Proteomics for the Discovery of Clinical Delirium Biomarkers: A Systematic Review of Major Studies

Protein Alterations in Patients with Delirium after Cardiac Surgery: An Exploratory Case-Control Substudy of the VISION Cardiac Surgery Biobank

BACKGROUND

Delirium is an acute state of confusion associated with adverse postoperative outcomes. Delirium is diagnosed clinically using screening tools; most cases go undetected. Identifying a delirium biomarker would allow for accurate diagnosis, application of therapies, and insight into causal pathways. To agnostically discover novel biomarkers of delirium, a case-control substudy was conducted using the Vascular Events in Surgery Patients Cohort Evaluation (VISION) Cardiac Surgery Biobank. The objective was to identify candidate biomarkers to investigate in future studies.

METHODS

The study gathered a convenience sample of 30 patients with delirium on postoperative day 1 matched to 30 controls by age, sex, ethnicity, center, and cardiopulmonary bypass time. The Olink Explore 3K platform was used to identify blood protein alterations on postoperative day 3. Protein concentrations were expressed as normalized protein expression units (log 2 fold scale). Protein expression was compared between cases and controls using a paired t test and identified significantly different biomarkers based on a false discovery rate-adjusted P value of less than 0.05.

RESULTS

Of 2,865 unique serum proteins, 26 (0.9%) were significantly associated with delirium status; all were elevated in cases versus controls at a false discovery rate of less than 0.05. Pathway analysis identified calcium-release channel activity ( Padj = 0.02) and GTP-binding ( Padj = 0.005) functions as characteristic of proteins associated with delirium. The top three differentially expressed biomarkers were FKBP1B ( Padj = 0.003), C2CD2L ( Padj = 0.004), and RAB6B ( Padj = 0.004). The inflammatory biomarker interleukin-8 (CXCL8; mean difference = 2.36; P = 3.6 × 10- 4 ) was also associated with delirium.

CONCLUSIONS

The study identified 26 biomarkers significantly associated with delirium; all are novel except for interleukin-8. An association between delirium and recognized neuroinflammatory proteins or markers of brain injury was not identifed, which supports using biomarkers to differentiate between delirium and other neurologic conditions. While exploratory, the study's findings support using biomarkers to diagnose postoperative delirium and validate using agnostic screens to identify potential delirium biomarkers.

A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study

BACKGROUND

Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.

OBJECTIVE

This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients.

METHODS

In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients.

RESULTS

In the development cohort, 201 out of 751 (33.5%) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method.

CONCLUSIONS

A real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients.

Postoperative Delirium and the Older Adult: Untangling the Confusion

Postoperative delirium is one of the most prevalent postoperative complications, affecting mostly older adults. Its incidence is expected to rise because of surgical advances, shifting demographics, and increased life expectancy. Although an acute alteration in brain function, postoperative delirium is associated with adverse outcomes, including progressive cognitive decline and dementia, that place significant burdens on patients' lives and healthcare systems. This has prompted efforts to understand the mechanisms of postoperative delirium to provide effective prevention and treatment. There are multiple mechanisms involved in the etiology of postoperative delirium that share similarities with the physiological changes associated with the aging brain. In addition, older patients often have multiple comorbidities including increased cognitive impairment that is also implicated in the genesis of delirium. These tangled connections pinpointed a shift toward creation of a holistic model of the pathophysiology of postoperative delirium. Scientific advancements integrating clinical risk factors, possible postoperative delirium biomarkers, genetic features, digital platforms, and other biotechnical and information technological innovations, will become available in the near future. Advances in artificial intelligence, for example, will aggregate cognitive testing platforms with patient-specific postoperative delirium risk stratification studies, panels of serum and cerebrospinal fluid molecules, electroencephalogram signatures, and gut microbiome features, along with the integration of novel polygenetic variants of sleep and cognition. These advances will allow for the enrollment of high-risk patients into prevention programs and help uncover new pharmacologic targets.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Butyrylcholinesterase activity in patients with postoperative delirium after cardiothoracic surgery or percutaneous valve replacement- an observational interdisciplinary cohort study

BACKGROUND AND OBJECTIVES

Postoperative delirium is a frequent and severe complication after cardiac surgery. Activity of butyrylcholinesterase (BChE) has been discussed controversially regarding a possible role in its development. This study aimed to investigate the relevance of BChE activity as a biomarker for postoperative delirium after cardiac surgery or percutaneous valve replacement.

