The aim of the study was to evaluate the ability of following biomarkers as diagnostic tools and risk predictors of AAA: C-reactive protein, interleukin-6, pentraxin-3, galectin-3, procollagen type III N-terminal peptide, C-terminal telopeptide of type I collagen, high-sensitive troponin I, and brain natriuretic peptide. Seventy-two patients with an AAA and 100 healthy individuals were enrolled into the study. We assessed individual biomarker performance and correlation between the AAA diameter and biomarker levels, and also, a multivariate logistic regression was used to design a possible predictive model of AAA growth and rupture risk. We identified following four parameters with the highest potential to find a useful place in AAA diagnostics: galectin-3, pentraxin-3, interleukin-6, and C-terminal telopeptide of type I. The best biomarkers in our evaluation (galectin-3 and pentraxin-3) were AAA diameter-independent. With the high AUC and AAA diameter correlation, the high-sensitive troponin I can be used as an independent prognostic biomarker of the upcoming heart complications in AAA patients. Authors recommend to add biomarkers as additional parameters to the current AAA patient management. Main addition value of biomarkers is in the assessment of the AAA with the smaller diameter. Elevated biomarkers can change the treatment decision, which would be done only based on AAA diameter size. The best way how to manage the AAA patients is to create a reliable predictive model of AAA growth and rupture risk. A created multiparameter model gives very promising results with the significantly higher efficiency compared with the use of the individual biomarkers.

• Keywords
• Abdominal aortic aneurism, Biomarker panel, Brain natriuretic peptide, C-Terminal telopeptide of type I collagen, Galectin-3, High-sensitive troponin I, Interleukin-6, Multivariate model, Multivariate stepwise logistic regression, Patient stratification, Pentraxin-3, Predictive preventive personalized medicine, Procollagen type III N-terminal peptide,
• Publication type
• Journal Article MeSH

AIM: Current diagnostics of bone metastatic disease is not satisfactory for early detection or regular process monitoring. The combination of biomarkers and the multiparametric approach was described as effective in other oncology diagnoses. The aim of the study was to improve the difference diagnostics between bone-metastatic disease and solid tumors using mutivariate logistic regression model. METHODS: We assessed the group of 131 patients with the following diagnoses: prostate cancer, breast cancer, lung cancer, and colorectal cancer. According to the results of scintigraphy, the cohort was divided into 2 groups based on the occurrence of bone metastases. Group 0 was a control group of 75 patients with no signs of bone metastases and group 1 included 56 patients with bone metastases. RESULTS: We used stepwise selection multivariate logistic regression for choosing the multimarker formula for calculation of risk score for bone metastases diagnostics. For detection of bone metastasis, it was shown to be most effective measurement of 3 biomarkers: procollagen type 1 N-terminal propeptide, growth differentiation factor-15, and osteonectin and combining with calculation of risk score by designating measured concentrations in mathematical formula: bone risk score = procollagen type 1 N-terminal propeptide × 0.0500 + growth differentiation factor-15 × 1.4179 + osteonectin × 0.00555. CONCLUSION: We identified growth differentiation factor-15 as the best individual marker for bone metastasis diagnostics. The best formula for risk score includes levels of 3 biomarkers-procollagen type 1 N-terminal propeptide, growth differentiation factor-15, and osteonectin. The new score has better performance described by higher area under the curve than individual biomarkers. A further study is necessary to confirm these findings incorporating a larger number of patients.

• Keywords
• biomarkers, bone metastasis, cancer, multivariate analysis, scintigraphy,
• MeSH
• Adult MeSH
• Cohort Studies MeSH
• Bone and Bones metabolism   pathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Multivariate Analysis MeSH
• Biomarkers, Tumor metabolism   MeSH
• Bone Neoplasms metabolism   pathology   secondary   MeSH
• Osteonectin metabolism   MeSH
• Radionuclide Imaging methods   MeSH
• Growth Differentiation Factor 15 metabolism   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Names of Substances
• Biomarkers, Tumor MeSH
• Osteonectin MeSH
• Growth Differentiation Factor 15 MeSH