risk of AKI with respective 95% confidence intervals. The spikes show the distribution of the peptidome-based scores. C ROC curves and corresponding AUROC 95% confidence intervals of the 204 peptides-based score in the external ICU validation cohort, according to the time until AKI diagnosis. D ROC curves and corresponding AUROC 95% confidence intervals of the 204 peptides-based score and reference urinary biomarker NGAL in the external ICU validation cohort

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