Metabolomics Identifies a Biomarker Revealing In Vivo Loss of Functional β-Cell Mass Before Diabetes Onset

Longitudinal Profiling of Fasting Plasma Metabolome in Response to Weight-Loss Interventions in Patients with Morbid Obesity

It is well recognized that patients with severe obesity exhibit remarkable heterogeneity in response to different types of weight-loss interventions. Those who undergo Roux-en-Y gastric bypass (RYGB) usually exhibit more favorable glycemic outcomes than those who receive adjustable gastric banding (BAND) or intensive medical intervention (IMI). The molecular mechanisms behind these observations, however, remain largely unknown. To identify the plasma metabolites associated with differential glycemic outcomes induced by weight-loss intervention, we studied 75 patients with severe obesity (25 each in RYGB, BAND, or IMI). Using untargeted metabolomics, we repeatedly measured 364 metabolites in plasma samples at baseline and 1-year after intervention. Linear regression was used to examine whether baseline metabolites or changes in metabolites are associated with differential glycemic outcomes in response to different types of weight-loss intervention, adjusting for sex, baseline age, and BMI as well as weight loss. Network analyses were performed to identify differential metabolic pathways involved in the observed associations. After correction for multiple testing (q < 0.05), 33 (RYGB vs. IMI) and 28 (RYGB vs. BAND) baseline metabolites were associated with changes in fasting plasma glucose (FPG) or glycated hemoglobin (HbA1c). Longitudinal changes in 38 (RYGB vs. IMI) and 38 metabolites (RYGB vs. BAND) were significantly associated with changes in FPG or HbA1c. The identified metabolites are enriched in pathways involved in the biosynthesis of aminoacyl-tRNA and branched-chain amino acids. Weight-loss intervention evokes extensive changes in plasma metabolites, and the altered metabolome may underlie the differential glycemic outcomes in response to different types of weight-loss intervention, independent of weight loss itself.

Lysophosphatidylinositols Are Upregulated After Human β-Cell Loss and Potentiate Insulin Release

In this study, we identified new lipid species associated with the loss of pancreatic β-cells triggering diabetes. We performed lipidomics measurements on serum from prediabetic mice lacking β-cell prohibitin-2 (a model of monogenic diabetes) patients without previous history of diabetes but scheduled for pancreaticoduodenectomy resulting in the acute reduction of their β-cell mass (∼50%), and patients with type 2 diabetes (T2D). We found lysophosphatidylinositols (lysoPIs) were the main circulating lipid species altered in prediabetic mice. The changes were confirmed in the patients with acute reduction of their β-cell mass and in those with T2D. Increased lysoPIs significantly correlated with HbA1c (reflecting glycemic control), fasting glycemia, and disposition index, and did not correlate with insulin resistance or obesity in human patients with T2D. INS-1E β-cells as well as pancreatic islets isolated from nondiabetic mice and human donors exposed to exogenous lysoPIs showed potentiated glucose-stimulated and basal insulin secretion. Finally, addition of exogenous lysoPIs partially rescued impaired glucose-stimulated insulin secretion in islets from mice and humans in the diabetic state. Overall, lysoPIs appear to be lipid species upregulated in the prediabetic stage associated with the loss of β-cells and that support the secretory function of the remaining β-cells.

ARTICLE HIGHLIGHTS

Circulating lysophosphatidylinositols (lysoPIs) are increased in situations associated with β-cell loss in mice and humans such as (pre-)diabetes, and hemipancreatectomy. Pancreatic islets isolated from nondiabetic mice and human donors, as well as INS-1E β-cells, exposed to exogenous lysoPIs exhibited potentiated glucose-stimulated and basal insulin secretion. Addition of exogenous lysoPIs partially rescued impaired glucose-stimulated insulin secretion in islets from mice and humans in the diabetic state. LysoPIs appear as lipid species being upregulated already in the prediabetic stage associated with the loss of β-cells and supporting the function of the remaining β-cells.

Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.

