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

BACKGROUND: Alpha-glucosidase inhibitors or dipeptidyl peptidase-4 inhibitors are both hypoglycemia agents that specifically impact on postprandial hyperglycemia. We compared the effects of acarbose and sitagliptin add on to metformin on time in range (TIR) and glycemic variability (GV) in Chinese patients with type 2 diabetes mellitus through continuous glucose monitoring (CGM). METHODS: This study was a randomized, open-label, active-con-trolled, parallel-group trial conducted at 15 centers in China from January 2020 to August 2022. We recruited patients with type 2 diabetes aged 18-65 years with body mass index (BMI) within 19-40 kg/m 2 and hemoglobin A1c (HbA1c) between 6.5% and 9.0%. Eligible patients were randomized to receive either metformin combined with acarbose 100 mg three times daily or metformin combined with sitagliptin 100 mg once daily for 28 days. After the first 14-day treatment period, patients wore CGM and entered another 14-day treatment period. The primary outcome was the level of TIR after treatment between groups. We also performed time series decomposition, dimensionality reduction, and clustering using the CGM data. RESULTS: A total of 701 participants received either acarbose or sitagliptin treatment in combination with metformin. There was no statistically significant difference in TIR between the two groups. Time below range (TBR) and coefficient of variation (CV) levels in acarbose users were significantly lower than those in sitagliptin users. Median (25th percentile, 75th percentile) of TBR below target level <3.9 mmol/L (TBR 3.9 ): Acarbose: 0.45% (0, 2.13%) vs . Sitagliptin: 0.78% (0, 3.12%), P = 0.042; Median (25th percentile, 75th percentile) of TBR below target level <3.0 mmol/L (TBR 3.0 ): Acarbose: 0 (0, 0.22%) vs . Sitagliptin: 0 (0, 0.63%), P = 0.033; CV: Acarbose: 22.44 ± 5.08% vs . Sitagliptin: 23.96 ± 5.19%, P <0.001. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV (group with small wave, moderate wave and big wave). No significant difference was found in the complexity of glucose time series index (CGI) between acarbose users and sitagliptin users. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV. CONCLUSIONS: Acarbose had slight advantages over sitagliptin in improving GV and reducing the risk of hypoglycemia. Time series analysis of CGM data may predict GV and the risk of hypoglycemia. TRIAL REGISTRATION Chinese Clinical Trial Registry: ChiCTR2000039424.
Humans ; Metformin/therapeutic use* ; Sitagliptin Phosphate/therapeutic use* ; Acarbose/therapeutic use* ; Diabetes Mellitus, Type 2/blood* ; Middle Aged ; Male ; Female ; Adult ; Blood Glucose/drug effects* ; Hypoglycemic Agents/therapeutic use* ; Aged ; Glycated Hemoglobin/metabolism* ; Adolescent ; Young Adult ; China ; East Asian People

BACKGROUND: The epidemiological pattern and disease burden of type 2 diabetes have been shifting in China over the past decades. This analysis described the epidemiological transition of type 2 diabetes in the past three decades and projected the trend in the future three decades in China. METHODS: Age-, sex-, and year-specific incidence, prevalence, death, and disability-adjusted life years (DALYs) for people with 15 years or older and diabetes or high fasting glucose in China and related countries from 1990 to 2021 were obtained from the Global Burden of Disease. We obtained the trends of age-, sex-, and year-specific rates and absolute numbers of incidence, prevalence, deaths, and DALYs attributable to type 2 diabetes in China from 1990 to 2021. Using the Lee-Carter model, we projected the incidence, prevalence, death, and DALYs attributable to type 2 diabetes to 2050 stratified by age and sex. RESULTS: The age-standardized incidence of type 2 diabetes was 341.5 per 100,000 persons (1.6 times in 1990) and the age-standardized prevalence was 9.96% (9960.0 per 100,000 persons, 2.5 times in 1990) in China 2021. In 2021, there were 0.9 million deaths and 26.8 million DALYs due to type 2 diabetes or hyperglycemia, as 2.9 and 2.7 times the data in 1990, respectively. The age-standardized rates of type 2 diabetes and hyperglycemia were projected to raise to 449.5 per 100,000 persons for incidence, 18.17% for prevalence, 244.6 per 100,000 persons for death, and 4720.2 per 100,000 persons for DALYs by 2050. The incidence of type 2 diabetes kept growing among individuals under the age of 20 years in the past three decades (128.7 per 100,000 persons in 1990 and 439.9 per 100,000 persons in 2021) and estimating 1870.8 per 100,000 in 2050. CONCLUSIONS The incidence, prevalence, and disease burden of type 2 diabetes grew rapidly in China in the past three decades. The prevention of type 2 diabetes in young people and the care for elder adults will be the greatest challenge for the country.
Humans ; Diabetes Mellitus, Type 2/mortality* ; China/epidemiology* ; Prevalence ; Female ; Male ; Incidence ; Middle Aged ; Adult ; Aged ; Adolescent ; Young Adult ; Disability-Adjusted Life Years ; Aged, 80 and over

