Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study.

AIMS/HYPOTHESIS: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. METHODS: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. RESULTS: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. CONCLUSIONS/INTERPRETATION: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.

Continuous Insulin Therapy to Prevent Post-Transplant Diabetes Mellitus: A Randomized Controlled Trial.

Rationale & Objectives: Hyperglycemia is frequently observed early after transplantation and associated with development of post-transplant diabetes mellitus (PTDM). Here, we assessed continuous subcutaneous insulin infusion (CSII) targeting afternoon hyperglycemia. Study Design: Open-label randomized parallel 3-arm design. Settings & Participants: In total, 85 kidney transplant recipients without previous diabetes diagnosis were randomized to postoperative CSII therapy, basal insulin, or control. Interventions: Insulin was to be initiated at afternoon capillary blood glucose level of ≥140 mg/dL (7.8 mmol/L; CSII and basal insulin) or fasting plasma glucose level of ≥200 mg/dL (11.1 mmol/L; control). Outcomes: Hemoglobin A1c (HbA1c) levels at 3 months post-transplant (primary endpoint). PTDM assessed using oral glucose tolerance test at 12 and 24 months. Results: CSII therapy lasted until median day 18 and maximum day 88. The median HbA1c value at month 3 was 5.6% (38 mmol/mol) in the CSII group versus 5.7% (39 mmol/mol) in the control group (P = 0.70) and 5.4% (36 mmol/mol) in the basal insulin group (P = 0.02). At months 12 and 24, the odds for PTDM were similar compared with the control group (odds ratios [95% confidence intervals], 0.80 [0.18-3.49] and 0.71 [0.15-3.16], respectively) and the basal insulin group (0.96 [0.18-5.68] and 1.51 [0.24-12.84], respectively). Mild hypoglycemia events occurred in the CSII and the basal insulin groups. Limitations: This study is limited by outdated insulin pump technology, frequent discontinuations of CSII, a complex protocol, and concerns regarding reliability of HbA1c measurements. Conclusions: CSII therapy was not superior at reducing HbA1c levels at month 3 or PTDM prevalence at months 12 and 24 compared with the control or basal insulin group.

Continuous glucose monitoring for the prediction of posttransplant diabetes mellitus and impaired glucose tolerance on day 90 after kidney transplantation-A prospective proof-of-concept study.

Posttransplant diabetes mellitus (PTDM) and prediabetes represent serious complications after kidney transplantation and are associated with increased cardiovascular morbidity and mortality. We assessed the predictive performance of continuous glucose monitoring (CGM) compared with plasma glucose and hemoglobin A1c in 46 kidney transplant recipients (KTRs) without known preexisting diabetes mellitus. CGM (14-day recording duration) was performed on days 8, 30, 45, 60, 90, and 180 posttransplant. Eight patients (17%) developed PTDM and nine (20%) impaired glucose tolerance (IGT), as diagnosed by oral glucose tolerance test (oGTT)-derived 2-hour plasma glucose (2hPG) or glucose-lowering therapy on day 90. CGM-readouts percent of time >140 mg/dL (%TAR (140 mg/dL)) and percent of time >180 mg/dL (%TAR (180 mg/dL)) showed excellent in-sample test characteristics regarding PTDM from day 8 onward (days 8-90 receiver operating characteristic area under the curve: 0.88-0.99) and regarding PTDM/IGT with the commencement of maintenance immunosuppression from day 30 onward (days 30-90 receiver operating characteristic area under the curve: 0.88-0.91). Exploratory CGM-%TAR (140 mg/dL)-screening thresholds of 31.8% on day 8 and 13.2% on day 30 yielded sensitivities/specificities of 88%/83% for PTDM and 94%/78% for PTDM/IGT on day 90, respectively. Although our findings need to be replicated in studies with larger sample sizes, CGM bears promising potential to facilitate clinical practice and research regarding PTDM.

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.

SUCNR1 regulates insulin secretion and glucose elevates the succinate response in people with prediabetes.

Pancreatic ß-cell dysfunction is a key feature of type 2 diabetes, and novel regulators of insulin secretion are desirable. Here we report that the succinate receptor (SUCNR1) is expressed in ß-cells and is up-regulated in hyperglycemic states in mice and humans. We found that succinate acts as a hormone-like metabolite and stimulates insulin secretion via a SUCNR1-Gq-PKC-dependent mechanism in human ß-cells. Mice with ß-cell-specific Sucnr1 deficiency exhibit 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 show an enhanced nutritional-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.

