"In conclusion, there is paucity of pharmacoeconomic studies specifically addressing efficacy, adverse events, and dependence associated with genetic markers of opioid therapy. With the rise in personalized medicine, the widespread use of genomic association in clinical practice remains to be seen [58, 65, 91–93]. The concept that individuals with divergent genetic polymorphisms respond differently to therapeutic compounds has gained widespread attention. "
[Show abstract][Hide abstract] ABSTRACT: Chronic non-cancer pain is a debilitating condition associated with high individual and societal costs. While opioid treatment for pain has been available for centuries, it is associated with high variability in outcome, and a considerable proportion of patients is unable to attain relief from symptoms while suffering adverse events and developing medication dependence. We performed a review of the efficacy of pharmacogenomic markers and their abilities to predict adverse events, dependence, and associated economic costs, focusing on two genes: OPRM1 and CYP2D6. Data sources were articles indexed by PubMed on or before August 6, 2013. Articles were first selected after review of their titles and abstracts, and full papers were read to confirm eligibility. Initially, fifty-two articles were identified. Of these, 17 were relevant to biological actions of pharmacogenomic markers and their effect on therapeutic efficacy, 16 to adverse events, 15 to opioid dependence, and eight to economic costs. In conclusion, increasing costs of opioid therapy have made the advances in pharmacogenomics an attractive solution to personalize care with unclear repercussions related to the impact on costs, morbidity, and outcomes. This intersection of pharmacoeconomics and pharmacogenomics presents a unique platform to further examine current advances in clinical medicine and their utility in cost-effective treatment of chronic pain.
Pain Research and Treatment 09/2013; 2013:943014. DOI:10.1155/2013/943014
"Consequently, for common diseases, individual variants have little diagnostic value (Fugger et al., 2012). To date, even combining results from many variants has provided limited value because only a proportion of causative loci have been identified and there are substantial environmental effects that contribute to most common diseases. "
[Show abstract][Hide abstract] ABSTRACT: Genetic factors contribute to risk for many common diseases affecting reproduction and fertility. In recent years, methods for genome-wide association studies (GWAS) have revolutionised gene discovery for common traits and diseases. Results of GWAS are documented in the Catalog of Published Genome-Wide Association Studies at the National Human Genome Research Institute and report over 70 publications for 32 traits and diseases associated with reproduction. These include endometriosis, uterine fibroids, age at menarche and age at menopause. Results that pass appropriate stringent levels of significance are generally well replicated in independent studies. Examples of genetic variation affecting twinning rate, infertility, endometriosis and age at menarche demonstrate that the spectrum of disease related variants for reproductive traits is similar to most other common diseases. GWAS "hits" provide novel insights into biological pathways and the translational value of these studies lies in discovery of novel gene targets for biomarkers, drug development and greater understanding of environmental factors contributing to disease risk. Results also show genetic data can help define sub-types of disease and co-morbidity with other traits and diseases. To date, many studies on reproductive traits have used relatively small samples. Future genetic marker studies in large samples with detailed phenotypic and clinical information will yield new insights into disease risk, disease classification and co-morbidity for many diseases associated with reproduction and infertility.
Molecular Human Reproduction 08/2013; 20(1). DOI:10.1093/molehr/gat058 · 3.75 Impact Factor
"During the last several decades, hypothesis-based research has revealed a number of genetic predispositions associated with HCC . Recently, genome-wide association studies (GWAS), a hypothesis-free approach, is now being used to study HCC , giving us better insight into the underlying mechanisms of hepatocarcinogenesis and providing information beyond those derived from logical analysis. Based on the results of genetic association studies, several loci are believed to be associated with HCC, such as MICA , HLA-DQA1/DRB1  and SATA4 . "
[Show abstract][Hide abstract] ABSTRACT: Background
Frequent deletions of the kinesin-like protein gene 1B (KIF1B) have been reported in neural tumors. Recently, a genome-wide association study revealed an association between polymorphisms in the KIF1B gene and the risk of hepatocellular carcinoma (HCC), and several case-control studies have further investigated this relationship. However, these studies have yielded controversial results. We therefore performed a meta-analysis to derive a more precise estimation of the association between the KIF1B gene polymorphisms and HCC risk.
PubMed, EMBASE, the ISI Web of Science and the CNKI databases were systematically searched to identify relevant studies. A total of 5 studies containing 13 cohorts with 5,773 cases and 6,404 controls were included. Odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were used to assess the strength of the associations. Subgroup analyses were conducted based on ethnicities, sample sizes and quality scores. Overall, the G allele at rs17401966 of the KIF1B gene was associated with a significantly decreased risk for HCC (OR = 0.81, 95%CI: 0.70–0.93; P = 0.003). Furthermore, subgroup analyses showed that the G allele at rs17401966 of the KIF1B gene significantly reduced the risk for HCC in Chinese cohorts (OR = 0.76, 95%CI: 0.64–0.90; P = 0.002), large-sample-size cohorts (OR = 0.80, 95%CI: 0.73–0.88, P<0.01) and high-quality cohorts (OR = 0.78, 95%CI: 0.71–0.87, P<0.01). However, no significant associations were found in small-sample-size cohorts, studies with low-quality scores and when excluding the cohorts from the study reporting the original discovery.
These findings demonstrate that the presence of the G allele at rs17401966 of the KIF1B gene may decrease the risk for HCC and suggest that KIF1B may play a critical role in the development of HCC. High-quality studies with larger sample sizes and different ethnic populations will be of great value to further confirm these findings.
PLoS ONE 04/2013; 8(4):e62571. DOI:10.1371/journal.pone.0062571 · 3.23 Impact Factor
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