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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.

Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient’s quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.

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 [...]

The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio was introduced as a tool to estimate insulin resistance, because circulating lipid measurements are available in routine settings. Insulin, C-peptide, and free fatty acids are components of other insulin-sensitivity indices but their measurement is expensive. Easier and more affordable tools are of interest for both pediatric and adult patients. Study participants from the Relationship Between Insulin Sensitivity and Cardiovascular Disease [43.9 (8.3) years, n = 1260] as well as the Beta-Cell Function in Juvenile Diabetes and Obesity study cohorts [15 (1.9) years, n = 29] underwent oral-glucose-tolerance tests and euglycemic clamp tests for estimation of whole-body insulin sensitivity and calculation of insulin sensitivity indices. To refine the TG/HDL ratio, mathematical modeling was applied including body mass index (BMI), fasting TG, and HDL cholesterol and compared to the clamp-derived M-value as an estimate of insulin sensitivity. Each modeling result was scored by identifying insulin resistance and correlation coefficient. The Single Point Insulin Sensitivity Estimator (SPISE) was compared to traditional insulin sensitivity indices using area under the ROC curve (aROC) analysis and χ(2) test. The novel formula for SPISE was computed as follows: SPISE = 600 × HDL-C(0.185)/(TG(0.2) × BMI(1.338)), with fasting HDL-C (mg/dL), fasting TG concentrations (mg/dL), and BMI (kg/m(2)). A cutoff value of 6.61 corresponds to an M-value smaller than 4.7 mg · kg(-1) · min(-1) (aROC, M:0.797). SPISE showed a significantly better aROC than the TG/HDL-C ratio. SPISE aROC was comparable to the Matsuda ISI (insulin sensitivity index) and equal to the QUICKI (quantitative insulin sensitivity check index) and HOMA-IR (homeostasis model assessment-insulin resistance) when calculated with M-values. The SPISE seems well suited to surrogate whole-body insulin sensitivity from inexpensive fasting single-point blood draw and BMI in white adolescents and adults.

Abstract Aims/Hypothesis Studies in permanent neonatal diabetes suggest that sulphonylureas lower blood glucose without causing hypoglycemia, in part by augmenting the incretin effect. This mechanism has not previously been attributed to sulphonylureas in patients with type 2 diabetes (T2DM). We therefore aimed to evaluate the impact of low-dose gliclazide on beta-cell function and incretin action in patients with T2DM. Methods Paired oral glucose tolerance tests and isoglycemic infusions were performed to evaluate the difference in the classical incretin effect in the presence and absence of low-dose gliclazide in 16 subjects with T2DM (hemoglobin A1c < 64 mmol/mol, 8.0%) treated with diet or metformin monotherapy. Beta-cell function modeling was undertaken to describe the relationship between insulin secretion and glucose concentration. Results A single dose of 20 mg gliclazide reduced mean glucose during the oral glucose tolerance test from 12.01 ± 0.56 to 10.82 ± 0.5mmol/l [P = 0.0006; mean ± standard error of the mean (SEM)]. The classical incretin effect was augmented by 20 mg gliclazide, from 35.5% (lower quartile 27.3, upper quartile 61.2) to 54.99% (34.8, 72.8; P = 0.049). Gliclazide increased beta-cell glucose sensitivity by 46% [control 22.61 ± 3.94, gliclazide 33.11 ± 7.83 (P = 0.01)] as well as late-phase incretin potentiation [control 0.92 ± 0.05, gliclazide 1.285 ± 0.14 (P = 0.038)]. Conclusions/Interpretation Low-dose gliclazide reduces plasma glucose in response to oral glucose load, with concomitant augmentation of the classical incretin effect. Beta-cell modeling shows that low plasma concentrations of gliclazide potentiate late-phase insulin secretion and increase glucose sensitivity by 50%. Further studies are merited to explore whether low-dose gliclazide, by enhancing incretin action, could effectively lower blood glucose without risk of hypoglycemia.

Pancreatic β cell dysfunction is a key feature of type 2 diabetes, and novel regulators of insulin secretion are desirable. Here, we report that succinate receptor 1 (SUCNR1) is expressed in β cells and is upregulated in hyperglycemic states in mice and humans. We found that succinate acted as a hormone-like metabolite and stimulated insulin secretion via a SUCNR1-Gq-PKC–dependent mechanism in human β cells. Mice with β cell–specific Sucnr1 deficiency exhibited impaired glucose tolerance and insulin secretion on a high-fat diet, indicating that SUCNR1 is essential for preserving insulin secretion in diet-induced insulin resistance. Patients with impaired glucose tolerance showed an enhanced nutrition-related succinate response, which correlates with the potentiation of insulin secretion during intravenous glucose administration. These data demonstrate that the succinate/SUCNR1 axis is activated by high glucose and identify a GPCR-mediated amplifying pathway for insulin secretion relevant to the hyperinsulinemia of prediabetic states.