Screening for new inhibitors of the human Mitochondrial Pyruvate Carrier and their effects on hepatic glucose production and diabetes

BACKGROUND

The mitochondrial pyruvate carrier (MPC) is a protein complex composed of two subunits, MPC1 and MPC2. This carrier is at the interface between glycolysis and mitochondrial metabolism and plays an essential role in hepatic glucose production.

METHODS

Here we describe an in vitro screen for small molecule inhibitors of the MPC using a strain of Lactococcus lactis that has been engineered to co-express the two subunits of the human MPC and is able to import exogenous 14C-pyruvate. We then tested the top candidates for potential antidiabetic effects through the repression of gluconeogenesis.

RESULTS

By screening the Prestwick compound library of 1'200 drugs approved by the Food and Drug Administration for inhibitors of pyruvate uptake, twelve hit molecules were identified. In a secondary screen, the most potent inhibitors were found to inhibit pyruvate-driven oxygen consumption in mouse C2C12 muscle cells. Assessment of gluconeogenesis showed that Zaprinast, as well as the established MPC inhibitor UK5099, inhibited in vitro and in vivo hepatic glucose production. However, when tested acutely in mice without the administration of gluconeogenic substrates, MPC inhibitors raised blood glucose levels, pointing to liver-independent effects. Furthermore, chronic treatment with Zaprinast failed to correct hyperglycemia in both lean and obese diabetic mouse models.

CONCLUSIONS

New MPC inhibitors have been identified, showing inhibitory effects on hepatic glucose production.

GENERAL SIGNIFICANCE

For potential antidiabetic applications, MPC inhibitors should target the liver without undesired inhibition of mitochondrial pyruvate metabolism in the skeletal muscles or pancreatic beta-cells in order to avoid dual effects on glycemia.

First-phase insulin secretion: can its evaluation direct therapeutic approaches?

Our work is aimed at unraveling the role of the first-phase insulin secretion in the natural history of type 2 diabetes mellitus (T2DM) and its interrelationship with insulin resistance and with β cell function and mass. Starting from pathophysiology, we investigate the impact of impaired secretion on glucose homeostasis and explore postmeal hyperglycemia as the main clinical feature, underlining its relevance in the management of the disease. We also review dietary and pharmacological approaches aimed at improving early secretory defects and restoring residual β cell function. Furthermore, we discuss possible approaches to detect early secretory defects in clinical practice. By providing a journey through human and animal data, we attempt a unification of the recent evidence in an effort to offer a new outlook on β cell secretion.

Intra-islet insulin synthesis defects are associated with endoplasmic reticulum stress and loss of beta cell identity in human diabetes

AIMS/HYPOTHESIS

Endoplasmic reticulum (ER) stress and beta cell dedifferentiation both play leading roles in impaired insulin secretion in overt type 2 diabetes. Whether and how these factors are related in the natural history of the disease remains, however, unclear.

METHODS

In this study, we analysed pancreas biopsies from a cohort of metabolically characterised living donors to identify defects in in situ insulin synthesis and intra-islet expression of ER stress and beta cell phenotype markers.

RESULTS

We provide evidence that in situ altered insulin processing is closely connected to in vivo worsening of beta cell function. Further, activation of ER stress genes reflects the alteration of insulin processing in situ. Using a combination of 17 different markers, we characterised individual pancreatic islets from normal glucose tolerant, impaired glucose tolerant and type 2 diabetic participants and reconstructed disease progression.

CONCLUSIONS/INTERPRETATION

Our study suggests that increased beta cell workload is accompanied by a progressive increase in ER stress with defects in insulin synthesis and loss of beta cell identity.