An in-silico modeling approach to separate exogenous and endogenous plasma insulin appearance, with application to inhaled insulin.

The aim of this study was to develop a dynamic model-based approach to separately quantify the exogenous and endogenous contributions to total plasma insulin concentration and to apply it to assess the effects of inhaled-insulin administration on endogenous insulin secretion during a meal test. A three-step dynamic in-silico modeling approach was developed to estimate the two insulin contributions of total plasma insulin in a group of 21 healthy subjects who underwent two equivalent standardized meal tests on separate days, one of which preceded by inhalation of a Technosphere® Insulin dose (22U or 20U). In the 30-120 min test interval, the calculated endogenous insulin component showed a divergence in the time course between the test with and without inhaled insulin. Moreover, the supra-basal area-under-the-curve of endogenous insulin in the test with inhaled insulin was significantly lower than that in the test without (2.1 ± 1.7 × 104 pmol·min/L vs 4.2 ± 1.8 × 104 pmol·min/L, p < 0.01). The percentage of exogenous insulin reaching the plasma, relative to the inhaled dose, was 42 ± 21%. The proposed in-silico approach separates exogenous and endogenous insulin contributions to total plasma insulin, provides individual bioavailability estimates, and can be used to assess the effect of inhaled insulin on endogenous insulin secretion during a meal.

ß-Cell Function, Incretin Effect, and Glucose Kinetics in Response to a Mixed Meal in Patients With Type 2 Diabetes Treated With Dapagliflozin Plus Saxagliptin.

OBJECTIVE: To explore the complementary effects of a combination of dipeptidyl peptidase 4 and sodium-glucose cotransporter 2 inhibitors added to metformin on hormonal and metabolic responses to meal ingestion. RESEARCH DESIGN AND METHODS: Forty-five patients (age 58 ± 8 years; HbA1c 58 ± 6 mmol/mol; BMI 30.7 ± 3.2 kg/m2) with type 2 diabetes uncontrolled with metformin were evaluated at baseline and 3 and 28 days after 5 mg saxagliptin (SAXA), 10 mg dapagliflozin (DAPA), or 5 mg saxagliptin plus 10 mg dapagliflozin (SAXA+DAPA) using a mixed-meal tolerance test (MMTT) spiked with dual-tracer glucose to assess glucose metabolism, insulin secretion, and sensitivity. RESULTS: At day 3, fasting and mean MMTT glucose levels were lower with SAXA+DAPA (-31.1 ± 1.6 and -91.5 ± 12.4 mg/dL) than with SAXA (-7.1 ± 2.1 and -53 ± 10.5 mg/dL) or DAPA (-17.0 ± 1.1 and -42.6 ± 10.0 mg/dL, respectively; P < 0.001). Insulin secretion rate (SAXA+DAPA +75%; SAXA +11%; DAPA +3%) and insulin sensitivity (+2.2 ± 1.7, +0.4 ± 0.7, and +0.4 ± 0.4 mg ⋅ kg-1⋅ min-1, respectively) improved with SAXA+DAPA (P < 0.007). Mean glucagon-like peptide 1 (GLP-1) was higher with SAXA+DAPA than with SAXA or DAPA. Fasting glucagon increased with DAPA and SAXA+DAPA but not with SAXA. Fasting endogenous glucose production (EGP) increased with SAXA+DAPA and DAPA. During MMTT, EGP suppression was greater (48%) with SAXA+DAPA (vs. SAXA 44%; P = 0.02 or DAPA 34%; P = 0.2). Metabolic clearance rate of glucose (MCRglu) increased more with SAXA+DAPA. At week 4, insulin secretion rate, ß-cell glucose sensitivity, and insulin sensitivity had further increased in the SAXA+DAPA group (P = 0.02), with no additional changes in GLP-1, glucagon, fasting or MMTT EGP, or MCRglu. CONCLUSIONS: SAXA+DAPA provided superior glycemic control compared with DAPA or SAXA, with improved ß-cell function, insulin sensitivity, GLP-1 availability, and glucose clearance.

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.

