Identification and validation of gestational diabetes subgroups by data-driven cluster analysis.

AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups. METHODS: In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters. RESULTS: Our optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort. CONCLUSIONS/INTERPRETATION: Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes.

Glucagon kinetics assessed by mathematical modelling during oral glucose administration in people spanning from normal glucose tolerance to type 2 diabetes.

Background/Objectives: Glucagon 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/Methods: We 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. Results: The 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 ± 0.50·10-2 vs. 0.26 ± 0.14·10-2 ng·L-1 GLUCA/pmol·L-1 INS, in NGT and IGR_T2D, respectively, p=0.009; "INS": "insulin"). Conclusions: The 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.

Glucometabolism in Kidney Transplant Recipients with and without Posttransplant Diabetes: Focus on Beta-Cell Function.

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.

Glucometabolic Alterations in Pregnant Women with Overweight or Obesity but without Gestational Diabetes Mellitus: An Observational Study.

INTRODUCTION: Maternal overweight is a risk factor for gestational diabetes mellitus (GDM). However, emerging evidence suggests that an increased maternal body mass index (BMI) promotes the development of perinatal complications even in women who do not develop GDM. This study aims to assess physiological glucometabolic changes associated with increased BMI. METHODS: Twenty-one women with overweight and 21 normal weight controls received a metabolic assessment at 13 weeks of gestation, including a 60-min frequently sampled intravenous glucose tolerance test. A further investigation was performed between 24 and 28 weeks in women who remained normal glucose tolerant. RESULTS: At baseline, mothers with overweight showed impaired insulin action, whereby the calculated insulin sensitivity index (CSI) was lower as compared to normal weight controls (3.5 vs. 6.7 10-4 min-1 [microU/mL]-1, p = 0.025). After excluding women who developed GDM, mothers with overweight showed higher average glucose during the oral glucose tolerance test (OGTT) at the third trimester. Moreover, early pregnancy insulin resistance and secretion were associated with increased placental weight in normal glucose-tolerant women. CONCLUSION: Mothers with overweight or obesity show an unfavorable metabolic environment already at the early stage of pregnancy, possibly associated with perinatal complications in women who remain normal glucose tolerant.

Maternal overweight and obesity and its association with metabolic changes and fetal overgrowth in the absence of gestational diabetes mellitus: A prospective cohort study.

INTRODUCTION: Previous studies indicated an association between fetal overgrowth and maternal obesity independent of gestational diabetes mellitus (GDM). However, the underlying mechanisms beyond this possible association are not completely understood. This study investigates metabolic changes and their association with fetal and neonatal biometry in overweight and obese mothers who remained normal glucose-tolerant during gestation. MATERIAL AND METHODS: In this prospective cohort study 893 women who did not develop GDM were categorized according to their pregestational body mass index (BMI): 570 were normal weight, 220 overweight and 103 obese. Study participants received a broad metabolic evaluation before 16 weeks and were followed up until delivery to assess glucose levels during the oral glucose tolerance test (OGTT) at mid-gestation as well as fetal biometry in ultrasound and pregnancy outcome data. RESULTS: Increased maternal BMI was associated with an adverse metabolic profile at the beginning of pregnancy, including a lower degree of insulin sensitivity (as assessed by the quantitative insulin sensitivity check index) in overweight (mean difference: -2.4, 95% CI -2.9 to -1.9, p < 0.001) and obese (mean difference: -4.3, 95% CI -5.0 to -3.7, p < 0.001) vs normal weight women. Despite not fulfilling diagnosis criteria for GDM, overweight and obese mothers showed higher glucose levels at fasting and during the OGTT. Finally, we observed increased measures of fetal subcutaneous tissue thickness in ultrasound as well as higher proportions of large-for-gestational-age infants in overweight (18.9%, odds ratio [OR] 1.74, 95% CI 1.08-2.78, p = 0.021) and obese mothers (21.0%, OR 1.99, 95% CI 1.06-3.59, p = 0.027) vs normal weight controls (11.8%). The risk for large for gestational age was further determined by OGTT glucose (60 min: OR 1.11, 95% CI 1.02-1.21, p = 0.013; 120 min: OR 1.13, 95% CI 1.02-1.27, P = 0.025, for the increase of 10 mg/dL) and maternal triglyceride concentrations (OR 1.11, 95% CI 1.01-1.22, p = 0.036, for the increase of 20 mg/dL). CONCLUSIONS: Mothers affected by overweight or obesity but not GDM had a higher risk for fetal overgrowth. An impaired metabolic milieu related to increased maternal BMI as well as higher glucose levels at mid-gestation may impact fetal overgrowth in women still in the range of normal glucose tolerance.