METHODS

A total of 237 patients who received elective cardiothoracic surgery or percutaneous valve replacement at a tertiary care centre were admitted preoperatively. These patients were tested with the Montreal Cognitive Assessment investigating cognitive deficits, and assessed for postoperative delirium twice daily for three days via the 3D-CAM or the CAM-ICU, depending on their level of consciousness. BChE activity was measured at three defined time points before and after surgery.

RESULTS

Postoperative delirium occurred in 39.7% of patients (n = 94). Univariate analysis showed an association of pre- and postoperative BChE activity with its occurrence (p = 0.037, p = 0.001). There was no association of postoperative delirium and the decline in BChE activity (pre- to postoperative, p = 0.327). Multivariable analysis including either preoperative or postoperative BChE activity as well as age, MoCA, type 2 diabetes mellitus, coronary heart disease, type of surgery and intraoperative administration of red-cell concentrates was performed. Neither preoperative nor postoperative BChE activity was independently associated with the occurrence of postoperative delirium (p = 0.086, p = 0.484). Preoperative BChE activity was lower in older patients (B = -12.38 (95% CI: -21.94 to -2.83), p = 0.011), and in those with a history of stroke (B = -516.173 (95% CI: -893.927 to -138.420), p = 0.008) or alcohol abuse (B = -451.47 (95% CI: -868.38 to -34.55), p = 0.034). Lower postoperative BChE activity was independently associated with longer procedures (B = -461.90 (95% CI: -166.34 to -757.46), p = 0.002), use of cardiopulmonary bypass (B = -262.04 (95% CI: -485.68 to -38.39), p = 0.022), the number of administered red cell-concentrates (B = -40.99 (95% CI: -67.86 to -14.12), p = 0.003) and older age (B = -9.35 (95% CI: -16.04 to -2.66), p = 0.006).

CONCLUSION

BChE activity is not independently associated with the occurrence of postoperative delirium. Preoperative BChE values are related to patients' morbidity and vulnerability, while postoperative activities reflect the severity, length and complications of surgery.

Plasma SOMAmer proteomics of postoperative delirium

BACKGROUND

Postoperative delirium is prevalent in older adults and has been shown to increase the risk of long-term cognitive decline. Plasma biomarkers to identify the risk for postoperative delirium and the risk of Alzheimer's disease and related dementias are needed.

METHODS

This biomarker discovery case-control study aimed to identify plasma biomarkers associated with postoperative delirium. Patients aged ≥65 years undergoing major elective noncardiac surgery were recruited. The preoperative plasma proteome was interrogated with SOMAmer-based technology targeting 1433 biomarkers.

RESULTS

In 40 patients (20 with vs. 20 without postoperative delirium), a preoperative panel of 12 biomarkers discriminated patients with postoperative delirium with an accuracy of 97.5%. The final model of five biomarkers delivered a leave-one-out cross-validation accuracy of 80%. Represented biological pathways included lysosomal and immune response functions.

CONCLUSION

In older patients who have undergone major surgery, plasma SOMAmer proteomics may provide a relatively non-invasive benchmark to identify biomarkers associated with postoperative delirium.

Serum proteomic analysis in esophagectomy patients with postoperative delirium: A case-control study

BACKGROUND

Postoperative delirium occurs in up to 80% of patients undergoing esophagectomy. We performed an exploratory proteomic analysis to identify protein pathways that may be associated with delirium post-esophagectomy.

OBJECTIVES

Identify proteins associated with delirium and delirium severity in a younger and higher-risk surgical population.

