Retos para el gobierno de las universidades en el marco del EEES

The European Higher Education Area (EHEA) requires changes in the way universities are managed. It is an opportunity for correcting imbalances whilst at the same time being a challenge that can mark important differences between universities. This study seeks to identify the challenges and information/training requirements of Spanish university management teams in order to facilitate their integration in the EHEA. Methodology: The Delphi study with the participation of 115 chancellors, vice-chancellors, deans and heads of service of Spanish Universities (EUE) and 26 managers of non-Spanish Universities included in European university quality and management agencies (ENE). For preparing the Delphi study questionnaire, two groups were formed using qualitative research techniques: a first discussion group with the participation of 12 Spanish university managers and a second group in which the Nominal Group technique was applied, with the participation of 18 non-Spanish university managers. Results: The most important challenges are improvement in the quality of education and redefinition of each university's strategy. The EUE group considers it necessary to increase coordination between subjects in order to offer a comprehensive education and promote the renovation of teaching methodologies. The ENE group gives priority to the need for professionalizing university management. The information/training requirements of university managers are: quality management, strategic management and change leadership. Conclusions: There is a notable effort to identify how EHEA integration affects the different disciplines but it is less frequent to address the structural changes needed in universities to be able to successfully accomplish this integration. These include improving the quality of teaching and management, for which managers must have the capacity for innovation and change leadership

Genomic and Metabolomic Profile Associated to Clustering of Cardio-Metabolic Risk Factors

<div><p>Background</p><p>To identify metabolomic and genomic markers associated with the presence of clustering of cardiometabolic risk factors (CMRFs) from a general population.</p><p>Methods and Findings</p><p>One thousand five hundred and two subjects, Caucasian, > 18 years, representative of the general population, were included. Blood pressure measurement, anthropometric parameters and metabolic markers were measured. Subjects were grouped according the number of CMRFs (Group 1: <2; Group 2: 2; Group 3: 3 or more CMRFs). Using SNPlex, 1251 SNPs potentially associated to clustering of three or more CMRFs were analyzed. Serum metabolomic profile was assessed by ¹H NMR spectra using a Brucker Advance DRX 600 spectrometer. From the total population, 1217 (mean age 54±19, 50.6% men) with high genotyping call rate were analysed. A differential metabolomic profile, which included products from mitochondrial metabolism, extra mitochondrial metabolism, branched amino acids and fatty acid signals were observed among the three groups. The comparison of metabolomic patterns between subjects of Groups 1 to 3 for each of the genotypes associated to those subjects with three or more CMRFs revealed two SNPs, the rs174577_AA of FADS2 gene and the rs3803_TT of GATA2 transcription factor gene, with minimal or no statistically significant differences. Subjects with and without three or more CMRFs who shared the same genotype and metabolomic profile differed in the pattern of CMRFS cluster. Subjects of <i>Group 3</i> and the AA genotype of the rs174577 had a lower prevalence of hypertension compared to the CC and CT genotype. In contrast, subjects of <i>Group 3</i> and the TT genotype of the rs3803 polymorphism had a lower prevalence of T2DM, although they were predominantly males and had higher values of plasma creatinine.</p><p>Conclusions</p><p>The results of the present study add information to the metabolomics profile and to the potential impact of genetic factors on the variants of clustering of cardiometabolic risk factors.</p></div

Patterns of statistical significance, calculated as p-values, for the comparison of metabolic profiles from subjects group 1 versus subjects from group 3 in the whole population (first column) and in individuals with different SNPs (rest of the columns).

<p>(white) p> 0.01; (white-gray) p< 0.01; (gray) p< 0.001; (black) p< 0.00001.</p

Metabolite relative levels in serum from subjects group 1 versus subjects from group 3.

<p>Metabolite relative levels in serum from subjects group 1 versus subjects from group 3.</p

Bar chart showing metabolic differences between Group 3 and Groups 1 normalized with respect to changes in group 3 (see Table 1).

