Growth hormone-releasing hormone (GHRH) polymorphisms associated with carcass traits of meat in Korean cattle

BACKGROUND: Cold carcass weight (CW) and longissimus muscle area (EMA) are the major quantitative traits in beef cattle. In this study, we found several polymorphisms of growth hormone-releasing hormone (GHRH) gene and examined the association of polymorphisms with carcass traits (CW and EMA) in Korean native cattle (Hanwoo). RESULTS: By direct DNA sequencing in 24 unrelated Korean cattle, we identified 12 single nucleotide polymorphisms within the 9 kb full gene region, including the 1.5 kb promoter region. Among them, six polymorphic sites were selected for genotyping in our beef cattle (n = 428) and five marker haplotypes (frequency > 0.1) were identified. Statistical analysis revealed that -4241A>T showed significant associations with CW and EMA. CONCLUSION: Our findings suggest that polymorphisms in GHRH might be one of the important genetic factors that influence carcass yield in beef cattle. Sequence variation/haplotype information identified in this study would provide valuable information for the production of a commercial line of beef cattle

Melatonin receptor 1 B polymorphisms associated with the risk of gestational diabetes mellitus

<p>Abstract</p> <p>Backgrounds</p> <p>Two SNPs in <it>melatonin receptor 1B </it>gene, <it>rs10830963 </it>and <it>rs1387153 </it>showed significant associations with fasting plasma glucose levels and the risk of Type 2 Diabetes Mellitus (T2DM) in previous studies. Since T2DM and gestational diabetes mellitus (GDM) share similar characteristics, we suspected that the two genetic polymorphisms in <it>MTNR1B </it>may be associated with GDM, and conducted association studies between the polymorphisms and the disease. Furthermore, we also examined genetic effects of the two polymorphisms with various diabetes-related phenotypes.</p> <p>Methods</p> <p>A total of 1,918 subjects (928 GDM patients and 990 controls) were used for the study. Two <it>MTNR1B </it>polymorphisms were genotyped using TaqMan assay. The allele distributions of SNPs were evaluated by <it>x</it>²models calculating odds ratios (ORs), 95% confidence intervals (CIs), and corresponding <it>P </it>values. Multiple regressions were used for association analyses of GDM-related traits. Finally, conditional analyses were also performed.</p> <p>Results</p> <p>We found significant associations between the two genetic variants and GDM, <it>rs10830963</it>, with a corrected <it>P </it>value of 0.0001, and <it>rs1387153</it>, with the corrected <it>P </it>value of 0.0008. In addition, we also found that the two SNPs were associated with various phenotypes such as homeostasis model assessment of beta-cell function and fasting glucose levels. Further conditional analyses results suggested that <it>rs10830963 </it>might be more likely functional in case/control analysis, although not clear in GDM-related phenotype analyses.</p> <p>Conclusion</p> <p>There have been studies that found associations between genetic variants of other genes and GDM, this is the first study that found significant associations between SNPs of <it>MTNR1B </it>and GDM. The genetic effects of two SNPs identified in this study would be helpful in understanding the insight of GDM and other diabetes-related disorders.</p

Limited Effect of CpG ODN in Preventing Type 1 Diabetes in NOD Mice

Type 1 diabetes is considered as Th1 cell mediated autoimmune disease and the suppression of Th1 cells or the activation of Th2 cells has been regarded as a plausible immunologic intervention for the prevention of type 1 diabetogenesis in a rodent model. CpG ODN is an immunostimulatory sequence primarily present in bacterial DNA, viral DNA and BCG. CpG ODN is conventionally classified as a Th1 cell activator, which has been clinically applied to cancer, allergy and infectious disease. Recently, there was a promising report of that CpG ODN administration suppressed the development of type 1 diabetes in NOD mice by inducing Th2 cell mediated cytokine. However, the antidiabetogenic effect of CpG ODN on NOD mice is controversial. Thus, two studies were serially undertaken with various kinds of CpG motif to find a more optimal sequence and administration method. In the first study, CpG ODN was vaccinated four times and pancreatic inflammation and the quantity of serum insulin subsequently evaluated. In the second study, the amounts of IFN γ and IL-4 in sera were measured as representative cytokines of Th1 and Th2 cells, respectively. As a result, vaccination or continuous injection of CpG ODN failed to show a preventive effect on type 1 diabetogenesis in NOD mice. Structural differences of CpG ODN also had no affect on the result. CpG ODN also consistently showed affect on the pancreatic pathology. The productions of IFN and IL-4 were γ detected only in the K and D type CpG ODN administration groups. Comparison of the two cytokines leads to the conclusion that CpG ODN generated a Th1-weighted response in both study groups. It was assumed that CpG ODN failed to produce Th2-weighted cytokine milieu, which can overcome the genetically determined phenotype of NOD mice. Given these results, it was concluded that the immunotherapeutic application of CpG ODN on Type 1 diabetes had clear limitations

The Genetic Effect of Copy Number Variations on the Risk of Type 2 Diabetes in a Korean Population

