Acta Diabetologica最新文献

Background: Objective and easily applicable biomarkers for diabetic polyneuropathy (DPN) are warranted. Circulating nerve-specific proteins have emerged as valuable biomarkers for central nervous system disease but few of these have been tested in peripheral neuropathy. Glial Fibrillary Acidic Protein (GFAP) is highly expressed in non-myelinating Schwann cells while UCH-L1 is a neuron expressed stress protein not previous analyzed in DPN. In this pilot study, we explore serum GFAP and UCH-L1 levels in patients with/without DPN and controls.

Methods: Persons with DPN (n = 28), without DPN (n = 31), and controls (n = 30) were evaluated in a cross-sectional design. Sural nerve conduction (velocity and amplitude) was evaluated by NC-stat DPNCheck™ and quantitative sensory testing of cold detection and pain was performed. GFAP and UCH-L1 levels were compared across study groups and the unadjusted correlation with nerve assessments evaluated.

Results: Serum GFAP were lower in persons with DPN (20.9 ± 10.9 pg/ml) than in persons without DPN (26.2 ± 14.1 pg/ml) (p = 0.04) or controls (31.7 ± 26.0 pg/ml) (p = 0.02). GFAP levels were not different in persons without DPN and controls (p = 0.61). UCH-L1 levels were not different between study groups (p = 0.48). GFAP levels correlated with cold pain threshold (Rho= - 0.320, p = 0.02) but failed to reach significance for cold detection (Rho= - 0.236, p = 0.09). No correlation was observed between GFAP and nerve amplitude (p = 0.58) or conductivity (p = 0.86).

Conclusion: Serum GFAP levels are reduced in persons with DPN compared to persons without DPN and controls. Reduced serum GFAP levels may be associated with reduced markers of small nerve fiber damage obtained from quantitative sensory testing in people with diabetes.

Aim: Previous studies have suggested that the triglyceride glucose (TyG) index and body roundness index (BRI) are indicators of insulin resistance (IR) and are associated with the incidence of cardiovascular disease (CVD) among middle and old-aged adults. BRI is considered a more accurate indicator of the proportion of body fat and visceral fat than body mass index (BMI). However, it remains unclear whether the combined use of the TyG index and BRI, specifically the triglyceride glucose-body roundness index (TyG-BRI), is associated with the incidence of CVD among Chinese middle and old-aged adults.

Methods: Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). The TyG-BRI index was computed using baseline measurements of triglycerides, fasting blood glucose, waist circumference, and height. Cox proportional hazards regression and restricted cubic spline (RCS) regression analyses were applied to assess the association between the TyG-BRI index and incident CVD (defined as cardiac events or stroke).

Results: A total of 8,113 Chinese adults participated in this study, with a median age of 58 years, including 56.3% males. The fully adjusted Cox regression analysis revealed that individuals in the highest quartile (Q4) of the TyG-BRI index had a 59.1% increased risk of developing incident CVD compared to those in the lowest quartile (HR, 1.591 [95% CI, 1.330-1.902]). The TyG-BRI index showed a significant linear association with CVD incidence (P for nonlinearity = 0.447, P < 0.001). This association persisted after conducting subgroup and sensitivity analyses.

Conclusions: This study introduced a novel TyG-BRI index, which integrated IR and body roundness as a comprehensive indicator, demonstrating its strong and independent association with increased CVD risk in a Chinese nationwide cohort. Our findings provide new insights into the interaction between metabolic dysfunction and cardiovascular risk, suggesting that the TyG-BRI index could serve as a practical tool for targeted preventive interventions.

The identification of mothers at risk for gestational diabetes mellitus (GDM) at start of pregnancy may be beneficial to improve perinatal outcomes. This study aims evaluating the predictive performance of fasting and dynamic indices of glucose metabolism at first trimester and their association with later GDM development. A cohort of 198 women received detailed metabolic assessment at median gestational age (13 weeks) including 75-g oral glucose tolerance test (OGTT) with assessment of glucose, insulin and C-peptide, and biochemical markers (including triglycerides) to calculate different indices of insulin sensitivity either at fasting and in the OGTT dynamic conditions. Moreover, parameters of β-cell function were assessed. A second OGTT was performed between 24 and 28 gestational weeks (GW) to identify women with GDM. We found that 28 women developed GDM, and, in univariable analysis, this was fairly predicted by several first trimester indices, both at fasting and in dynamic conditions. However, fasting indices containing maternal triglycerides showed better accuracy as compared to traditional indices (even the dynamic ones). In multivariable analysis, the best predictive model of GDM development included fasting and OGTT glucose values, HbA1c, and an insulin sensitivity marker that includes triglycerides (e.g. the improved triglyceride-glucose index, TyGIS). β-Cell function was not included in such predictive model, but at 24-28 GW it showed remarkable impairment in women with GDM. In conclusion, both fasting and dynamic parameters of glucose homeostasis at early pregnancy showed fair predictive accuracy for later GDM, with TyGIS showing excellent performance. β-Cell dysfunction role needs being further elucidated.

