Thoroughly modern medicine

DNA methylation signatures at endoplasmic reticulum stress genes are associated with adiposity and insulin resistance

A sustained activation of the unfolded protein response and the subsequent endoplasmic reticulum (ER) stress has been involved in the onset and severity of several metabolic diseases. The aim of this study was to analyze the association of DNA methylation signatures at ER stress genes with adiposity traits and related metabolic disorders. An epigenomic analysis within the Methyl Epigenome Network Association (MENA) project was conducted in an adult population (n=474). DNA methylation status in peripheral white blood cells was analyzed by a microarray approach. KEGG database was used to the characterization and discrimination of genes involved in the "protein processing in endoplasmic reticulum pathway". Anthropometric measurements and plasma metabolic profiles were analyzed. A total of 15 CpG sites at genes participating in ER pathway were strongly correlated with BMI after adjusted linear regression analyses (p<0.0001). These included cg08188400 (MAP2K7), cg20541779 (CASP12), cg24776411 (EIF2AK1), cg14190817 (HSPA5), cg21376454 (ERN1), cg06666486 (EIF2AK1), cg03211481 (DNAJC1), cg18357645 (OS9), cg05801879 (MBTPS1), cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg03384128 (EIF2AK4), cg02712587 (EIF2AK4), cg04972384 (SELS), cg02240686 (EIF2AK2). Noteworthy, most of them were implicated in ER stress (p=2.9E-09). However, only methylation levels at cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg05801879 (MBTPS1), and cg03384128 (EIF2AK4) also correlated with total fat mass. Interestingly, significant associations between methylation patterns at cg20964082 (ERO1LB) and cg17300868 (NFE2L2) and insulin and HOMA-IR index were found, whereas cg05801879 (MBTPS1) and cg03384128 (EIF2AK4) were correlated with triglyceride levels. This study suggests associations of methylation signatures at ER stress genes with adiposity and insulin resistance, as revealed by discriminative pathway analyses.

Incorporation of personal single nucleotide polymorphism (SNP) data into a national level electronic health record for disease risk assessment, part 1: an overview of requirements

BACKGROUND

Personalized medicine approaches provide opportunities for predictive and preventive medicine. Using genomic, clinical, environmental, and behavioral data, tracking and management of individual wellness is possible. A prolific way to carry this personalized approach into routine practices can be accomplished by integrating clinical interpretations of genomic variations into electronic medical records (EMRs)/electronic health records (EHRs). Today, various central EHR infrastructures have been constituted in many countries of the world including Turkey.

OBJECTIVE

The objective of this study was to concentrate on incorporating the personal single nucleotide polymorphism (SNP) data into the National Health Information System of Turkey (NHIS-T) for disease risk assessment, and evaluate the performance of various predictive models for prostate cancer cases. We present our work as a miniseries containing three parts: (1) an overview of requirements, (2) the incorporation of SNP into the NHIS-T, and (3) an evaluation of SNP incorporated NHIS-T for prostate cancer.

METHODS

For the first article of this miniseries, the scientific literature is reviewed and the requirements of SNP data integration into EMRs/EHRs are extracted and presented.

RESULTS

In the literature, basic requirements of genomic-enabled EMRs/EHRs are listed as incorporating genotype data and its clinical interpretation into EMRs/EHRs, developing accurate and accessible clinicogenomic interpretation resources (knowledge bases), interpreting and reinterpreting of variant data, and immersing of clinicogenomic information into the medical decision processes. In this section, we have analyzed these requirements under the subtitles of terminology standards, interoperability standards, clinicogenomic knowledge bases, defining clinical significance, and clinicogenomic decision support.

CONCLUSIONS

In order to integrate structured genotype and phenotype data into any system, there is a need to determine data components, terminology standards, and identifiers of clinicogenomic information. Also, we need to determine interoperability standards to share information between different information systems of stakeholders, and develop decision support capability to interpret genomic variations based on the knowledge bases via different assessment approaches.

Achieving the "triple aim" for inborn errors of metabolism: a review of challenges to outcomes research and presentation of a new practice-based evidence framework

Across all areas of health care, decision makers are in pursuit of what Berwick and colleagues have called the “triple aim”: improving patient experiences with care, improving health outcomes, and managing health system impacts. This is challenging in a rare disease context, as exemplified by inborn errors of metabolism. There is a need for evaluative outcomes research to support effective and appropriate care for inborn errors of metabolism. We suggest that such research should consider interventions at both the level of the health system (e.g., early detection through newborn screening, programs to provide access to treatments) and the level of individual patient care (e.g., orphan drugs, medical foods). We have developed a practice-based evidence framework to guide outcomes research for inborn errors of metabolism. Focusing on outcomes across the triple aim, this framework integrates three priority themes: tailoring care in the context of clinical heterogeneity; a shift from “urgent care” to “opportunity for improvement”; and the need to evaluate the comparative effectiveness of emerging and established therapies. Guided by the framework, a new Canadian research network has been established to generate knowledge that will inform the design and delivery of health services for patients with inborn errors of metabolism and other rare diseases.

Genet Med 2013:15(6):415–422

Personalized nanomedicine advancements for stem cell tracking

Recent technological developments in biomedicine have facilitated the generation of data on the anatomical, physiological and molecular level for individual patients and thus introduces opportunity for therapy to be personalized in an unprecedented fashion. Generation of patient-specific stem cells exemplifies the efforts toward this new approach. Cell-based therapy is a highly promising treatment paradigm; however, due to the lack of consistent and unbiased data about the fate of stem cells in vivo, interpretation of therapeutic effects remains challenging hampering the progress in this field. The advent of nanotechnology with a wide palette of inorganic and organic nanostructures has expanded the arsenal of methods for tracking transplanted stem cells. The diversity of nanomaterials has revolutionized personalized nanomedicine and enables individualized tailoring of stem cell labeling materials for the specific needs of each patient. The successful implementation of stem cell tracking will likely be a significant driving force that will contribute to the further development of nanotheranostics. The purpose of this review is to emphasize the role of cell tracking using currently available nanoparticles.