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

BACKGROUND: The American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) health and stage, reflecting the interaction among metabolism, chronic kidney disease (CKD), and the cardiovascular system. However, the association between CKM stage and the long-term risk of cardiovascular disease (CVD) has not been validated. This study aimed to evaluate the long-term CVD risk associated with CKM health metrics and CKM stage using data from a population-based cohort study. METHODS: In total, 5293 CVD-free participants were followed up to around 13 years in the Chinese Multi-provincial Cohort Study (CMCS). Considering the pathophysiologic progression of CKM health metrics abnormalities (comprising obesity, central adiposity, prediabetes, diabetes, hypertriglyceridemia, CKD, and metabolic syndrome), participants were divided into CKM stages 0, 1, and 2. The time-dependent Cox regression models were used to estimate the cardiovascular risk associated with CKM health metrics and stage. Additionally, broader CVD outcomes were examined, with a specific assessment of the impact of stage 3 in 2581 participants from the CMCS-Beijing subcohort. RESULTS: Among participants, 91.2% (4825/5293) had at least one abnormal CKM health metric, 8.8% (468/5293), 13.3% (704/5293), and 77.9% (4121/5293) were in CKM stages 0, 1, and 2, respectively; and 710 incident CVD cases occurred during a median follow-up time of 13.3 years (interquartile range: 12.1 to 13.6 years). Participants with each poor CKM health metric exhibited significantly higher CVD risk. Compared with stage 0, the hazard ratio (HR) (95% confidence interval [CI]) for CVD incidence was 1.31 (0.84-2.04) in stage 1 and 2.27 (1.57-3.28) in stage 2. Significant interactive impacts existed between CKM stage and age or sex, with higher CVD risk related to increased CKM stages in participants aged <60 years or females. CONCLUSION These findings highlight the contribution of CKM health metrics and CKM stage to the long-term risk of CVD, suggesting the importance of multi-component recognition and management of poor CKM health in CVD prevention.
Humans ; Female ; Male ; Cardiovascular Diseases/etiology* ; Middle Aged ; Adult ; Cohort Studies ; Renal Insufficiency, Chronic/metabolism* ; Aged ; Risk Factors ; Metabolic Syndrome/metabolism* ; China ; East Asian People