Background: Type 2 diabetes and coronary heart disease exhibit heightened prevalence in the Chinese population, posing as leading causes of mortality. The combination of diabetes and coronary heart disease, due to its challenging diagnosis and poor prognosis, imposes a significant disease burden. In recent years, machine learning has frequently been employed in diagnostic applications within medical fields; however, predictive models for type 2 diabetes complicated by coronary heart disease have been confronted with issues such as lower predictive performance and interference from other comorbidities during prediction.

Methods: This study enhances the predictive accuracy, sensitivity, specificity, F1 score, and AUC of models forecasting the coexistence of diabetes and coronary heart disease. We developed an advanced prediction model using XGBoost combined with SHAP for feature analysis. Through comparative feature selection, hyperparameter optimization, and computational efficiency analysis, we identified optimal conditions for model performance. External validation with independent datasets confirmed the model's robustness and generalizability, supporting its potential implementation in clinical practice.

Results: This study compared three models-Random Forest, LightGBM, and XGBoost-and found that XGBoost exhibited superior performance in both efficacy and computational efficiency. The accuracy (Acc) of the XGBoost model was 0.8910, which improved to 0.8942 after hyperparameter tuning. External validation using datasets from Pingyang Hospital and Heji Hospital in Shanxi Province, China, yielded an AUC of 0.7897, demonstrating robust generalizability. By integrating SHAP (SHapley Additive exPlanations) for interpretability, our study identified bilirubin levels, basophil count, cholesterol levels, and age as key features for predicting the coexistence of type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD). These findings are seamlessly consistent with the feature importance rankings determined by the XGBoost algorithm. The model demonstrates moderate predictive performance (AUC = 0.7879 in external validation) with practical interpretability, offering potential utility in improving diagnostic efficiency for T2DM-CHD comorbidity in resource-limited settings. However, its clinical implementation requires further validation in diverse populations.

Background and aims: The relationship between the TyG index and outcomes in non-diabetic patients with ischemic stroke admitted to intensive care unit (ICU) has not been validated. This study aims to investigate the correlation between the TyG index and mortality in non-diabetic ICU patients with ischemic stroke.

Methods: We collected data from ICU patients (≥ 18 years) with ischemic stroke and no diabetes. The primary outcome was hospital mortality, and the secondary outcomes were 30-day mortality following admission, hospital length of stay (LOS) and ICU LOS. Cox proportional hazards models and generalized linear models were employed to evaluate association between the TyG index and mortality and LOS, respectively. Nonlinear associations between the TyG index and outcomes were assessed using restricted cubic spline regression models.

Results: A total of 1122 eligible patients were included in this study. The hospital mortality was 10.61%, and 30-day mortality was 16.93%. Multivariate Cox proportional hazards models and generalized linear models revealed the higher of TyG was significantly associated with increased hospital mortality [adjusted HR (95% CI) 1.22 (1.02-1.46), P = 0.0264], 30-day mortality [adjusted HR (95% CI) 1.26 (1.10-1.44), P = 0.0011] and prolonged hospital LOS [adjusted difference (95% CI) 0.52 (0.06-0.97), P = 0.0276].

Conclusions: TyG index is a significant predictor of hospital mortality, 30-day mortality, and LOS in non-diabetic ICU patients with ischemic stroke, which could aid clinical decision-making in managing ischemic stroke.

Objective: The ratio of γ-glutamyl transferase (GGT) and high-density lipoprotein cholesterol (HDL-C) is a novel noninsulin-based marker for assessing the risk of nonalcoholic fatty liver disease and type 2 diabetes mellitus. However, it is unclear whether the GGT/HDL-C ratio is related to all-cause mortality. Therefore, we aimed to investigate the longitudinal association of GGT/HDL-C on all-cause mortality in a large cohort of Korean adults.

Methods: Data were assessed for 87,668 participants (25,767 men and 61,901 women) from the Korean Genome and Epidemiology Study-Health Examinees cohort. These data were combined with the death certificate database from the National Statistical Office. The participants were divided into four groups according to GGT/HDL-C quartiles. We prospectively assessed hazard ratios (HRs) with 95% confidence intervals (CIs) for all-cause mortality in the 11.7 years following the baseline survey using multivariate Cox proportional hazard regression models including age, BMI, smoking status, and drinking habits, which are known to be major confounders.

Results: During the follow-up period, 3,214 individuals (3.6%; 1,728 men and 1,486 women) died. The respective HRs (95% CIs) of mortality for GGT/HDL-C quartiles 2-4 were 1.15 (0.99-1.33), 1.48 (1.28-1.71), and 1.97 (1.70-2.29) in men and 1.22 (1.02-1.45), 1.36 (1.15-1.61), and 1.69 (1.42-2.00) in women after adjusting for confounders.

Conclusions: GGT/HDL-C may be a useful predictive marker for all-cause mortality in men and women. We believe that GGT/HDL-C ratio will provide a complementary tool to help clinicians make decisions about prevention and disease management to improve survival.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) and type 2 diabetes mellitus (T2DM) are increasingly prevalent metabolic disorders worldwide, with a complex bidirectional relationship between them. Currently, there is a lack of research on the trajectories of blood glucose and insulin concentration over time after eating in patients with MASLD and T2DM.

