Biomarker-Driven Subphenotypes Of Acute Kidney Injury

Many blood and urine biomarkers have been shown to predict the development of AKI, such as plasma or urine neutrophil gelatinase-associated lipase (NGAL), urinary kidney injury molecule 1 (KIM-1), urinary tissue metalloproteinases Inhibitor-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7), biomarkers were also used to identify AKI subphenotypes. One of the biomarker-derived AKI subphenotypes is subclinical AKI. Subclinical AKI refers to the clinical situation in which there is structural kidney damage without elevated creatinine. Studies have shown that elevated urinary NGAL or KIM-1 without elevated creatinine subsequently heralds the onset of KRT or in-hospital mortality.

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In a cardiac surgery cohort, elevated urinary interleukin-18 and KIM-1 were independently associated with increased 3-year mortality in patients without AKI. More recently, surrogate plasma and urine biomarkers have again demonstrated a progressive trend toward increased poor outcomes in patients with subclinical AKI compared with those with established AKI. This association underscores the problem with creatinine, that renal damage can occur even without a significant increase in creatinine, and correlates with patient-centered outcomes. Whether this damage is limited to the kidney, as suggested by kidney-specific biomarkers, or whether it also includes other organs, such as the endothelium, needs further elucidation.

Biomarkers were also found to predict the irreversibility of AKI. In an analysis of 331 patients, the C-C motif chemokine ligand 14 (CCL14) was found to stratify patients according to the likelihood of persistent severe AKI. These results suggest that in the future if validated, the biomarker could help divide patients into two trials looking for ways to improve AKI recovery and ensure adequate follow-up of renal function.

Another approach to deducing AKI subphenotypes is to use unsupervised cluster analysis such as LCA. Bhatraju and colleagues applied LCA to 29 different variables in two groups of critically ill patients with AKI. They identified two AKI subphenotypes (AKI-SP1 and AKI-SP2) with distinct clinical features associated with clinical outcomes, even after adjusting for disease and AKI severity. They also found heterogeneity in treatment effect in a post hoc analysis of patients with sepsis-associated AKI in the Vaspressin and Septic Shock Trial (VASST), with the early addition of vasopressin and norepinephrine in patients with AKI-SP1 Treatment mortality was lower. At the same time, there was no difference in mortality among patients with AKI-SP2.

These findings contrast with the overall results of the VASST trial, which showed no mortality benefit with the early addition of vasopressin to shock. The findings also highlight the importance of identifying biologically distinct AKI subphenotypes, as they may respond differently to treatment in clinical trials. Other groups have also applied LCA to ICU cohorts with AKI and identified two AKI subphenotypes. Wiersema and colleagues studied 301 patients with sepsis-associated AKI and applied LCA to 30 different variables, including 12 variables involving systemic inflammation and endothelial dysfunction. They identified two AKI subphenotypes with distinct clinical features and outcomes.

The heterogeneity of AKI clinical syndromes may also limit the identification of novel mechanisms and genetic risks of AKI. A 2009 systematic review concluded that genetic research on AKI has been inconsistent and contradictory. One reason was the lack of consensus on the definition of AKI when this report was published. For example, the authors identified five different definitions of AKI in previous genetic studies. Although the KDIGO definition of AKI is now widely used, AKI remains a syndromic diagnosis that may be too heterogeneous to identify genetic risk factors. Thus, the heterogeneity in the clinical definition of AKI can be overcome by utilizing AKI subphenotypes. Bhatraju et al utilized previously described AKI subphenotypes to assess genetic risk for AKI development.

They performed a targeted genetic study to identify single nucleotide polymorphisms (SNPs) within 50 kb of the AKI-SP2-associated ANGPT1, ANGPT2, and TNFRSF1A genes in 452 subjects. They demonstrated that an SNP near ANGPT2 (rs2920656) was associated with a reduced risk of AKI-SP2 and that this SNP was associated with reduced plasma concentrations of angiopoietin-2 (Ang-2). These findings support a pathophysiological role for Ang-2 in AKI and also support its use as a therapeutic target. In addition, genetic susceptibility may be concentrated in certain populations, such as another genetic polymorphism associated with increased Ang-2 concentrations in infectious ARDS in subjects of European ancestry. Several studies in other fields, such as diabetes and asthma, also use disease subphenotypes to discover new genetic variants associated with the disease.

The work accomplished to date in identifying biomarker-based AKI subphenotypes raises the question of whether these AKI subtypes are unique to AKI or are found in other clinical syndromes, ARDS, or sepsis. As in ARDS, sepsis, and other diseases, circulating biomarkers of endothelial activation and inflammation are associated with AKI, but not specifically. These findings suggest underlying similarities between critical illness syndromes and common pathophysiological mechanisms among diseases. Some researchers have suggested transitioning from disease-specific models to cross-disease "treatable trait" models. Potential examples in cancer include immunomodulatory therapies that are not specific to one type of cancer but are effective in many cancers, with high PD-1 expression on tumor cells. As another example, mepolizumab, a monoclonal antibody that blocks interleukin-5 signaling, can be used to treat eosinophilic lung disease regardless of whether the patient has asthma or COPD.