BACKGROUND: Studies on the impact of blood glucose indicators on metabolism remain relatively scarce. The aim of this study was to investigate the associations between blood glucose indicators and metabolic disorders in China. METHODS: Data were from the Thyroid disorders, Iodine status and Diabetes Epidemiological survey (TIDE survey), which randomly selected 31 cities from 31 provinces in the Chinese mainland. A total of 68,383 participants without preexisting diabetes and have complete data on blood glucose, lipids, and blood pressure were included in the analysis. The diabetic population was divided into seven groups based on different types of elevated blood glucose levels, including fasting plasma glucose (FPG), postprandial glucose (PPG), and hemoglobin A1c (HbA1c): FPG ≥7 mmol/L; PPG ≥11.1 mmol/L; HbA1c ≥6.5%; FPG ≥7 mmol/L and PPG ≥11.1 mmol/L; FPG ≥7 mmol/L and HbA1c ≥6.5%; PPG ≥11.1 mmol/L and HbA1c ≥6.5%; FPG ≥7 mmol/L, PPG ≥11.1 mmol/L, and HbA1c ≥6.5%. The effects of each blood glucose indicator on metabolism were investigated separately. Weighted calculation was applied during the analysis, with the weighting coefficient based on the number of people corresponding to the population characteristics of each sample in the 2010 Chinese Census. A logistic regression model with restricted cubic splines (RCS) was employed to characterize the nonlinear associations of age and body mass index (BMI) with the risk of diabetes subtypes defined by distinct blood glucose indicators elevations, as well as the relationships between different blood glucose indicators (FPG, PPG, HbA1c) and the risk of metabolic disorders such as hypertension, hypertriglyceridemia, hypercholesterolemia, high low-density lipoprotein cholesterol (high LDL-C) and low high-density lipoprotein cholesterol (low HDL-C). RESULTS: Among individuals with diabetes, elevated PPG alone was the most common abnormality, affecting 26.96% (1382/5127) of the population. Among the seven groups with only one elevated blood glucose indicator, individuals with elevated PPG alone exhibited the highest mean levels of triglycerides (TG) at 2.11 mmol/L (95% confidence interval [CI]: 1.97-2.25 mmol/L, P = 0.004), total cholesterol (TC) at 5.26 mmol/L (95% CI: 5.18-5.33 mmol/L, P <0.001), and low-density lipoprotein cholesterol (LDL-C) at 3.12 mmol/L, (95% CI: 3.06-3.19 mmol/L, P = 0.001). Individuals with elevated PPG alone showed a high prevalence of hypertension (806/1382, 58.32%), hypertriglyceridemia (676/1382, 48.91%), hypercholesterolemia (694/1382, 50.22%), High LDL-C (525/1382, 37.94%), and Low HDL-C (364/1382, 26.34%). The association of age and BMI with the risk of diabetes revealed that the older the patient, the steeper the RCS curve for the odds ratio (OR) of diabetes with elevated PPG alone (age = 60, OR = 2.79, 95% CI [2.49-3.12], P <0.01). Similarly, as BMI increased, the RCS curve for the OR of diabetes with elevated HbA1c alone also steepened (BMI = 35, OR = 3.75, 95% CI [3.23-4.35], P <0.001). Additionally, the RCS yielded a positive association between blood glucose indicators and metabolic diseases risk. In individuals with diabetes, RCS for both the ORs of metabolic diseases (hypertension, hypertriglyceridemia, hypercholesterolemia, high LDL-C, low HDL-C) and the levels of metabolic indicators (TG, TC, LDL-C, HDL-C) revealed some inflection points within the ranges of FPG 5-6 mmol/L, PPG 6-8 mmol/L, and HbA1c 5.5-6.0%. CONCLUSIONS PPG is more closely related to metabolic disorders than FPG and HbA1c in people with diabetes. For patients with diabetes and metabolic disorders, it may be necessary to monitor blood glucose fluctuations within specific ranges (FPG 5-6 mmol/L, PPG 6-8 mmol/L, and HbA1c 5.5-6.0%).
Humans ; Female ; Cross-Sectional Studies ; Male ; Blood Glucose/metabolism* ; Middle Aged ; Glycated Hemoglobin/metabolism* ; Adult ; Metabolic Diseases/epidemiology* ; Aged ; China ; Diabetes Mellitus/blood* ; East Asian People

Peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α) is a core member of the PGC-1 family and serves as a transcriptional coactivator, playing a crucial regulatory role in various diseases. Mitochondria, the main site of cellular energy metabolism, are essential for maintaining cell growth and function. Their function is regulated by various transcription factors and coactivators. PGC-1α regulates the biogenesis, dynamics, energy metabolism, calcium homeostasis, and autophagy processes of mitochondria by interacting with multiple nuclear transcription factors, thereby exerting significant effects on mitochondrial function. This review explores the biological functions of PGC-1α and its regulatory effects and related mechanisms on mitochondria, providing important information for our in-depth understanding of the role of PGC-1α in cellular metabolism. The potential role of PGC-1α in metabolic diseases, cardiovascular diseases, and neurodegenerative diseases was also discussed, providing a theoretical basis for the development of new treatment strategies.
Humans ; Mitochondria/metabolism* ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha/physiology* ; Animals ; Energy Metabolism/physiology* ; Neurodegenerative Diseases/physiopathology* ; Autophagy/physiology* ; Transcription Factors/physiology* ; Metabolic Diseases/physiopathology* ; Cardiovascular Diseases/physiopathology*

