Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy最新文献

Background: Early diagnosis and management of diabetic retinopathy are essential to avoid vision impairment. We explored different glycated albumin levels in patients with or without diabetic retinopathy, and further constructed a glycated albumin based-model to predict the presence of diabetic retinopathy.

Methods: This cross-sectional study, which was conducted at a physical examination center from June 2022 to June 2024, collected clinical information, laboratory test results, and information on diabetic retinopathy from type 2 diabetes adults. Least absolute shrinkage and selection operator regression was applied to select the variables associated with diabetic retinopathy, with optimal threshold determined by receiver operating characteristic curve analysis. Eight machine learning algorithms, including eXtreme Gradient Boosting, logistic regression, Light Gradient-Boosting Machine, random forest, adaptive boosting, K-nearest neighbors, support vector machine, and Gaussian naïve Bayes, were compared to select the model with the best performance in predicting the risk of diabetic retinopathy.

Results: Of the 809 eligible patients, 85 (10.5%) and 724 (89.5%) had or had no diabetic retinopathy. Glycated albumin and glycated hemoglobin levels were higher in patients with diabetic retinopathy than in those without diabetic retinopathy. Glycated albumin had satisfactory performance for predicting diabetic retinopathy (area under the curve 0.657 at a threshold of 18.0%). Multivariate regression logistic analysis revealed that glycated albumin was independently correlated with diabetic retinopathy. Machine learning algorithm analysis illustrated that the random forest model had the best performance in predicting the presence of diabetic retinopathy (area under the curve 0.648 and 0.725 in validation and final test sets, respectively). Both calibration curve and decision curve analyses suggested high clinical predication value and applicability for this model.

Conclusion: High glycated albumin levels were associated with diabetic retinopathy in patients with type 2 diabetes. A novel prediction model based on glycated albumin could be used to predict the risk of diabetic retinopathy.

Background: Metabolic Syndrome (MetS) is a significant public health concern globally, driven by factors like obesity and sedentary lifestyles. In Iran, numerous meta-analyses have estimated its prevalence, but results vary. This umbrella review aims to synthesize these findings to provide a definitive summary of MetS prevalence across both general and high-risk populations in Iran.

Methods: We systematically searched international (PubMed/Medline, Scopus, Web of Science) and Persian databases (SID, Magiran) for meta-analyses published between January 2014 and August 2025 reporting pooled MetS prevalence in Iranian populations. Data extraction covered overall prevalence, subgroup analyses (gender, diagnostic criteria, population risk strata), and study characteristics. Methodological quality was assessed using AMSTAR 2, and evidence quality was evaluated using GRADE framework. Meta-analysis of pooled estimates was performed using random-effects models.

Results: Nineteen meta-analyses comprising 119 prevalence estimates and 1,954,049 participants were included. The overall pooled prevalence of MetS in Iran was 30.4% (95% CI: 28.44-32.33). Prevalence was higher in females (33.05%) than in males (27.57%). The prevalence varied by diagnostic criteria, with the Joint Interim Statement (JIS) criteria yielding the highest estimate (36.47%), followed by the International Diabetes Federation (IDF) criteria (32.03%), and the NCEP-ATP III criteria (26.86%). High heterogeneity (I² > 99%) was observed. The methodological quality of included studies was predominantly moderate, and the GRADE evidence quality for most outcomes was moderate.

Conclusion: Approximately one in three Iranian adults is affected by MetS, with disproportionately higher burden among women and specific high-risk populations. The choice of diagnostic criteria significantly influences prevalence estimates, and considerable heterogeneity across studies highlights the context-dependent nature of these findings. These results underscore the urgent need for targeted public health interventions and systematic screening strategies tailored to different population risk profiles.

Registration information: The study protocol was registered in the Prospero with ID, CRD420251182425.

Purpose: Maternal metabolic health is increasingly recognized as a critical factor influencing early childhood development. This study examined the relationship between maternal pre-pregnancy glycemic status and neurodevelopmental outcomes in early childhood using a large national cohort in Korea.

Patients and methods: We conducted a population-based retrospective cohort study using national health examination and Korean Developmental Screening Test data from 2014 to 2021. Maternal fasting plasma glucose levels measured within one year prior to conception through delivery were used to classify women into normal, prediabetes, and diabetes mellitus (DM) groups. Neurodevelopmental outcomes were assessed at 18-24 and 30-36 months across six domains: gross motor, fine motor, cognition, language, sociality, and self-care. Inverse probability of treatment weighting was applied to adjust for baseline differences and weighted relative risks (RRs) and 95% confidence intervals (CIs) were estimated.