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IntroductionSarcopenia is characterised by loss of muscle mass and strength. Although ageing is the most likely risk factor of sarcopenia, sarcopenia is prevalent even in non-elderly people. Type 2 diabetes (T2D) is a risk factor for sarcopenia, as T2D shares with sarcopenia several aetiological factors. Meanwhile, gestational diabetes mellitus (GDM) is characterised by metabolic alterations that resemble those observed in T2D, including increased insulin resistance (present even in physiologic pregnancies). Hence, GDM presents two major risk factors for sarcopenia, that is, dysglycaemia and insulin resistance. Moreover, the number of pregnancies at age >40 years is increasing, which is in an age range in which sarcopenia prevalence is already not negligible. However, data on the prevalence of sarcopenia prevalence in GDM and its effect on pregnancy outcomes are limited. Thus, this study aims to evaluate the prevalence of sarcopenia in women with GDM (and in pregnant women without GDM), identify risk factors and determine its effect on delivery and maternal and fetal outcomes.Methods and analysisFor this study, 100 each of women with and without GDM will be recruited. Women will undergo an oral glucose tolerance test within weeks 24–28 for possible GDM diagnosis (in weeks 16–18 for high-risk women). Muscle/physical performance tests will be conducted at weeks 28–32 for possible diagnosis of sarcopenia/presarcopenia. Cognitive function will also be assessed. For all women, information regarding pregnancy progression, along with any complications, will be collected. Collected data will be analysed according to the main objectives of the study: (i) determine the prevalence of sarcopenia/presarcopenia in pregnancy with and without GDM, (ii) identify factors associated with sarcopenia risk, (iii) determine the effect of sarcopenia/presarcopenia on pregnancy outcomes, (iv) explore the relationship between sarcopenia and cognitive function. Therefore, this study will provide information on sarcopenia/presarcopenia prevalence in GDM and, possibly, in pregnancy not complicated by dysglycaemia. Furthermore, the study will provide knowledge on the main factors associated with sarcopenia/presarcopenia in GDM/pregnancy. The identification of such factors will be relevant for an initial guidance for treatments that may prevent sarcopenia in GDM/pregnant women. This will become of even greater interest if sarcopenia/presarcopenia influences pregnancy outcomes, especially in GDM women.Ethics and disseminationThe study protocol has been approved by the Comitato Etico Regione Toscana - Area Vasta Nord Ovest (CEAVNO) on 25 July 2024 and by the Local Ethics Committee of the Medical University of Vienna on 17 June 2024. Participants’ enrolment began in May 2025. The results of the study will be presented at national and international conferences and in peer-reviewed journals.Trial registration numberClinicalTrials.gov Identifier: NCT06876090; Registration Date: 2025-03-14

Aims/hypothesisThe euglycaemic–hyperinsulinaemic clamp is the gold-standard method for measuring insulin sensitivity, but is less suitable for large clinical trials. Thus, several indices have been developed for evaluating insulin sensitivity from the oral glucose tolerance test (OGTT). However, most of them yield values different from those obtained by the clamp method. The aim of this study was to develop a new index to predict clamp-derived insulin sensitivity (M value) from the OGTT-derived oral glucose insulin sensitivity index (OGIS).MethodsWe analysed datasets of people that underwent both a clamp and an OGTT or meal test, thereby allowing calculation of both the M value and OGIS. The population was divided into a training and a validation cohort (n = 359 and n = 154, respectively). After a stepwise selection approach, the best model for M value prediction was applied to the validation cohort. This cohort was also divided into subgroups according to glucose tolerance, obesity category and age.ResultsThe new index, called PREDIcted M (PREDIM), was based on OGIS, BMI, 2 h glucose during OGTT and fasting insulin. Bland–Altman analysis revealed a good relationship between the M value and PREDIM in the validation dataset (only 9 of 154 observations outside limits of agreement). Also, no significant differences were found between the M value and PREDIM (equivalence test: p < 0.0063). Subgroup stratification showed that measured M value and PREDIM have a similar ability to detect intergroup differences (p < 0.02, both M value and PREDIM).Conclusions/interpretationThe new index PREDIM provides excellent prediction of M values from OGTT or meal data, thereby allowing comparison of insulin sensitivity between studies using different tests.

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 (R 2  > 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.