Dataset Information

Proteomic and clinical biomarkers for acute mountain sickness diagnosis, prognosis, protection, and pathogenesis in a longitudinal cohort

ABSTRACT: We redefined AMS by using the combination of Olink’s antibody-based proximity extension assay and the multiple reaction monitoring mass spectrometry proteome with 106 paired plasma samples of Chinese Han, 22 AMS-related phenotypes, and 65 clinical indexes. We revealed a systematic proteomic and clinical index profile of individuals with AMS and without AMS on the plateau

ORGANISM(S): Homo Sapiens

SUBMITTER: Kunlun He

PROVIDER: PXD029063 | iProX | Tue Oct 12 00:00:00 BST 2021

REPOSITORIES: iProX

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Proteomic and clinical biomarkers for acute mountain sickness in a longitudinal cohort.

Yang Jing J Jia Zhilong Z Song Xinyu X Shi Jinlong J Wang Xiaoreng X Zhao Xiaojing X He Kunlun K

Communications biology 20220606 1

Ascending to high-altitude by non-high-altitude natives is a well-suited model for studying acclimatization to extreme environments. Acute mountain sickness (AMS) is frequently experienced by visitors. The diagnosis of AMS mainly depends on a self-questionnaire, revealing the need for reliable biomarkers for AMS. Here, we profiled 22 AMS symptom phenotypes, 65 clinical indexes, and plasma proteomic profiles of AMS via a combination of proximity extension assay and multiple reaction monitoring of ...[more]

PMID: 35668171

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Project description:BackgroundCurrent prognostic prediction models of colorectal cancer (CRC) include only the preoperative measurement of tumor markers, with their available repeated postoperative measurements underutilized. CRC prognostic prediction models were constructed in this study to clarify whether and to what extent the inclusion of perioperative longitudinal measurements of CEA, CA19-9, and CA125 can improve the model performance, and perform a dynamic prediction.MethodsThe training and validating cohort included 1453 and 444 CRC patients who underwent curative resection, with preoperative measurement and two or more measurements within 12 months after surgery, respectively. Prediction models to predict CRC overall survival were constructed with demographic and clinicopathological variables, by incorporating preoperative CEA, CA19-9, and CA125, as well as their perioperative longitudinal measurements.ResultsIn internal validation, the model with preoperative CEA, CA19-9, and CA125 outperformed the model including CEA only, with the better area under the receiver operating characteristic curves (AUCs: 0.774 vs 0.716), brier scores (BSs: 0.057 vs 0.058), and net reclassification improvement (NRI = 33.5%, 95% CI: 12.3 ~ 54.8%) at 36 months after surgery. Furthermore, the prediction models, by incorporating longitudinal measurements of CEA, CA19-9, and CA125 within 12 months after surgery, had improved prediction accuracy, with higher AUC (0.849) and lower BS (0.049). Compared with preoperative models, the model incorporating longitudinal measurements of the three markers had significant NRI (40.8%, 95% CI: 19.6 to 62.1%) at 36 months after surgery. External validation showed similar results to internal validation. The proposed longitudinal prediction model can provide a personalized dynamic prediction for a new patient, with estimated survival probability updated when a new measurement is collected during 12 months after surgery.ConclusionsPrediction models including longitudinal measurements of CEA, CA19-9, and CA125 have improved accuracy in predicting the prognosis of CRC patients. We recommend repeated measurements of CEA, CA19-9, and CA125 in the surveillance of CRC prognosis.

Dataset Information

Proteomic and clinical biomarkers for acute mountain sickness diagnosis, prognosis, protection, and pathogenesis in a longitudinal cohort

Publications

Proteomic and clinical biomarkers for acute mountain sickness in a longitudinal cohort.

Similar Datasets

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