ArticleJournal of translational medicine2023
Establishment of a potent weighted risk model for determining the progression of diabetic kidney disease.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Metabolomics approaches for the early detection and therapeutics: type 2 diabetes-induced diabetic kidney disease-a systematic review and meta-analysis.Metabolomics : Official journal of the Metabolomic Society · 2025Pooled it
- Association Between Short-Term Glycemic Variability Measured by Continuous Glucose Monitoring and Early Diabetic Kidney Disease in Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Targeting senescence to prevent diabetic kidney disease: Exploring molecular mechanisms and potential therapeutic targets for disease management.Diabetic medicine : a journal of the British Diabetic Association · 2025Review
- The association between vitamin D receptor gene polymorphism FokI and type 2 diabetic kidney disease and its molecular mechanism: a case control study.BMC medical genomics · 2024Article
- Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population.Frontiers in endocrinology · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiabetic kidney disease (DKD) is a severe complication of diabetes. Currently, no effective measures are available to reduce the risk of DKD progression. This study aimed to establish a weighted risk model to determine DKD progression and provide effective treatment strategies.
methodsThis was a hospital-based, cross-sectional study. A total of 1104 patients with DKD were included in this study. The random forest method was used to develop weighted risk models to assess DKD progression. Receiver operating characteristic curves were used to validate the models and calculate the optimal cutoff values for important risk factors.
resultsWe developed potent weighted risk models to evaluate DKD progression. The top six risk factors for DKD progression to chronic kidney disease were hemoglobin, hemoglobin A1c (HbA1c), serum uric acid (SUA), plasma fibrinogen, serum albumin, and neutrophil percentage. The top six risk factors for determining DKD progression to dialysis were hemoglobin, HbA1c, neutrophil percentage, serum albumin, duration of diabetes, and plasma fibrinogen level. Furthermore, the optimal cutoff values of hemoglobin and HbA1c for determining DKD progression were 112 g/L and 7.2%, respectively.
conclusionWe developed potent weighted risk models for DKD progression that can be employed to formulate precise therapeutic strategies. Monitoring and controlling combined risk factors and prioritizing interventions for key risk factors may help reduce the risk of DKD progression.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.