Non-diabetic urine glucose in idiopathic membranous nephropathy

Liu, Lingling; Zuo, Ke; Le, Weibo; Lu, M X; Liu, Zhihong; Xu, Weiwei

doi:10.1080/0886022x.2022.2094806

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Patients with CKD showed varying degrees of impaired glucose tolerance [ 42 ]. In a cohort study of 1313 IMN patients, Cox regression showed that urinary glucose and baseline SCr were risk factors for a 50% reduction in eGFR [ 43 ]. The reason may be that with the progression of kidney disease, patients developed insulin resistance [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning

Liu,

Lu,

et al. 2023

Renal Failure

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Background Established prognostic models of idiopathic membranous nephropathy (IMN) were limited to traditional modeling methods and did not comprehensively consider clinical and pathological patient data. Based on the electronic medical record (EMR) system, machine learning (ML) was used to construct a risk prediction model for the prognosis of IMN. Methods Data from 418 patients with IMN were diagnosed by renal biopsy at the Fifth Clinical Medical College of Shanxi Medical University. Fifty-nine medical features of the patients could be obtained from EMR, and prediction models were established based on five ML algorithms. The area under the curve, recall rate, accuracy, and F1 were used to evaluate and compare the performances of the models. Shapley additive explanation (SHAP) was used to explain the results of the best-performing model. Results One hundred and seventeen patients (28.0%) with IMN experienced adverse events, 28 of them had compound outcomes (ESRD or double serum creatinine (SCr)), and 89 had relapsed. The gradient boosting machine (LightGBM) model had the best performance, with the highest AUC (0.892 ± 0.052, 95% CI 0.840–0.945), accuracy (0.909 ± 0.016), recall (0.741 ± 0.092), precision (0.906 ± 0.027), and F1 (0.905 ± 0.020). Recursive feature elimination with random forest and SHAP plots based on LightGBM showed that anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), aspartate transaminase (AST), β2-microglobulin (BMG), SCr, and fasting plasma glucose (FPG) were important risk factors for the prognosis of IMN. Increased risk of adverse events in IMN patients was correlated with high anti-PLA2R and low IHC IgG4. Conclusions This study established a risk prediction model for the prognosis of IMN using ML based on clinical and pathological patient data. The LightGBM model may become a tool for personalized management of IMN patients.

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Section: Discussionmentioning

confidence: 99%

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning

Liu,

Lu,

et al. 2023

Renal Failure

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“…In observing GU, this study also showed significant differences in GU levels in the R. norvegicus group before and after injection of L-NAME, which were observed for five days of the experiment. One study showed that GU patients had higher proteinuria, serum creatinine, and a lower estimated glomerular filtration rate (eGFR) [27]. Other studies have shown that a history of PE may serve as an additional risk factor for gestational diabetes mellitus (GDM) in subsequent pregnancies, as GDM is independently associated with PE in singletons.…”

Section: -Discussionmentioning

confidence: 99%

Concentration of B-CG and sFlt-1 in Rattus Norvegicus Model of Preeclampsia with Swimming Exercise Treatment

et al. 2023

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Preeclampsia (PE) is a life-threatening pregnancy complication for the mother and fetus. High concentrations of human chorionic gonadotrophin (hCG) and soluble fms-like tyrosine kinase-1 (sFLt-1) during pregnancy may have a role in the pathophysiology of PE. Swimming Exercise (SE) is one of the physical activities recommended for pregnant women and carries a minimal risk. This study is aimed at analyzing the interaction between the conditions of rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes) on the concentrations of B-CG and sFlt-1 in the Rattus norvegicus (R. norvegicus) model of PE. 72 R. norvegicus were included in this study and divided into 12 experimental groups (each group n = 6 individuals). R. norvegicus PE was prepared by inducing L-Nitro-Arginine-Methyl Ester (L-NAME) at a 75 mg/kg BW/day dose. The determination of PE was supported by the observation of differences in the values of urine protein (PU), urine glucose (GU), and urine leukocytes (LU) in R. norvegicus before and after injection of L-NAME. The three-factorial statistical test showed a significant interaction between the concentration of B-CG and the condition of R. norvegicus, the onset of PE, and the time of SE, with a p-value <0.001. The three-factorial statistical test also showed a significant interaction between the sFLt-1 concentration and the condition of R. norvegicus, the onset of PE, and the time of SE with p<0.05. The difference in the concentration of B-CG and sFLt-1 R. norvegicus in each treatment group was influenced by the condition of the rats (normal and PE), the onset of PE (early onset and late onset), and the time of SE (SE 0 minutes; SE 5 minutes; SE 10 minutes). Research related to SE on PE still needs to be continued to decide on recommendations on whether SE can be used as a preventive measure in complementary midwifery care for preventing and reducing symptoms of PE in pregnancy. Doi: 10.28991/ESJ-2023-07-03-021 Full Text: PDF

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