IntroductionAcromegaly is a rare, insidious disease resulting from the overproduction of growth hormone (GH) and insulin-like growth factor 1 (IGF-1), and is associated with a range of comorbidities. The extent of associated complications and mortality risk is related to length of exposure to the excess GH and IGF-1, thus early diagnosis and treatment is imperative. Unfortunately, acromegaly is often diagnosed late, when patients already have a wide range of comorbidities. The presence of comorbid conditions contributes significantly to patient morbidity/mortality and impaired quality of life.MethodsWe conducted a retrospective literature review for information relating to the diagnosis of acromegaly, and its associated comorbidities using PubMed. The main aim of this review is to highlight the issues of comorbidities in acromegaly, and to reinforce the importance of early diagnosis and treatment.Findings and conclusionsSuccessful management of acromegaly goes beyond treating the disease itself, since many patients are diagnosed late in disease evolution, they present with a range of comorbid conditions, such as cardiovascular disease, diabetes, hypertension, and sleep apnea. It is important that patients are screened carefully at diagnosis (and thereafter), for common associated complications, and that biochemical control does not become the only treatment goal. Mortality and morbidities in acromegaly can be reduced successfully if patients are treated using a multimodal approach with comprehensive comorbidity management.

BackgroundPersonalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics.MethodsWe developed a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants. To provide high sensitivity in known cancer mutation hotspots, Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was also employed. We integrated the resulting data with cancer knowledge bases and developed a specific workflow for each cancer type to improve interpretation of genomic data.ResultsWe returned genomics findings to 46 patients and their physicians describing somatic alterations and predicting drug response, toxicity, and prognosis. Mean 17.3 cancer-relevant somatic mutations per patient were identified, 13.3-fold, 6.9-fold, and 4.7-fold more than could have been detected using CHPv2, Oncomine Cancer Panel (OCP), and FoundationOne, respectively. Our approach delineated the underlying genetic drivers at the pathway level and provided meaningful predictions of therapeutic efficacy and toxicity. Actionable alterations were found in 91 % of patients (mean 4.9 per patient, including somatic mutations, copy number alterations, gene expression alterations, and germline variants), a 7.5-fold, 2.0-fold, and 1.9-fold increase over what could have been uncovered by CHPv2, OCP, and FoundationOne, respectively. The findings altered the course of treatment in four cases.ConclusionsThese results show that a comprehensive, integrative genomic approach as outlined above significantly enhanced genomics-based PCT strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-016-0313-0) contains supplementary material, which is available to authorized users.

The amount of patient knowledge about diabetes-related issues was low in this representative Mexican population. The educational efforts were focused on those with the worst metabolic control and/or with diabetes complications.

In the past decade there has been an explosion in genetic research that has resulted in the generation of enormous quantities of disease-related data. In the current study, we have compiled disease risk gene variant information and Electronic Medical Record (EMR) classification codes from various repositories for 305 diseases. Using such data, we developed a pipeline to test for clinical prevalence, gene-variant overlap, and literature presence for all 46,360 unique diseases pairs. To determine whether disease pairs were enriched we systematically employed both Fishers' Exact (medical and literature) and Term Frequency-Inverse Document Frequency (genetics) methodologies to test for enrichment, defining statistical significance at a Bonferonni adjusted threshold of (p < 1x10 -6 ) and weighted q<0.05 accordingly. We hypothesize that disease pairs that are statistically enriched in medical and genetic spheres, but not so in the literature have the potential to reveal non-obvious connections between clinically disparate phenotypes. Using this pipeline, we identified 2,316 disease pairs that were significantly enriched within an EMR and 213 enriched genetically. Of these, 65 disease pairs were statistically enriched in both, 19 of which are believed to be novel. These identified non-obvious relationships between disease pairs are suggestive of a shared underlying etiology with clinical presentation. Further investigation of uncovered disease-pair relationships has the potential to provide insights into the architecture of complex diseases, and update existing knowledge of risk factors.