This study aimed to explore the novel classification of inpatients with new-onset diabetes in Eastern China by the cluster-based classification method and compare the clinical characteristics among the different subgroups.A total of 1017 Inpatients with new-onset diabetes of five hospitals in Eastern China were included in the study. Clustering analysis was used to cluster the data into five subgroups according to six basic variables. The differences in clinical characteristics, treatments, and the prevalence of diabetes-related diseases among the five subgroups were analyzed by multiple groups comparisons and pairwise comparisons. The risk of diabetes-related diseases in the five subgroups was compared by calculating odd ratio (OR). P value < 0.05 was considered significant.Five subgroups were obtained by clustering analysis with the highest proportion of patients with severe insulin-deficient diabetes (SIDD) 451 (44.35%), followed by patients with mild age-related diabetes (MARD) 236 (23.21%), patients with mild obesity-related diabetes (MOD) 207 (20.35%), patients with severe insulin-resistant diabetes (SIRD) 81 (7.96%), and patients with severe autoimmune diabetes (SAID) 42 (4.13%). Five subtypes had their own unique characteristics and treatments. The prevalence and risk of diabetes-related complications and comorbidities were also significantly different among the five subtypes. Diabetic kidney disease (DKD) was the most common in SIRD group. Patients in SIDD, SIRD, and MARD groups were more likely to develop cardiovascular disease (CVD) and/or stroke, diabetic peripheral vascular disease (DPVD), and diabetic distal symmetric polyneuropathy (DSPN). The prevalence and risk of metabolic syndrome (MS) were the highest in MOD and SIRD groups. Patients in SAID group had the highest prevalence and risk of diabetic ketoacidosis (DKA). Patients with MOD were more likely to develop non-alcoholic fatty liver disease (NAFLD).The inpatients with new-onset diabetes in Eastern China had the unique clustering distribution. The clinical characteristics, treatments, and diabetes-related complications and comorbidities of the five subgroups were different, which may provide the basis for precise treatments of diabetes.
Motivation: GLP-1 receptor agonists have shown favorable effects in improving obesity and ectopic fat deposition in type 2 diabetes patients. MRI-PDFF can serve as a tool for continuous, dynamic assessment of lipid content changes in patients. Goal(s): Dynamic monitoring of fat content and lipid deposition changes in type 2 diabetes obese patients receiving glucagon-like peptide-1 receptor agonist treatment using MRI-PDFF. Approach: 21 male diabetic patients received 3-month treatment: semaglutide (n=12) or loxenatide (n=9). Clinical data collected, pre/post 3.0T MRI scans (T1WI, T2WI, MRI-PDFF) measured fat. Results: The semaglutide group showed significant fat reduction in multiple organs, while the loxenatide group reduced pancreatic fat fraction. Impact: Semaglutide effectively controls blood glucose and body weight in type 2 diabetes obese patients. It also significantly reduces SAT and VAT while alleviating ectopic fat deposition. MRI-PDFF represents a non-invasive tool for continuous assessment of lipid content changes following treatment.
Background. Exocrine pancreatic insufficiency (EPI) is common in patients with type 2 diabetes. However, the prevalence of EPI varies significantly in different studies. Untreated EPI in these patients can adversely affect their nutrition and metabolism. The aim of this study is to estimate the pooled prevalence of EPI in patients with type 2 diabetes and to explore the potential risk factors. Methods. A systematic search was performed in PubMed, Web of Science, and Embase, which included studies meeting inclusion criteria from 1960 to 1 April 2022. Relevant articles were searched using the combination of Medical Subject Heading (MeSH) terms of “Type 2 diabetes” and “pancreatic exocrine insufficiency.” The Stata 16.0 software was used for data analyses. The random-effects model was used to estimate the pooled prevalence rates and 95% CI using “metaprop program.” Results. The pooled prevalence of EPI was 22% (95% CI: 15%–31%) in patients with type 2 diabetes and 8% (95% CI: 4%–14%) of them developed severe pancreatic insufficiency. In the subgroup analyses, the prevalence of EPI in type 2 diabetes was correlated with geographic location. The prevalence in Asian countries (35%, 95% CI: 22%–49%) is higher than in Europe (18%, 95% CI: 10%–29%) and Australia (9%, 95% CI: 4%–16%). Furthermore, patients with higher insulin requirements, who are more likely to be insulin-deficient, have a higher prevalence of EPI. The pooled prevalence was 27% (95% CI: 17%–37%) in type 2 diabetes with higher insulin requirement (1 group) and 15% (95% CI: 1%–40%) in patients with lower insulin requirement (2 group). In addition, the morbidity of severe EPI in the higher insulin requirement group (12%, 95% CI: 7%–19%) was sextuple as much as the lower insulin requirement group (2%, 95% CI: 0%–13%). EPI was more common in subjects younger than 60 compared with elderlies (25% vs. 19%). Conclusion. The prevalence of EPI in type 2 diabetes may be overestimated. Furthermore, the higher prevalence may be closely related to β-cell function. Endocrine disease therapy would potentially represent a novel therapeutic approach for patients with type 2 diabetes and EPI.