Obesity is a worldwide health problem. An imbalance in energy metabolism is an important cause of obesity and related metabolic diseases. Our previous studies showed that inhibition of miR-429 increased the protein level of uncoupling protein 1 (UCP1) in beige adipocytes; however, whether local inhibition of miR-429 in subcutaneous adipose tissue affects diet-induced obesity and related metabolic disorders remains unclear. The aim of this study was to investigate the effect of local overexpression of miR-429 sponge in subcutaneous adipose tissue on obesity and related metabolic disorders. The control adeno-associated virus (AAV) or AAV expressing the miR-429 sponge was injected into mouse inguinal white adipose tissue. Seven days later, the mice were fed a high-fat diet for 10 weeks to induce obesity. The effects of the miR-429 sponge on body weight, adipose tissue weight, plasma glucose and lipid levels, and hepatic lipid content were explored. The results showed that the overexpression of miR-429 sponge in subcutaneous white adipose tissue reduced body weight and fat mass, decreased fasting blood glucose and plasma cholesterol levels, improved glucose tolerance, and alleviated hepatic lipid deposition in mice. Mechanistic investigation showed that the inhibition of miR-429 significantly upregulated the expression of UCP1 in adipocytes and adipose tissue. These results suggest that local inhibition of miR-429 in subcutaneous white adipose tissue ameliorates obesity and related metabolic disorders potentially by upregulating UCP1, and miR-429 is a potential therapeutic target for the treatment of obesity and related metabolic disorders.
Animals ; MicroRNAs/physiology* ; Obesity/metabolism* ; Mice ; Adipose Tissue, White/metabolism* ; Metabolic Diseases ; Subcutaneous Fat/metabolism* ; Male ; Uncoupling Protein 1/metabolism* ; Diet, High-Fat ; Mice, Inbred C57BL

Metabolic diseases characterized by an imbalance in energy homeostasis represent a significant global health challenge. Individuals with metabolic diseases often suffer from complications related to disorders in lipid metabolism, such as obesity and non-alcoholic fatty liver disease (NAFLD). Understanding core genes involved in lipid metabolism can advance strategies for the prevention and treatment of these conditions. Stearoyl-CoA desaturase 1 (SCD1) is a key enzyme in lipid metabolism that converts saturated fatty acids into monounsaturated fatty acids. SCD1 plays a crucial regulatory role in numerous physiological and pathological processes, including energy homeostasis, glycolipid metabolism, autophagy, and inflammation. Abnormal transcription and epigenetic activation of <i>Scd1i> contribute to abnormal lipid accumulation by regulating multiple signaling axes, thereby promoting the development of obesity, NAFLD, diabetes, and cancer. This review comprehensively summarizes the key role of SCD1 as a metabolic hub gene in various (patho)physiological contexts. Further it explores potential translational avenues, focusing on the development of novel SCD1 inhibitors across interdisciplinary fields, aiming to provide new insights and approaches for targeting SCD1 in the prevention and treatment of metabolic diseases.
Stearoyl-CoA Desaturase/metabolism* ; Humans ; Metabolic Diseases/physiopathology* ; Lipid Metabolism/physiology* ; Animals ; Obesity/enzymology* ; Non-alcoholic Fatty Liver Disease

The circadian rhythm regulates the 24-hour physiological and behavioral cycles through endogenous molecular clocks governed by core clock genes via the transcription-translation feedback loop (TTFL). In mammals, the suprachiasmatic nucleus (SCN) serves as the central pacemaker, coordinating the timing of physiological processes throughout the body by regulating clock genes such as CLOCK, BMAL1, PER, and CRY. The molecular clocks of peripheral tissues and cells are synchronized by the SCN through TTFLs to regulate metabolism, immunity, and energy homeostasis. Numerous studies indicate that circadian rhythm disruption is closely related to obesity, type 2 diabetes, metabolic syndrome and other diseases, and the mechanism involves the dysregulation of glucose and lipid metabolism, abnormal insulin signaling and low-grade inflammation. In recent years, small-molecule compounds targeting the core clock components such as CRY, REV-ERB, and ROR have been identified and shown potential to modulate metabolic diseases by stabilizing or inhibiting the activity of key clock proteins. This review summarizes the mechanisms and advances in these compounds, and explores the challenges and future directions for their clinical translation, providing insights for chronotherapy-based metabolic disease interventions.
Humans ; Metabolic Diseases/physiopathology* ; Animals ; Circadian Rhythm/physiology* ; Biological Clocks/drug effects* ; CLOCK Proteins/physiology* ; Circadian Clocks/physiology* ; Suprachiasmatic Nucleus/physiology*