Methods: This clinical cohort included diagnosed T2DM patients in a large hospital over the past five years, was divided into an observation group (with MASLD) and a control group (without MASLD). The postprandial time trends of blood glucose and insulin concentration were analysed within two hours after eating. A strategy of backward iterative feature elimination combined with propensity score matching (PSM) was employed to screen for potential associated factors that might influence these trends.

Results: In total, there were 521 in the observation group and 373 in the control group. In terms of blood glucose, the postprandial time-concentration trajectories for both groups shown a significant time main effect (F = 1145.567, P < 0.001), a significant group main effect (F = 15.340, P < 0.001), and a significant time*group interaction effect (F = 2.873, P = 0.035); After matching all the factors, the time*group interaction effect of blood glucose was not significant, but the differences from group main effect still existed. In terms of insulin, the postprandial time-concentration trajectories for both groups shown a significant time main effect (F = 309.429, P < 0.001), a significant group main effect (F = 6.319, P < 0.012), and a significant time*group interaction effect (F = 20.057, P < 0.001), but the trajectories crossed; After matching 4 factors such as Smoking, Essential Hypertension (EH), Body Mass Index (BMI), Triglyceride (TG) and Ca²⁺, neither the group main effect nor the time*group interaction effect on insulin was significant any more.

Conclusion: The postprandial trends of blood glucose and insulin concentration over time shown significant differences between T2DM patients with and without MASLD. IL-6 might be associated with the insulin resistance, while EH and Ca²⁺ might be related to the islet β-cell function. Smoking and TG might participate in both of the above processes. The strategy of backward iterative with PSM had demonstrated a relatively satisfactory effect in feature screening.

Fufang Zhenzhu Tiaozhi capsule (FTZ) is a patented traditional Chinese medicine preparation that has been used clinically for nearly 10 years to treat hyperglycemia, hyperlipidemia, and other glucolipid metabolic diseases. Previous studies have shown that FTZ can improve diabetic kidney disease (DKD). However, the role and mechanism of FTZ in reducing renal lipid accumulation in DKD remain unclear. Phosphorylation of Adenosine 5'-Monophosphate-Activated Protein Kinase (AMPK), a key regulator of energy homeostasis, inhibits Acetyl-CoA Carboxylase (ACC) signaling, thereby reducing fatty acid synthesis and promoting fatty acid oxidation via carnitine palmitoyltransferase-1 (CPT-1). Sterol regulatory element-binding protein 1 (SREBP-1), a transcription factor, regulates lipid metabolism through fatty acid synthesis. This study investigated the anti-lipid accumulation effect and mechanism of FTZ in vitro and in vivo. Streptozotocin (40 mg/kg/d, i.p. for 5 days, consecutively) combined with a high-fat diet (HFD) were used to induce a DKD model in C57BL/6J mice, followed by FTZ (1, 2 g/kg/d, i.g.) or Losartan (30 mg/kg/d, i.g.) treatments for 12 weeks. High glucose (HG, 30 mM) combined with palmitic-acid (PA, 250 µM) were used to induce HK-2 cells injury, followed by FTZ (25, 50, or 100 µg/ml) or Compound C (an AMPK inhibitor, 10 µM) treatments for 24 h. Results showed that FTZ reduced blood lipids and improved renal function in DKD mice. In addition, compared with the control group, DKD mice and cells exhibited significantly increased lipid deposition. However, the effect of FTZ in alleviating lipid accumulation was reversed by Compound C. Furthermore, FTZ increased p-AMPK, p-ACC and CPT-1 protein expression while decreasing SREBP-1. These results indicate that FTZ effectively protects against lipid accumulation in DKD by regulating the AMPK/ACC/SREBP pathway, inhibiting de novo lipogenesis, providing a novel therapeutic strategy for DKD.

Normal pregnancy is characterized by changes in lipid metabolism with significant implications for the health of both mother and offspring. When these changes develop into maternal dyslipidemia, a significant association with adverse pregnancy outcomes has been observed, including the development of gestational diabetes (GD), modulation of the inflammatory response, and excessive fetal growth. In the present study, we performed a lipidomic assessment of patients at GD diagnosis (24-28 weeks of gestation) and 12 weeks after diagnosis. We found higher levels of esterified oleic acid in plasma at the time of GD diagnosis in women who subsequently required pharmacological therapy to control blood glucose levels compared to those who did not require additional treatment, suggesting that the measurement of plasma oleic acid might be an additional tool for the early identification of patients with a more severe form of gestational diabetes. Moreover, plasma oleic acid levels showed a positive correlation with fetal growth in the context of adequate glycemic control, supporting a metabolic dysregulation of other pathways whose identification could help clinicians to discriminate different cases within the spectrum of severity of the disease. Finally, the correlation between plasma oleic acid and circulating BAFF levels at the time of diagnosis and 12 weeks later adds a possible mechanism to support the pro-inflammatory and pro-diabetic state in the metabolic set of GD. Overall, these findings strongly support the role of plasma oleic acid as a possible early marker for GD severity stratification during pregnancy.