Sarinnapha M. Vasunilashorn

Purpose: (1) To describe cognitive trajectory patterns over 6 years after major surgery in older adults, and (2) To identify patient characteristics associated with severe cognitive decline. Methods: Group‐based semiparametric trajectory modeling was performed on longitudinal cognitive data from the SAGES study, which enrolled patients aged ≥ 70 years undergoing major elective noncardiac surgery. Participants received comprehensive neuropsychological testing prior to surgery and postoperatively every 6–12 months up to 72 months. The primary outcome was change in general cognitive performance score, a composite of neuropsychological tests, at each of 11 follow‐up timepoints relative to baseline. Generalized linear models were used to assess the associations of pre‐surgical patient characteristics and incidence of postoperative delirium with cognitive trajectory. Results: Of 560 participants, 326 were women (58%) and the average age was 76.7 (standard deviation 5.2) years. They underwent orthopedic (81%), gastrointestinal (13%), and vascular surgeries (6%), and 24% experienced postoperative delirium. We found the 3‐group cognitive trajectory model to be optimal, with the groups characterized as severe decline trajectory (SDT) (15% of the cohort), slight decline (59%), or stable (26%). Of pre‐surgical factors, age (relative risk [RR]: 1.06, 95% confidence interval [CI] 1.03–1.10 per 1 year increase) and 3MS (Modified‐Mini‐Mental) score (RR 0.95, 95% CI 0.92–0.99 per one point increase) were significantly associated with SDT. Participants who developed delirium had over two‐fold higher risk of SDT compared to those who did not (RR: 2.15, 95% CI: 1.35–3.42). Conclusions: Among older adults undergoing major surgery, 15% experienced severe cognitive decline over the ensuing 6 years, 59% experienced slight decline, and 26% remained stable. Older age, baseline cognitive impairment, and delirium were associated with severe decline, with delirium having the strongest association. Our findings provide valuable information for older patients considering major surgery and may help clinicians target interventions.

Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case–no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85–1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology.

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.

Delirium is a common postoperative complication among older patients with many adverse outcomes. Due to a lack of validated biomarkers, prediction and monitoring of delirium by biological testing is not currently feasible. Circulating proteins in cerebrospinal fluid (CSF) may reflect biological processes causing delirium. Our goal was to discover and investigate candidate protein biomarkers in preoperative CSF that were associated with the development of postoperative delirium in older surgical patients. We employed a nested case–control study design coupled with high multiplex affinity proteomics analysis to measure 1305 proteins in preoperative CSF. Twenty-four matched delirium cases and non-delirium controls were selected from the Healthier Postoperative Recovery (HiPOR) cohort, and the associations between preoperative protein levels and postoperative delirium were assessed using t-test statistics with further analysis by systems biology to elucidate delirium pathophysiology. Proteomics analysis identified 32 proteins in preoperative CSF that significantly associate with delirium (t-test p < 0.05). Due to the limited sample size, these proteins did not remain significant by multiple hypothesis testing using the Benjamini–Hochberg correction and q-value method. Three algorithms were applied to separate delirium cases from non-delirium controls. Hierarchical clustering classified 40/48 case–control samples correctly, and principal components analysis separated 43/48. The receiver operating characteristic curve yielded an area under the curve [95% confidence interval] of 0.91 [0.80–0.97]. Systems biology analysis identified several key pathways associated with risk of delirium: inflammation, immune cell migration, apoptosis, angiogenesis, synaptic depression and neuronal cell death. Proteomics analysis of preoperative CSF identified 32 proteins that might discriminate individuals who subsequently develop postoperative delirium from matched control samples. These proteins are potential candidate biomarkers for delirium and may play a role in its pathophysiology.

Background: Although growing evidence highlights inflammation as a common biological mechanism of delirium and Alzheimer’s disease (AD), not all individuals with high inflammation develop delirium, AD, and/or cognitive decline. Therefore other predisposing factors (e.g., brain vulnerability) likely influence the effect of inflammation on the brain.Method: We used the Role of Inflammation after Surgery for Elders (RISE) study of adults aged ≥65 who underwent elective surgery under spinal anesthesia (n = 40), to investigate the interaction of brain vulnerability and inflammation on persistent neuronal injury. Brain vulnerability was examined from preoperative cerebrospinal fluid: 1) amyloid status, and 2) ATN classification (ATN+ vs. negative for all groups [ATN‐]). Plasma levels of C‐reactive protein (CRP), interleukin‐6, and chitinase 3‐like protein (CHI3L1/YKL‐40), previously associated with delirium, were assigned values of 1‐4 for each marker within sample‐based quartiles. For each patient, we summed these values for the three markers to create an inflammation score (range 0‐12). Persistent neuronal injury, measured using postoperative 1 month (PO1MO) plasma, was measured from neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP). Linear regression models examined the association between inflammation score and neuronal injury, stratified by brain vulnerability.Result: On average, participants were age 75 years, 65% female, and 7% had Charlson scores ≥2. We identified a brain vulnerability x inflammation interaction on neuronal injury. Among amyloid+ patients, those in the highest inflammation score tertile [T3] vs T1 had 0.16 pg/ml higher GFAP (p<.05); however, among amyloid‐ patients, no inflammation and GFAP association was observed. No differences between inflammation and neuronal injury, by ATN classification, was observed.Conclusion: The association between inflammation and persistent neuronal injury differs by amyloid pathology, suggesting the potential value of identifying older patients vulnerable to adverse postoperative cognitive outcomes who may benefit from interventions under conditions of high inflammation.