Subphenotypes of other ICU syndromes

Similar to AKI, there is great interest in defining subtypes of other forms of critical illness, including sepsis and ARDS. In the context of ARDS, both subtypes can be reliably identified in clinical trial populations. The hyperinflammatory subphenotype is characterized by high levels of proinflammatory biomarkers, including interleukin-6, interleukin-8, soluble tumor necrosis factor receptor-1, and plasminogen activator inhibitor- 1. Higher mortality rate. In contrast, the low-inflammatory subphenotype was associated with higher levels of protein C and bicarbonate and higher systolic blood pressure.

Patients with inflammatory and inflammatory subtypes respond differently to multiple treatments, including fluid management, positive end-expiratory pressure, and statins. For example, in a reanalysis of the HARP-2 trial, simvastatin improved survival in patients with the hyperinflammatory subtype. Both subtypes have also been identified in more generalized prospective observational cohort studies. Finally, these subphenotypes could be reliably identified using a parsimonious subset of the three biomarkers, raising the potential for near real-time point-of-care biomarker measurements and clinical trial predictive enrichment.

In an analysis that identified subphenotypes using only biomarkers, the 'non-inflammatory' and 'reactive' subphenotypes were identified and associated with a number of signaling pathways in a whole blood transcriptomic study. These subphenotypes share many of the same biomarkers as the 'hyper-inflammation' and 'hypo-inflammation' subphenotypes and underscore the notion that hyper-inflammatory states are associated with a wide range of adverse outcomes in a variety of serious diseases and could be used in clinical trials targeting specific therapeutic interventions.

Subphenotypes of sepsis in children and adults have been studied. Here, the number of subphenotypes varied more than in the ARDS study, possibly because the overall population studied was larger and thus able to derive more subphenotypes. Nonetheless, some important themes emerged from these studies. First, large-scale clinical subphenotype studies are possible using data from electronic health records due to the large number of patients with sepsis admitted to the hospital. Some of these studies focused on the trajectories of a limited number of variables (such as temperature), while others focused on clustering patients based on the data available at the time of presentation.

These subphenotypes vary according to clinical characteristics and mortality; however, these subphenotypes may not add significantly to our biological insights into sepsis. In some cases, subsequent analyzes linked inflammatory responses to clinical subphenotypes, including temperature-based subphenotypes. Second, subphenotypes that respond differently to treatment can be identified in children and adults. For example, a reanalysis of clinical trials of interleukin-1 receptor antagonists has shown different results for different subphenotypes. However, these studies also emphasize the importance of studying children and adults and the need for replication. That is, when analyzing the effect of steroids on sepsis outcomes, while a subcategory of children had higher mortality rates when treated with steroids, adults did not. Clearly, more work is needed to better define and understand sepsis subclasses, as underlying comorbidities (such as immunosuppressive states) increase the risk of infection and may also alter the host response to infection, which may have additional Complexity. Finally, numerous studies linking sepsis subphenotypes to gene expression patterns and host responses are critical for identifying precise treatments for sepsis. However, more work is clearly needed to define subtypes of this complex syndrome.

Current and future impact

To date, the vast majority of AKI subphenotyping efforts have focused on differences in prognosis, specifically identifying which AKI patients are likely to experience adverse outcomes, including death. For example, several groups have now identified clinical and/or biomarker-based subphenotypes of sepsis-associated AKI in adults and children that are associated with various interesting outcomes, including increased likelihood of requiring KRT, renal function Non-recovery, and mortality. These subphenotype strategies could be applied at the bedside to inform clinical care (i.e. early consideration of KRT in high-risk patients) and, perhaps more importantly, guide risk-informed patients into future treatment trials (i.e. Only high-risk patients were included in the trial).

Unfortunately, to date, most of these tools have failed to reach the bedside, likely due to a lack of large-scale validation, as well as issues of timely availability of the included biomarkers, the overall complexity of genetic testing, and subphenotype models. These are issues that need to be addressed. Regarding the identification of ARDS subphenotypes, ongoing projects are already looking at feasible solutions for identifying subphenotypes at the point of care using machine learning or point-of-care biomarker analysis (NCT04009330).

Application of two distinct subtypes of AKI (AKI-SP1 and AKI-SP2) to a cohort of patients from the VASST trial showed that patients with AKI-SP1 who had less endothelial activation and inflammation than AKI-SP2 were less likely to experience vasopressin treatment after 28 Day and 90-day mortality improved, although renal recovery rates did not differ. While there is no direct link between the underlying biology of AKI-SP1 patients and their response to vasopressin, ongoing molecular subphenotyping of AKI using such strategies requires substantial work to apply these tools to the patient's bedside. In particular, the ability to rapidly identify subphenotypes in patients with AKI remains the most important hurdle.

in conclusion

Subphenotypes help differentiates all AKI patients among patients with different pathophysiological mechanisms, disease severity, and prognosis. Cluster analysis including data from unconventional measured biomarkers revealed subphenotypes with potentially distinct pathophysiology in terms of inflammatory responses, opening avenues for research into targeted therapies. More studies are needed to validate the AKI subphenotypes identified and to develop methods to rapidly differentiate between the various subphenotypes at the bedside.

for more information:ali.ma@wecistanche.com