Results: Among 258,367 mother-child dyads, both prediabetes and DM were associated with significantly increased risks of developmental delays in multiple domains. Offspring of mothers with DM exhibited significantly increased risks of developmental delay across all domains at both 18-24 and 30-36 months, with the highest risk observed for self-care at 30-36 months (RR 1.466, 95% CI 1.408-1.525). Children of mothers with prediabetes also demonstrated increased risks in cognition (RR 1.061, 95% CI 1.008-1.117) and self-care (RR 1.119, 95% CI 1.058-1.184) at 18-24 months. Additionally, statistically significant elevations were observed in gross motor (RR 1.101, 95% CI 1.037-1.169), language (RR 1.058, 95% CI 1.019-1.100), and sociality (RR 1.106, 95% CI 1.060-1.154) skills at 30-36 months.

Conclusion: Maternal pre-pregnancy glycemic abnormalities, even at the prediabetic level, were associated with an increased risk of developmental delays in early childhood. Optimizing maternal glycemic control prior to conception may promote more favorable developmental outcomes in offspring.

Background: To examine the role of the triglyceride glucose(TyG) index and epicardial adipose tissue(EAT) in the progression of type 2 diabetes mellitus (T2DM) with concurrent coronary atherosclerotic heart disease(CHD), thereby establishing a theoretical foundation for early clinical intervention in CHD among T2DM patients.

Methods: This retrospective study analysed 96 patients with T2DM who underwent Coronary CT angiography at the Department of Endocrinology, Qinhuangdao First Hospital, between January 2020 and June 2023. Participants were categorised into two groups: those with T2DM and CHD (n=48) and those with T2DM alone (n=48). Clinical indicators were compared between the groups. Logistic regression analysis assessed the association between T2DM with CHD and both the TyG index and EAT. Receiver operating characteristic (ROC) curve analysis evaluated the predictive value of these parameters.

Results: Significant differences (P < 0.05) were found between the two groups in weight, uric acid(UA), triglycerides(TG), high-density lipoprotein cholesterol (HDL-C), epicardial adipose tissue volume (EATV) and the TyG index. Multivariate analysis identified both EATV and the TyG index as independent risk factors for T2DM complicated by CHD (P < 0.05). ROC curve analysis demonstrated that both EATV and TyG index showed statistically significant predictive power for CHD development in T2DM patients (P<0.05). The EATV exhibited an AUC of 0.739 with a predictive cut-off value of 128.45 mL (sensitivity 66.7%, specificity 72.9%). The TyG index demonstrated an AUC of 0.724 with a predictive cut-off value of 9.47 (sensitivity 58.3%, specificity 81.2%).

Conclusion: The TyG index and EAT are significant risk factors for CHD in T2DM patients. These findings provide a theoretical basis for early screening, monitoring, and clinical intervention in this population.

Purpose: Oxidative stress and the newly characterized mode of regulated cell death, cuproptosis, drive the progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Electroacupuncture (EA) is a promising non-drug treatment, but its mechanisms are unclear. This study investigated the benefits of EA in a high-fat diet (HFD)-induced MASLD mouse model, focusing on the NRF2/ARE pathway, cuproptosis, and the gut-liver axis.

Methods: MASLD was established in C57BL/6J mice via a 16-week HFD. Mice were arbitrarily categorized into four groups: control (C, normal chow diet), model (M, HFD), EA (HFD + EA), and EA plus NRF2 inhibitor (EM, HFD + EA + inhibitor). A 4-week intervention was then conducted. Post-intervention, body weight alterations were tracked, and serum, liver tissue, and fecal specimens were gathered for subsequent examination.

Results: EA treatment significantly improved the metabolic profile, reducing body weight, lipids, and hepatic inflammation. It attenuated oxidative stress by enhancing antioxidant capacity and activated the NRF2/ARE pathway. EA also modulated cuproptosis-related genes (upregulating dihydrolipoamide S-acetyltransferase (Dlat), downregulating solute carrier family 31 member 1 (Slc31a1), ferredoxin reductase (Fdx1), and heat shock protein 70 (Hsp70)). Furthermore, EA remodeled the gut microbiota (increasing Limosilactobacillusand Ligilactobacillus) and elevated related metabolites.

Conclusion: EA ameliorates MASLD through multiple mechanisms, including regulating lipid metabolism, attenuating inflammation and oxidative stress, activating NRF2, suppressing cuproptosis, and altering the gut-liver axis, collectively contributing to hepatic improvement.

