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Background/ObjectivesGlucagon is important in the maintenance of glucose homeostasis, with also effects on lipids. In this study, we aimed to apply a recently developed model of glucagon kinetics to determine the sensitivity of glucagon variations (especially, glucagon inhibition) to insulin levels (“alpha-cell insulin sensitivity”), during oral glucose administration.Subjects/MethodsWe studied 50 participants (spanning from normal glucose tolerance to type 2 diabetes) undergoing frequently sampled 5-hr oral glucose tolerance test (OGTT). The alpha-cell insulin sensitivity and the glucagon kinetics were assessed by a mathematical model that we developed previously.ResultsThe alpha-cell insulin sensitivity parameter (named SGLUCA; “GLUCA”: “glucagon”) was remarkably variable among participants (CV=221%). SGLUCA was found inversely correlated with the mean glycemic values, as well as with 2-hr glycemia of the OGTT. When stratifying participants into two groups (normal glucose tolerance, NGT, N=28, and impaired glucose regulation/type 2 diabetes, IGR_T2D, N=22), we found that SGLUCA was lower in the latter (1.50 ± 0.50·10-2vs. 0.26 ± 0.14·10-2 ng·L-1GLUCA/pmol·L-1INS, in NGT and IGR_T2D, respectively, p=0.009; “INS”: “insulin”).ConclusionsThe alpha-cell insulin sensitivity is highly variable among subjects, and it is different in groups at different glucose tolerance. This may be relevant for defining personalized treatment schemes, in terms of dietary prescriptions but also for treatments with glucagon-related agents.

Conclusion: Identifying threshold values for insulin therapy and risk stratification in twin pregnancy are crucial for optimal patient management.

Women with preexisting diabetes and gestational diabetes mellitus (GDM) are at higher risk for adverse maternal and neonatal outcomes. However, there is no consensus on a uniform approach regarding mode of birth (MOB) for all forms of diabetes. The aim of the study is to compare MOB in women with preexisting diabetes and GDM and possible factors influencing it. A retrospective cohort study of women with GDM and preexisting diabetes between 2015 and 2021 at a tertiary referral center was conducted. One thousand three hundred eighty-five singleton pregnancies were included. One thousand twenty-two (74.4%) women had a vaginal birth (VB) and 351 (25.6%) a caesarean section. Preexisting diabetes was significantly associated with caesarean section compared to GDM (OR 2.43). Five hundred fifty-one (40.1%) women underwent induction of labor, and 122 (22.1%) women had a secondary caesarean after IOL. Women induced due to spontaneous rupture of membrane (SROM) achieved the highest rate of VB at 93%. The lowest rates of VB occurred if indication for induction was for preeclampsia or hypertension. IOL was significantly less successful in preexisting diabetes with a VB achieved in 56.4% for type 1 diabetes and 52.6% of type 2 diabetes compared to GDM (78.2% in GDM; 81.2% in IGDM; OR 3.25, 95% CI 1.70–6.19, p

Abstract Background The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy. Methods At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition. Results The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p

Posttransplant diabetes mellitus (PTDM) is a common complication after kidney transplantation. Pathophysiologically, whether beta-cell dysfunction rather than insulin resistance may be the predominant defect in PTDM has been a matter of debate. The aim of the present analysis was to compare glucometabolism in kidney transplant recipients with and without PTDM. To this aim, we included 191 patients from a randomized controlled trial who underwent oral glucose tolerance tests (OGTTs) 6 months after transplantation. We derived several basic indices of beta-cell function and insulin resistance as well as variables from mathematical modeling for a more robust beta-cell function assessment. Mean ± standard deviation of the insulin sensitivity parameter PREDIM was 3.65 ± 1.68 in PTDM versus 5.46 ± 2.57 in NON-PTDM. Model-based glucose sensitivity (indicator of beta-cell function) was 68.44 ± 57.82 pmol∙min−1∙m−2∙mM−1 in PTDM versus 143.73 ± 112.91 pmol∙min−1∙m−2∙mM−1 in NON-PTDM, respectively. Both basic indices and model-based parameters of beta-cell function were more than 50% lower in patients with PTDM, indicating severe beta-cell impairment. Nonetheless, some defects in insulin sensitivity were also present, although less marked. We conclude that in PTDM, the prominent defect appears to be beta-cell dysfunction. From a pathophysiological point of view, patients at high risk for developing PTDM may benefit from intensive treatment of hyperglycemia over the insulin secretion axis.

