BACKGROUND: Approximately 40% of individuals with diabetes worldwide are at risk of developing diabetic kidney disease (DKD), which is not only the leading cause of kidney failure, but also significantly increases the risk of cardiovascular disease, causing significant societal health and financial burdens. This study aimed to describe the burden of DKD and explore its cross-country epidemiological status, predict development trends, and assess its risk factors and sociodemographic transitions. METHODS: Based on the Global Burden of Diseases (GBD) Study 2021, data on DKD due to type 1 diabetes (DKD-T1DM) and type 2 diabetes (DKD-T2DM) were analyzed by sex, age, year, and location. Numbers and age-standardized rates were used to compare the disease burden between DKD-T1DM and DKD-T2DM among locations. Decomposition analysis was used to assess the potential drivers. Locally weighted scatter plot smoothing and Frontier analysis were used to estimate sociodemographic transitions of DKD disability-adjusted life years (DALYs). RESULTS: The DALYs due to DKD increased markedly from 1990 to 2021, with a 74.0% (from 2,227,518 to 3,875,628) and 173.6% (from 4,122,919 to 11,278,935) increase for DKD-T1DM and DKD-T2DM, respectively. In 2030, the estimated DALYs for DKD-T1DM surpassed 4.4 million, with that of DKD-T2DM exceeding 14.6 million. Notably, middle-sociodemographic index (SDI) quintile was responsible for the most significant DALYs. Decomposition analysis revealed that population growth and aging were major drivers for the increased DKD DALYs in most regions. Interestingly, the most pronounced effect of positive DALYs change from 1990 to 2021 was presented in high-SDI quintile, while in low-SDI quintile, DALYs for DKD-T1DM and DKD-T2DM presented a decreasing trend over the past years. Frontiers analysis revealed that there was a negative association between SDI quintiles and age-standardized DALY rates (ASDRs) in DKD-T1DM and DKD-T2DM. Countries with middle-SDI shouldered disproportionately high DKD burden. Kidney dysfunction (nearly 100.0% for DKD-T1DM and DKD-T2DM), high fasting plasma glucose (70.8% for DKD-T1DM and 87.4% for DKD-T2DM), and non-optimal temperatures (low and high, 5.0% for DKD-T1DM and 5.1% for DKD-T2DM) were common risk factors for age-standardized DALYs in T1DM-DKD and T2DM-DKD. There were other specific risk factors for DKD-T2DM such as high body mass index (38.2%), high systolic blood pressure (10.2%), dietary risks (17.8%), low physical activity (6.2%), lead exposure (1.2%), and other environmental risks. CONCLUSIONS DKD markedly increased and varied significantly across regions, contributing to a substantial disease burden, especially in middle-SDI countries. The rise in DKD is primarily driven by population growth, aging, and key risk factors such as high fasting plasma glucose and kidney dysfunction, with projections suggesting continued escalation of the burden by 2030.
Humans ; Global Burden of Disease ; Risk Factors ; Male ; Female ; Disability-Adjusted Life Years ; Diabetic Nephropathies/epidemiology* ; Middle Aged ; Diabetes Mellitus, Type 2/epidemiology* ; Adult ; Diabetes Mellitus, Type 1/complications* ; Aged ; Adolescent ; Young Adult ; Quality-Adjusted Life Years

