RESUMO
BACKGROUND AND OBJECTIVES: We investigated if a combination of plasma or salivary interleukin-2 (IL-2), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), transforming growth factor-beta (TGF-ß), and troponin can improve estimation of the pretest probability of the left ventricular systolic dysfunction (LVSD). SUBJECTS AND METHODS: Eighty patients with newly-diagnosed myocardial infarction (MI) were echocardiographically examined for LVSD (ejection fraction ≤40%). Measurements included traditional MI risk factors, plasma and salivary concentrations of troponin, IL-2, IL-6, TNF-α, and TGF-ß. With the LVSD as the outcome variable, we developed logistic regression models, starting with a basic model incorporating traditional risk factors and consecutively adding salivary and plasma biomarkers. Models were compared using several criteria, including (but not limited to) C statistic (discrimination) and net reclassification improvement index (NRI). RESULTS: APART FROM TROPONIN, PLASMA, AND SALIVARY VALUES OF THE BIOMARKERS WERE CORRELATED: spearman's ρ was 0.19 (p=0.088) for troponin, 0.36 (p=0.001) for IL-2, 0.74 (p<0.001) for IL-6, 0.61 (p<0.001) for TNF-α, and 0.65 (p<0.001) for TGF-ß. The predictive performances of the basic model for estimating the pretest probability of the presence of LVSD considerably improved when cytokines were added (salivary added: C-statistic from 0.77 to 0.82 and NRI 77%; plasma added: C-statistic to 0.80 and NRI 134%). CONCLUSION: Multiple biomarkers added diagnostic value to the standard risk factors for predicting the presence of post-MI LVSD.
RESUMO
OBJECTIVE: This study defines the relationship between salivary beta-2 microglobulin (ß2-M) and intensity of uremia in male patients diagnosed with chronic renal failure (CRF). MATERIALS AND METHODS: In total of 42 males were enrolled in a case-control study. There were 21 cases of CRF and 21 control cases. We collected 10cc of saliva plus 5 cc of blood from all patients to determine ß2-M, blood urea nitrogen (BUN) and creatinine (Cr) levels. RESULTS: There was a correlation between the level of serum BUN and salivary urea in controls and patients, which was statistically significant for controls (p=0.028).The correlation between serum and salivary Cr was 0.195 in controls (p=0.398) and 0.598 in patients (p=0.006), which was statistically significant in patients. The correlation between serum and saliva was 0.133 (p=0.566) in controls and 0.078 (p=0.737) in patients, which was not statistically significant. The correlation between serum BUN and ß2-M was 0.168 (p=0.469) in the control group and 0.629 (p=0.002) in patients, which was statistically significant in patients. The correlation between serum Cr and ß2-M was 0.110 (p=0.635) in the control group and 0.678 (p=0.001) in patients, which was statistically significant in patients. The correlation between serum BUN and salivary ß2-M was 0.093 (p=0.0690) in controls and 0.152 (p=0.152) in patients, which was not statistically significant. The correlation between serum Cr and salivary ß2-M was 0.072 (p=0.070) in the control group and 0.286 (p=0.209) in patients, which was not statistically significant in either group. CONCLUSION: The results of the study indicated that salivary ß2-M cannot be used as a noninvasive indicator to detect the severity of renal failure.