Aim: This study was to evaluate the association between triglyceride glucose-body mass index (TyG-BMI) and osteoporosis in patients with type 2 diabetes mellitus (T2DM).

Methods: In this cross-sectional study, 892 patients with T2DM hospitalized at Zhengrzhou Central Hospital between April 2024 and March 2025 were included. Participants were grouped into TyG-BMI tertiles (T1 ≤ 209.31, T2 209.31-243.39, T3 > 243.39). Logistic regression analysis, subgroup and sensitivity analyses, receiver operating characteristic (ROC) curve analysis, and restricted cubic spline (RCS) modeling were performed to evaluate the relationship between TyG-BMI and osteoporosis.

Results: After adjusting for potential confounders, each one-unit increase in TyG-BMI was associated with a 1.5% lower odds of osteoporosis (OR = 0.985, 95% CI: 0.978-0.992). Compared with T1, the odds of osteoporosis were significantly reduced in T2 and T3 (T2: OR = 0.565, 95% CI: 0.352-0.908; T3: OR = 0.322, 95% CI: 0.167-0.620). Subgroup analyses confirmed that the inverse association remained stable across most demographic and clinical strata. Sensitivity analyses further supported this trend: participants in the highest TyG-BMI quartile had a 74.8% lower risk of osteoporosis compared with those in the lowest quartile. Individuals with TyG-BMI above the median also showed a markedly reduced osteoporosis risk. ROC analysis showed TyG-BMI had a higher predictive value (AUC = 0.657) for osteoporosis than TyG index (AUC = 0.554) or BMI (AUC = 0.647) alone. RCS analysis indicated a significant linear negative relationship without evidence of nonlinearity (P for nonlinearity = 0.720).

Conclusion: Higher TyG-BMI levels are inversely associated with the risk of osteoporosis in patients with T2DM.

Background: Hypertension (HTN) and dyslipidemia (DYS) frequently complicate type 2 diabetes mellitus (T2DM), increasing cardiovascular risk. Genetic variation within the DPP4-ABCC8-INSR-IGF1 axis may underlie this clustering.

Methods: A total of 444 T2DM patients were stratified into T2DM (n = 256), T2DM with HTN (T2MH, n = 134), and T2DM with HTN and DYS (T2MH-DYS, n = 54). Six single nucleotide polymorphisms (SNPs) were genotyped, and associations were assessed by logistic regression and haplotype analysis with Bonferroni correction.

Results: Clinical profiling showed higher C-reactive protein (CRP) and adrenocorticotropic hormone (ACTH) in T2MH and more severe metabolic derangements in T2MH-DYS. DPP4 rs3788979 was strongly linked to hypertension: CT (adjusted OR = 0.370, P = 0.001) and CC (adjusted OR = 0.424, P = 0.001) were protective versus TT, while in the T2MH vs T2MH-DYS comparison, the same CT and CC genotypes conferred increased dyslipidemia risk (adjusted OR = 5.418, P = 0.001; OR = 5.620, P = 0.002). In the comparison between T2DM and T2MH-DYS, the same genotypes also increase the susceptibility risk. IGF1 rs972936 TC genotype also reduced T2MH risk (adjusted OR = 0.460, P = 0.006). Haplotype analysis identified GAATGT as protective against hypertension (OR = 0.312, P = 0.0014) and GACCGT as a risk haplotype for dyslipidemia (OR = 4.113, P = 0.0021); both remained significant after Bonferroni correction.

Conclusion: Variants within the DPP4 axis influence susceptibility to HTN and DYS in T2DM, with GAATGT and GACCGT emerging as robust haplotype markers. Notably, the risk conferred by DPP4 rs3788979 genotypes was modulated by lipid status: CT/CC were protective against hypertension alone but became risk factors when dyslipidemia co-occurred.

Purpose: The ratio of gamma-glutamyl transferase (GGT) to high-density lipoprotein cholesterol (HDL-C) (GHR) represents a novel non-insulin-based biomarker for evaluating the risk of NAFLD and T2DM. However, its correlation with diabetic kidney disease (DKD) remains unexplored. This study aims to explore the association between GHR and DKD in patients with T2DM.

Patients and methods: In this cross-sectional study, 2798 patients diagnosed as T2DM admitted to the hospital from 2018 to 2023 were assessed. The analysis was conducted through restricted cubic spline (RCS) and logistic regression methodologies, complemented by additional stratified and interaction analyses.