OBJECTIVES: To investigate the current status and influencing factors of quality of life in children and adolescents with type 1 diabetes (T1DM) in Xinjiang. METHODS: A convenience sampling method was used to select 259 children with T1DM and their primary caregivers who attended three tertiary hospitals in Xinjiang from January 2023 to February 2024. The Pediatric Quality of Life Inventory^TM Version 4.0 Generic Core Scales (PedsQL^TM4.0) and Pediatric Quality of Life Inventory^TM Version 3.2 Diabetes Module (PedsQL^TM3.2-DM) were used to assess the quality of life of the children. Information on family demographics, caregiver burden, and caregiving ability was also collected. Multiple linear regression analysis was employed to identify factors associated with the quality of life of the children. RESULTS: The scores for PedsQL^TM4.0 and PedsQL^TM3.2-DM were 77±16 and 71±16, respectively. Both were negatively correlated with caregiver burden (P<0.05) and positively correlated with caregiving ability (P<0.05). Multiple linear regression analysis indicated that caregiver burden, caregiving ability, family income, and parent-child relationship were significantly associated with generic quality of life (P<0.05), whereas caregiver burden, caregiving ability, disease duration, place of residence, and glycated hemoglobin level were significantly associated with diabetes-specific quality of life (P<0.05). CONCLUSIONS The overall quality of life of children and adolescents with T1DM in Xinjiang is relatively low. The quality of life is influenced by a combination of factors including family caregiver burden, caregiving ability, family income, parent-child relationship, disease duration, place of residence, and glycated hemoglobin level. Strategies to improve quality of life should consider the combined impact of individual disease characteristics and family factors.
Humans ; Quality of Life ; Diabetes Mellitus, Type 1/psychology* ; Adolescent ; Child ; Male ; Female ; Caregivers/psychology* ; Child, Preschool ; Linear Models

The prevalence of type 1 diabetes (T1DM) is increasing annually, and its complications seriously impair the quality of life of affected children. Early screening for T1DM helps reduce the occurrence of diabetic ketoacidosis, protect β-cell function, and delay disease onset in high-risk populations. This article summarizes current domestic and international screening technologies for T1DM. Screening methods remain centered on detection of diabetes-related antibodies and glycometabolic markers, while factors related to disease pathogenesis hold promise as sensitive screening markers. Expanding T1DM screening in China is expected to improve early diagnosis and treatment.
Diabetes Mellitus, Type 1/diagnosis* ; Humans ; Early Diagnosis ; Autoantibodies/blood* ; Mass Screening/methods*

OBJECTIVE: To elucidate the effect of Huanglian-Renshen-Decoction (HRD) on ameliorating type 2 diabetes mellitus by maintaining islet β -cell identity through regulating paracrine and endocrine glucagon-like peptide-1 (GLP-1)/GLP-1 receptor (GLP-1R) in both islet and intestine. METHODS: The db/db mice were divided into the model (distilled water), low-dose HRD (LHRD, 3 g/kg), high-dose HRD (HHRD, 6 g/kg), and liraglutide (400 µ g/kg) groups using a random number table, 8 mice in each group. The db/m mice were used as the control group (n=8, distilled water). The entire treatment of mice lasted for 6 weeks. Blood insulin, glucose, and GLP-1 levels were quantified using enzyme-linked immunosorbent assay kits. The proliferation and apoptosis factors of islet cells were determined by immunohistochemistry (IHC) and immunofluorescence (IF) staining. Then, GLP-1, GLP-1R, prohormone convertase 1/3 (PC1/3), PC2, v-maf musculoaponeurotic fibrosarcoma oncogene homologue A (MafA), and pancreatic and duodenal homeobox 1 (PDX1) were detected by Western blot, IHC, IF, and real-time quantitative polymerase chain reaction, respectively. RESULTS: HRD reduced the weight and blood glucose of the db/db mice, and improved insulin sensitivity at the same time (P<0.05 or P<0.01). HRD also promoted mice to secrete more insulin and less glucagon (P<0.05 or P<0.01). Moreover, it also increased the number of islet β cell and decreased islet α cell mass (P<0.01). After HRD treatment, the levels of GLP-1, GLP-1R, PC1/3, PC2, MafA, and PDX1 in the pancreas and intestine significantly increased (P<0.05 or P<0.01). CONCLUSION HRD can maintain the normal function and identity of islet β cell, and the underlying mechanism is related to promoting the paracrine and endocrine activation of GLP-1 in pancreas and intestine.
Animals ; Glucagon-Like Peptide 1/metabolism* ; Diabetes Mellitus, Type 2/metabolism* ; Glucagon-Like Peptide-1 Receptor/metabolism* ; Insulin-Secreting Cells/pathology* ; Drugs, Chinese Herbal/pharmacology* ; Male ; Blood Glucose/metabolism* ; Insulin/blood* ; Mice ; Intestinal Mucosa/pathology* ; Apoptosis/drug effects* ; Cell Proliferation/drug effects* ; Islets of Langerhans/pathology*

