Mapping Metabolite and ICD-10 Associations

被引:1
作者
Taalberg, Egon
Kilk, Kalle
机构
[1] Department of Biochemistry, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu
[2] Centre of Excellence for Genomics and Translational Medicine, 19 Ravila Street, Tartu
关键词
metabolomics; ICD-10; AUC-ROC; specificity; sensitivity; biomarker; comorbidity; SERUM URIC-ACID; PERSONALIZED MEDICINE; BIOMARKER DISCOVERY; SLEEP-APNEA; DISEASE; METABOLOMICS; OMICS;
D O I
10.3390/metabo10050196
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The search for novel metabolic biomarkers is intense but has had limited practical outcomes for medicine. Part of the problem is that we lack knowledge of how different comorbidities influence biomarkers' performance. In this study, 49 metabolites were measured by targeted LC/MS protocols in the serum of 1011 volunteers. Their performance as potential biomarkers was evaluated by the area under the curve of receiver operator characteristics (AUC-ROC) for 105 diagnosis codes or code groups from the 10th revision of the international classification of diseases (ICD-10). Additionally, the interferences between diagnosis codes were investigated. The highest AUC-ROC values for individual metabolites and ICD-10 code combinations reached a moderate (0.7) range. Most metabolites that were found to be potential markers remained so independently of the control group composition or comorbidities. The precise value of the AUC-ROC, however, could vary depending on the comorbidities. Moreover, networks of metabolite and disease associations were built in order to map diseases, which may interfere with metabolic biomarker research on other diseases.
引用
收藏
页数:14
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