Metabolomic Selection in the Progression of Type 2 Diabetes Mellitus: A Genetic Algorithm Approach

According to the World Health Organization (WHO), type 2 diabetes mellitus (T2DM) is a result of the inefficient use of insulin by the body. More than 95% of people with diabetes have T2DM, which is largely due to excess weight and physical inactivity. This study proposes an intelligent feature selection of metabolites related to different stages of diabetes, with the use of genetic algorithms (GA) and the implementation of support vector machines (SVMs), K-Nearest Neighbors (KNNs) and Nearest Centroid (NEARCENT) and with a dataset obtained from the Instituto Mexicano del Seguro Social with the protocol name of the following: "Análisis metabolómico y transcriptómico diferencial en orina y suero de pacientes pre diabéticos, diabéticos y con nefropatía diabética para identificar potenciales biomarcadores pronósticos de daño renal" (differential metabolomic and transcriptomic analyses in the urine and serum of pre-diabetic, diabetic and diabetic nephropathy patients to identify potential prognostic biomarkers of kidney damage). In order to analyze which machine learning (ML) model is the most optimal for classifying patients with some stage of T2DM, the novelty of this work is to provide a genetic algorithm approach that detects significant metabolites in each stage of progression. More than 100 metabolites were identified as significant between all stages; with the data analyzed, the average accuracies obtained in each of the five most-accurate implementations of genetic algorithms were in the range of 0.8214-0.9893 with respect to average accuracy, providing a precise tool to use in detections and backing up a diagnosis constructed entirely with metabolomics. By providing five potential biomarkers for progression, these extremely significant metabolites are as follows: "Cer(d18:1/24:1) i2", "PC(20:3-OH/P-18:1)", "Ganoderic acid C2", "TG(16:0/17:1/18:1)" and "GPEtn(18:0/20:4)".

Circulating 1,5-Anhydroglucitol as a Biomarker of ß-cell Mass Independent of a Diabetes Phenotype in Human Subjects

CONTEXT

During an asymptomatic prediabetic state, the functional ß-cell mass decreases to a critical threshold, triggering diabetes and related symptoms. To date, there are no reliable readouts able to capture in vivo a potential drop of the ß-cell mass.

OBJECTIVE

Beside its use as a short-term marker of glycemic control, the deoxyhexose 1,5-anhydroglucitol was identified in rodents as a circulating biomarker of the functional ß-cell mass already in the asymptomatic prediabetic stage. The present study investigated the putative corresponding relevance of circulating 1,5-anhydroglucitol in different human cohorts.

METHODS

We analyzed clinical and blood parameters in patients with established type 2 diabetes and subjects considered at high risk of developing diabetes, as well as patients with no history of diabetes scheduled for pancreaticoduodenectomy.

RESULTS

Circulating 1,5-anhydroglucitol was reduced in type 2 diabetic patients, negatively correlating with fasting plasma glucose (P < 0.0001) and hemoglobin A1c (P < 0.0001). In healthy subjects, 1,5-AG levels positively correlated with body mass index (P = 0.004) and Homeostatic Model Assessment of Insulin Resistance %S (P < 0.03) and was particularly high in nondiabetic obese individuals, potentially accounting for compensatory ß-cell expansion. Patients with no history of diabetes undergoing pancreaticoduodenectomy exhibited a 50% reduction of circulating 1,5-anhydroglucitol levels following surgery leading to an acute loss of their ß-cell mass (P = 0.002), regardless their glucose tolerance status.

CONCLUSION

In summary, plasma concentration of 1,5-anhydroglucitol follows the ß-cell mass and its noninvasive monitoring may alert about the loss of ß cells in subjects at risk for diabetes, an event that cannot be captured by other clinical parameters of glycemic control.

Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents

Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

Metabolic Profiling Implicates a Critical Role of Cyclooxygenase-2-Mediated Arachidonic Acid Metabolism in Radiation-Induced Esophageal Injury in Rats