OBJECTIVES: The gut microbiota plays a crucial role in the pathophysiology of various types of diabetes. However, the causal relationship between them has yet to be systematically elucidated. This study aims to explore the potential causal associations between gut microbiota and diabetes using a two-sample Mendelian randomization (MR) analysis, based on multiple taxonomic levels. METHODS: Eligible instrumental variables were extracted from the selected genome-wide association study (GWAS) data on gut microbiota. These were combined with GWAS datasets on type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes mellitus (GDM) to conduct forward MR analysis, sensitivity analysis, reverse MR analysis, and validation of significant estimates. Microbial taxa with causal effects on T1D, T2D, and GDM were identified based on a comprehensive assessment of all analytical stages. RESULTS: A total of 2 179, 2 176, and 2 166 single nucleotide polymorphisms (SNP) were included in the MR analyses for gut microbiota with T1D, T2D, and GDM, respectively. MR results indicated causal associations between: Six microbial taxa (Eggerthella, Lachnospira, Bacillales, Desulfovibrionales, Parasutterella, and Turicibacter) and T1D; 9 microbial taxa (Verrucomicrobia, Deltaproteobacteria, Actinomycetales, Desulfovibrionale, Actinomycetaceae, Desulfovibrionaceae, Actinomyces, Alcaligenaceae, and Lachnospiraceae NC2004 group) and T2D; 10 microbial taxa (Betaproteobacteria, Coprobacter, Ruminococcus2, Tenericutes, Clostridia, Methanobacteria, Mollicutes, Methanobacteriales, Methanobacteriaceae, and Methanobrevibacter) and GDM. CONCLUSIONS This study identified specific gut microbial taxa that may significantly increase or decrease the risk of developing diabetes. Some findings were fully replicated in independent validation datasets. However, the underlying biological mechanisms of these causal relationships warrant further investigation through mechanistic studies and population-based research.
Gastrointestinal Microbiome/genetics* ; Humans ; Mendelian Randomization Analysis ; Genome-Wide Association Study ; Diabetes Mellitus, Type 2/genetics* ; Diabetes Mellitus, Type 1/genetics* ; Female ; Polymorphism, Single Nucleotide ; Diabetes, Gestational/genetics* ; Pregnancy

OBJECTIVES: To investigate the effects of live combined Bacillus subtilis and Enterococcus faecium (LCBE) on glucose and lipid metabolism in mice with type 2 diabetes mellitus (T2DM) and circadian rhythm disorder (CRD) and explore the possible mechanisms. METHODS: KM mice were randomized into normal diet (ND) group (n=8), high-fat diet (HFD) group (n=8), and rhythm-intervention with HFD group (n=16). After 8 weeks of feeding, the mice were given an intraperitoneal injection of streptozotocin (100 mg/kg) to induce T2DM. The mice in CRD-T2DM group were further randomized into two equal groups for treatment with LCBE (225 mg/kg) or saline by gavage; the mice in ND and HFD groups also received saline gavage for 8 weeks. Blood glucose level of the mice was measured using a glucometer, and serum levels of Bmal1, PER2, insulin, C-peptide and lipids were determined with ELISA. Colon morphology and hepatic lipid metabolism of the mice were examined using HE staining and Oil Red O staining, respectively, and fecal short-chain fatty acids (SCFAs) was detected using LC-MS; GPR43 and GLP-1 expression levels were analyzed using RT-qPCR and Western blotting. RESULTS: Compared with those in CRD-T2DM group, the LCBE-treated mice exhibited significant body weight loss, lowered levels of PER2, insulin, C-peptide, total cholesterol (TC) and LDL-C, and increased levels of Bmal1 and HDL-C levels. LCBE treatment significantly increased SCFAs, upregulated GPR43 and GLP-1 expressions at both the mRNA and protein levels, and improved hepatic steatosis and colon histology. CONCLUSIONS LCBE ameliorates lipid metabolism disorder in CRD-T2DM mice by reducing body weight and improving lipid profiles and circadian regulators possibly via the SCFAs/GPR43/GLP-1 pathway.
Animals ; Mice ; Lipid Metabolism ; Diabetes Mellitus, Type 2/metabolism* ; Enterococcus faecium ; Glucagon-Like Peptide 1/metabolism* ; Bacillus subtilis ; Diabetes Mellitus, Experimental/metabolism* ; Circadian Rhythm ; Blood Glucose/metabolism* ; Receptors, G-Protein-Coupled/metabolism* ; Fatty Acids, Volatile/metabolism* ; Male ; Chronobiology Disorders/metabolism*

