MTNR1B Genetic Variability Is Associated with Gestational Diabetes in Czech Women

The gene MTNR1B encodes a receptor for melatonin. Melatonin receptors are expressed in human -cells, which implies that genetic variants might affect glucose tolerance. Meta-analysis confirmed that the rs10830963 shows the most robust association. The aim of the study was to assess the rs10830963 in Czech GDM patients and controls and to study relations between the SNP and biochemical as well as anthropometric characteristics. Our cohort consisted of 880 women; 458 were diagnosed with GDM, and 422 were normoglycemic controls without history of GDM. Despite similar BMI, the GDM group showed higher WHR, waist circumference, abdominal circumference, and total body fat content. The risk allele G was more frequent in the GDM group (38.3 versus 29.4% in controls, OR 1.49 CI95% [1.22; 1.82]; OR = 0.0001). In spite of higher frequency, the G allele in the GDM group was not associated with any markers of glucose metabolism. In contrast, controls showed significant association of the allele G with FPG and with postchallenge glycemia during the oGTT. Frequency analysis indicates that rs10830963 is involved in gestational diabetes in Czech women. However, the association of the SNP with glucose metabolism, which is obvious in controls, is covert in women who have experienced GDM

MTNR1B Genetic Variability Is Associated with Gestational Diabetes in Czech Women

The gene MTNR1B encodes a receptor for melatonin. Melatonin receptors are expressed in human β-cells, which implies that genetic variants might affect glucose tolerance. Meta-analysis confirmed that the rs10830963 shows the most robust association. The aim of the study was to assess the rs10830963 in Czech GDM patients and controls and to study relations between the SNP and biochemical as well as anthropometric characteristics. Our cohort consisted of 880 women; 458 were diagnosed with GDM, and 422 were normoglycemic controls without history of GDM. Despite similar BMI, the GDM group showed higher WHR, waist circumference, abdominal circumference, and total body fat content. The risk allele G was more frequent in the GDM group (38.3 versus 29.4% in controls, OR 1.49 CI95% [1.22; 1.82]; POR=0.0001). In spite of higher frequency, the G allele in the GDM group was not associated with any markers of glucose metabolism. In contrast, controls showed significant association of the allele G with FPG and with postchallenge glycemia during the oGTT. Frequency analysis indicates that rs10830963 is involved in gestational diabetes in Czech women. However, the association of the SNP with glucose metabolism, which is obvious in controls, is covert in women who have experienced GDM

Insulin sensitivity and secretion in obese Type 2 diabetic women after various bariatric operations

Objective: To compare the effects of biliopancreatic diversion (BPD) and laparoscopic gastric banding (LAGB) on insulin sensitivity and secretion with the effects of laparoscopic gastric plication (P). Methods: A total of 52 obese women (age 30-66 years) suffering from type 2 diabetes mellitus (T2DM) were prospectively recruited into three study groups: 16 BPD; 16 LAGB, and 20 P. Euglycemic clamps and mixed meal tolerance tests were performed before, at 1 month and at 6 months after bariatric surgery. Beta cell function derived from the meal test parameters was evaluated using mathematical modeling. Results: Glucose disposal per kilogram of fat free mass (a marker of peripheral insulin sensitivity) increased significantly in all groups, especially after 1 month. Basal insulin secretion decreased significantly after all three types of operations, with the most marked decrease after BPD compared with P and LAGB. Total insulin secretion decreased significantly only following the BPD. Beta cell glucose sensitivity did not change significantly post-surgery in any of the study groups. Conclusion: We documented similar improvement in insulin sensitivity in obese T2DM women after all three study operations during the 6-month postoperative follow-up. Notably, only BPD led to decreased demand on beta cells (decreased integrated insulin secretion), but without increasing the beta cell glucose sensitivity

BRAF V600E Status Sharply Differentiates Lymph Node Metastasis-associated Mortality Risk in Papillary Thyroid Cancer