Clinical Factors for Predicting Pharmacotherapy in Twin Pregnancies Complicated by Gestational Diabetes Mellitus.

Controlling blood glucose levels is the main target in pregnant women with gestational diabetes mellitus (GDM). Twin pregnancies are offered the same screening selection and have the same diagnostic criteria as well as treatment of gestational diabetes as singleton pregnancies, even though the risks for pregnancy complications are increased. The aim of this study was to assess the association between predicting factors, OGTT glucose levels and pharmacotherapy requirements in twin pregnancies with GDM. This retrospective cohort study included 446 GDM patients with twin pregnancies (246 managed with lifestyle modifications and 200 requiring pharmacotherapy) over a time period of 18 years. An evaluation of maternal characteristics and a standardized 75 g oral glucose test (OGGT) for glucose concentrations at fasting, 1 h and 2 h were conduced. OGTT glucose levels at fasting (=0 min, p < 0.01) and 1 h (p < 0.01) were significantly associated with the later requirement of pharmacotherapy. Also, clinical risk factors (pre-pregnancy BMI p < 0.01, multiparity p < 0.05, GDM in previous pregnancy p < 0.01, assisted reproduction p < 0.05) showed a predictive accuracy for insulin therapy in twin pregnancies complicated by GDM, whereas age and chorionicity had no effect. OGTT glucose measures in addition to clinical risk factors are promising variables for risk stratification in mothers with GDM and twin pregnancy.

Sensitivity, specificity, and diagnostic accuracy of WHO 2013 criteria for diagnosis of gestational diabetes mellitus in low risk early pregnancies: international, prospective, multicentre cohort study.

Objective: To evaluate the predictability of gestational diabetes mellitus wth a 75 g oral glucose tolerance test (OGTT) in early pregnancy, based on the 2013 criteria of the World Health Organization, and to test newly proposed cut-off values. Design: International, prospective, multicentre cohort study. Setting: Six university or cantonal departments in Austria, Germany, and Switzerland, from 1 May 2016 to 31 January 2019. Participants: Low risk cohort of 829 participants aged 18-45 years with singleton pregnancies attending first trimester screening and consenting to have an early 75 g OGTT at 12-15 weeks of gestation. Participants and healthcare providers were blinded to the results. Main outcome measures: Fasting, one hour, and two hour plasma glucose concentrations after an early 75 g OGTT (12-15 weeks of gestation) and a late 75 g OGTT (24-28 weeks of gestation). Results: Of 636 participants, 74 (12%) developed gestational diabetes mellitus, according to World Health Organization 2013 criteria, at 24-28 weeks of gestation. Applying WHO 2013 criteria to the early OGTT with at least one abnormal value gave a low sensitivity of 0.35 (95% confidence interval 0.24 to 0.47), high specificity of 0.96 (0.95 to 0.98), positive predictive value of 0.57 (0.41 to 0.71), negative predictive value of 0.92 (0.89 to 0.94), positive likelihood ratio of 10.46 (6.21 to 17.63), negative likelihood ratio of 0.65 (0.55 to 0.78), and diagnostic odds ratio of 15.98 (8.38 to 30.47). Lowering the postload glucose values (75 g OGTT cut-off values of 5.1, 8.9, and 7.8 mmol/L) improved the detection rate (53%, 95% confidence interval 41% to 64%) and negative predictive value (0.94, 0.91 to 0.95), but decreased the specificity (0.91, 0.88 to 0.93) and positive predictive value (0.42, 0.32 to 0.53) at a false positive rate of 9% (positive likelihood ratio 5.59, 4.0 to 7.81; negative likelihood ratio 0.64, 0.52 to 0.77; and diagnostic odds ratio 10.07, 6.26 to 18.31). Conclusions: The results of this prospective low risk cohort study indicated that the 75 g OGTT as a screening tool in early pregnancy is not sensitive enough when applying WHO 2013 criteria. Postload glucose values were higher in early pregnancy complicated by diabetes in pregnancy. Lowering the postload cut-off values identified a high risk group for later development of gestational diabetes mellitus or those who might benefit from earlier treatment. Results from randomised controlled trials showing a beneficial effect of early intervention are unclear. Trial registration: ClinicalTrials.gov NCT02035059.