Radiation-induced esophageal injury (RIEL) is a major dose-limiting complication of radiotherapy, especially for esophageal and thoracic cancers. RIEL is a multi-factorial and multi-step process, which is regulated by a complex network of DNA, RNA, protein and metabolite. However, it is unclear which esophageal metabolites are altered by ionizing radiation and how these changes affect RIEL progression. In this work, we established a rat model of RIEL with 0-40 Gy X-ray irradiation. Esophageal irradiation using ≥25 Gy induced significant changes to rats, such as body weight, food intake, water intake and esophageal structure. The metabolic changes and related pathways of rat esophageal metabolites were investigated by liquid chromatography-mass spectrometry (LC-MS). One hundred eighty metabolites showed an up-regulation in a dose-dependent manner (35 Gy ≥ 25 Gy > controls), and 199 metabolites were downregulated with increasing radiation dose (35 Gy ≤ 25 Gy < controls). The KEGG analysis showed that ionizing radiation seriously disrupted multiple metabolic pathways, and arachidonic acid metabolism was the most significantly enriched pathway. 20 metabolites were dysregulated in arachidonic acid metabolism, including up-regulation of five prostaglandins (PGA2, PGJ2, PGD2, PGH2, and PGI2) in 25 or 35 Gy groups. Cyclooxygenase-2 (COX-2), the key enzyme in catalyzing the biosynthesis of prostaglandins from arachidonic acid, was highly expressed in the esophagus of irradiated rats. Additionally, receiver operating characteristic (ROC) curve analysis revealed that PGJ2 may serve as a promising tissue biomarker for RIEL diagnosis. Taken together, these findings indicate that ionizing radiation induces esophageal metabolic alterations, which advance our understanding of the pathophysiology of RIEL from the perspective of metabolism.

Baseline Plasma Metabotype Correlates With Direct-Acting Antiviral Therapy Nonresponse for HCV in HIV–HCV Coinfected Patients

Introduction: With the advent of direct-acting antiviral (DAA) therapy for HCV, the cure is achieved at similar rates among HIV–HCV coinfected patients as in HCV mono-infected patients. The present study evaluates host plasma metabolites as putative indicators in predicting the treatment response in baseline HIV–HCV patients.

Methods: Non-cirrhotic HIV–HCV (N = 43) coinfected patients were treated with sofosbuvir and daclatasvir for 12 weeks. Plasma metabolite profiling of pre- and post-therapy was analyzed in 20/43 patients. Of the 20 selected, 10 (50%) attained the sustained viral response [(SVR) (responders)] as defined by the absence of HCV RNA at 12 weeks after the treatment, and 10 (50%) did not attain the cure for HCV (nonresponders).

Results: A total of 563 features were annotated (metabolomic/spectral databases). Before therapy, 39 metabolites differentiated (FC ±1.5, p < 0.05) nonresponders from responders. Of these, 20 upregulated and 19 downregulated were associated with tryptophan metabolism, nicotinamide metabolism, and others. Post therapy, 62 plasma metabolites (12 upregulated and 50 downregulated, FC±1.5, p < 0.05) differentiated nonresponders from responders and highlighted a significant increase in the steroid and histidine metabolism and significant decrease in tryptophan metabolism and ascorbate and pyruvate metabolism in the nonresponders. Based on random forest and multivariate linear regression analysis, the baseline level of N-acetylspermidine (FC > 2, AUC = 0.940, Bfactor = −0.267) and 2-acetolactate (FC > 2, AUC = 0.880, Bfactor = −0.713) significantly differentiated between nonresponders from responders in HIV–HCV coinfected patients and was able to predict the failure of treatment response.

Conclusion: Increased baseline levels of N-acetylspermidine and 2-acetolactate levels are associated with the likeliness of failure to attain the cure for HCV in HIV–HCV coinfected patients.

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Metabolite Biomarkers of Response (BoRs): Towards a fingerprint for the evolution of metastatic breast cancer

Breast cancer is the most common cancer in women worldwide and despite improved treatment strategies, it persists as the second leading cause of death of women globally. Overall prognosis drops drastically once the cancer has metastasized, which is also associated with resistance to therapy. The evolution from a localized breast cancer to metastatic disease is complex and multifactorial. Metabolic reprogramming is a pre-requisite for this transition. In this graphical review, we provide an overview of altered metabolic pathways observed in metastatic breast cancer (mBC) and detail how metabolite biomarkers could serve as a novel class of precision medicine tools to improve the diagnosis, monitoring, and treatment of mBC.

