The expression level of BAALC-associated microRNA miR-3151 is an independent prognostic factor in younger patients with cytogenetic intermediate-risk acute myeloid leukemia.

Acute myeloid leukemia (AML) is a heterogeneous disease whose prognosis is mainly related to the biological risk conferred by cytogenetics and molecular profiling. In elderly patients (60 years) with normal karyotype AML miR-3151 have been identified as a prognostic factor. However, miR-3151 prognostic value has not been examined in younger AML patients. In the present work, we have studied miR-3151 alone and in combination with BAALC, its host gene, in a cohort of 181 younger intermediate-risk AML (IR-AML) patients. Patients with higher expression of miR-3151 had shorter overall survival (P=0.0025), shorter leukemia-free survival (P=0.026) and higher cumulative incidence of relapse (P=0.082). Moreover, in the multivariate analysis miR-3151 emerged as independent prognostic marker in both the overall series and within the unfavorable molecular prognostic category. Interestingly, the combined determination of both miR-3151 and BAALC improved this prognostic stratification, with patients with low levels of both parameters showing a better outcome compared with those patients harboring increased levels of one or both markers (P=0.003). In addition, we studied the microRNA expression profile associated with miR-3151 identifying a six-microRNA signature. In conclusion, the analysis of miR-3151 and BAALC expression may well contribute to an improved prognostic stratification of younger patients with IR-AML.

Zinc-α2-Glycoprotein Modulates AKT-Dependent Insulin Signaling in Human Adipocytes by Activation of the PP2A Phosphatase.

OBJECTIVE Evidence from mouse models suggests that zinc-α2-glycoprotein (ZAG) is a novel anti-obesity adipokine. In humans, however, data are controversial and its physiological role in adipose tissue (AT) remains unknown. Here we explored the molecular mechanisms by which ZAG regulates carbohydrate metabolism in human adipocytes. METHODS ZAG action on glucose uptake and insulin action was analyzed. β1 and β2-adrenoreceptor (AR) antagonists and siRNA targeting PP2A phosphatase were used to examine the mechanisms by which ZAG modulates insulin sensitivity. Plasma levels of ZAG were measured in a lean patient cohort stratified for HOMA-IR. RESULTS ZAG treatment increased basal glucose uptake, correlating with an increase in GLUT expression, but induced insulin resistance in adipocytes. Pretreatment of adipocytes with propranolol and a specific β1-AR antagonist demonstrated that ZAG effects on basal glucose uptake and GLUT4 expression are mediated via β1-AR, whereas inhibition of insulin action is dependent on β2-AR activation. ZAG treatment correlated with an increase in PP2A activity. Silencing of the PP2A catalytic subunit abrogated the negative effect of ZAG on insulin-stimulated AKT phosphorylation and glucose uptake but not on GLUT4 expression and basal glucose uptake. ZAG circulating levels were unchanged in a lean patient cohort stratified for HOMA-IR. Neither glucose nor insulin was associated with plasma ZAG. CONCLUSIONS ZAG inhibits insulin-induced glucose uptake in human adipocytes by impairing insulin signaling at the level of AKT in a β2-AR- and PP2A-dependent manner.

Identification of serum and urine proteins responsible for enhanced pigment production by group B streptococci as amylases.

The serum and urine proteins responsible for enhanced pigment production in Streptococcus agalactiae in culture media were purified by chromatography and were identified as amylases by comparison of their amino acid composition with that calculated for proteins with known sequences. Similar pigment-enhancing activity was displayed by other amylases of nonanimal origin and by maltooligosaccharides.

A Nested Case-Control Study of Metabolically Defined Body Size Phenotypes and Risk of Colorectal Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC).

BACKGROUND Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. METHODS AND FINDINGS The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case-control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of <0.05 was considered statistically significant. In multivariable-adjusted conditional logistic regression models with BMI used to define adiposity, compared with metabolically healthy/normal weight individuals, we observed a higher colorectal cancer risk among metabolically unhealthy/normal weight (odds ratio [OR] = 1.59, 95% CI 1.10-2.28) and metabolically unhealthy/overweight (OR = 1.40, 95% CI 1.01-1.94) participants, but not among metabolically healthy/overweight individuals (OR = 0.96, 95% CI 0.65-1.42). Among the overweight individuals, lower colorectal cancer risk was observed for metabolically healthy/overweight individuals compared with metabolically unhealthy/overweight individuals (OR = 0.69, 95% CI 0.49-0.96). These associations were generally consistent when waist circumference was used as the measure of adiposity. To our knowledge, there is no universally accepted clinical definition for using C-peptide level as an indication of hyperinsulinaemia. Therefore, a possible limitation of our analysis was that the classification of individuals as being hyperinsulinaemic-based on their C-peptide level-was arbitrary. However, when we used quartiles or the median of C-peptide, instead of tertiles, as the cut-point of hyperinsulinaemia, a similar pattern of associations was observed. CONCLUSIONS These results support the idea that individuals with the metabolically healthy/overweight phenotype (with normal insulin levels) are at lower colorectal cancer risk than those with hyperinsulinaemia. The combination of anthropometric measures with metabolic parameters, such as C-peptide, may be useful for defining strata of the population at greater risk of colorectal cancer.