[Context]: How lymph node metastasis (LNM)-associated mortality risk is affected by BRAF V600E in papillary thyroid cancer (PTC) remains undefined. [Objective]: To study whether BRAF V600E affected LNM-associated mortality in PTC. [Design, Setting, and Participants]: We retrospectively analyzed the effect of LNM on PTC-specific mortality with respect to BRAF status in 2638 patients (2015 females and 623 males) from 11 centers in 6 countries, with median age of 46 [interquartile range (IQR) 35-58] years and median follow-up time of 58 (IQR 26-107) months. [Results]: Overall, LNM showed a modest mortality risk in wild-type BRAF patients but a strong one in BRAF V600E patients. In conventional PTC (CPTC), LNM showed no increased mortality risk in wild-type BRAF patients but a robustly increased one in BRAF V600E patients; mortality rates were 2/659 (0.3%) vs 4/321 (1.2%) in non-LNM vs LNM patients (P = 0.094) with wild-type BRAF, corresponding to a hazard ratio (HR) (95% CI) of 4.37 (0.80-23.89), which remained insignificant at 3.32 (0.52-21.14) after multivariate adjustment. In BRAF V600E CPTC, morality rates were 7/515 (1.4%) vs 28/363 (7.7%) in non-LNM vs LNM patients (P < 0.001), corresponding to an HR of 4.90 (2.12-11.29) or, after multivariate adjustment, 5.76 (2.19-15.11). Adjusted mortality HR of coexisting LNM and BRAF V600E vs absence of both was 27.39 (5.15-145.80), with Kaplan-Meier analyses showing a similar synergism. [Conclusions]: LNM-associated mortality risk is sharply differentiated by the BRAF status in PTC; in CPTC, LNM showed no increased mortality risk with wild-type BRAF but a robust one with BRAF mutation. These results have strong clinical relevance.This work was supported partly by the following funding at the individual participating centers: Polish National Center of Research and Development MILESTONE Project—molecular diagnostics and imaging in individualized therapy for breast, thyroid and prostate cancer, grant No. STRATEGMED2/267398/4/ NCBR/2015 (Poland, AC, BJ); Grants No. PID2019-105303RB-I00 (AEI from MICINN), GCB14142311CRES (AECC Foundation), and B2017/BMD-3724 TIRONET2-CM (Spain; PS and GR-E); Grant No. AZV 16-32665A and MH CZ-DRO (Institute of Endocrinology-EU, 00023761) (Czech Republic; BB, VS); NIH/ National Institute on Aging Grant No. 5R03AG042334-02 (LY); and grants from the Qingdao Science and Technology Project for People’s Livelihood No.13-1-3-58-nsh (China; FW) and the Innovative Platform Project of Qingdao No.12-1-2-15-jch (China; YW)

<i>RET</i> Variants and Haplotype Analysis in a Cohort of Czech Patients with Hirschsprung Disease

<div><p>Hirschsprung disease (HSCR) is a congenital aganglionosis of myenteric and submucosal plexuses in variable length of the intestine. This study investigated the influence and a possible modifying function of <i>RET</i> proto-oncogene's single nucleotide polymorphisms (SNPs) and haplotypes in the development and phenotype of the disease in Czech patients. Genotyping of 14 SNPs was performed using TaqMan Genotyping Assays and direct sequencing. The frequencies of SNPs and generated haplotypes were statistically evaluated using chi-square test and the association with the risk of HSCR was estimated by odds ratio. SNP analysis revealed significant differences in frequencies of 11 polymorphic <i>RET</i> variants between 162 HSCR patients and 205 unaffected controls. Particularly variant alleles of rs1864410, rs2435357, rs2506004 (intron 1), rs1800858 (exon 2), rs1800861 (exon 13), and rs2565200 (intron 19) were strongly associated with increased risk of HSCR (p<0.00000) and were over-represented in males vs. females. Conversely, variant alleles of rs1800860, rs1799939 and rs1800863 (exons 7, 11, 15) had a protective role. The haploblock comprising variants in intron 1 and exon 2 was constructed. It represented a high risk of HSCR, however, the influence of other variants was also found after pruning from effect of this haploblock. Clustering patients according to genotype status in haploblock revealed a strong co-segregation with several SNPs and pointed out the differences between long and short form of HSCR. This study involved a large number of SNPs along the entire <i>RET</i> proto-oncogene with demonstration of their risk/protective role also in haplotype and diplotype analysis in the Czech population. The influence of some variant alleles on the aggressiveness of the disease and their role in gender manifestation differences was found. These data contribute to worldwide knowledge of the genetics of HSCR.</p></div

Routine OGTT: A Robust Model Including Incretin Effect for Precise Identification of Insulin Sensitivity and Secretion in a Single Individual

