Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Guang Dong Clinical Research Center for Metabolic Diseases, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Front Endocrinol (Lausanne). 2023 Oct 5;14:1164822. doi: 10.3389/fendo.2023.1164822. eCollection 2023.
Diabetic nephropathy (DN) is one of the most prevalent complications of diabetes mellitus (DM). However, there is still a lack of effective methods for non-invasive diagnosis of DN in clinical practice. We aimed to explore biomarkers from plasma cell-free DNA as a surrogate of renal biopsy for the differentiation of DN patients from patients with DM.
The plasma cell-free DNA (cfDNA) was sequenced from 53 healthy individuals, 53 patients with DM but without DN, and 71 patients with both DM and DN. Multidimensional features of plasma DNA were analyzed to dissect the cfDNA profile in the DM and DN patients and identify DN-specific cfDNA features. Finally, a classification model was constructed by integrating all informative cfDNA features to demonstrate the clinical utility in DN detection.
In comparison with the DM patients, the DN individuals exhibited significantly increased cfDNA concentration in plasma. The cfDNA from the DN patients showed a distinct fragmentation pattern with an altered size profile and preferred motifs that start with "CC" in the cfDNA ending sites, which were associated with deoxyribonuclease 1 like 3 () expression in the kidney. Moreover, patients with DM or DN were found to carry more alterations in whole-genome cfDNA coverage when compared with healthy individuals. We integrated DN-specific cfDNA features (cfDNA concentration, size, and motif) into a classification model, which achieved an area under the receiver operating characteristic curve (AUC) of 0.928 for the differentiation of DN patients from DM patients.
Our findings showed plasma cfDNA as a reliable non-invasive biomarker for differentiating DN patients from DM patients. The utility of cfDNA in clinical practice in large prospective cohorts is warranted.
糖尿病肾病（DN）是糖尿病（DM）最常见的并发症之一。然而，在临床实践中，仍然缺乏用于非侵入性诊断 DN 的有效方法。我们旨在探索血浆无细胞游离 DNA（cfDNA）中的生物标志物，作为肾活检的替代物，用于区分 DN 患者和 DM 患者。
对 53 名健康个体、53 名无 DN 的 DM 患者和 71 名 DM 合并 DN 患者的血浆无细胞游离 DNA（cfDNA）进行测序。分析血浆 DNA 的多维特征，以剖析 DM 和 DN 患者的 cfDNA 图谱，并鉴定 DN 特异性 cfDNA 特征。最后，通过整合所有有信息的 cfDNA 特征构建分类模型，以证明其在 DN 检测中的临床应用价值。
与 DM 患者相比，DN 个体的血浆 cfDNA 浓度明显升高。DN 患者的 cfDNA 表现出明显的碎片化模式，大小谱发生改变，cfDNA 末端的偏好基序以“CC”开头，这与肾脏中的脱氧核糖核酸酶 1 样 3（）表达有关。此外，与健康个体相比，DM 或 DN 患者的全基因组 cfDNA 覆盖度变化更多。我们将 DN 特异性 cfDNA 特征（cfDNA 浓度、大小和基序）整合到分类模型中，该模型在区分 DN 患者和 DM 患者方面的曲线下面积（AUC）为 0.928。
我们的研究结果表明，血浆 cfDNA 是区分 DN 患者和 DM 患者的可靠非侵入性生物标志物。cfDNA 在大的前瞻性队列临床实践中的应用价值尚需进一步研究。
