Metabolic syndrome in polycystic ovary syndrome

Zespół metaboliczny (MS, metabolic syndrome) i zespół policystycznych jajników (PCOS, polycystic ovary syndrome) są często rozpoznawanymi zaburzeniami u kobiet. Częstość występowania zespołu metabolicznego u kobiet z PCOS zależy od zastosowanych kryteriów diagnostycznych. W prezentowanej pracy autorzy rozważają tezę, że przyczyną obu zaburzeń może być insulinooporność, będąca następstwem otyłości brzusznej. Ponadto dokonują przeglądu literatury dotyczącej występowania MS u kobiet z PCOS oraz omawiają wpływ wyboru określonych kryteriów diagnostycznych MS i PCOS na oszacowanie czêstoœci ich występowania.Both metabolic syndrome (MS) and polycystic ovary syndrome (PCOS) are common among women. The exact prevalence of MS in women with PCOS is dependent upon the diagnostic criteria used for each. However, the frequent co-occurrence of both MS and PCOS in women is suggestive of a common aetiology. In this short review article we argue that insulin resistance, as a consequence of abdominal obesity, may represent such a common aetiology. We also review the literature on the prevalence of MS in women with PCOS and consider the impact that the particular criteria used to diagnose both MS and PCOS may have had on these estimates of prevalence

Low Frequency Variants in the Exons Only Encoding Isoform A of HNF1A Do Not Contribute to Susceptibility to Type 2 Diabetes

Background: There is considerable interest in the hypothesis that low frequency, intermediate penetrance variants contribute to the proportion of Type 2 Diabetes (T2D) susceptibility not attributable to the common variants uncovered through genome-wide association approaches. Genes previously implicated in monogenic and multifactorial forms of diabetes are obvious candidates in this respect. In this study, we focussed on exons 8-10 of the HNF1A gene since rare, penetrant mutations in these exons (which are only transcribed in selected HNF1A isoforms) are associated with a later age of diagnosis of Maturity onset diabetes of the young (MODY) than mutations in exons 1-7. The age of diagnosis in the subgroup of HNF1A-MODY individuals with exon 8-10 mutations overlaps with that of early multifactorial T2D, and we set out to test the hypothesis that these exons might also harbour low-frequency coding variants of intermediate penetrance that contribute to risk of multifactorial T2D. Methodology and principal findings: We performed targeted capillary resequencing of HNF1A exons 8-10 in 591 European T2D subjects enriched for genetic aetiology on the basis of an early age of diagnosis (≤ 45 years) and/or family history of T2D (≥ 1 affected sibling). PCR products were sequenced and compared to the published HNF1A sequence. We identified several variants (rs735396 [IVS9-24T>C], rs1169304 [IVS8+29T>C], c.1768+44C>T [IVS9+44C>T] and rd61953349 [c.1545G>A, p.T515T] but no novel non-synonymous coding variants were detected. Conclusions and significance: We conclude that low frequency, nonsynonymous coding variants in the terminal exons of HNF1A are unlikely to contribute to T2D-susceptibility in European samples. Nevertheless, the rationale for seeking low-frequency causal variants in genes known to contain rare, penetrant mutations remains strong and should motivate efforts to screen other genes in a similar fashion

Combining Information from Common Type 2 Diabetes Risk Polymorphisms Improves Disease Prediction

BACKGROUND: A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed. METHODS AND FINDINGS: Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles. CONCLUSIONS: Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases

Variability of gene expression profiles in human blood and lymphoblastoid cell lines

