Serum lipoprotein(a) and high-density lipoprotein cholesterol associate with diabetic nephropathy: Evidence from machine learning perspectives
Open accessAbstract
Purpose: Diabetic nephropathy (DN) is a common complication of type 2 diabetes mellitus (T2DM) that significantly impacts the quality of life for affected patients. Dyslipidemia is a known risk factor for developing cardiovascular complications in T2DM patients. However, the association between serum lipoprotein(a) (Lp(a)) and high-density lipoprotein cholesterol (HDL-C) with DN requires further investigation. Patients and Methods: For this cross-sectional study, we randomly selected T2DM patients with nephropathy (DN, n = 211) and T2DM patients without nephropathy (T2DM, n = 217) from a cohort of 142,611 patients based on predefined inclusion and exclusion criteria. We collected clinical data from the patients to identify potential risk factors for DN using binary logistic regression and machine learning. After obtaining the feature importance score of clinical indicators by building a random forest classifier, we examined the correlations between Lp(a), HDL-C and the top 10 indicators. Finally, we trained decision tree models with top 10 features using training data and evaluated their performance with independent testing data. Results: = 0.028). Lp(a) was identified as a risk factor for DN, while HDL-C was found to be protective. We identified the top 10 indicators that were associated with Lp(a) and/or HDL-C, including urinary albumin (uALB), uALB to creatinine ratio (uACR), cystatin C, creatinine, urinary ɑ1-microglobulin, estimated glomerular filtration rate (eGFR), urinary β2-microglobulin, urea nitrogen, superoxide dismutase and fibrinogen. The decision tree models trained using the top 10 features and with uALB at a cut-off value of 31.1 mg/L showed an average area under the receiver operating characteristic curve (AUC) of 0.874, with an AUC range of 0.870 to 0.890. Conclusion: Our findings indicate that serum Lp(a) and HDL-C are associated with DN and we have provided a decision tree model with uALB as a predictor for DN.
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Gao et al. applied DT to serum metabolites for DN prediction (AUC = 0.874).
“Gao et al. [78] applied DT to serum metabolites for DN prediction (AUC = 0.874).”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [78]
The Results section, “Decision Tree Models for DN Prediction” (page unavailable), reports decision-tree models for DN prediction with an average AUC of 0.874. But the predictor was uALB, which the Methods section, “Calculations of uACR and eGFR” (page unavailable), defines as urinary microalbumin—not a serum metabolite. (LLM, conf=0.94)
Experimental — accuracy not yet measuredAnother study involving 428 T2D patients applied ML to urinary and blood biomarkers, identifying features such as urinary albumin (uALB), uALB to creatinine ratio, cystatin C, and creatinine as strong predictors with DT.
“another study involving 428 T2D patients applied ML to urinary and blood biomarkers, identifying features such as urinary albumin (uALB), uALB to creatinine ratio, cystatin C, and creatinine as strong predictors with DT [78].”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [78]
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- Authors
- Gao, R.-H., Liu, B., Yang, Y., Ran, R., Zhou, Y., & Liu, S.-M.
- Affiliations
- Zhongnan Hospital of Wuhan University (CN)
- Michigan State University (US)
- Corresponding author
- Liu, Song-Mei
- Venue
- Diabetes, Metabolic Syndrome and Obesity
- Publisher
- Informa UK Limited
- Published
- 2023-06
- Volume
- Volume 16
- Pages
- 1847-1858
- ISSN
- 1178-7007 (ISSN-L)
- PMC
- PMC10292662
- OpenAlex
- W4381618155
- Cited by
- 17
- References
- 43
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 3
- FWCI
- 3.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diabetes Metabolic Syndrome and Obesity (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dove Medical Press (Taylor and Francis Group) (submittedVersion, cc-by-nc)
- Funders
- Health Commission of Hubei Province
- National Natural Science Foundation of China (82172359, 81972009)
- Topics
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- diabetic nephropathy, lipoprotein(a), HDL cholesterol, type 2 diabetes, urinary albumin, albumin-creatinine ratio, cystatin C, estimated glomerular filtration rate, urinary biomarkers, risk factors, decision tree, random forest, superoxide dismutase, fibrinogen
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