Modification Effects of Albuminuria on the Association Between Kidney Function and Development of Anemia in Diabetes

A Machine Learning Model for Predicting Diabetic Nephropathy Based on TG/Cys-C Ratio and Five Clinical Indicators

Objective

Distinguishing diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) remains challenging. This study developed and validated a machine learning model for differential diagnosis of DN and NDRD.

Methods

We included 100 type 2 diabetes mellitus (T2DM) patients with proteinuria from four Xuzhou hospitals (2013-2021), divided into DN (n=50) and NDRD (n=50) groups based on renal biopsy. Clinical data were used to build a predictive model. External validation was performed on 55 patients from The Affiliated Taian City Central Hospital of Qingdao University (2019-2023). Models were constructed using Python's scikit-learn library (v1.4.2), with feature selection via Recursive Feature Elimination (RFE).

Results

Compared to NDRD, DN patients had lower TG/Cys-c ratio [1.45 (0.75, 1.99) vs 2.78 (1.81, 4.48)], higher systolic blood pressure (156.80 ± 20.14 vs 137.66 ± 17.67), longer diabetes duration [78 (24, 120) vs 18 (6, 48) months], higher diabetic retinopathy prevalence (60% vs 40%), higher HbA1c [7.98 (6.50, 10.40) vs 7.10 (6.70, 7.90)], and lower hemoglobin (115.66 ± 22.20 vs 135.64 ± 18.59). The logistic regression (LR) model, incorporating TG/Cys-c ratio, SBP, diabetes duration, DR, HbA1c, and Hb, achieved an AUC of 0.9305, accuracy of 0.8333, sensitivity of 0.8283, and specificity of 0.8701. External validation showed an AUC of 0.9642, accuracy of 0.9455, sensitivity of 0.9615, and specificity of 0.9310. We named this method PDN (Prediction of Diabetic Nephropathy) and developed an online platform: http://cppdd.cn/service/PDN.

Conclusion

This machine learning-based method effectively differentiates DN from NDRD, aiding clinicians in diagnosis and treatment planning.

Anemia in diabetes mellitus: Pathogenetic aspects and the value of early erythropoietin therapy

Anemia is a frequent, yet increasingly recognized, comorbidity in diabetes mellitus (DM), with prevalence often driven by multifactorial mechanisms. Hematinic deficiencies, common in this population, may arise from associated comorbidities or medications, such as metformin, as well as other drugs commonly employed for DM-related conditions. Among contributing factors, diabetic kidney disease (DKD) plays a pivotal role, with anemia developing more frequently and being more pronounced in earlier stages, than in CKD of other causes. This enhanced susceptibility stems primarily from the combined impact of impaired renal oxygen sensing and deficient erythropoietin (EPO) production linked to tubulointerstitial fibrosis. Additional mechanisms comprise glomerular dysfunction, shortened erythrocyte lifespan, uremia-induced bone marrow suppression, and increased bleeding risk. DM is also recognized as a chronic low-grade inflammatory condition, with its inflammatory burden driving iron maldistribution, suppression of erythropoiesis, and resistance to EPO. The diagnostic approach of anemia in DM mirrors that in the general population. Addressing modifiable causes such as hematinic deficiencies, and other chronic conditions, such as DKD and bone marrow disorders, is paramount. In total, the underlying pathophysiology of anemia in DM primarily reflects a state of absolute or relative EPO deficiency and/or diminished bone marrow responsiveness, effectively corresponding to 'anemia of chronic disease. Early initiation of EPO therapy, even in DM patients without overt DKD, may mitigate disease progression and improve outcomes. Future research should focus on diabetes-specific strategies integrating optimal EPO use, potentially implementing targeted management of renal and inflammatory contributors to anemia.

Modification effect of receipt of diabetes care on the association between COVID-19 infection and HbA1c level during the first year of the coronavirus pandemic using a nationwide population-based database

AIMS/INTRODUCTION

We assessed the modification effect of adherence to diabetes care on the association between HbA1c levels and the risk of coronavirus disease 2019 (COVID-19) among individuals with diabetes using a population-based database.

MATERIAL AND METHODS

We retrospectively identified individuals with diabetes during routine health checkups performed in 2019 in Japan using a population-based claims database (JMDC, Tokyo, Japan). We assessed the risk of COVID-19 infection in 2020 in relation to HbA1c levels during routine checkups, stratified by the presence/absence of follow-up for diabetes care in 2019. Several sensitivity analyses were performed.

RESULTS

We identified 65,956 individuals with an HbA1c ≥6.5% and fasting glucose ≥126 mg/dL in routine checkups, including 52,637 and 13,319 with and without at least one physician consultation for diabetes care in 2019, respectively. Although high HbA1c levels were associated with an increased risk of COVID-19 infection in a dose-dependent manner among individuals without diabetes care in 2019 (odds ratios, 1.53 and 2.17 in individuals with HbA1c of 7.0-7.9% and ≥8.0%, respectively) with a reference to HbA1c of 6.5-6.9%, individuals with diabetes care had no such trend in 2019 (odds ratios, 0.99 and 0.97 among individuals with HbA1c of 7.0-7.9% and ≥8.0%, respectively). Sensitivity analyses yielded consistent results when the variable definitions were changed and after multivariable adjustment with multiple imputation.

CONCLUSIONS

This population-based study suggests that adherence to diabetes care may modify the association between HbA1c levels and the risk of COVID-19 infection.