Molecular and clinical signatures in Acute Kidney Injury define distinct subphenotypes that associate with death, kidney, and cardiovascular events
Abstract Introduction AKI is a heterogeneous syndrome defined via serum creatinine and urine output criteria. However, these markers are insufficient to capture the biological complexity of AKI and not necessarily inform on future risk of kidney and clinical events.Methods Data from ASSESS-AKI was obtained and analyzed to uncover different clinical and biological signatures within AKI. We utilized a set of unsupervised machine learning algorithms incorporating a comprehensive panel of systemic and organ-specific biomarkers of inflammation, injury, and repair/health integrated into electronic data. Furthermore, the association of these novel biomarker-enriched subphenotypes with kidney and cardiovascular events and death was determined. Clinical and biomarker concentration differences among subphenotypes were evaluated via classic statistics. Kaplan-Meier and cumulative incidence curves were obtained to evaluate longitudinal outcomes.Results Among 1538 patients from ASSESS-AKI, we included 748 AKI patients in the analysis. The median follow-up time was 4.8 years. We discovered 4 subphenotypes via unsupervised learning. Patients with AKI subphenotype 1 (‘injury’ cluster) were older (mean age ± SD): 71.2 ± 9.4 (p<0.001), with high ICU admission rates (93.9%, p<0.001) and highly prevalent cardiovascular disease (71.8%, p<0.001). They were characterized by the highest levels of KIM-1, troponin T, and ST2 compared to other clusters (P<0.001). AKI subphenotype 2 (‘benign’ cluster) is comprised of relatively young individuals with the lowest prevalence of comorbidities and highest levels of systemic anti-inflammatory makers (IL-13). AKI Subphenotype 3 (‘chronic inflammation and low injury’) comprised patients with markedly high pro-BNP, TNFR1, and TNFR2 concentrations while presenting low concentrations of KIM-1 and NGAL. Patients with AKI subphenotype 4 (‘inflammation-injury’) were predominantly critically ill individuals with the highest prevalence of sepsis and stage 3 AKI. They had the highest systemic (IL-1B, CRP, IL-8) and kidney inflammatory biomarker activity (YKL-40, MCP-1) as well as high kidney injury levels (NGAL, KIM-1). AKI subphenotype 3 and 4 were independently associated with a higher risk of death compared to subphenotype 2. Moreover, subphenotype 3 was independently associated with CKD outcomes and CVD events.Conclusion We discovered four clinically meaningful AKI subphenotypes with statistical differences in biomarker composites that associate with longitudinal risks of adverse clinical events. Our approach is a novel look at the potential mechanisms underlying AKI and the putative role of biomarkers investigation..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 14. Nov. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Vasquez-Rios, George [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2021.12.14.21267738 |
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| PPN (Katalog-ID): |
XBI033237166 |
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| 520 | |a Abstract Introduction AKI is a heterogeneous syndrome defined via serum creatinine and urine output criteria. However, these markers are insufficient to capture the biological complexity of AKI and not necessarily inform on future risk of kidney and clinical events.Methods Data from ASSESS-AKI was obtained and analyzed to uncover different clinical and biological signatures within AKI. We utilized a set of unsupervised machine learning algorithms incorporating a comprehensive panel of systemic and organ-specific biomarkers of inflammation, injury, and repair/health integrated into electronic data. Furthermore, the association of these novel biomarker-enriched subphenotypes with kidney and cardiovascular events and death was determined. Clinical and biomarker concentration differences among subphenotypes were evaluated via classic statistics. Kaplan-Meier and cumulative incidence curves were obtained to evaluate longitudinal outcomes.Results Among 1538 patients from ASSESS-AKI, we included 748 AKI patients in the analysis. The median follow-up time was 4.8 years. We discovered 4 subphenotypes via unsupervised learning. Patients with AKI subphenotype 1 (‘injury’ cluster) were older (mean age ± SD): 71.2 ± 9.4 (p<0.001), with high ICU admission rates (93.9%, p<0.001) and highly prevalent cardiovascular disease (71.8%, p<0.001). They were characterized by the highest levels of KIM-1, troponin T, and ST2 compared to other clusters (P<0.001). AKI subphenotype 2 (‘benign’ cluster) is comprised of relatively young individuals with the lowest prevalence of comorbidities and highest levels of systemic anti-inflammatory makers (IL-13). AKI Subphenotype 3 (‘chronic inflammation and low injury’) comprised patients with markedly high pro-BNP, TNFR1, and TNFR2 concentrations while presenting low concentrations of KIM-1 and NGAL. Patients with AKI subphenotype 4 (‘inflammation-injury’) were predominantly critically ill individuals with the highest prevalence of sepsis and stage 3 AKI. They had the highest systemic (IL-1B, CRP, IL-8) and kidney inflammatory biomarker activity (YKL-40, MCP-1) as well as high kidney injury levels (NGAL, KIM-1). AKI subphenotype 3 and 4 were independently associated with a higher risk of death compared to subphenotype 2. Moreover, subphenotype 3 was independently associated with CKD outcomes and CVD events.Conclusion We discovered four clinically meaningful AKI subphenotypes with statistical differences in biomarker composites that associate with longitudinal risks of adverse clinical events. Our approach is a novel look at the potential mechanisms underlying AKI and the putative role of biomarkers investigation. | ||
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| 700 | 1 | |a Nadkarni, Girish N. |4 aut | |
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