Humans ; Diabetes Mellitus, Type 1 ; Hypoglycemia/chemically induced* ; Hypoglycemic Agents/adverse effects* ; China ; Blood Glucose ; Insulin

Humans ; Glycated Hemoglobin ; Diabetes Mellitus, Type 1 ; Blood Glucose ; Diabetes Mellitus, Type 2

Diabetic Ketoacidosis/diagnosis* ; Humans ; Child ; Diabetes Mellitus, Type 1/diagnosis* ; China ; Insulin/administration & dosage* ; Hypoglycemic Agents/therapeutic use*

OBJECTIVES: Diabetes mellitus is closely associated with sudden sensorineural hearing loss (SSNHL), but no definitive evidence has established a causal relationship between type 1 diabetes mellitus (T1DM) and SSNHL. This study aims to investigate the impact of T1DM on SSNHL from a genetic perspective, providing insights for risk prediction and treatment strategies. METHODS: Genetic data related to exposure (T1DM) and outcome (SSNHL) were obtained from publicly available genome-wide association studies (GWAS). Instrumental variables were selected, and Mendelian randomization (MR) analysis was conducted to explore the causal association between T1DM and SSNHL. Inverse variance weighted (IVW) analysis was used as the primary method, with random-effects IVW serving as the main analytical approach. MR-Egger, weighted median, simple mode, and weighted mode analyses were utilized as supplementary methods. Cochran's Q test was applied to evaluate the heterogeneity of the selected instrumental variables, MR-PRESSO was applied to detect outliers, MR-Egger regression was used to assess horizontal pleiotropy and leave-one-out analysis was conducted to examine the robustness of individual single nucleotide polymorphisms (SNPs) on the overall results. RESULTS: A total of 127 SNPs were selected as instrumental variables for the MR analysis. IVW analysis demonstrated a genetically determined association between T1DM and SSNHL (OR=1.036, 95% CI 1.002 to 1.071, P=0.038). Forest plots and scatter plots indicated a causal relationship, suggesting that T1DM increases the risk of SSNHL. Cochran's Q test demonstrated no significant heterogeneity among SNPs (MR-Egger: Q=126.030, P=0.356; IVW: Q=126.450, P=0.373). The funnel plot appeared symmetrical, indicating that the selected instrumental variables were primarily related to exposure rather than potential confounding factors. The MR-Egger intercept was not significantly different from zero (P=0.527), indicating no evidence of horizontal pleiotropy among the SNPs. MR-PRESSO analysis did not identify any outlier SNPs (P=0.356). Leave-one-out analysis confirmed the robustness of the findings, as the results remained stable after removing individual SNPs. CONCLUSIONS Two-sample MR analysis supports the conclusion that T1DM patients have an increased risk of developing SSNHL.
Humans ; Mendelian Randomization Analysis ; Hearing Loss, Sensorineural/etiology* ; Diabetes Mellitus, Type 1/genetics* ; Genome-Wide Association Study ; Hearing Loss, Sudden/etiology* ; Polymorphism, Single Nucleotide ; Risk Factors ; Genetic Predisposition to Disease