The triglyceride glucose (TyG) index has been proved to be a reliable marker of diabetic kidney disease (DKD).We further investigated the association between TyG index, and its derivative, triglyceride-glucose body mass index (TyG-BMI), and the risk of DKD among adults with newly diagnosed type 2 diabetes (T2D).This cross-sectional study was conducted among patients with newly diagnosed T2D. We assessed the correlation between TyG index, TyG-BMI, and the risk of DKD using logistic regression analysis, restricted cubic spline analysis, trend tests, receiver operating characteristic curve, and subgroup analyses.Among the 924 included patients, 199 (21.5%) had DKD. Logistic regression revealed that TyG index (odds ratio [OR] 1.232, 95% confidence interval [CI] 1.064–1.428, p = 0.005) and TyG-BMI (OR 1.003, 95% CI 1.000–1.006, p = 0.021) were risk factors for DKD. The trend test demonstrated a dose–response association between TyG index (p for trend = 0.004), TyG-BMI (p for trend = 0.035), and the risk of DKD. Restricted cubic spline analysis indicated a nonlinear correlation between TyG index and the risk of DKD, with an increase in the risk of DKD when the TyG index was greater than 9.68 (p for nonlinearity = 0.014). In contrast, TyG-BMI and the risk of DKD exhibited a linear dose–response relationship, with an increase in the risk of DKD when the TyG-BMI was greater than 243 (p for nonlinearity = 0.034). According to the receiver operating characteristic curve, the optimal cutoff values for TyG index and TyG-BMI were 10.08 and 221.5, respectively.Among newly diagnosed T2D patients, the risk of DKD increases with the increase of TyG index and TyG-BMI, with their respective cut-off values being 9.68 and 243. Both TyG index and TyG-BMI have poor diagnostic value for the risk of DKD.

Association between the triglyceride glucose index, triglyceride-glucose body mass index and diabetic kidney disease in adults with newly diagnosed type 2 diabetes

Background: In this study, we aimed to investigate the relationship between diabetic nephropathy (DN) and triglyceride/glucose (TyG) index and triglyceride/high-density lipoprotein cholesterol ratio (Tg/HDL-C) as surrogate markers of insulin resistance. Method: Medical records of 15,378 individuals between February 2019 and May 2024 were examined. Serum glucose, Tg, HDL-C, HbA1c, estimated glomerular filtration rate (eGFR), and urine albumin/creatinine ratio (UACR) were evaluated and the TyG index and TG/HDL-C ratios were calculated for each individual. DN was defined as a UACR ≥ 30 mg/g and/or eGFR &lt;60 mL/min/1.73 m2. Results: Of 10,714 patients, DN was detected in 3763 (35.1%). Females had 10% higher odds of developing DN compared to males. A TyG index at or above the determined cutoff point (9.58) indicated a risk of DN and the sensitivity and specificity values were 44.01% and 71.28%, respectively. The risk of DN was 1.95 times higher in individuals with a TyG index value of ≥9.58 compared to those with a TyG index &lt;9.58. While the Tg/HDL ratio was significant in detecting DN in the univariate analysis (odds ratio (OR) 1.59; 95% confidence interval 1.46–1.73), this significance was not found in the multivariate analysis (OR 1.15; 95% confidence interval 0.94–1.40). Conclusions: A high TyG index is associated with DN in patients with type 2 diabetes and it might be a potential marker in predicting DN. 

The Association Between Diabetic Nephropathy and Triglyceride/Glucose Index and Triglyceride/High-Density Lipoprotein Cholesterol Ratio in Patients with Type 2 Diabetes Mellitus

This study systematically reviews and meta-analyzes existing risk prediction models for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models. 

Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis

The triglyceride-glucose (TyG) index is identified as an alternative indicator of insulin resistance (IR) and is associated with macro- and micro-vascular diseases among patients with type 2 diabetes mellitus. The relationship between the TyG index and IR and its impact on diabetic kidney disease (DKD) remains unclear among adults with type 1 diabetes mellitus(T1DM). 

Correlation between triglyceride-glucose index and diabetic kidney disease risk in adults with type 1 diabetes mellitus

Inhibiting the activity of intestinal α-glucosidase is considered an effective approach for treating type II diabetes mellitus (T2DM). In this study, we employed an in vitro enzymatic synthesis approach to synthesize four derivatives of natural products (NPs) for the discovery of therapeutic drugs for T2DM. Network pharmacology analysis revealed that the betulinic acid derivative P3 exerted its effects in the treatment of T2DM through multiple targets. Neuroactive ligand–receptor interaction and the calcium signaling pathway were identified as key signaling pathways involved in the therapeutic action of compound P3 in T2DM. The results of molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations indicate that compound P3 exhibits a more stable binding interaction and lower binding energy (−41.237 kcal/mol) with α-glucosidase compared to acarbose. In addition, compound P3 demonstrates excellent characteristics in various pharmacokinetic prediction models. Therefore, P3 holds promise as a lead compound for the development of drugs for T2DM and warrants further exploration. Finally, we performed site-directed mutagenesis to achieve targeted synthesis of betulinic acid derivative. This work demonstrates a practical strategy of discovering novel anti-hyperglycemic drugs from derivatives of NPs synthesized through in vitro enzymatic synthesis technology, providing potential insights into compound P3 as a lead compound for anti-hyperglycemic drug development.

A New Potent Inhibitor against α-Glucosidase Based on an In Vitro Enzymatic Synthesis Approach

Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study.

Background Diabetic kidney disease (DKD), a common microvascular complication of diabetes mellitus (DM), is always asymptomatic until it develops to the advanced stage. Thus, we aim to develop a nomogram prediction model for progression to DKD in newly diagnosed type 2 diabetes mellitus (T2DM). Methods This was a single-center analysis of prospective data collected from 521 newly diagnosed patients with T2DM. All related clinical records were incorporated, including the triglyceride-glucose index (TyG index). The least absolute shrinkage and selection operator (LASSO) was used to build a prediction model. In addition, discrimination, calibration, and clinical practicality of the nomogram were evaluated. Results In this study, 156 participants were incorporated as the validation set, while the remaining 365 were incorporated into the training set. The predictive factors included in the individualized nomogram prediction model included 5 variables. The area under the curve (AUC) for the prediction model was 0.826 (95% CI 0.775 to 0.876), indicating excellent discrimination performance. The model performed exceptionally well in terms of predictive accuracy and clinical applicability, according to calibration curves and decision curve analysis. Conclusion The predictive nomogram for the risk of DKD in newly diagnosed T2DM patients had outstanding discrimination and calibration, which could help in clinical practice.

https://www.dovepress.com/getfile.php?fileID=91039

Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus

Purification efficiency of Pyropia-processing wastewater and microalgal biomass production by the combination of Chlorella sp. C2 cultivated at different culture temperatures and chitosan.

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Correlation between alternative insulin resistance indexes and diabetic kidney disease: a retrospective study.

Prediction of Diabetic Kidney Disease in Newly Diagnosed Type 2 Diabetes Mellitus

Purification efficiency of Pyropia-processing wastewater and microalgal biomass production by the combination of Chlorella sp. C2 cultivated at different culture temperatures and chitosan.