Background Studies on postoperative cognitive dysfunction （POCD） have attracted extensive attention and achieved significant progress. However, the diagnosis of POCD is not very satisfactory as no specific biomarker...Background Studies on postoperative cognitive dysfunction （POCD） have attracted extensive attention and achieved significant progress. However, the diagnosis of POCD is not very satisfactory as no specific biomarkers have been classified. The aim of the present study was to evaluate differences in serum protein composition between POCD and Non-POCD patients, identify potential biomarkers associated with early POCD, and study the mechanism underlying POCD. Methods Sixty-eight elderly patients （age 〉65 years） received isoflurane inhalation anesthesia for arthroplasty surgeries. One day before and seven days after the surgery, these patients were subjected to a neuropsychological test and venous blood sample collection. Postoperative cognitive dysfunction was determined using Z test scores. Based on the results, the patients were divided into POCD and non-POCD groups. Twenty-five randomly chosen blood samples obtained seven days after the surgery from each group were analyzed on a Bruker ultraFlexTM time of flight （TOF）/TOF mass spectrophotometer. The resulting peptide fingerprints were compared with those from the pre-surgery samples to identify differences in serum protein composition. The model designed to distinguish between a non-POCD group and a POCD group were established and validated. Three proteins with the most significant changes were selected for further characterization. Results Thirty-three cases were diagnosed as POCD. Using the Clinprotools software, 58 polypeptides were found to display differential expression （P 〈0.05）. Using a support vector algorithm method, seven differential peaks were isolated to establish a diagnostic model to distinguish POCD patients from normal individuals. The prediction rate and recognition rate were 96.89% and 100%, respectively. Validation of this model showed that the accuracy rates were 100% and 85% using samples from the POCD and non-POCD groups, respectively. Protein analysis also led to the identification of fibrinopeptide A （FPA） as a pot展开更多
基金This work was supported by a grant from the Natural Science Foundation of Beijing （No. 7062026）.
文摘Background Studies on postoperative cognitive dysfunction （POCD） have attracted extensive attention and achieved significant progress. However, the diagnosis of POCD is not very satisfactory as no specific biomarkers have been classified. The aim of the present study was to evaluate differences in serum protein composition between POCD and Non-POCD patients, identify potential biomarkers associated with early POCD, and study the mechanism underlying POCD. Methods Sixty-eight elderly patients （age 〉65 years） received isoflurane inhalation anesthesia for arthroplasty surgeries. One day before and seven days after the surgery, these patients were subjected to a neuropsychological test and venous blood sample collection. Postoperative cognitive dysfunction was determined using Z test scores. Based on the results, the patients were divided into POCD and non-POCD groups. Twenty-five randomly chosen blood samples obtained seven days after the surgery from each group were analyzed on a Bruker ultraFlexTM time of flight （TOF）/TOF mass spectrophotometer. The resulting peptide fingerprints were compared with those from the pre-surgery samples to identify differences in serum protein composition. The model designed to distinguish between a non-POCD group and a POCD group were established and validated. Three proteins with the most significant changes were selected for further characterization. Results Thirty-three cases were diagnosed as POCD. Using the Clinprotools software, 58 polypeptides were found to display differential expression （P 〈0.05）. Using a support vector algorithm method, seven differential peaks were isolated to establish a diagnostic model to distinguish POCD patients from normal individuals. The prediction rate and recognition rate were 96.89% and 100%, respectively. Validation of this model showed that the accuracy rates were 100% and 85% using samples from the POCD and non-POCD groups, respectively. Protein analysis also led to the identification of fibrinopeptide A （FPA） as a pot
