Gene promoter evolution targets the center of the human protein interaction network.

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P=0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P=0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Metabolic syndrome in bipolar disorder: a cross-sectional assessment of a Health Management Organization database.

OBJECTIVE: To estimate the prevalence of metabolic syndrome (MS) in patients with bipolar disorder (BD) included in a Health Management Organization (HMO) database. METHODS: A cross-sectional analysis of the administrative claim database of Badalona Serveis Assistencials (BSA) was performed. All patients of either sex over 16 years of age and receiving treatment for BD for more than three weeks were included in the study group. The reference group comprised the rest of patients in the BSA database without BD. MS was defined according to the National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III modified criteria and required fulfillment of at least three of the following five components: body mass index (BMI) >or=28.8 kg/m(2), triglycerides >or=150 mg/dL, high-density lipoprotein (HDL) cholesterol <40 mg/dL (males)/<50 mg/dL (females), blood pressure >or=130/85 mmHg, and fasting glucose >or=110 mg/dL. Descriptive statistics, bivariate analysis, and logistic regression models were applied. RESULTS: We identified 178 patients with BD out of 86,028 subjects (50.5% women; 45.5 +/- 17.8 years, mean +/- SD) included in the BSA database. MS prevalence was significantly higher in BD: 24.7% [95% confidence interval (CI): 18.6-31.7] versus 14.4% (CI: 14.2-14.7) with no statistically significant differences between genders; age-adjusted odds ratio (OR) = 1.65 (1.11-2.44, p = 0.013). All MS components were higher in the BD group, particularly BMI >28.8 kg/m(2) [33.1% (26.3-40.6) versus 17.9% (17.7-18.2), adjusted OR = 2.05 (1.46-2.87, p < 0.001)], high triglyceride levels [23.0% (17.1-29.9) versus 11.3% (11.1-11.5), adjusted OR = 2.09 (1.45-3.02, p < 0.001)], and low HDL cholesterol levels [54.5% (46.9-62.0) versus 29.4% (29.1-29.7), adjusted OR = 2.77 (2.02-3.80, p < 0.001)]. Furthermore, patients with BD showed a significantly higher frequency of obesity [41.4% (32.3-50.9) versus 27.1% (26.6-27.5); adjusted OR = 1.83 (1.24-2.68, p = 0.002)]. CONCLUSIONS: Compared with the general population managed by the BSA, the prevalence of MS was significantly higher in patients with BD, mainly due to a higher prevalence of obesity, high triglyceride levels, and low HDL cholesterol levels. These findings strongly support the development of health policies addressing this problem in BD patients.

Variability in prescription drug expenditures explained by adjusted clinical groups (ACG) case-mix: a cross-sectional study of patient electronic records in primary care.

BACKGROUND: In view of rapidly increasing prescription costs, case-mix adjustment should be considered for effective control of costs. We have estimated the variability in pharmacy costs explained by ACG in centers using patient electronic records, profiled centers and physicians and analyzed the correlation between cost and quality of prescription. METHODS: We analyzed 65,630 patient records attending five primary care centers in Spain during 2005. Variables explored were age, gender, registered diagnosed episodes of care during 2005, total cost of prescriptions, physician and center. One ACG was assigned to each patient with ACG case-mix software version 7.1. In a two-part model, logistic regression was used to explain the incurrence of drug expenditure at the first stage and a linear mixed model that considered the multilevel structure of data modeled the cost, conditional upon incurring any expense. Risk and efficiency indexes in pharmacy cost adjusted for ACG were obtained for centers and physicians. Spearman rank correlation between physician expenditure, adjusted for ACG, and a prescription quality index was also obtained. Pediatric and adult data were analyzed separately. RESULTS: No prescription was recorded for 13% of adults and 39.6% of children. The proportion of variance of the incurrence of expenditure explained by ACGs was 0.29 in adults and 0.21 in children. For adults with prescriptions, the variance of cost explained by ACGs was 35.4%, by physician-center was 1.8% and age 10.5% (residual 52.3%). For children, ACGs explained 22.4% of cost and physician-center 10.9% (residual 66.7%). Center efficiency index for adults ranged 0.58 to 1.22 and for children 0.32 to 2.36. Spearman correlation between expenditure and prescription quality index was -0.36 in family physicians (p = 0.019, N = 41) and -0.52 in pediatricians (p = 0.08, N = 12). CONCLUSION: In our setting, ACG is the variable studied that explains more variability in pharmacy cost in adults compared to physician and center. In children there is greater variability among physicians and centers not related to case-mix. In our sites, ACG is useful to profile physicians and centers using electronic records in real practical conditions. Physicians with lower pharmaceutical expenditure have higher scores for a prescription quality index.