BACKGROUND: The global rise in diabetes prevalence is a pressing concern. Despite initiatives like "The Healthy Beijing Action 2020-2030" advocating for increased awareness, treatment, and control, the specific situation in Beijing remains unexplored. This study aimed to analyze the trends in diabetes prevalence, awareness, treatment, and control among Beijing adults. METHODS: Through a stratified multistage probability cluster sampling method, a series of representative cross-sectional surveys were conducted in Beijing from 2005 to 2022, targeting adults aged 18-79 years. A face-to-face questionnaire, along with body measurements and laboratory tests, were administered to 111,943 participants. Data from all survey were age- and/or gender-standardized based on the 2020 Beijing census population. Annual percentage rate change (APC) or average annual percentage rate change (AAPC) was calculated to determine prevalence trends over time. Complex sampling logistic regression models were employed to explore the relationship between various characteristics and diabetes. RESULTS: From 2005 to 2022, the total prevalence of diabetes among Beijing adults aged 18-79 years increased from 9.6% (95% CI: 8.8-10.4%) to 13.9% (95% CI: 13.1-14.7%), with an APC/AAPC of 2.1% (95% CI: 1.1-3.2%, P <0.05). Significant increases were observed among adults aged 18-39 years and rural residents. Undiagnosed diabetes rose from 3.5% (95% CI: 3.2-4.0%) to 7.2% (95% CI: 6.6-7.9%) with an APC/AAPC of 4.1% (95% CI: 0.5-7.3%, P <0.05). However, diabetes awareness and treatment rates showed annual declines of 1.4% (95% CI: -3.0% to -0.2%, P <0.05) and 1.3% (95% CI: -2.6% to -0.2%, P <0.05), respectively. The diabetes control rate decreased from 21.5% to 19.1%, although not statistically significant (APC/AAPC = -1.5%, 95% CI: -5.6% to 1.9%). Overweight and obesity were identified as risk factors for diabetes, with ORs of 1.65 (95% CI: 1.38-1.98) and 2.48 (95% CI: 2.07-2.99), respectively. CONCLUSIONS The prevalence of diabetes in Beijing has significantly increased between 2005 and 2022, particularly among young adults and rural residents. Meanwhile, there has been a concerning decrease in diabetes awareness and treatment rates, while control rates have remained stagnant. Regular blood glucose testing, especially among adults aged 18-59 years, should be warranted. Furthermore, being male, elderly, overweight, or obese was associated with higher diabetes risk, suggesting the needs for targeted management strategies.
Humans ; Adult ; Middle Aged ; Male ; Female ; Aged ; Adolescent ; Young Adult ; Cross-Sectional Studies ; Diabetes Mellitus/epidemiology* ; Beijing/epidemiology* ; Prevalence ; China/epidemiology* ; Surveys and Questionnaires

Oxidative stress due to aberrant metabolism is considered as a crucial contributor to diabetes and its complications. Hyperglycemia and hyperlipemia boost excessive reactive oxygen species generation by elevated mitochondrial respiration, increased nicotinamide adenine dinucleotide phosphate oxidase activity, and enhanced pro-oxidative processes, including protein kinase C pathways, hexosamine, polyol, and advanced glycation endproducts, which exacerbate oxidative stress. Oxidative stress plays a significant role in the onset of diabetes and its associated complications by impairing insulin production, increasing insulin resistance, maintaining hyperglycemic memory, and inducing systemic inflammation. A more profound comprehension of the molecular processes that link oxidative stress to diabetes is crucial to new preventive and therapeutic strategies. Therefore, this review discusses the mechanisms underlying how oxidative stress contributes to diabetes mellitus and its complications. We also summarize the current approaches for prevention and treatment by targeting the oxidative stress pathways in diabetes.
Oxidative Stress/physiology* ; Humans ; Diabetes Mellitus/physiopathology* ; Diabetes Complications/metabolism* ; Reactive Oxygen Species/metabolism* ; Glycation End Products, Advanced/metabolism* ; Animals

BACKGROUND: The role of inflammation in the development of gestational diabetes mellitus (GDM) has recently become a focus of research. The systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), novel indices, reflect the body's chronic immune-inflammatory state. This study aimed to investigate the associations between the SII or SIRI and GDM. METHODS: A prospective birth cohort study was conducted at Beijing Obstetrics and Gynecology Hospital from February 2018 to December 2020, recruiting participants in their first trimester of pregnancy. Baseline SII and SIRI values were derived from routine clinical blood results, calculated as follows: SII = neutrophil (Neut) count × platelet (PLT) count/lymphocyte (Lymph) count, SIRI = Neut count × monocyte (Mono) count/Lymph count, with participants being grouped by quartiles of their SII or SIRI values. Participants were followed up for GDM with a 75-g, 2-h oral glucose tolerance test (OGTT) at 24-28 weeks of gestation using the glucose thresholds of the International Association of Diabetes and Pregnancy Study Groups (IADPSG). Logistic regression was used to analyze the odds ratios (ORs) (95% confidence intervals [CIs]) for the the associations between SII, SIRI, and the risk of GDM. RESULTS: Among the 28,124 women included in the study, the average age was 31.8 ± 3.8 years, and 15.76% (4432/28,124) developed GDM. Higher SII and SIRI quartiles were correlated with increased GDM rates, with rates ranging from 12.26% (862/7031) in the lowest quartile to 20.10% (1413/7031) in the highest quartile for the SII ( Ptrend <0.001) and 11.92-19.31% for the SIRI ( Ptrend <0.001). The ORs (95% CIs) of the second, third, and fourth SII quartiles were 1.09 (0.98-1.21), 1.21 (1.09-1.34), and 1.39 (1.26-1.54), respectively. The SIRI findings paralleled the SII outcomes. For the second through fourth quartiles, the ORs (95% CIs) were 1.24 (1.12-1.38), 1.41 (1.27-1.57), and 1.64 (1.48-1.82), respectively. These associations were maintained in subgroup and sensitivity analyses. CONCLUSION The SII and SIRI are potential independent risk factors contributing to the onset of GDM.
Humans ; Female ; Pregnancy ; Diabetes, Gestational/immunology* ; Prospective Studies ; Adult ; Inflammation/immunology* ; Glucose Tolerance Test ; Birth Cohort