Effects of Consuming Beverages Sweetened with Fructose, Glucose, High-Fructose Corn Syrup, Sucrose, or Aspartame on OGTT-Derived Indices of Insulin Sensitivity in Young Adults.

(1) Background: Clinical results on the effects of excess sugar consumption on insulin sensitivity are conflicting, possibly due to differences in sugar type and the insulin sensitivity index (ISI) assessed. Therefore, we compared the effects of consuming four different sugars on insulin sensitivity indices derived from oral glucose tolerance tests (OGTT). (2) Methods: Young adults consumed fructose-, glucose-, high-fructose corn syrup (HFCS)-, sucrose-, or aspartame-sweetened beverages (SB) for 2 weeks. Participants underwent OGTT before and at the end of the intervention. Fasting glucose and insulin, Homeostatic Model Assessment-Insulin Resistance (HOMA-IR), glucose and insulin area under the curve, Surrogate Hepatic Insulin Resistance Index, Matsuda ISI, Predicted M ISI, and Stumvoll Index were assessed. Outcomes were analyzed to determine: (1) effects of the five SB; (2) effects of the proportions of fructose and glucose in all SB. (3) Results: Fructose-SB and the fructose component in mixed sugars negatively affected outcomes that assess hepatic insulin sensitivity, while glucose did not. The effects of glucose-SB and the glucose component in mixed sugar on muscle insulin sensitivity were more negative than those of fructose. (4) Conclusion: the effects of consuming sugar-SB on insulin sensitivity varied depending on type of sugar and ISI index because outcomes assessing hepatic insulin sensitivity were negatively affected by fructose, and outcomes assessing muscle insulin sensitivity were more negatively affected by glucose.

Glucagon-like peptide-1 and interleukin-6 interaction in response to physical exercise: An in-silico model in the framework of immunometabolism.

BACKGROUND AND OBJECTIVE: Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity. METHODS: The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects. RESULTS: Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects. CONCLUSIONS: The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise.

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.

Diurnal Cycling of Insulin Sensitivity in Type 2 Diabetes: Evidence for Deviation From Physiology at an Early Stage.

The aim of this study was to establish the contribution of insulin resistance to the morning (a.m.) versus afternoon (p.m.) lower glucose tolerance of people with type 2 diabetes (T2D). Eleven subjects with T2D (mean [SD] diabetes duration 0.79 [0.23] years, BMI 28.3 [1.8] kg/m2, A1C 6.6% [0.26%] [48.9 (2.9) mmol/mol]), treatment lifestyle modification only) and 11 matched control subjects without diabetes were monitored between 5:00 and 8:00 a.m. and p.m. (in random order) on one occasion (study 1), and on a subsequent occasion, they underwent an isoglycemic clamp (a.m. and p.m., both between 5:00 and 8:00, insulin infusion rate 10 mU/m2/min) (study 2). In study 1, plasma glucose, insulin, C-peptide, and glucagon were higher and insulin clearance lower in subjects with T2D a.m. versus p.m. and versus control subjects (P < 0.05), whereas free fatty acid, glycerol, and ß-hydroxybutyrate were lower a.m. versus p.m. However, in study 2 at identical hyperinsulinemia a.m. and p.m. (∼150 pmol/L), glucose Ra and glycerol Ra were both less suppressed a.m. versus p.m. (P < 0.05) in subjects with T2D. In contrast, in control subjects, glucose Ra was more suppressed a.m. versus p.m. Leucine turnover was no different a.m. versus p.m. In conclusion, in subjects with T2D, insulin sensitivity for glucose (liver) and lipid metabolism has diurnal cycles (nadir a.m.) opposite that of control subjects without diabetes already at an early stage, suggesting a marker of T2D. ARTICLE HIGHLIGHTS: In people with type 2 diabetes (T2D), fasting hyperglycemia is greater in the morning (a.m.) versus the afternoon (p.m.), and insulin sensitivity for glucose and lipid metabolism is lower a.m. versus p.m. This pattern is the reverse of the physiological diurnal cycle of people without diabetes who are more insulin sensitive a.m. versus p.m. These new findings have been observed in the present study in people without obesity but with recent-onset T2D, with good glycemic control, and in the absence of confounding pharmacological treatment. It is likely that the findings represent a specific marker of T2D, possibly present even in prediabetes before biochemical and clinical manifestations.