Abstract It has not been elucidated whether incretins affect insulin clearance in type 2 diabetes (T2D). We aimed exploring possible associations between insulin clearance and endogenously secreted or exogenously administered incretins in T2D patients. Twenty T2D patients were studied (16 males/4 females, 59 ± 2 years (mean ± standard error), BMI = 31 ± 1 kg/m2, HbA1c = 7.0 ± 0.1%). Patients were treated with metformin, sitagliptin, metformin/sitagliptin combination, and placebo (randomized order). On each treatment period, oral and isoglycemic intravenous glucose infusion tests were performed (OGTT, IIGI, respectively). We also studied twelve T2D patients (9 males/3 females, 61 ± 3 years, BMI = 30 ± 1 kg/m2, HbA1c = 7.3 ± 0.4%) that underwent infusion of GLP-1(7–36)-amide, GIP, GLP-1/GIP combination, and placebo. Plasma glucose, insulin, C-peptide, and incretins were measured. Insulin clearance was assessed as insulin secretion to insulin concentration ratio. In the first study, we found OGTT/IIGI insulin clearance ratio weakly inversely related to OGTT/IIGI total GIP and intact GLP-1 (R2 = 0.13, p 0.5). Thus, our data suggest that in T2D there are no relevant incretin effects on insulin clearance. Conversely, different antidiabetic treatments may determine insulin clearance variations.

Amino acids (AAs) are well known to be involved in the regulation of glucose metabolism and, in particular, of insulin secretion. However, the effects of different AAs on insulin release and kinetics have not been completely elucidated. The aim of this study was to propose a mathematical model that includes the effect of AAs on insulin kinetics during a mixed meal tolerance test. To this aim, five different models were proposed and compared. Validation was performed using average data, derived from the scientific literature, regarding subjects with normal glucose tolerance (CNT) and with type 2 diabetes (T2D). From the average data of the CNT and T2D people, data for two virtual populations (100 for each group) were generated for further model validation. Among the five proposed models, a simple model including one first-order differential equation showed the best results in terms of model performance (best compromise between model structure parsimony, estimated parameters plausibility, and data fit accuracy). With regard to the contribution of AAs to insulin appearance/disappearance (kAA model parameter), model analysis of the average data from the literature yielded 0.0247 (confidence interval, CI: 0.0168 – 0.0325) and -0.0048 (CI: -0.0281 – 0.0185) μU·ml-1/(μmol·l-1·min), for CNT and T2D, respectively. This suggests a positive effect of AAs on insulin secretion in CNT, and negligible effect in T2D. In conclusion, a simple model, including single first-order differential equation, may help to describe the possible AAs effects on insulin kinetics during a physiological metabolic test, and provide parameters that can be assessed in the single individuals.

BackgroundThis study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to apply it to study women with previous gestational diabetes mellitus (pGDM).MethodsCSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from normal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each subject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG.ResultsCSG was described as CSG=1.06×10−2+5.71×10−2×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10–25- and 25–50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (

Women with a previous history of gestational diabetes mellitus (GDM) have increased risk of developing GDM in future pregnancies (i.e. recurrent GDM) and also Type 2 Diabetes (T2D). Insulin clearance represents one of the processes regulating glucose tolerance but has been scarcely investigated for its possible impairment in high-risk subjects. The aim of this study was to identify possible determinants of insulin clearance in women with a previous history of GDM. A detailed model-based analysis of a regular 3-hour, insulin-modified intravenous glucose tolerance test (IM-IVGTT) has been performed in women with a previous history of GDM (pGDM, n = 115) and in women who had a healthy pregnancy (CNT, n = 41) to assess total, first-phase and second-phase insulin clearance (ClINS-TOT, ClINS-FP and ClINS-SP) and other metabolic parameters (insulin sensitivity SI, glucose effectiveness SG, beta-cell function and disposition index DI). CLINS-SP was found increased in pGDM with respect to CNT and was found significantly inversely linearly correlated with SG (r = -0.20, p = 0.03, slope: -16.2, 95% CI -30.9 to -1.4, intercept: 1.1, 95% CI 0.7–1.4) and also with DI (r = -0.22, p = 0.02, slope: -10.0, 95% CI -18.5 to -1.6, intercept: 0.9, 95% CI 0.7–1.3). Disposition index, accounting for the combined contribution of insulin sensitivity and beta-cell function, and glucose effectiveness were identified as possible determinants of insulin clearance in women with a previous history of GDM. This may be of relevance for more accurate estimation and prevention of the risk for recurrent GDM and T2D.

Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.