<div><p>In order to provide a method for precise identification of insulin sensitivity from clinical Oral Glucose Tolerance Test (OGTT) observations, a relatively simple mathematical model (Simple Interdependent glucose/insulin MOdel SIMO) for the OGTT, which coherently incorporates commonly accepted physiological assumptions (incretin effect and saturating glucose-driven insulin secretion) has been developed. OGTT data from 78 patients in five different glucose tolerance groups were analyzed: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), impaired fasting glucose (IFG), IFG+IGT, and Type 2 Diabetes Mellitus (T2DM). A comparison with the 2011 Salinari (COntinuos GI tract MOdel, COMO) and the 2002 Dalla Man (Dalla Man MOdel, DMMO) models was made with particular attention to insulin sensitivity indices IS_COMO, IS_DMMO and <i>k_xgi</i> (the insulin sensitivity index for SIMO). ANOVA on <i>k_xgi</i> values across groups resulted significant overall (P<0.001), and post-hoc comparisons highlighted the presence of three different groups: NGT (8.62×10⁻⁵±9.36×10⁻⁵ min⁻¹pM⁻¹), IFG (5.30×10⁻⁵±5.18×10⁻⁵) and combined IGT, IFG+IGT and T2DM (2.09×10⁻⁵±1.95×10⁻⁵, 2.38×10⁻⁵±2.28×10⁻⁵ and 2.38×10⁻⁵±2.09×10⁻⁵ respectively). No significance was obtained when comparing IS_COMO or IS_DMMO across groups. Moreover, <i>k_xgi</i> presented the lowest sample average coefficient of variation over the five groups (25.43%), with average CVs for IS_COMO and IS_DMMO of 70.32% and 57.75% respectively; <i>k_xgi</i> also presented the strongest correlations with all considered empirical measures of insulin sensitivity. While COMO and DMMO appear over-parameterized for fitting single-subject clinical OGTT data, SIMO provides a robust, precise, physiologically plausible estimate of insulin sensitivity, with which habitual empirical insulin sensitivity indices correlate well. The <i>k_xgi</i> index, reflecting insulin secretion dependency on glycemia, also significantly differentiates clinically diverse subject groups. The SIMO model may therefore be of value for the quantification of glucose homeostasis from clinical OGTT data.</p></div

Visual Predictive Check.

<p>Visual Predictive Check (VPC) for each of the 5 groups (panel A for NGT, IFG and IGT; panel B for IFG+IGT and T2DM). For each patient 200 simulations were performed with the model: the shaded area represents the 90% prediction interval, dashed lines represent the 25-th, 50-th and 75-th percentile. Observed data are reported as circles.</p

Block diagram of the model.

<p>Schematic representation of the six-compartment model. D is the orally administered quantity of glucose. S represents the quantity of glucose in the stomach while J, R and L represent the glucose content in the jejunum, in a delay compartment and in the Ileum respectively. G indicates the compartment for the plasma glucose concentration and I indicates the insulin plasma concentration. Measurements were taken for plasma glucose and insulin concentrations. Continuous lines represent entry or exit fluxes while dotted lines represent stimulation (arrows) or inhibition (black circles) mechanisms.</p

Correlations among empirical measures of insulin sensitivity and model-derived insulin sensitivity indices.

<p>Asterisks indicate significance of the correlations: * P<0.01, **P<0.001, NS Not Significant.</p><p><b>HOMA-IS:</b> Homeostasis Model Assessment.</p><p><b>ISIcomp:</b> Insulin Sensitivity Index Composite.</p><p><b>MCRrest</b>: glucose Metabolic Clearance Rate.</p><p><b>OGIS</b>: Oral Glucose Insulin Sensitivity index as estimated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070875#pone.0070875-Mari1" target="_blank">[6]</a>.</p><p><b>IS_BREDA</b>: Insulin Sensitivity index as derived in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070875#pone.0070875-Breda1" target="_blank">[9]</a>.</p><p><b>IS_NAIF</b>: Insulin Sensitivity index computed as the inverse of the mean of the observed glycemias during the OGTT.</p><p><b>IS_DMMO:</b> Insulin Sensitivity index as derived from the Dalla Man model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070875#pone.0070875-DallaMan1" target="_blank">[10]</a>.</p><p><b>IS_COMO:</b> Insulin Sensitivity index as derived from the Salinari model <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070875#pone.0070875-Salinari1" target="_blank">[13]</a>.</p><p><b>k_xgi:</b> Insulin Sensitivity index as derived from the proposed SIMO model.</p

Glucose prediction diagnostic plot.

<p>Panel A reports weighted residuals versus time, panel B reports weighted residuals versus glucose predictions and panel C reports observed concentrations versus predicted concentrations.</p