Gadolinium-Based Paramagnetic Relaxation Enhancement Agent Enhances Sensitivity for NUS Multidimensional NMR-Based Metabolomics

Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.

Pancreaticoduodenectomy model demonstrates a fundamental role of dysfunctional β cells in predicting diabetes

BACKGROUNDThe appearance of hyperglycemia is due to insulin resistance, functional deficits in the secretion of insulin, and a reduction of β cell mass. There is a long-standing debate as to the relative contribution of these factors to clinically manifesting β cell dysfunction. The aim of this study was to verify the acute effect of one of these factors, the reduction of β cell mass, on the subsequent development of hyperglycemia.METHODSTo pursue this aim, nondiabetic patients, scheduled for identical pancreaticoduodenectomy surgery, underwent oral glucose tolerance tests (OGTT) and hyperglycemic clamp (HC) procedures, followed by arginine stimulation before and after surgery. Based on postsurgery OGTT, subjects were divided into 3 groups depending on glucose tolerance: normal glucose tolerance (post-NGT), impaired glucose tolerance (post-IGT), or having diabetes mellitus (post-DM).RESULTSAt baseline, the 3 groups showed similar fasting glucose and insulin levels; however, examining the various parameters, we found that reduced first-phase insulin secretion, reduced glucose sensitivity, and rate sensitivity were predictors of eventual postsurgery development of IGT and diabetes.CONCLUSIONDespite comparable functional mass and fasting glucose and insulin levels at baseline and the very same 50% mass reduction, only reduced first-phase insulin secretion and glucose sensitivity predicted the appearance of hyperglycemia. These functional alterations could be pivotal to the pathogenesis of type 2 diabetes (T2DM).TRIAL REGISTRATIONClinicalTrials.gov NCT02175459.FUNDINGUniversità Cattolica del Sacro Cuore; Italian Ministry of Education, University and Research; European Foundation for the Study of Diabetes.

The effects of beta-cell mass and function, intercellular coupling, and islet synchrony on [Formula: see text] dynamics

Type 2 diabetes (T2D) is a challenging metabolic disorder characterized by a substantial loss of [Formula: see text]-cell mass and alteration of [Formula: see text]-cell function in the islets of Langerhans, disrupting insulin secretion and glucose homeostasis. The mechanisms for deficiency in [Formula: see text]-cell mass and function during the hyperglycemia development and T2D pathogenesis are complex. To study the relative contribution of [Formula: see text]-cell mass to [Formula: see text]-cell function in T2D, we make use of a comprehensive electrophysiological model of human [Formula: see text]-cell clusters. We find that defect in [Formula: see text]-cell mass causes a functional decline in single [Formula: see text]-cell, impairment in intra-islet synchrony, and changes in the form of oscillatory patterns of membrane potential and intracellular [Formula: see text] concentration, which can lead to changes in insulin secretion dynamics and in insulin levels. The model demonstrates a good correspondence between suppression of synchronizing electrical activity and published experimental measurements. We then compare the role of gap junction-mediated electrical coupling with both [Formula: see text]-cell synchronization and metabolic coupling in the behavior of [Formula: see text] concentration dynamics within human islets. Our results indicate that inter-[Formula: see text]-cellular electrical coupling depicts a more important factor in shaping the physiological regulation of islet function and in human T2D. We further predict that varying the whole-cell conductance of delayed rectifier [Formula: see text] channels modifies oscillatory activity patterns of [Formula: see text]-cell population lacking intercellular coupling, which significantly affect [Formula: see text] concentration and insulin secretion.