Background: Flash glucose monitoring (FGM) is a technology with considerable differences compared to continuous glucose monitoring (CGM), but it has been scarcely studied in hemodialysis patients. Thus, we aimed assessing the performance of FGM in such patients by comparison to self-monitoring of blood glucose (SMBG). We will also focus on estimation of glycemic control and variability, and their relationships with parameters of glucose homeostasis.Methods: Thirty-one patients (20 with type 2 diabetes, T2DM, 11 diabetes-free, NODM) collected readings by FGM and SMBG for about 12 days on average. Readings by FGM and SMBG were compared by linear regression, Clarke error grid, and Bland-Altman analyses. Several indices of glycemic control and variability were computed. Ten patients also underwent oral glucose tolerance test (OGTT) for assessment of insulin sensitivity/resistance and insulin secretion/beta-cell function.Results: Flash glucose monitoring and SMBG readings showed very good agreement in both T2DM and NODM (on average, 97 and 99% of readings during hemodialysis in A+B Clarke regions, respectively). Some glycemic control and variability indices were similar by FGM and SMBG (p = 0.06–0.9), whereas others were different (p = 0.0001–0.03). The majority of control and variability indices were higher in T2DM than in NODM, according to both FGM and SMBG (p = 0.0005–0.03). OGTT-based insulin secretion was inversely related to some variability indices according to FGM (R < −0.72, p < 0.02).Conclusions: Based on our dataset, FGM appeared acceptable for glucose monitoring in hemodialysis patients, though partial disagreement with SMBG in glycemic control/variability assessment needs further investigations.

Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., SGLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for SGLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 – -0.64) between SGLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.

Mathematical modelling in glucose metabolism has proven very useful for different reasons. Several models have allowed deeper understanding of the relevant physiological and pathophysiological aspects and promoted new experimental activity to reach increased knowledge of the biological and physiological systems of interest. Glucose metabolism modelling has also proven useful to identify the parameters with specific physiological meaning in single individuals, this being relevant for clinical applications in terms of precision diagnostics or therapy. Among those model-based physiological parameters, an important role resides in those for the assessment of different functional aspects of the pancreatic beta cell. This study focuses on the mathematical models of incretin hormones and other endogenous substances with known effects on insulin secretion and beta-cell function, mainly amino acids, non-esterified fatty acids, and glucagon. We found that there is a relatively large number of mathematical models for the effects on the beta cells of incretin hormones, both at the cellular/organ level or at the higher, whole-body level. In contrast, very few models were identified for the assessment of the effect of other insulin secretagogues. Given the opportunities offered by mathematical modelling, we believe that novel models in the investigated field are certainly advisable.

Sarcopenia is emerging as a severe complication in type 2 diabetes (T2DM). On the other hand, it has been documented that nutritional aspects, such as insufficient protein or total energy intake, increase sarcopenia risk. The analysis of body composition is a relevant approach to assess nutritional status, and different techniques are available. Among such techniques, bioelectrical impedance analysis (BIA) is particularly interesting, since it is non-invasive, simple, and less expensive than the other techniques. Therefore, we conducted a review study to analyze the studies using BIA for body composition analysis in T2DM patients with sarcopenia or at risk of catching it. Revised studies have provided important information concerning relationships between body composition parameters (mainly muscle mass) and other aspects of T2DM patients’ conditions, including different comorbidities, and information on how to avoid muscle mass deterioration. Such relevant findings suggest that BIA can be considered appropriate for body composition analysis in T2DM complicated by sarcopenia/muscle loss. The wide size of the patients’ cohort in many studies confirms that BIA is convenient for clinical applications. However, studies with a specific focus on the validation of BIA, in the peculiar population of patients with T2DM complicated by sarcopenia, should be considered.

Sarcopenia has been defined as a progressive and generalized loss of muscle mass that can be observed after the age of 40 years, with a rate of deterioration of about 8% every ten years up to 70 years, and 15–25% thereafter [...]