Results: As the quartiles of GHR increase, there is a notable increase in the prevalence of DKD, with the rates of 43.2%, 47.2%, 52.1%, and 57.4%, respectively. Logistic regression analysis showed a positive association between GHR and DKD (OR=1.17, 95% CI: 1.05-1.30), which was consistently observed across all subgroups through stratified analysis. RCS analysis identified an inverted L-shaped association, with an inflection point at 84.5. Additionally, AUC for GHR (AUC = 0.637, 95% CI: 0.616-0.657) was significantly higher compared to those of GGT and HDL alone.

Conclusion: GHR exhibits a positive association with the risk of DKD, underscoring its potential utility as a cost-effective biomarker for stratifying the risk of DKD.

Introduction: Diabetic nephropathy (DN) is a leading cause of end-stage kidney disease (ESKD), with renal fibrosis as a key pathological hallmark. However, the cellular and molecular drivers of fibrosis remain incompletely defined. Here, we employed single-cell RNA sequencing (scRNA-seq) to delineate pro-fibrotic cell subsets and their key regulatory factors in human DN kidneys, providing a higher-resolution view compared to previous fibrosis-related scRNA-seq studies.

Methods: Publicly available scRNA-seq datasets from human DN and control kidneys were analyzed to identify fibrosis-associated fibroblast subsets. A Tmsb10-high fibroblast population was prioritized. Functional validation was performed through Tmsb10 knockdown in NIH-3T3 fibroblasts and in a diabetic mouse model, followed by assessment of fibrosis markers, extracellular matrix (ECM) deposition, and TGF-β/SMAD signaling.

Results: scRNA-seq revealed a significant expansion of Tmsb10-high fibroblasts in DN kidneys, exhibiting strong enrichment of ECM-related and TGF-β/SMAD-responsive genes. Tmsb10 knockdown reduced Fn1, Col1a1, and α-Sma expression by approximately 50-70% and markedly attenuated ECM accumulation in vivo. Mechanistically, TMSB10 deficiency suppressed phosphorylation of SMAD2/3, mitigating fibroblast activation and matrix deposition.

Discussion: This study identifies TMSB10 as a novel fibroblast-specific regulator of renal fibrosis in DN, acting through the TGF-β/SMAD pathway. These findings expand current understanding of fibroblast heterogeneity and highlight TMSB10 as a potential therapeutic target for DN and other fibrotic diseases. Limitations include validation in a limited sample size and the use of murine fibroblast models, warranting further confirmation in human primary cells.

Background: The developmental origins of health and disease (DOHaD) framework highlights the importance of the intrauterine environment in shaping lifelong health outcomes. Maternal nutrition, toxic exposures, and epigenetic reprogramming are key factors influencing offspring susceptibility to obesity and cardiometabolic disorders. However, prior reviews have typically addressed nutrition and toxicants separately, limiting insights into their combined effects on the fetal epigenome. This review integrates current evidence on how maternal nutrition and toxicant exposures converge through epigenetic mechanisms to influence obesity risk, while outlining translational opportunities for mitigating intergenerational metabolic disease.

Methods: A narrative review was conducted of studies published from 2000 to 2025, sourced from PubMed, Scopus, and Web of Science, supplemented by manual screening. Search terms included maternal nutrition, environmental toxicants, epigenetic mechanisms, and offspring obesity outcomes. Studies on animal models, human cohorts, and intervention trials were included, focusing on links between maternal exposures, epigenetic changes, and metabolic disease.

Results: Maternal dietary imbalances, such as deficiencies in one-carbon donors or excess caloric intake, cause persistent epigenetic changes on genes regulating adipogenesis and energy homeostasis, increasing offspring obesity risk. Prenatal exposure to environmental toxicants, including endocrine disruptors and heavy metals, amplifies these vulnerabilities by altering DNA methylation, histone modifications, and noncoding RNA networks. Combined nutritional deficits and toxicant exposures, particularly in low- and middle-income countries (LMICs), create a "dual burden" that intensifies epigenetic instability. Nutrients like methyl donors and antioxidants may mitigate toxicant-induced epimutations, offering potential for precision maternal nutrition interventions.

Conclusion: Maternal nutrition and toxicant exposures interact through epigenetic mechanisms to program obesity and related diseases. Addressing these factors through precision nutrition, stricter environmental regulations, and early-life epigenetic biomarkers offers promising prevention strategies. Large, diverse, multi-generational cohorts and multi-omics approaches are needed to strengthen causal inference and inform equitable policies to break the intergenerational cycle of metabolic disease.