Beyond the Evidence of the New Hypertension Guidelines. Blood pressure measurement – is it good enough for accurate diagnosis of hypertension? Time might be in, for a paradigm shift (I)

Despite widespread availability of a large body of evidence in the area of hypertension, the translation of that evidence into viable recommendations aimed at improving the quality of health care is very difficult, sometimes to the point of questionable acceptability and overall credibility of the guidelines advocating those recommendations. The scientific community world-wide and especially professionals interested in the topic of hypertension are witnessing currently an unprecedented debate over the issue of appropriateness of using different drugs/drug classes for the treatment of hypertension. An endless supply of recent and less recent "drug-news", some in support of, others against the current guidelines, justifying the use of selected types of drug treatment or criticising other, are coming out in the scientific literature on an almost weekly basis. The latest of such debate (at the time of writing this paper) pertains the safety profile of ARBs vs ACE inhibitors. To great extent, the factual situation has been fuelled by the new hypertension guidelines (different for USA, Europe, New Zeeland and UK) through, apparently small inconsistencies and conflicting messages, that might have generated substantial and perpetuating confusion among both prescribing physicians and their patients, regardless of their country of origin. The overwhelming message conveyed by most guidelines and opinion leaders is the widespread use of diuretics as first-line agents in all patients with blood pressure above a certain cut-off level and the increasingly aggressive approach towards diagnosis and treatment of hypertension. This, apparently well-justified, logical and easily comprehensible message is unfortunately miss-obeyed by most physicians, on both parts of the Atlantic. Amazingly, the message assumes a universal simplicity of both diagnosis and treatment of hypertension, while ignoring several hypertension-specific variables, commonly known to have high level of complexity, such as: - accuracy of recorded blood pressure and the great inter-observer variability, - diversity in the competency and training of diagnosing physician, - individual patient/disease profile with highly subjective preferences, - difficulty in reaching consensus among opinion leaders, - pharmaceutical industry's influence, and, nonetheless, - the large variability in the efficacy and safety of the antihypertensive drugs. The present 2-series article attempts to identify and review possible causes that might have, at least in part, generated the current healthcare anachronism (I); to highlight the current trend to account for the uncertainties related to the fixed blood pressure cut-off point and the possible solutions to improve accuracy of diagnosis and treatment of hypertension (II)

Identification of nine new susceptibility loci for endometrial cancer

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study

Breast cancer subtype predictors revisited: From consensus to concordance?

Background: At the molecular level breast cancer comprises a heterogeneous set of subtypes associated with clear differences in gene expression and clinical outcomes. Single sample predictors (SSPs) are built via a two-stage approach consisting of clustering and subtype predictor construction based on the cluster labels of individual cases. SSPs have been criticized because their subtype assignments for the same samples were only moderately concordant (Cohen’s κ<0.6). Methods: We propose a semi-supervised approach where for five datasets, consensus sets were constructed consisting of those samples that were concordantly subtyped by a number of different predictors. Next, nine subtype predictors - three SSPs, three subtype classification models (SCMs) and three novel rule-based predictors based on the St. Gallen surrogate intrinsic subtype definitions (STGs) - were constructed on the five consensus sets and their associated consensus subtype labels. The predictors were validated on a compendium of over 4,000 uniformly preprocessed Affymetrix microarrays. Concordance between subtype predictors was assessed using Cohen’s kappa statistic. Results: In this standardized setup, subtype predictors of the same type (either SCM, SSP, or STG) but with a different gene list and/or consensus training set were associated with almost perfect levels of agreement (median κ>0.8). Interestingly, for a given predictor type a change in consensus set led to higher concordance than a change to another gene list. The more challenging scenario where the predictor type, gene list and training set were all different resulted in predictors with only substantial levels of concordance (median κ=0.74) on independent validation data. Conclusions: Our results demonstrate that for a given subtype predictor type stringent standardization of the preprocessing stage, combined with carefully devised consensus training sets, leads to predictors that show almost perfect levels of concordance. However, predictors of a different type are only substantially concordant, despite reaching almost perfect levels of concordance on training data.Pattern Recognition and Bioinformatic

Identification of nine new susceptibility loci for endometrial cancer

Endometrial cancer is the most commonly diagnosed cancer of the female reproductive tract in developed countries. Through genome-wide association studies (GWAS), we have previously identified eight risk loci for endometrial cancer. Here, we present an expanded meta-analysis of 12,906 endometrial cancer cases and 108,979 controls (including new genotype data for 5624 cases) and identify nine novel genome-wide significant loci, including a locus on 12q24.12 previously identified by meta-GWAS of endometrial and colorectal cancer. At five loci, expression quantitative trait locus (eQTL) analyses identify candidate causal genes; risk alleles at two of these loci associate with decreased expression of genes, which encode negative regulators of oncogenic signal transduction proteins (SH2B3 (12q24.12) and NF1 (17q11.2)). In summary, this study has doubled the number of known endometrial cancer risk loci and revealed candidate causal genes for future study