Background: glucagon secretion and inhibition should be mainly determined by glucose and insulin levels, but the relative relevance of each factor is not clarified, especially following ingestion of different macronutrients. We aimed to investigate the associations between plasma glucagon, glucose, and insulin after ingestion of single macronutrients or mixed-meal. Methods: thirty-six participants underwent four metabolic tests, based on administration of glucose, protein, fat, or mixed-meal. Glucagon, glucose, insulin, and C-peptide were measured at fasting and for 300 min following food ingestion. We analyzed relationships between time samples of glucagon, glucose, and insulin in each individual, as well as between suprabasal area-under-the-curve of the same variables (ΔAUCGLUCA, ΔAUCGLU, ΔAUCINS) over the whole participants’ cohort. Results: in individuals, time samples of glucagon and glucose were related in only 26 cases (18 direct, 8 inverse relationships), whereas relationship with insulin was more frequent (60 and 5, p < 0.0001). The frequency of significant relationships was different among tests, especially for direct relationships (p ≤ 0.006). In the whole cohort, ΔAUCGLUCA was weakly related to ΔAUCGLU (p ≤ 0.02), but not to ΔAUCINS, though basal insulin secretion emerged as possible covariate. Conclusions: glucose and insulin are not general and exclusive determinants of glucagon secretion/inhibition after mixed-meal or macronutrients ingestion.

The aim of this study was to investigate whether incretins, at physiological levels, affect hepatic and/or extrahepatic insulin clearance. Hepatic and extrahepatic insulin clearance was studied in 31 double incretin receptor knockout (DIRKO) and 45 wild-type (WT) mice, which underwent an Intravenous Glucose Tolerance Test (IVGTT). A novel methodology based on mathematical modeling was designed to provide two sets of values (FEL-P1, CLP-P1; FEL-P2, CLP-P2) accounting for hepatic and extrahepatic clearance in the IVGTT first and second phases, respectively, plus the respective total clearances, CLT-P1 and CLT-P2. A statistically significant difference between DIRKO and WT was found in CLT-P1 (0.61 [0.48–0.82] vs. 0.51 [0.46–0.65] (median [interquartile range]); p = 0.02), which was reflected in the peripheral component, CLP-P1 (0.18 [0.13–0.27] vs. 0.15 [0.11–0.22]; p = 0.04), but not in the hepatic component, FEL-P1 (29.7 [26.7–34.9] vs. 28.9 [25.7–32.0]; p = 0.18). No difference was detected between DIRKO and WT in CLT-P2 (1.38 [1.13–1.75] vs. 1.69 [1.48–1.87]; p = 0.10), neither in CLP-P2 (0.72 [0.64–0.81] vs. 0.79 [0.69–0.87]; p = 0.27) nor in FEL-P2 (37.8 [35.1–43.1] vs. 39.8 [35.8–44.2]; p = 0.46). In conclusion, our findings suggest that the higher insulin clearance observed in DIRKO compared with WT during the IVGTT first phase may be due to its extrahepatic component.

Gestational diabetes (GDM) is a common complication during pregnancy that is strongly associated with adverse fetal and maternal outcomes. Advanced maternal age (≥35 years) is a known risk factor for GDM. Studies advocate that GDM comprises distinctive metabolic entities, suggesting an individualized approach based on early pregnancy characteristics (such as 75 g oGTT values, maternal age, obstetric history). The oGTT blood glucose levels of 1,664 women were categorized into isolated fasting hyperglycemia (GDM-IFH), isolated postprandial hyperglycemia (GDM-IPH) and combined hyperglycemia (GDM-CH), using the levels of the fasting, 1 h and 2 h values after glucose application. These three subtypes were analysed regarding baseline characteristics as well as fetal and maternal outcome in the context of maternal age. This analysis reveals that the 75 g oGTT levels and maternal age can distinguish metabolic phenotypes in women with GDM. The overall rate of insulin therapy required was higher in women from the GDM-CH group and increased with maternal age (31.7 %, 38.2 %, <35 years, ≥35–39 years respectively, vs. total insulin rate 22.3 %, p-value <0.001). Women ≥35 years displayed a significantly higher caesarean delivery (CD) rate (<35 years 34.6 %, 38.4 %, 41.1 % vs. ≥35 years 54.8 %, 47.6 %, 46.5 %, GDM-IFH, GDM-IPH, GDM-CH respectively, p-value <0.001). Women with fasting hyperglycemia, especially those with combined hyperglycemia and advanced maternal age (AMA) display a higher risk for unfavorable perinatal outcome. A categorization based on oGTT values and maternal age, as well as other characteristics can facilitate a basis for clinical risk stratification. Women at risk should receive an individualized and intensified perinatal care as well as interventional therapies. [ABSTRACT FROM AUTHOR]

