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Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Insulin is an essential regulator of blood glucose homeostasis that is produced exclusively byβcells within the pancreatic islets of healthy individuals. In those affected by diabetes, immune inflammation, damage, and destruction of isletβcells leads to insulin deficiency and hyperglycemia. Current efforts to understand the mechanisms underlyingβcell damage in diabetes rely onin vitro-cultured cadaveric islets. However, isolation of these islets involves removal of crucial matrix and vasculature that supports islets in the intact pancreas. Unsurprisingly, these islets demonstrate reduced functionality over time in standard culture conditions, thereby limiting their value for understanding native islet biology. Leveraging a novel, vascularized micro-organ (VMO) approach, we have recapitulated elements of the native pancreas by incorporating isolated human islets within a three-dimensional matrix nourished by living, perfusable blood vessels. Importantly, these islets show long-term viability and maintain robust glucose-stimulated insulin responses. Furthermore, vessel-mediated delivery of immune cells to these tissues provides a model to assess islet-immune cell interactions and subsequent islet killing-key steps in type 1 diabetes pathogenesis. Together, these results establish the islet-VMO as a novel,ex vivoplatform for studying human islet biology in both health and disease.

Insulin-mechanistic target of rapamycin (mTOR) signaling drives anabolic growth during organismal development; its late-life dysregulation contributes to aging and limits lifespans. Age-related regulatory mechanisms and functional consequences of insulin-mTOR remain incompletely understood. Here, we identify LPD-3 as a megaprotein that orchestrates the tempo of insulin-mTOR signaling during C. elegans aging. We find that an agonist insulin, INS-7, is drastically overproduced from early life and shortens lifespan in lpd-3 mutants. LPD-3 forms a bridge-like tunnel megaprotein to facilitate non-vesicular cellular lipid trafficking. Lipidomic profiling reveals increased hexaceramide species in lpd-3 mutants, accompanied by up-regulation of hexaceramide biosynthetic enzymes, including HYL-1. Reducing the abundance of HYL-1, insulin receptor/DAF-2 or mTOR/LET-363, normalizes INS-7 levels and rescues the lifespan of lpd-3 mutants. LPD-3 antagonizes SINH-1, a key mTORC2 component, and decreases expression with age. We propose that LPD-3 acts as a megaprotein brake for organismal aging and that its age-dependent decline restricts lifespan through the sphingolipid-hexaceramide and insulin-mTOR pathways.

We assessed the causal relation of four glycemic traits and type 2 diabetes liability with 167 metabolites using Mendelian randomization with various sensitivity analyses and a reverse Mendelian randomization analysis. We extracted instruments for fasting glucose, 2-h glucose, fasting insulin, and glycated hemoglobin from the Meta-Analyses of Glucose and Insulin-related traits Consortium (n = 200,622), and those for type 2 diabetes liability from a meta-analysis of multiple cohorts (148,726 cases, 965,732 controls) in Europeans. Outcome data were from summary statistics of 167 metabolites from the UK Biobank (n = 115,078). Fasting glucose and 2-h glucose were not associated with any metabolite. Higher glycated hemoglobin was associated with higher free cholesterol in small low-density lipoprotein. Type 2 diabetes liability and fasting insulin were inversely associated with apolipoprotein A1, total cholines, lipoprotein subfractions in high-density-lipoprotein and intermediate-density lipoproteins, and positively associated with aromatic amino acids. These findings indicate hyperglycemia-independent patterns and highlight the role of insulin in type 2 diabetes development. Further studies should evaluate these glycemic traits in type 2 diabetes diagnosis and clinical management.

OBJECTIVE: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients differently affect post-prandial gastric retention (GR), rate of appearance (R[Formula: see text]), and insulin sensitivity (S[Formula: see text]). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. METHODS: Forty-seven individuals with T1D (weight =78±13 kg, age =42±10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. RESULTS: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R[Formula: see text] model parameters were not significantly different using the MI-OMM and R-OMM (p 0.05) and the correlation between the two S[Formula: see text] was satisfactory ( ρ =0.77). CONCLUSION: The MI-OMM is usable to estimate GR, R[Formula: see text], and S[Formula: see text] from data collected in real-life conditions with minimally-invasive technologies. SIGNIFICANCE: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R[Formula: see text], and S[Formula: see text]. DSS could finally exploit this information to improve diabetes management.

OBJECTIVE: To study the impact of vigorous vs. moderate exercise on metabolic parameters in polycystic ovary syndrome (PCOS). DESIGN: Randomized controlled trial. SETTING: Unsupervised home-based exercise program. PATIENTS: Patients with PCOS on the basis of the Rotterdam criteria with insulin resistance. INTERVENTIONS: Participants were block randomized to a home-based exercise program of 75 minutes of vigorous exercise or 150 minutes of moderate exercise per week, for 8 weeks total. MAIN OUTCOME MEASURES: Changes in glucose, insulin, and insulin resistance. RESULTS: In total, 36 participants were randomized, of whom 20 completed the study. The percentage changes from baseline at 4 and 8 weeks for fasting glucose, insulin, and homeostatic model assessment for insulin resistance did not significantly differ between the groups, except for the change in the 8-week glucose level, which was more favorable in the moderate arm (8.06% [standard deviation, 6.44%] in the vigorous group compared with -0.32% [standard deviation, 4.91%] in the moderate group). The absolute values of the main outcomes (fasting glucose, insulin, and homeostatic model assessment for insulin resistance) at baseline and 4 and 8 weeks did not significantly differ between trial arms. When assessing the change from baseline at 4 and 8 weeks, overall and within each trial arm, only the 8-week fasting glucose level was significantly greater than the baseline value in the vigorous arm (93.5 [95% confidence interval, 88.7-98.3] vs. 86.8 [95% confidence interval, 81.1-92.4]). CONCLUSIONS: Unsupervised short-term exercise programs may not achieve significant metabolic improvements in patients with PCOS, regardless of vigorous vs. moderate intensity. Future studies should investigate this question in larger sample sizes and longer or structured exercise programs. CLINICAL TRIAL REGISTRATION NUMBER: ClinicalTrials.gov identifier, NCT02303470.