BACKGROUND: Readily accessible samples such as peripheral blood or cell lines are increasingly being used in large cohorts to characterise gene expression differences between a patient group and healthy controls. However, cell and RNA isolation procedures and the variety of cell types that make up whole blood can affect gene expression measurements. We therefore systematically investigated global gene expression profiles in peripheral blood from six individuals collected during two visits by comparing five of the following cell and RNA isolation methods: whole blood (PAXgene), peripheral blood mononuclear cells (PBMCs), lymphoblastoid cell lines (LCLs), CD19 and CD20 specific B-cell subsets. RESULTS: Gene expression measurements were clearly discriminated by isolation method although the reproducibility was high for all methods (range rho = 0.90-1.00). The PAXgene samples showed a decrease in the number of expressed genes (P < 1*10(-16)) with higher variability (P < 1*10(-16)) compared to the other methods. Differentially expressed probes between PAXgene and PBMCs were correlated with the number of monocytes, lymphocytes, neutrophils or erythrocytes. The correlations (rho = 0.83; rho = 0.79) of the expression levels of detected probes between LCLs and B-cell subsets were much lower compared to the two B-cell isolation methods (rho = 0.98). Gene ontology analysis of detected genes showed that genes involved in inflammatory responses are enriched in B-cells CD19 and CD20 whereas genes involved in alcohol metabolic process and the cell cycle were enriched in LCLs. CONCLUSION: Gene expression profiles in blood-based samples are strongly dependent on the predominant constituent cell type(s) and RNA isolation method. It is crucial to understand the differences and variability of gene expression measurements between cell and RNA isolation procedures, and their relevance to disease processes, before application in large clinical studies

Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci

We performed fine-mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine-mapping implicated rs10830963 as driving T2D association. We confirmed that this T2D-risk allele increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D-risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease

Sex differences in the risk of coronary heart disease associated with type 2 diabetes:a Mendelian Randomization analysis

OBJECTIVE Observational studies have demonstrated that type 2 diabetes is a stronger risk factor for coronary heart disease (CHD) in women compared with men. However, it is not clear whether this reflects a sex differential in the causal effect of diabetes on CHD risk or results from sex-specific residual confounding. RESEARCH DESIGN AND METHODS Using 270 single nucleotide polymorphisms (SNPs) for type 2 diabetes identified in a type 2 diabetes genome-wide association study, we performed a sex-stratified Mendelian randomization (MR) study of type 2 diabetes and CHD using individual participant data in UK Biobank (251,420 women and 212,049 men). Weighted median, MR-Egger, MR-pleiotropy residual sum and outlier, and radial MR from summary-level analyses were used for pleiotropy assessment. RESULTS MR analyses showed that genetic risk of type 2 diabetes increased the odds of CHD for women (odds ratio 1.13 [95% CI 1.08–1.18] per 1-log unit increase in odds of type 2 diabetes) and men (1.21 [1.17–1.26] per 1-log unit increase in odds of type 2 diabetes). Sensitivity analyses showed some evidence of directional pleiotropy; however, results were similar after correction for outlier SNPs. CONCLUSIONS This MR analysis supports a causal effect of genetic liability to type 2 diabetes on risk of CHD that is not stronger for women than men. Assuming a lack of bias, these findings suggest that the prevention and management of type 2 diabetes for CHD risk reduction is of equal priority in both sexes

H-alpha Spectral diversity of type II supernovae

We present a spectroscopic analysis of the H-alpha profiles of hydrogen-rich type II supernovae. A total of 52 type II supernovae having well sampled optical light curves and spectral sequences were analyzed. Concentrating on the H-alpha P-Cygni profile we measure its velocity from the FWHM of emission and the ratio of absorption to emission (a/e) at a common epoch at the start of the recombination phase, and search for correlations between these spectral parameters and photometric properties of the V-band light curves. Testing the strength of various correlations we find that a/e appears to be the dominant spectral parameter in terms of describing the diversity in our measured supernova properties. It is found that supernovae with smaller a/e have higher H-alpha velocities, more rapidly declining light curves from maximum, during the plateau and radioactive tail phase, are brighter at maximum light and have shorter optically thick phase durations. We discuss possible explanations of these results in terms of physical properties of type II supernovae, speculating that the most likely parameters which influence the morphologies of H-alpha profiles are the mass and density profile of the hydrogen envelope, together with additional emission components due to circumstellar interaction.Comment: Accepted for publication in ApJ letters. 9 pages, 3 figures, 2 table

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect