Cerebrospinal Fluid Spermidine, Glutamine and Putrescine Predict Postoperative Delirium Following Elective Orthopaedic Surgery.
Xiaobei PanEmma L CunninghamAnthony P PassmoreBernadette McGuinnessDaniel F McAuleyDavid BeverlandSeamus O'BrienTim MawhinneyJonathan M SchottHenrik ZetterbergBrian D GreenPublished in: Scientific reports (2019)
Delirium is a marker of brain vulnerability, associated with increasing age, pre-existing cognitive impairment and, recently, cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease. This nested case-control study used a targeted quantitative metabolomic methodology to profile the preoperative CSF of patients (n = 54) who developed delirium following arthroplasty (n = 28) and those who did not (n = 26). The aim was to identify novel preoperative markers of delirium, and to assess potential correlations with clinical data. Participants without a diagnosis of dementia (≥65 years) undergoing elective primary hip or knee arthroplasty were postoperatively assessed for delirium once-daily for three days. Groups were compared using multivariate, univariate and receiving operator characteristic (ROC) methods. Multivariate modelling using Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) of metabolomic data readily distinguished between delirium and control groups (R2 ≤ 0.56; Q2 ≤ 0.10). Three metabolites (spermidine, putrescine and glutamine) significantly differed between groups (P < 0.05; FDR < 0.07), and performed well as CSF biomarkers (ROC > 0.75). The biomarker performance of the two polyamines (spermidine/putrescine) was enhanced by ratio with CSF Aβ42 (ROC > 0.8), and spermidine significantly correlated with Aβ42 (pearson r = -0.32; P = 0.018). These findings suggest that spermidine and putrescine levels could be useful markers of postoperative delirium risk, particularly when combined with Aβ42, and this requires further investigation.
Keyphrases
- cerebrospinal fluid
- cardiac surgery
- patients undergoing
- hip fracture
- cognitive impairment
- end stage renal disease
- chronic kidney disease
- data analysis
- electronic health record
- ejection fraction
- climate change
- big data
- high resolution
- newly diagnosed
- machine learning
- drug delivery
- cognitive decline
- acute coronary syndrome
- prognostic factors
- risk assessment
- coronary artery bypass
- white matter
- cancer therapy
- coronary artery disease
- subarachnoid hemorrhage
- artificial intelligence
- blood brain barrier
- patient reported
- breast cancer risk