Abstract Post-acute pancreatitis diabetes mellitus (PPDM-A) is the main component of pancreatic exocrine diabetes mellitus. Timely diagnosis of PPDM-A improves patient outcomes and the mitigation of burdens and costs. We aimed to determine risk factors prospectively and predictors of PPDM-A in China, focusing on giving personalized treatment recommendations. Here, we identify and evaluate the best set of predictors of PPDM-A prospectively using retrospective data from 820 patients with acute pancreatitis at four centers by machine learning approaches. We used the L1 regularized logistic regression model to diagnose early PPDM-A via nine clinical variables identified as the best predictors. The model performed well, obtaining the best AUC = 0.819 and F1 = 0.357 in the test set. We interpreted and personalized the model through nomograms and Shapley values. Our model can accurately predict the occurrence of PPDM-A based on just nine clinical pieces of information and allows for early intervention in potential PPDM-A patients through personalized analysis. Future retrospective and prospective studies with multicentre, large sample populations are needed to assess the actual clinical value of the model.
Abstract Context Distinguishing different types of diabetes is important in directing optimized treatment strategies and correlated epidemiological studies. Objective Through detailed analysis of hormone responses to mixed meal tolerance test (MMTT), we aimed to find representing characteristics of post-acute pancreatitis diabetes mellitus (PPDM-A) and post-chronic pancreatitis diabetes mellitus (PPDM-C). Methods Participants with PPDM-A, PPDM-C, type 1 diabetes, type 2 diabetes, and normal controls (NCs) underwent MMTT. Fasting and postprandial responses of serum glucose, C-peptide, insulin, glucagon, pancreatic polypeptide (PP), ghrelin, gastric inhibitory peptide (GIP), glucagon like peptide-1 (GLP-1), and peptide YY (PYY) were detected and compared among different groups. Focused analysis on calculated insulin sensitivity and secretion indices were performed to determine major causes of hyperglycemia in different conditions. Results Participants with PPDM-A were characterized by increased C-peptide, insulin, glucagon, and PP, but decreased ghrelin, GIP, and PYY compared with NCs. Patients with PPDM-C showed secretion insufficiency of C-peptide, insulin, ghrelin, and PYY, and higher postprandial responses of glucagon and PP than NCs. In particular, both fasting and postprandial levels of ghrelin in PPDM-C were significantly lower than other diabetes groups. PYY responses in patients with PPDM-A and PPDM-C were markedly reduced. Additionally, the insulin sensitivity of PPDM-A was decreased, and the insulin secretion for PPDM-C was decreased. Conclusion Along with the continuum from acute to chronic pancreatitis, the pathological mechanism of PPDM changes from insulin resistance to insulin deficiency. Insufficient PYY secretion is a promising diagnostic marker for distinguishing PPDM from type 1 and type 2 diabetes. Absent ghrelin secretion to MMTT may help identify PPDM-C.
Diabetes of the exocrine pancreas (DEP), also commonly described as pancreatogenic diabetes mellitus, is a type of diabetes secondary to abnormalities in pancreatic or exocrine secretion of the pancreas. However, its pathogenesis is not yet known. The aim of this article was to explore the biomarkers of DEP and their potential molecular mechanisms. Based on GSE76896 dataset, which was acquired from Gene Expression Omnibus (GEO), we identified 373 genes by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. In addition, protein-protein interaction (PPI) network analysis and cytoHubba were used to screen potential hub genes. Five hub genes were determined, comprising Toll-like receptor 4 (TLR4), ITGAM, ITGB2, PTPRC, and CSF1R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways suggested macrophage activation and Toll-like receptor signaling pathway as important pathophysiological features of DEP. CIBERSORT suggested that TLR4 may regulate the immune pathway via macrophages. Next, we validated the expression and receiver operating characteristic curve (ROC) of the hub genes using the GSE164416 dataset. In addition, we used miRNet to predict the target miRNAs of hub genes and intersected them with common miRNAs in diabetes from the Human MicroRNA Disease Database (HMDD), which was used to propose a possible mechanistic model for DEP. The miRNA-mRNA network showed that has-miR-155-5p/has-miR-27a-3p/has-miR-21-5p-TLR4 might lead to TLR4 signaling pathway activation in DEP. In conclusion, we identified five hub genes, namely, TLR4, ITGAM, ITGB2, PTPRC, and CSF1R, as biomarkers to aid in the diagnosis of DEP and conducted an in-depth study of the pathogenesis of DEP at the genetic level.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)