Serum cystatin C and βeta-2 microglobulin as potential biomarkers in children with lupus nephritis

Baraka, Eman; Hashaad, Nashwa Ismail; Abdelhalim, Walid; Elolemy, Gehan

doi:10.46497/archrheumatol.2023.8520

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…Although previous studies have revealed a significant correlation between CysC levels and the severity and pathological grades of LN (25,26), the specific connection between it and PLN Conversely, cystatin C or creatinine show a strong negative correlation with red blood cells or hematocrit (Figure 6). These findings are consistent with the clinical presentations of the patients and the characteristics listed in Table 1.…”

Section: Discussionmentioning

confidence: 74%

“…Although previous studies have revealed a significant correlation between CysC levels and the severity and pathological grades of LN ( 25 , 26 ), the specific connection between it and PLN remains insufficiently supported by empirical evidence. The exact association between neutrophils and PLN is also subject to controversy ( 27 , 28 ).…”

Section: Discussionmentioning

confidence: 93%

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Machine learning models predicts risk of proliferative lupus nephritis

Yang,

Liu,

et al. 2024

Front. Immunol.

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ObjectiveThis study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe.MethodsThis study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers.ResultsInvolving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835–0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN.ConclusionThis study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.

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Section: Discussionmentioning

confidence: 74%

“…Although previous studies have revealed a significant correlation between CysC levels and the severity and pathological grades of LN ( 25 , 26 ), the specific connection between it and PLN remains insufficiently supported by empirical evidence. The exact association between neutrophils and PLN is also subject to controversy ( 27 , 28 ).…”

Section: Discussionmentioning

confidence: 93%

Machine learning models predicts risk of proliferative lupus nephritis

Yang,

Liu,

et al. 2024

Front. Immunol.

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Biomarkers for systemic lupus erythematosus: A scoping review

Zhang,

Xu,

Kang

2024

Immunity Inflam & Disease

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BackgroundIn recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE).ObjectiveIn this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed.MethodsIn PubMed and Google Scholar databases, “SLE,” “biomarkers,” “predictor,” “autoimmune diseases,” “lupus nephritis,” “neuropsychiatric SLE,” “diagnosis,” “monitoring,” and “disease activity” were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article.ConclusionsRecent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta‐analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

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A nomogram to predict the risk of proliferative lupus nephritis in patients with systemic lupus erythematosus involving the kidneys

Yang,

Tang,

et al. 2024

Clinical Immunology

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Serum cystatin C and βeta-2 microglobulin as potential biomarkers in children with lupus nephritis

Cited by 3 publications

References 51 publications

Machine learning models predicts risk of proliferative lupus nephritis

Machine learning models predicts risk of proliferative lupus nephritis

Biomarkers for systemic lupus erythematosus: A scoping review

A nomogram to predict the risk of proliferative lupus nephritis in patients with systemic lupus erythematosus involving the kidneys

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