Near-Infrared Spectroscopy: A Free-Living Neuroscience Tool to Better Understand Diabetes and Obesity.

The human brain is the least accessible of all organs and attempts to study it in vivo rely predominantly on neuroimaging. Functional near-infrared spectroscopy (fNIRS) allows for the study of cortical neural activity in a non-invasive manner that may resemble free-living conditions. Moreover, compared to other neuroimaging tools, fNIRS is less expensive, it does not require the use of ionizing radiation, and can be applied to all study populations (patients suffering from claustrophobia, or neonates). In this narrative review, we provide an overview of the available research performed using fNIRS in patients with diabetes and obesity. The few studies conducted to date have presented controversial results regarding patients with diabetes, some reporting a greater hemodynamic response and others reporting a reduced hemodynamic response compared to the controls, with an unclear distinction between types 1 and 2. Subjects with obesity or a binge eating disorder have reduced prefrontal activation in response to inhibitory food or non-food stimuli; however, following an intervention, such as cognitive treatment, prefrontal activation is restored. Moreover, we discuss the potential of future applications of fNIRS for a better understanding of cortical neural activity in the context of metabolic disorders.

The Glycemic Curve during the Oral Glucose Tolerance Test: Is It Only Indicative of Glycoregulation?

The shape of the glycemic curve during the oral glucose tolerance test (OGTT), interpreted in the correct context, can predict impaired glucose homeostasis. Our aim was to reveal information inherent in the 3 h glycemic trajectory that is of physiological relevance concerning the disruption of glycoregulation and complications beyond, such as components of metabolic syndrome (MS). METHODS: In 1262 subjects (1035 women, 227 men) with a wide range of glucose tolerance, glycemic curves were categorized into four groups: monophasic, biphasic, triphasic, and multiphasic. The groups were then monitored in terms of anthropometry, biochemistry, and timing of the glycemic peak. RESULTS: Most curves were monophasic (50%), then triphasic (28%), biphasic (17.5%), and multiphasic (4.5%). Men had more biphasic curves than women (33 vs. 14%, respectively), while women had more triphasic curves than men (30 vs. 19%, respectively) (p < 0.01). Monophasic curves were more frequent in people with impaired glucose regulation and MS compared to bi-, tri-, and multiphasic ones. Peak delay was the most common in monophasic curves, in which it was also most strongly associated with the deterioration of glucose tolerance and other components of MS. CONCLUSION: The shape of the glycemic curve is gender dependent. A monophasic curve is associated with an unfavorable metabolic profile, especially when combined with a delayed peak.

Short-chain fatty acids are associated with adiposity, energy and glucose homeostasis among different metabolic phenotypes in the Nutritionists' Health Study.

BACKGROUND AND AIMS: The gut microbiome is associated with obesity, mainly mediated by bacteria-produced short-chain fatty acids (SCFAs). It is unknown how SCFA concentrations are associated with the phenotypes metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and metabolically unhealthy obese/overweight (MUO). We compared plasma and fecal SCFA concentrations among adult women categorized according to the metabolic phenotypes mentioned above and examined associations between SCFA and adiposity and components of energy and glucose homeostasis. METHODS: This was a cross-sectional study involving 111 participants. Body composition was assessed by DEXA. Energy and glycemic homeostasis were assessed by the standard mixed-meal tolerance test coupled with indirect calorimetry. SCFAs were quantified by gas chromatography and mass spectrometry. RESULTS: Only plasma propionate was increased in the MHNW phenotype compared to the MHO and MUO phenotypes [p < 0.05]. Fecal propionate and butyrate concentrations and plasma propionate concentrations were inversely associated with total and visceral adiposity [p < 0.05]. Fecal and plasma SCFA concentrations were associated with reduced glucose, insulin and HbA1c levels, increased fasting and postprandial GLP-1 levels; and more preserved beta-cell function [p < 0.05]. Fecal and plasma SCFA concentrations were positively correlated with resting energy expenditure and lipid oxidation rate and inversely correlated with the oxidation rate of carbohydrates [p < 0.05]. CONCLUSION: These findings reinforce the concept that fecal and plasma SCFA concentrations are linked to specific components of energy and glucose homeostasis; and body adiposity. However, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentrations.

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.