BACKGROUND: Unlike Caucasian populations, genetic factors contributing to the risk of type 2 diabetes mellitus (T2DM) are not well studied in Asian populations. In light of this, and the fact that copy number variation (CNV) is emerging as a new way to understand human genomic variation, the objective of this study was to identify type 2 diabetes-associated CNV in a Korean cohort. METHODOLOGY/PRINCIPAL FINDINGS: Using the Illumina HumanHap300 BeadChip (317,503 markers), genome-wide genotyping was performed to obtain signal and allelic intensities from 275 patients with type 2 diabetes mellitus (T2DM) and 496 nondiabetic subjects (Total n = 771). To increase the sensitivity of CNV identification, we incorporated multiple factors using PennCNV, a program that is based on the hidden Markov model (HMM). To assess the genetic effect of CNV on T2DM, a multivariate logistic regression model controlling for age and gender was used. We identified a total of 7,478 CNVs (average of 9.7 CNVs per individual) and 2,554 CNV regions (CNVRs; 164 common CNVRs for frequency>1%) in this study. Although we failed to demonstrate robust associations between CNVs and the risk of T2DM, our results revealed a putative association between several CNVRs including chr15:45994758-45999227 (P = 8.6E-04, P(corr) = 0.01) and the risk of T2DM. The identified CNVs in this study were validated using overlapping analysis with the Database of Genomic Variants (DGV; 71.7% overlap), and quantitative PCR (qPCR). The identified variations, which encompassed functional genes, were significantly enriched in the cellular part, in the membrane-bound organelle, in the development process, in cell communication, in signal transduction, and in biological regulation. CONCLUSION/SIGNIFICANCE: We expect that the methods and findings in this study will contribute in particular to genome studies of Asian populations

Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: Rigorous analysis of levels and trends in exposure to leading risk factors and quantification of their effect on human health are important to identify where public health is making progress and in which cases current efforts are inadequate. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a standardised and comprehensive assessment of the magnitude of risk factor exposure, relative risk, and attributable burden of disease. Methods: GBD 2019 estimated attributable mortality, years of life lost (YLLs), years of life lived with disability (YLDs), and disability-adjusted life-years (DALYs) for 87 risk factors and combinations of risk factors, at the global level, regionally, and for 204 countries and territories. GBD uses a hierarchical list of risk factors so that specific risk factors (eg, sodium intake), and related aggregates (eg, diet quality), are both evaluated. This method has six analytical steps. (1) We included 560 risk–outcome pairs that met criteria for convincing or probable evidence on the basis of research studies. 12 risk–outcome pairs included in GBD 2017 no longer met inclusion criteria and 47 risk–outcome pairs for risks already included in GBD 2017 were added based on new evidence. (2) Relative risks were estimated as a function of exposure based on published systematic reviews, 81 systematic reviews done for GBD 2019, and meta-regression. (3) Levels of exposure in each age-sex-location-year included in the study were estimated based on all available data sources using spatiotemporal Gaussian process regression, DisMod-MR 2.1, a Bayesian meta-regression method, or alternative methods. (4) We determined, from published trials or cohort studies, the level of exposure associated with minimum risk, called the theoretical minimum risk exposure level. (5) Attributable deaths, YLLs, YLDs, and DALYs were computed by multiplying population attributable fractions (PAFs) by the relevant outcome quantity for each age-sex-location-year. (6) PAFs and attributable burden for combinations of risk factors were estimated taking into account mediation of different risk factors through other risk factors. Across all six analytical steps, 30 652 distinct data sources were used in the analysis. Uncertainty in each step of the analysis was propagated into the final estimates of attributable burden. Exposure levels for dichotomous, polytomous, and continuous risk factors were summarised with use of the summary exposure value to facilitate comparisons over time, across location, and across risks. Because the entire time series from 1990 to 2019 has been re-estimated with use of consistent data and methods, these results supersede previously published GBD estimates of attributable burden. Findings: The largest declines in risk exposure from 2010 to 2019 were among a set of risks that are strongly linked to social and economic development, including household air pollution; unsafe water, sanitation, and handwashing; and child growth failure. Global declines also occurred for tobacco smoking and lead exposure. The largest increases in risk exposure were for ambient particulate matter pollution, drug use, high fasting plasma glucose, and high body-mass index. In 2019, the leading Level 2 risk factor globally for attributable deaths was high systolic blood pressure, which accounted for 10·8 million (95% uncertainty interval [UI] 9·51–12·1) deaths (19·2% [16·9–21·3] of all deaths in 2019), followed by tobacco (smoked, second-hand, and chewing), which accounted for 8·71 million (8·12–9·31) deaths (15·4% [14·6–16·2] of all deaths in 2019). The leading Level 2 risk factor for attributable DALYs globally in 2019 was child and maternal malnutrition, which largely affects health in the youngest age groups and accounted for 295 million (253–350) DALYs (11·6% [10·3–13·1] of all global DALYs that year). The risk factor burden varied considerably in 2019 between age groups and locations. Among children aged 0–9 years, the three leading detailed risk factors for attributable DALYs were all related to malnutrition. Iron deficiency was the leading risk factor for those aged 10–24 years, alcohol use for those aged 25–49 years, and high systolic blood pressure for those aged 50–74 years and 75 years and older. Interpretation: Overall, the record for reducing exposure to harmful risks over the past three decades is poor. Success with reducing smoking and lead exposure through regulatory policy might point the way for a stronger role for public policy on other risks in addition to continued efforts to provide information on risk factor harm to the general public

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings: In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning