Population attributable fractions of modifiable risk factors for microvascular complications of type 2 diabetes in China: An analysis using national cross‐sectional data

Lin, Chu; Zhu, Xiaoxi; Jiao, Ruoyang; Cai, Xiaoling; Hu, Suiyuan; Lv, Fang; Yang, Wenjia; Li, Zonglin; Ji, Linong

doi:10.1111/dom.15029

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Risk factors associated with DKD in T2DM patients with elevated lactate levels were identified in the present study, which included diabetes duration, total cholesterol, creatinine, and hypertension, while these findings corroborate existing literature. 23,[45][46][47] Emerging evidence suggested potential links: A study found that the media fold change of urinary lactate and creatinine revealed a trend corresponding to stages of DKD and gradually increased. 48 DM patients with longer durations (>20 years) exhibited higher LDH levels compared to those with shorter durations (<5 years).…”

Section: Dovepressmentioning

confidence: 99%

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Jiang,

Ma,

Chen

et al. 2024

DMSO

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Purpose To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Patients and methods A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Results T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group ( P <0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17–99% and 0.36, and 24–99% and 0.24 for the models in normal lactate and high lactate groups, respectively. Conclusion The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

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Section: Dovepressmentioning

confidence: 99%

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Jiang,

Ma,

Chen

et al. 2024

DMSO

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“…Up to now, previous studies have recommended some early diagnosis or risk prediction models for DKD based on clinical serum/urine biomarkers of renal function, transcriptomics markers and/or metabonomics markers (11)(12)(13)(14)(15). Several clinical risk factors associated with the occurrence and progression of DKD have been reported, including age, gender, hypertension, dyslipidemia, glycated hemoglobin (HbA1c), the drug use of angiotensin converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB), diabetes duration, body mass index (BMI), and smoking, among others (7,11,13,16). Additionally, there have been many studies on urinary biomarkers of renal injury commonly used in clinical practice, such as urinary albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1microglobulin to creatinine ratio (A1MCR), neutrophil gelatinaseassociated lipocalin to creatinine ratio (NGAL/Cr), transferrin to creatinine ratio (UTRF/Cr), retinol-binding protein to creatinine ratio (URBP/Cr), and orosomucoid to creatinine ratio (UORM/Cr), among others (7,(13)(14)(15)(16)(17).Recently, one of our previous crosssectional studies has reported that the serum fasting C-peptide is an independent risk factor for the odds of renal dysfunction in patients with type 2 diabetes, furthermore, the association between them is not linear, but non-linear (6).…”

Section: Introductionmentioning

confidence: 99%

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Sun

et al. 2023

Front. Endocrinol.

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BackgroundOur previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.MethodsThe patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.ResultsDuring a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.ConclusionOur results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

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Current challenges of diabetes and metabolic disorders in China

2023

Diabetes Obesity Metabolism

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Population attributable fractions of modifiable risk factors for microvascular complications of type 2 diabetes in China: An analysis using national cross‐sectional data

Cited by 3 publications

References 28 publications

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Current challenges of diabetes and metabolic disorders in China

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