## 3 Results
### Wine's Phytochemical Composition  
The chromatogram was analyzed and compared using online databases to identify the principal compounds from the Cabernet Sauvignon Malbec wine used in healthy volunteer participants [37; 38]. It is well known that wine is a rich source of phenolic compounds, and the polyphenic profile in wine changes due to different conditions including grape variety, environmental factors in the vineyard, storage conditions and management, for this reason we decided to conduct this fast analysis to confirm the presence of these compounds [39; 40; 41]. For this reason, we determined a preliminary finger-printing using a UPLC-QDa-MS, looking for information to link its effects. Phenolic compounds present in wine can be divided in two major groups according to their carbon skeletons: flavonoids and nonflavonoids. The main nonflavonoid phenolics include cinnamic acids (caffeic, p-coumaric, and ferulic acids), benzoic acids (gallic, vanillic, and syrigenic acids), and stilbenes (resveratrol).The chromatogram Figure S1 (Supplementary Material) and mass spectrum analysis in Figure S2 showed the presence of polyphenic such as gallotannins, stilbenes, and tannin fragments (Table S3 Supplementary Material) [42]. According to our findings we can classify the wine used as richer in nonflavonoids compounds like benzoic acids derivatives and stilbenes such as resveratrol, and resveratrol fragments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**AREase**} \\ \hline
**Nutrient** & **OR** & **95\% CI** & \(p\) \\ \hline Total fiber (g) & 1.002 & 1.0004–1.004 & **0.02** \\ \hline \(\beta\)-tocopherol (mg) & 0.085 & 0.0088–0.813 & **0.03** \\ \hline \(\gamma\)-tocopherol (mg) & 0.814 & 0.668–0.992 & **0.04** \\ \hline \(\delta\)-tocopherol (mg) & 0.517 & 0.281–0.950 & **0.03** \\ \hline Polyunsaturated fat (g) & 0.906 & 0.823–0.998 & **0.04** \\ \hline \multicolumn{3}{c}{**PONase**} \\ \hline
**Nutrient** & \(\beta\) & **95\% CI** & \(p\) \\ \hline Total protein (g) & \(-0.008\) & \(-0.0139\)—\(0.0013\) & **0.02** \\ \hline Animal protein (g) & \(-0.012\) & \(-0.0214\)—\(0.0032\) & **0.01** \\ \hline Calcium (mg) & \(-0.0007\) & \(-0.0014\)—\(0.00006\) & **0.03** \\ \hline Potassium (mg) & \(-0.0001\) & \(-0.0003\)—\(4.6\times 10^{-6}\) & **0.04** \\ \hline Vitamin B2 (mg) & \(-0.302\) & \(-0.581\)—\(0.0234\) & **0.03** \\ \hline Vitamin B3 (mg) & \(-0.023\) & \(-0.045\)—\(0.0024\) & **0.03** \\ \hline Vitamin B6 (mg) & \(-0.217\) & \(-0.416\)—\(0.0178\) & **0.03** \\ \hline Animal fat (g) & \(-0.008\) & \(-0.016\)—\(0.0003\) & **0.04** \\ \hline Saturated fat (g) & \(-0.017\) & \(-0.032\)—\(0.0011\) & **0.04** \\ \hline \hline \end{tabular}  
* A Results are expressed as odds ratio (OR) and 95% confidence interval (95% CI) and \(\beta\): beta coefficient. AREase associations were conducted by logistic regression analysis. Geometric mean (47.38 U/ug) of dichotomized specific AREase activity was used as the cutoff point. PONase associations were performed by linear regression analysis with specific PONase activity logarithmic transformation.  
\end{table}
Table 4: Associations between initial PON1 AREase and PONase specific activities and nutrient intake.