Dysfunction of Persisting β Cells Is a Key Feature of Early Type 2 Diabetes Pathogenesis

Type 2 diabetes is characterized by peripheral insulin resistance and insufficient insulin release from pancreatic islet β cells. However, the role and sequence of β cell dysfunction and mass loss for reduced insulin levels in type 2 diabetes pathogenesis are unclear. Here, we exploit freshly explanted pancreas specimens from metabolically phenotyped surgical patients using an in situ tissue slice technology. This approach allows assessment of β cell volume and function within pancreas samples of metabolically stratified individuals. We show that, in tissue of pre-diabetic, impaired glucose-tolerant subjects, β cell volume is unchanged, but function significantly deteriorates, exhibiting increased basal release and loss of first-phase insulin secretion. In individuals with type 2 diabetes, function within the sustained β cell volume further declines. These results indicate that dysfunction of persisting β cells is a key factor in the early development and progression of type 2 diabetes, representing a major target for diabetes prevention and therapy.

Therapeutic potential of stem cells from human exfoliated deciduous teeth infusion into patients with type 2 diabetes depends on basal lipid levels and islet function

Mesenchymal stem cells (MSCs) hold great potential in treating patients with diabetes, but the therapeutic effects are not always achieved. Particularly, the clinical factors regulating MSC therapy in this setting are largely unknown. In this study, 24 patients with type 2 diabetes mellitus (T2DM) treated with insulin were selected to receive three intravenous infusions of stem cells from human exfoliated deciduous teeth (SHED) over the course of 6 weeks and were followed up for 12 months. We observed a significant reduction of glycosylated serum albumin level (P < .05) and glycosylated hemoglobin level (P < .05) after SHED transplantation. The total effective rate was 86.36% and 68.18%, respectively, at the end of treatment and follow‐up periods. Three patients ceased insulin injections after SHED transplantation. A steamed bread meal test showed that the serum levels of postprandial C‐peptide at 2 hours were significantly higher than those at the baseline (P < .05). Further analysis showed that patients with a high level of blood cholesterol and a low baseline level of C‐peptide had poor response to SHED transplantation. Some patients experienced a transient fever (11.11%), fatigue (4.17%), or rash (1.39%) after SHED transplantation, which were easily resolved. In summary, SHED infusion is a safe and effective therapy to improve glucose metabolism and islet function in patients with T2DM. Blood lipid levels and baseline islet function may serve as key factors contributing to the therapeutic outcome of MSC transplantation in patients with T2DM.

Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes

PURPOSE OF REVIEW

Machine learning approaches-which seek to predict outcomes or classify patient features by recognizing patterns in large datasets-are increasingly applied to clinical epidemiology research on diabetes. Given its novelty and emergence in fields outside of biomedical research, machine learning terminology, techniques, and research findings may be unfamiliar to diabetes researchers. Our aim was to present the use of machine learning approaches in an approachable way, drawing from clinical epidemiological research in diabetes published from 1 Jan 2017 to 1 June 2020.

RECENT FINDINGS

Machine learning approaches using tree-based learners-which produce decision trees to help guide clinical interventions-frequently have higher sensitivity and specificity than traditional regression models for risk prediction. Machine learning approaches using neural networking and "deep learning" can be applied to medical image data, particularly for the identification and staging of diabetic retinopathy and skin ulcers. Among the machine learning approaches reviewed, researchers identified new strategies to develop standard datasets for rigorous comparisons across older and newer approaches, methods to illustrate how a machine learner was treating underlying data, and approaches to improve the transparency of the machine learning process. Machine learning approaches have the potential to improve risk stratification and outcome prediction for clinical epidemiology applications. Achieving this potential would be facilitated by use of universal open-source datasets for fair comparisons. More work remains in the application of strategies to communicate how the machine learners are generating their predictions.

Palmatine ameliorates high fat diet induced impaired glucose tolerance

BACKGROUND

The impaired glucose tolerance (IGT) is a representative prediabetes characterized by defective glucose homeostasis, and palmatine (PAL) is a natural isoquinoline alkaloid with multiple pharmacological effects. Our study aims to investigate the therapeutic effect of PAL on the impaired glucose tolerance.