METHODS

We performed a case-control study using blood samples collected from patients enrolled in a negative, randomized, double-blind clinical trial. English speaking adults aged 18 years or older, undergoing esophagectomy, who had blood samples obtained were included. Cases were defined by a positive delirium screen after surgery while controls were patients with negative delirium assessments. Delirium was assessed using Richmond Agitation Sedation Scale and Confusion Assessment Method for the Intensive Care Unit, and delirium severity was assessed by Delirium Rating Scale-Revised-98. Blood samples were collected pre-operatively and on post-operative day 1, and discovery proteomic analysis was performed. Between-group differences in median abundance ratios were reported using Wilcoxon-Mann-Whitney Odds (WMWodds1) test.

RESULTS

52 (26 cases, 26 controls) patients were included in the study with a mean age of 64 (SD 9.6) years, 1.9% were females and 25% were African American. The median duration of delirium was 1 day (IQR: 1-2), and the median delirium/coma duration was 2.5 days (IQR: 2-4). Two proteins with greater relative abundance ratio in patients with delirium were: Coagulation factor IX (WMWodds: 1.89 95%CI: 1.0-4.2) and mannosyl-oligosaccharide 1,2-alpha-mannosidase (WMWodds: 2.4 95%CI: 1.03-9.9). Protein abundance ratios associated with mean delirium severity at postoperative day 1 were Complement C2 (Spearman rs = -0.31, 95%CI [-0.55, -0.02]) and Mannosyl-oligosaccharide 1,2-alpha-mannosidase (rs = 0.61, 95%CI = [0.29, 0.81]).

CONCLUSIONS

We identified changes in proteins associated with coagulation, inflammation, and protein handling; larger, follow-up studies are needed to confirm our hypothesis-generating findings.

Exploring the Pathophysiology of Delirium: An Overview of Biomarker Studies, Animal Models, and Tissue-Engineered Models

Delirium is an acute brain disorder associated with disorganized thinking, difficulty focusing, and confusion that commonly follows major surgery, severe infection, and illness. Older patients are at high risk for developing delirium during hospitalization, which may contribute to increased morbidity, longer hospitalization, and increased risk of institutionalization following discharge. The pathophysiology underlying delirium remains poorly studied. This review delves into the findings from biomarker studies and animal models, and highlights the potential for tissue-engineered models of the brain in studying this condition. The aim is to bring together the existing knowledge in the field and provide insight into the future direction of delirium research.

Application of Multiple Omics to Understand Postoperative Delirium Pathophysiology in Humans

Delirium, an acute change in cognition, is common, morbid, and costly, particularly among hospitalized older adults. Despite growing knowledge of its epidemiology, far less is known about delirium pathophysiology. Initial work understanding delirium pathogenesis has focused on assaying single or a limited subset of molecules or genetic loci. Recent technological advances at the forefront of biomarker and drug target discovery have facilitated application of multiple "omics" approaches aimed to provide a more complete understanding of complex disease processes such as delirium. At its basic level, "omics" involves comparison of genes (genomics, epigenomics), transcripts (transcriptomics), proteins (proteomics), metabolites (metabolomics), or lipids (lipidomics) in biological fluids or tissues obtained from patients who have a certain condition (i.e., delirium) and those who do not. Multi-omics analyses of these various types of molecules combined with machine learning and systems biology enable the discovery of biomarkers, biological pathways, and predictors of delirium, thus elucidating its pathophysiology. This review provides an overview of the most recent omics techniques, their current impact on identifying delirium biomarkers, and future potential in enhancing our understanding of delirium pathogenesis. We summarize challenges in identification of specific biomarkers of delirium and, more importantly, in discovering the mechanisms underlying delirium pathophysiology. Based on mounting evidence, we highlight a heightened inflammatory response as one common pathway in delirium risk and progression, and we suggest other promising biological mechanisms that have recently emerged. Advanced multiple omics approaches coupled with bioinformatics methodologies have great promise to yield important discoveries that will advance delirium research.