A low-cost laboratory-based method for predicting newly diagnosed biopsy-proven diabetic nephropathy in people with type 2 diabetes

Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus

PURPOSE

There are few studies on the establishment of diagnostic models for diabetic nephropathy (DN) in in type 2 diabetes mellitus (T2DM) patients based on biomarkers. This study was to establish a model for diagnosing DN in T2DM.

METHODS

In this cross-sectional study, data were collected from the Second Hospital of Shijiazhuang between August 2018 to March 2021. Totally, 359 eligible participants were included. Clinical characteristics and laboratory data were collected. LASSO regression analysis was used to screen out diagnostic factors, and the selected factors were input into the decision tree for fivefold cross validation; then a diagnostic model was established. The performances of the diagnosis model were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The diagnostic performance of the model was also validated through risk stratifications.

RESULTS

Totally, 199 patients (55.43%) were diagnosed with DN. Age, diastolic blood pressure (DBP), fasting blood glucose, insulin treatment, mean corpuscular hemoglobin concentration (MCHC), platelet distribution width (PDW), uric acid (UA), serum creatinine (SCR), fibrinogen (FIB), international normalized ratio (INR), and low-density lipoprotein cholesterol (LDL-C) were the diagnostic factors for DN in T2DM. The diagnostic model presented good performances, with the sensitivity, specificity, PPV, NPV, AUC, and accuracy being 0.849, 0.969, 0.971, 0.838, 0.965, and 0.903, respectively. The diagnostic model based on the stratifications also showed excellent diagnostic performance for diagnosing DN in T2DM patients.

CONCLUSION

Our diagnostic model with simple and accessible factors provides a noninvasive method for the diagnosis of DN.

Coagulation Function and Type 2 Diabetic Kidney Disease: A Real-World Observational Study

Objectives

Type 2 diabetic kidney disease (DKD), a chronic microvascular complication of diabetes, may exhibit a complex interrelation with coagulation function. This study is aimed at elucidating the association between coagulation function and DKD.

Methods

This was a real-world observational study conducted in Beijing, involving 2,703 participants. All patients with diabetes were classified into two groups, viz., DKD and non-DKD groups. Effect magnitudes are denoted as odds ratios (OR) with a 95% confidence interval (CI). To mitigate potential bias in group comparisons, we employed propensity score matching (PSM).

Results

After adjusting for variables such as age, gender, systolic blood pressure (SBP), hemoglobin A1c (HbA1c), triglyceride (TG), c-reactive protein (CRP), platelet (PLT), and serum albumin (sALB), it was discerned that fibrinogen (FIB) (OR, 95% CI, P: 1.565, 1.289-1.901, <0.001) and fibrinogen degradation products (FDP) (1.203, 1.077-1.344, 0.001) were significantly correlated with an increased risk of DKD. To facilitate clinical applications, a nomogram prediction model was established, demonstrating commendable accuracy for DKD prediction.

Conclusions

Our findings suggest that elevated levels of FIB and FDP serve as potential risk indicators for DKD, and coagulation function may play an important role in the occurrence and development of DKD.