Delirium is a common, morbid, and costly syndrome that is closely linked to Alzheimer's disease (AD) and AD‐related dementias (ADRD) as a risk factor and outcome. Human studies of delirium have advanced our knowledge of delirium incidence and prevalence, risk factors, biomarkers, outcomes, prevention, and management. However, understanding of delirium neurobiology remains limited. Preclinical and translational models for delirium, while challenging to develop, could advance our knowledge of delirium neurobiology and inform the development of new prevention and treatment approaches. We discuss the use of preclinical and translational animal models in delirium, focusing on (1) a review of current animal models, (2) challenges and strategies for replicating elements of human delirium in animals, and (3) the utility of biofluid, neurophysiology, and neuroimaging translational markers in animals. We conclude with recommendations for the development and validation of preclinical and translational models for delirium, with the goal of advancing awareness in this important field.

Delirium, an acute change in cognition, is a common, morbid, and costly syndrome, affecting up to 64% of older medical patients and up to 50% of older surgical patients.¹ Delirium is associated with longer hospital stays,² greater iatrogenic complications,³ higher rates of institutionalization,⁴ increased risks of incident dementia,⁵ and increased in-hospital mortality (25%-33%).^6,7 The estimated annual US health care costs attributable to delirium range up to $182 billion (2011 US dollars), and estimates of $146 358 per year per patient are attributable to delirium, with postsurgery health care costs increasing substantially and directly with the level of delirium severity (no or mild delirium, $83 534 per patient per year; moderate delirium, $99 756 per patient per year; and severe delirium, $140 008 per patient per year).^8,9 Moreover, with its increased incidence during the COVID-19 pandemic,^10,11 delirium has garnered widespread attention as a worldwide public health and patient safety priority.^12-14

Background: Our understanding of the relationship between plasma and cerebrospinal fluid (CSF) remains limited, which poses an obstacle to the identification of blood-based markers of neuroinflammatory disorders. To better understand the relationship between peripheral and central nervous system (CNS) markers of inflammation before and after surgery, we aimed to examine whether surgery compromises the blood-brain barrier (BBB), evaluate postoperative changes in inflammatory markers, and assess the correlations between plasma and CSF levels of inflammation. Methods: We examined the Role of Inflammation after Surgery for Elders (RISE) study of adults aged ≥ 65 who underwent elective hip or knee surgery under spinal anesthesia who had plasma and CSF samples collected at baseline and postoperative 1 month (PO1MO) (n = 29). Plasma and CSF levels of three inflammatory markers previously identified as increasing after surgery were measured using enzyme-linked immunosorbent assay: interleukin-6 (IL-6), C-reactive protein (CRP), and chitinase 3-like protein (also known as YKL-40). The integrity of the BBB was computed as the ratio of CSF/plasma albumin levels (Qalb). Mean Qalb and levels of inflammation were compared between baseline and PO1MO. Spearman correlation coefficients were used to determine the correlation between biofluids. Results: Mean Qalb did not change between baseline and PO1MO. Mean plasma and CSF levels of CRP and plasma levels of YKL-40 and IL-6 were higher on PO1MO relative to baseline, with a disproportionally higher increase in CRP CSF levels relative to plasma levels (CRP tripled in CSF vs. increased 10% in plasma). Significant plasma-CSF correlations for CRP (baseline r = 0.70 and PO1MO r = 0.89, p < .01 for both) and IL-6 (PO1MO r = 0.48, p < .01) were observed, with higher correlations on PO1MO compared with baseline. Conclusions: In this elective surgical sample of older adults, BBB integrity was similar between baseline and PO1MO, plasma-CSF correlations were observed for CRP and IL-6, plasma levels of all three markers (CRP, IL-6, and YKL-40) increased from PREOP to PO1MO, and CSF levels of only CRP increased between the two time points. Our identification of potential promising plasma markers of inflammation in the CNS may facilitate the early identification of patients at greatest risk for neuroinflammation and its associated adverse cognitive outcomes.