Comment: pregnancy after bariatric surgery - achievements and open questions.

Bariatric surgery confers potential advantages for obese patients, but also risks for pregnancy. Perinatal outcomes may be varying between surgical procedures. This topic was recently addressed by a systematic review in BMC Pregnancy and Childbirth. This commentary will discuss the scientific background and implications for future research.

Prevalence of gestational diabetes mellitus in women with a family history of type 2 diabetes in first- and second-degree relatives.

AIMS: A family history of type 2 diabetes mellitus (T2DM) markedly increases an individual's lifetime risk of developing the disease. For gestational diabetes (GDM), this risk factor is less well characterized. This study aimed to investigate the relationship between family history of T2DM in first- and second-degree relatives in women with GDM and the differences in metabolic characteristics at early gestation. METHODS: This prospective cohort study included 1129 pregnant women. A broad risk evaluation was performed before 16 + 0 weeks of gestation, including a detailed family history of the different types of diabetes and a laboratory examination of glucometabolic parameters. Participants were followed up until delivery and GDM assessed according to the latest diagnosis criteria. RESULTS: We showed that pregnant women with first- (FHD1, 26.6%, OR 1.91, 95%CI 1.16 to 3.16, p = 0.005), second- (FHD2, 26.3%, OR 1.88, 95%CI 1.16 to 3.05, p = 0.005) or both first- and second-degree relatives with T2DM (FHD1 + D2, 33.3%, OR 2.64, 95%CI 1.41 to 4.94, p < 0.001) had a markedly increased risk of GDM compared to those with negative family history (FHN) (n = 100, 15.9%). The association was strongest if both parents were affected (OR 4.69, 95%CI 1.33 to 16.55, p = 0.009). Women with FHD1 and FHD1 + D2 had adverse glucometabolic profiles already in early pregnancy. CONCLUSIONS: Family history of T2DM is an important risk factor for GDM, also by applying the current diagnostic criteria. Furthermore, we showed that the degree of kinship plays an essential role in quantifying the risk already at early pregnancy.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data.

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

The impact of regional origin on the incidence of gestational diabetes mellitus in a multiethnic European cohort.

Introduction: Women with migration background present specific challenges related to risk stratification and care of gestational diabetes mellitus (GDM). Therefore, this study aims to investigate the role of ethnic origin on the risk of developing GDM in a multiethnic European cohort. Methods: Pregnant women were included at a median gestational age of 12.9 weeks and assigned to the geographical regions of origin: Caucasian Europe (n = 731), Middle East and North Africa countries (MENA, n = 195), Asia (n = 127) and Sub-Saharan Africa (SSA, n = 48). At the time of recruitment maternal characteristics, glucometabolic parameters and dietary habits were assessed. An oral glucose tolerance test was performed in mid-gestation for GDM diagnosis. Results: Mothers with Caucasian ancestry were older and had higher blood pressure and an adverse lipoprotein profile as compared to non-Caucasian mothers, whereas non-Caucasian women (especially those from MENA countries) had a higher BMI and were more insulin resistant. Moreover, we found distinct dietary habits. Non-Caucasian mothers, especially those from MENA and Asian countries, had increased incidence of GDM as compared to the Caucasian population (OR 1.87, 95%CI 1.40 to 2.52, p < 0.001). Early gestational fasting glucose and insulin sensitivity were consistent risk factors across different ethnic populations, however, pregestational BMI was of particular importance in Asian mothers. Discussion: Prevalence of GDM was higher among women from MENA and Asian countries, who already showed adverse glucometabolic profiles at early gestation. Fasting glucose and early gestational insulin resistance (as well as higher BMI in women from Asia) were identified as important risk factors in Caucasian and non-Caucasian patients.

New Developments, Challenges and Open Questions in Diagnosis and Treatment of Gestational Diabetes Mellitus.

The prevalence of gestational diabetes mellitus (GDM) is increasing alongside a rising maternal age at conception, an increasing number of people making unhealthy lifestyle choices and, especially, an increasing pregestational body weight [...].

Assessment of glucose levels in pregnant women with history of COVID-19 in a case-control study.

Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection may negatively affect glucose metabolism. This study aims to assess glucose levels, prevalence of gestational diabetes mellitus (GDM) and perinatal outcome in women with history of COVID-19. To this purpose, a group of 65 patients with history of COVID-19 and 94 control patients were retrospectively recruited among pregnant women who attended the pregnancy outpatient department between 01/2020 and 02/2022. Glucose data from an oral glucose tolerance test (OGTT), GDM status and obstetric complications were assessed. We observed no differences in average (p = 0.37), fasting (p = 0.62) or post-load glucose concentrations (60 min: p = 0.19; 120 min: p = 0.95) during OGTT. A total of 15 (23.1%) women in the COVID-19 group and 18 (19.1%) women in the control group developed GDM (p = 0.55). Moreover, caesarean section rate, weight percentiles and pregnancy outcomes were comparable between the groups (p = 0.49). In conclusion, in this study we did not identify a possible impact of COVID-19 on glucose metabolism in pregnancy, especially with regard to glucose concentrations during the OGTT and prevalence of GDM.

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy.

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 < 0.0001). Bland-Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG. CONCLUSIONS: We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.

Mathematical model of insulin kinetics accounting for the amino acids effect during a mixed meal tolerance test.

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.

Impact Of Prepregnancy Overweight And Obesity On Treatment Modality And Pregnancy Outcome In Women With Gestational Diabetes Mellitus.

Background: We aim to evaluate the impact of prepregnancy overweight on treatment modalities of Gestational Diabetes Mellitus (GDM). We assessed the association of increased pregravid Body Mass Index (BMI) with dosing of basal and rapid acting insulin as well as pregnancy outcome. Methods: We included 509 gestational diabetic women (normal weight: 200, overweight: 157, obese: 152), attending the pregnancy outpatient clinic at the Department of Obstetrics and Gynecology, Medical University of Vienna, in this retrospective study. We used a prospectively compiled database to assess patient characteristics, treatment approaches - particularly maximum doses of basal and rapid acting insulin or metformin - and pregnancy outcome. Results: Increased BMI was associated with the need of glucose lowering medication (odds ratio (OR): 1.08 for the increase of 1 kg/m² BMI, 95%CI 1.05-1.11, p<0.001). Mothers with pregestational obesity received the highest amount of insulin. Metformin was more often used in patients with obesity who also required higher daily doses. Maternal BMI was associated with increased risk of cesarean section (OR 1.04, 95%CI 1.01-1.07, p<0.001) and delivering large for gestational age offspring (OR 1.09, 95%CI 1.04-1.13, p<0.001). Birthweight percentiles were highest in patients with obesity who required glucose lowering therapy. Conclusions: Treatment modalities and outcome in GDM pregnancies are closely related to the extent of maternal BMI. Patients with obesity required glucose lowering medication more often and were at higher risk of adverse pregnancy outcomes. It is crucial to further explore the underlying pathophysiologic mechanisms to optimize clinical management and individual treatment approaches.

Fatty liver indices and their association with glucose metabolism in pregnancy - An observational cohort study.

AIMS: Non-invasive hepatic steatosis indices can be used to assess the risk for metabolic (dysfunction) associated fatty liver disease (MAFLD). This may be helpful to detect metabolic disorders in pregnancy, specifically gestational diabetes (GDM). We aimto examine the association of these indices with parameters of glucose metabolism. METHODS: 109 women underwent a metabolic characterization at 16 weeks of gestation andwere classified according to the fatty-liver index (FLI) andhepatic-steatosis index (HSI) into low (G1), intermediate (G2) and high risk (G3). At 26 weeks, participants received an oral glucose tolerance test (OGTT) to assess insulin action, ß-cell function and GDM status. RESULTS: Both MAFLD indices wereassociated with impaired insulin sensitivityand compensatory increase of insulin release. G3 groups showedimpaired insulin action. The higher circulating insulin concentrations were not able to compensate for insulin resistance in women with higher MAFLD scores, resulting in an increased risk of GDM(OR: 1.05, 95% CI 1.03 to 1.08, p < 0.001 for FLI). MAFLD scores were associated with fetal overgrowth. CONCLUSIONS: Maternal MAFLD represents a high-risk obstetric condition. Hepatic steatosis indices are associated with impaired glucose regulation and may provide a useful tool for early risk assessment for impaired glucose metabolism.

Assessing the Effect of Incretin Hormones and Other Insulin Secretagogues on Pancreatic Beta-Cell Function: Review on Mathematical Modelling Approaches.

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.

Bioelectrical Impedance Analysis for the Assessment of Body Composition in Sarcopenia and Type 2 Diabetes.

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.