METHODS

Male Sprague-Dawley rats were used to establish an IGT model with high fat diet (HFD). Oral glucose tolerance test (OGTT) and further biochemical analysis were conducted to determine the effect of PAL on glucose intolerance in vivo. Molecular details were clarified in a cellular model of IGT induced by Palmitate (PA) on INS-1 cells.

RESULTS

Our study demonstrated a relief of IGT with improved insulin resistance in HFD induced rats after PAL treatment. Besides, promoted pancreas islets function was validated with significantly increased β cell mass after the treatment of PAL. We further found out that PAL could alleviate the β cell apoptosis that accounts for β cell mass loss in IGT model. Moreover, MAPK signaling was investigated in vivo and vitro with the discovery that PAL regulated the MAPK signaling by restricting the ERK and JNK cascades. The insulin secretion assay indicated that PAL significantly promoted the defective insulin secretion in PA-induced INS-1 cells via JNK rather than ERK signaling. Furthermore, PAL treatment was determined to significantly suppress β cell apoptosis in PA-induced cells. We thus thought that PAL promoted the PA-induced impaired insulin release by inhibiting the β cell apoptosis and JNK signaling in vitro.

CONCLUSION

In summary, PAL ameliorates HFD-induced IGT with novel mechanisms.

Evaluation of Non-Uniform Sampling 2D 1H-13C HSQC Spectra for Semi-Quantitative Metabolomics

Metabolomics is the comprehensive study of metabolism, the biochemical processes that sustain life. By comparing metabolites between healthy and disease states, new insights into disease mechanisms can be uncovered. NMR is a powerful analytical method to detect and quantify metabolites. Standard one-dimensional (1D) ¹H-NMR metabolite profiling is informative but challenged by significant chemical shift overlap. Multi-dimensional NMR can increase resolution, but the required long acquisition times lead to limited throughput. Non-uniform sampling (NUS) is a well-accepted mode of acquiring multi-dimensional NMR data, enabling either reduced acquisition times or increased sensitivity in equivalent time. Despite these advantages, the technique is not widely applied to metabolomics. In this study, we evaluated the utility of NUS ¹H–¹³C heteronuclear single quantum coherence (HSQC) for semi-quantitative metabolomics. We demonstrated that NUS improved sensitivity compared to uniform sampling (US). We verified that the NUS measurement maintains linearity, making it possible to detect metabolite changes across samples and studies. Furthermore, we calculated the lower limit of detection and quantification (LOD/LOQ) of common metabolites. Finally, we demonstrate that the measurements are repeatable on the same system and across different systems. In conclusion, our results detail the analytical capability of NUS and, in doing so, empower the future use of NUS ¹H–¹³C HSQC in metabolomic studies.

Association between 1,5-Anhydroglucitol and Acute C Peptide Response to Arginine among Patients with Type 2 Diabetes

OBJECTIVE

The aim of this study was to explore the association of 1,5-anhydroglucitol with acute C peptide response (ACPR) to arginine among patients with type 2 diabetes.

METHODS

Patients with type 2 diabetes were enrolled from the Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital. ACPR was assessed using arginine stimulation test. Decreased β-cell function was defined as ACPR < 2.1. Multivariable logistic regression models were used to demonstrate the association between 1,5-anhydroglucitol and decreased β-cell function.

RESULTS

Finally, 623 patients with type 2 diabetes were enrolled into the analysis. Multivariable-adjusted odds ratios for decreased β-cell function across quartiles of 1,5-anhydroglucitol were 1.00, 0.47 (95% confidence interval (CI) 0.23-0.99), 0.41 (95% CI 0.20-0.84), and 0.27 (95% CI 0.13-0.57) (P _trend = 0.042), respectively. When 1,5-anhydroglucitol was considered as a continuous variable after logarithm, the corresponding odds ratio was 0.40 (95% CI 0.23-0.71).

CONCLUSIONS

We demonstrated a dose-response linear association between 1,5-anhydroglucitol and ACPR. 1,5-Anhydroglucitol was likely to be associated with β-cell function. Further analysis with large sample size and prospective study design is warranted to validate our findings.