The prevalence of obesity is growing worldwide, and strategies to overcome this epidemic need to be developed urgently. Bariatric surgery is a very effective treatment option to reduce excess weight and often performed in women of reproductive age. Weight loss influences fertility positively and can resolve hormonal imbalance. So far, guidelines suggest conceiving after losing maximum weight and thus recommend conception at least 12–24 months after surgery. As limited data of these suggestions exist, further evidence is urgently needed as well for weight gain in pregnancy. Oral glucose tolerance tests for the diagnosis of gestational diabetes mellitus (GDM) should not be performed after bariatric procedures due to potential hypoglycaemic adverse events and high variability of glucose levels after glucose load. This challenges the utility of the usual diagnostic criteria for GDM in accurate prediction of complications. Furthermore, recommendations on essential nutrient supplementation in pregnancy and lactation in women after bariatric surgery are scarce. In addition, nutritional deficiencies or daily intake recommendations in pregnant women after bariatric surgery are not well investigated. This review summarizes current evidence, proposes clinical recommendations in pregnant women after bariatric surgery, and highlights areas of lack of evidence and the resulting urgent need for more clinical investigations.

Objective. The objective of the present study is to investigate the relationship of cardiac fat depots with disturbances of the carbohydrate metabolism in women with PCOS. Methods. An oral glucose tolerance test (OGTT) was realized, and metabolic parameters were collected in 48 women with PCOS and in 20 controls. Intramyocardial fat (MYCL) and pericardial fat (PERI) were measured using 1H-magnetic resonance spectroscopy and imaging. Results. Only in PCOS women, PERI was positively and independently related to parameters of glucose metabolism (HbA1c: p=0.001, fasting plasma glucose: p

Objective. The aim of this study was to evaluate sex specific differences of metabolic and clinical characteristics of treated hyperlipidemic men and women (HL-men and HL-women). Methods. In this study vascular and metabolic characteristics of 35 HL-women and 64 HL-men were assessed. In addition a sex specific analysis of metabolic and nutritional habits of HL-patients with prediabetes (HL-IGR) was done. Results. HL-women were older and had favourable concentrations of high density lipoprotein cholesterol (HDL-cholesterol), triglycerides (TG), and triglyceride/HDL-cholesterol ratio (TG/HDL-ratio) but were also shown to have higher concentrations of lipoprotein-a compared to HL-men. HL-men were characterized as having higher levels of liver-specific parameters and body weight as well as being more physically active compared to HL-women. Brain natriuretic peptide (pro-BNP) was higher in HL-women than HL-men, while no differences in metabolic syndrome and glycemic parameters were shown. HL-IGR-women were also older and still had a better profile of sex specific lipid parameters, as well as a lower body weight compared to HL-IGR-men. No differences were seen in vascular parameters such as the intima media thickness (IMT). Conclusion. HL-women were older and had overall more favourable concentrations of lipid parameters and liver enzymes but did not differ regarding vascular morphology and insulin sensitivity compared to HL-men of comparable body mass index (BMI).

[This corrects the article DOI: 10.1371/journal.pone.0128317.].

Meiotic errors during oocyte maturation are considered the major contributors to embryonic aneuploidy and failures in human IVF treatment. Various technologies have been developed to screen polar bodies, blastomeres and trophectoderm cells for chromosomal aberrations. Array-CGH analysis using bacterial artificial chromosome (BAC) arrays is widely applied for preimplantation genetic diagnosis (PGD) using single cells. Recently, an increase in the pregnancy rate has been demonstrated using array-CGH to evaluate trophectoderm cells. However, in some countries, the analysis of embryonic cells is restricted by law. Therefore, we used BAC array-CGH to assess the impact of polar body analysis on the live birth rate. A disadvantage of polar body aneuploidy screening is the necessity of the analysis of both the first and second polar bodies, resulting in increases in costs for the patient and complex data interpretation. Aneuploidy screening results may sometimes be ambiguous if the first and second polar bodies show reciprocal chromosomal aberrations. To overcome this disadvantage, we tested a strategy involving the pooling of DNA from both polar bodies before DNA amplification. We retrospectively studied 351 patients, of whom 111 underwent polar body array-CGH before embryo transfer. In the group receiving pooled polar body array-CGH (aCGH) analysis, 110 embryos were transferred, and 29 babies were born, corresponding to live birth rates of 26.4% per embryo and 35.7% per patient. In contrast, in the control group, the IVF treatment was performed without preimplantation genetic screening (PGS). For this group, 403 embryos were transferred, and 60 babies were born, resulting in live birth rates of 14.9% per embryo and 22.7% per patient. In conclusion, our data show that in the aCGH group, the use of aneuploidy screening resulted in a significantly higher live birth rate compared with the control group, supporting the benefit of PGS for IVF couples in addition to the suitability and effectiveness of our polar body pooling strategy.