Microalbuminuria, was associated to the presence of diabetes and/or hypertension. In the present population and independent of these clinical conditions, the increment of UAE was weakly associated to genotypes of SNPs located in the chromosomes 11, 12 and 16, replicating previous studies. These SNPs were located in genes such as G protein beta polypeptide 3, ACEI and RPH3A, associated previously to UAE and to metabolic pathways not previously associated with UAE. However, the degree of association was not high enough to be considered as a positive association per se. Then we used the data from the metabolomic study to gain further insight into the potential relationship between genotypes and microalbuminuria. A characteristic metabolomic profile associated to microalbuminuria was identified by using a multivariate model, which allows for discrimination between normoalbuminuric and microalbuminuric individuals. The good match between the results in training and cross-validation datasets provides further support to the model. Whereas previous studies reported correlations between metabolic profile and different CVD risk factors and disease states such as insulin resistance, diabetes, obesity, the present study represents the first description of metabolic profiles of microalbuminuria in a general population. The differential metabolic profiles show that branched amino acids are reduced in microalbuminuria. The statistical significance of different spectral regions containing resonances of BCAA and related metabolites, like 3-OH-isovalerate, supports the association. BCAA can act as signaling molecules in many processes. Although many studies report increased BCAA levels in diabetes and insulin resistance, the association with microalbuminuria has not been previously Cichoric-Acid explored. Early studies showed that idiopathic portal hypertension correlates to decreased levels of leucine, isoleucine and valine. Diet-induced insulin resistant obese mice also display a depletion of BCAA serum levels.For individuals with the corresponding SNPs, we calculated the average metabolic level and standard deviation for each individual metabolite in microalbuminuria and no microalbuminuria normalized with respect groups stratified by SNPs. For each polymorphism normalized to the same differences at global levels, irrespective of genotype. Differences in the 26 metabolite values for each SNP in subjects with and without microalbuminuria of each Acetylcorynoline genotype were calculated. Finally, the metabolic profile and the most relevant metabolites of each genotype and allele were compared between normoalbuminuric and microalbuminuric subjects. The data was adjusted for the potential confounders in the study population age, sex, BMI, type 2 diabetes, and SBP. Statistical analyses were performed using the IBM SPSS Statistics 19 software. Principal component analysis was initially performed with the normalized peak areas obtained from all the samples to evaluate the quality of sample analysis and to view the holistic distribution, clustering, and outlier of samples.