<p>The bars represent the difference in the average metabolic levels between Group 3 and group 1 for each SNP divided by the same difference calculated for the entire cohort. SNPs with bars closer to 1 (dotted line) show CMRFs associated metabolic changes similar to those of the global population (irrespective of genotype). On the other hand, SNPs with bars closer to 0 exhibit minimal or no metabolic changes associated to CMRFs. Bars with negative values indicate a CMRF associated metabolic change opposite to that detected in global population. Metabolites from top to bottom are: tryptophan + choline; creatinine; phosphoethanolamine; creatine phosphate; tyrosine; creatine; methanol; proline; trimethylamine; lipids (= CH-CH2-CH2 =); citrate; 3-hydroxybutyrate; pyruvate; acetone; lipids (-CH2-CH3); N-acetylglutamine; acetate; lipids (-CH2-CH2_CO); alanine; 2-phenylpropionate; lactate; lipids (-CH2-)n; isobutyrate; valine; isoleucine; leucine; lipids (-CH3) and cholesterol.</p

Genomic and Metabolomic Profile Associated to Microalbuminuria

<div><p>To identify factors related with the risk to develop microalbuminuria using combined genomic and metabolomic values from a general population study. One thousand five hundred and two subjects, Caucasian, more than 18 years, representative of the general population, were included. Blood pressure measurement and albumin/creatinine ratio were measured in a urine sample. Using SNPlex, 1251 SNPs potentially associated to urinary albumin excretion (UAE) were analyzed. Serum metabolomic profile was assessed by ¹H NMR spectra using a Brucker Advance DRX 600 spectrometer. From the total population, 1217 (mean age 54±19, 50.6% men, ACR>30 mg/g in 81 subjects) with high genotyping call rate were analysed. A characteristic metabolomic profile, which included products from mitochondrial and extra mitochondrial metabolism as well as branched amino acids and their derivative signals, were observed in microalbuminuric as compare to normoalbuminuric subjects. The comparison of the metabolomic profile between subjects with different UAE status for each of the genotypes associated to microalbuminuria revealed two SNPs, the rs10492025_TT of <i>RPH3A</i> gene and the rs4359_CC of <i>ACE</i> gene, with minimal or no statistically significant differences. Subjects with and without microalbuminuria, who shared the same genotype and metabolomic profile, differed in age. Microalbuminurics with the CC genotype of the rs4359 polymorphism and with the TT genotype of the rs10492025 polymorphism were seven years older and seventeen years younger, respectively as compared to the whole microalbuminuric subjects. With the same metabolomic environment, characteristic of subjects with microalbuminuria, the TT genotype of the rs10492025 polymorphism seems to increase and the CC genotype of the rs4359 polymorphism seems to reduce risk to develop microalbuminuria.</p></div

Key metabolite differences subjects from group 1 and from group 3 in the whole population.

<p>Key metabolite differences subjects from group 1 and from group 3 in the whole population.</p

General characteristics of the study population grouped by CMRFs.

<p>General characteristics of the study population grouped by CMRFs.</p

General characteristics of the study population grouped by urinary albumin excretion.

<p><i>() percentage</i>.</p

Bar chart showing metabolic differences between microalbuminuria and no microalbuminuria normalized with respect to changes in the entire cohort.

<p>The bars represent the difference in the average metabolic levels between microalbuminuria and no microalbuminuria for each SNP divided by the same difference calculated for the entire cohort. SNPs with bars closer to 1 (dotted line) show UAE associated metabolic changes similar to those of the global population (irrespective of genotype). On the other hand, SNPs with bars closer to 0 exhibit minimal or no metabolic changes associated to UAE. Bars with negative values indicate a UAE associated metabolic change opposite to that detected in global population. Metabolites from top to bottom are: creatinine; creatine phosphate; leucine; glucose; proline; phosphocholine; choline; creatine+creatine phosphate; albumin; trimethylamine; citrate+dimethylamine; glutamine; 3-hydroxyisovalerate; pyruvate; N-acetylglutamine; lipids ( = CH-CH2-CH2-)+aminobutyrate; isoleucine; lipids (βCH2); alanine; lactate; lipids (-CH2-)n; valine; valine+isoleucine; leucine+isoleucine; lipids (-CH3) and cholesterol.</p