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: 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

BACKGROUND: Epoxyeicosatrienoic acids (EETs), which are metabolites of arachidonic acid catalyzed by cytochrome P450 epoxygenase, are degraded into inactive dihydroxyeicosatrienoic acids by soluble epoxide hydrolase (sEH). Many studies have revealed that sEH gene deletion exerts protective effects against diabetes. Vascular calcification is a common complication of diabetes, but the potential effects of sEH on diabetic vascular calcification are still unknown. METHODS: The level of aortic calcification in wild-type and Ephx2-/- C57BL/6 diabetic mice induced with streptozotocin was evaluated by measuring the aortic calcium content through alizarin red staining, immunohistochemistry staining, and immunofluorescence staining. Mouse vascular smooth muscle cell lines (MOVAS cells) treated with β-glycerol phosphate (0.01 mol/L) plus advanced glycation end products (50 mg/L) were used to investigate the effects of sEH inhibitors or sEH knockdown and EETs on the calcification of vascular smooth muscle cells, which was detected by Western blotting, alizarin red staining, and Von Kossa staining. RESULTS: sEH gene deletion significantly inhibited diabetic vascular calcification by increasing levels of EETs in the aortas of mice. EETs (especially 11,12-EET and 14,15-EET) efficiently prevented the osteogenic transdifferentiation of MOVAS cells by decreasing nidogen-2 (NID2) expression. Interestingly, suppressing sEH activity by small interfering ribonucleic acid or specific inhibitors did not block osteogenic transdifferentiation of MOVAS cells induced by β-glycerol phosphate and advanced glycation end products. NID2 overexpression significantly abolished the inhibitory effect of sEH gene deletion on diabetic vascular calcification. Moreover, NID2 overexpression mediated by adeno-associated virus 9 vectors markedly increased insulin-like growth factor 2 (IGF2) and phospho-ERK1/2 expression in MOVAS cells. Overall, sEH gene knockout inhibited diabetic vascular calcification by decreasing aortic NID2 expression and, then, inactivating the downstream IGF2-ERK1/2 signaling pathway. CONCLUSIONS sEH gene deletion markedly inhibited diabetic vascular calcification through repressed osteogenic transdifferentiation of vascular smooth muscle cells mediated by increased aortic EET levels, which was associated with decreased NID2 expression and inactivation of the downstream IGF2-ERK1/2 signaling pathway.
Animals ; Mice ; Vascular Calcification/metabolism* ; Mice, Inbred C57BL ; Epoxide Hydrolases/metabolism* ; Diabetes Mellitus, Experimental/genetics* ; Male ; Gene Deletion ; MAP Kinase Signaling System/genetics* ; Cell Line ; Immunohistochemistry ; Muscle, Smooth, Vascular/metabolism* ; Signal Transduction/genetics* ; Mice, Knockout

Diabetes mellitus (DM) has become one of the most serious and common chronic diseases around the world, leading to various complications and a reduction in life expectancy. Increased sedentary behavior (SB) and decreased physical activity (PA) are important contributors to the rising prevalence of DM. This article reviews the research progress on the pathogenesis of DM, the effects of SB and PA on the risk of DM, aiming to explore the influence of different PA intensities, amounts, frequencies, durations and types on the incidence of DM. Research has shown that blood glucose levels tend to increase with the prolongation of SB. Within a certain range, PA intensity and amount are negatively correlated with the risk of DM; Performing PA for more than 3 days per week maintains normal glucose tolerance and lower blood pressure; Engaging in 150-300 min of moderate-intensity exercise or 75-150 min of high-intensity exercise per week reduces the risk of DM; PA during leisure time reduces the risk of DM, while PA during work increases the risk of DM; Both aerobic training and resistance training reduce the risk of DM, and the combination of the two training methods produces better benefits; Various types of exercises, such as cycling, soccer, aerobics, yoga and tai chi, all reduce the risk of DM. In summary, prolonged SB increases the risk of DM, while appropriate PA reduces the risk of DM. As the intensity, amount, and frequency of PA increase, the effect of reducing DM risk becomes more significant. Different exercise methods have different effects on reducing DM risk.
Humans ; Sedentary Behavior ; Exercise/physiology* ; Diabetes Mellitus/prevention & control*