A major complication of type 1 diabetes is diabetic kidney disease (DKD). Albuminuria and impaired glomerular filtration rate are the main characteristics of DKD. Neutrophil gelatinase-associated lipocalin (NGAL) levels may rise even in the early stages of DKD, even in patients with normoalbuminuria. We present the first systematic review and meta-analysis examining the prognostic role of NGAL exclusively in pediatric patients with type 1 diabetes.

A search through major databases was carried out until September 24, 2024, investigating the prognostic role of NGAL in kidney injury in pediatric patients with type 1 diabetes. A p < 0.05 was considered statistically significant. A study quality assessment was conducted using the Newcastle-Ottawa Scale.

The standardized mean difference in urinary NGAL (uNGAL) levels between the type 1 diabetes group and healthy controls was statistically significant (SMD = 0.63, 95%CI [0.36,0.90]). A moderate positive relationship between uNGAL and ACR was identified (r = 0.53, 95 % CI [0.31-0.70]). The uNGAL revealed a high overall diagnostic accuracy (AUC = 0.881).

Urinary NGAL appears to be a valuable biomarker for early detection and understanding of DKD in individuals with type 1 diabetes. Future clinical studies should prioritize assessing the accuracy of NGAL in identifying kidney injury in pediatric patients with type 1 diabetes and the association of NGAL with traditional biomarkers in groups with similar characteristics.

Diabetes mellitus (DM) has emerged as the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) globally in recent years. Diabetic nephropathy (DN), or diabetic kidney disease (DKD) that occurs as a direct consequence of DM, has complex pathophysiological mechanisms, such as various inflammatory processes and genetic and epigenetic factors, often accentuated by comorbid illnesses like hypertension and dyslipidemia. Therefore, management of DKD involves targeting these etio-pathological processes. Various medications with remarkable disease modifying properties have been introduced for treatment of DN in recent years. We update the current and future diagnostic and therapeutic landscapes against DKD in this article.

Diabetic kidney disease (DKD) is one of the common microvascular complications of diabetes. The exploration of serum biomarkers holds promise for improving the efficiency and accuracy of early DKD diagnosis. This study aims to investigate the diagnostic value of transforming growth factor-β1 (TGF-β1) and cystatin C (CysC) in DKD patients.

A total of 126 patients with type 2 diabetes mellitus (T2DM) diagnosed at Dongzhimen Hospital, Beijing University of Chinese Medicine, between May 2021 and March 2023 were enrolled. Patients were categorized based on proteinuria levels and estimated glomerular filtration rate (eGFR). Correlation analyses were conducted to examine the relationships between serum TGF-β1, CysC, and clinical parameters. Logistic regression was applied to identify correlation factors for DKD and renal function impairment in T2DM patients. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic efficacy.

Significant differences in TGF-β1 and CysC levels were observed across groups with varying proteinuria levels. CysC was positively correlated with TGF-β1 (r = 0.640, p < 0.001). TGF-β1 has been associated with proteinuria levels in T2DM patients. Each unit increase in TGF-β1 was associated with a 1.122-fold and 1.470-fold higher odds of the presence of microalbuminuria and proteinuria, respectively, in the normal proteinuria (NP) group. TGF-β1 and CysC showed varying diagnostic performance. TGF-β1 better distinguished microalbuminuria group (MP) from NP, while CysC alone was less effective. T2DM patients with impaired renal function exhibited significantly higher CysC and TGF-β1 levels compared to those with normal renal function. CysC emerged as an associated factor of renal function decline (OR = 2.255, p = 0.008). CysC demonstrated superior diagnostic efficacy compared to TGF-β1 in predicting renal function impairment (AUC = 0.974).

CysC and TGF-β1 can serve as potential biomarkers for assessing renal impairment and proteinuria in T2DM patients. Their combined evaluation demonstrates diagnostic value and clinical application potential.

To investigate the expression level of miR-25-3p in patients with type 2 diabetes mellitus (T2DM) and diabetic nephropathy (DN), and its effect on proliferation, apoptosis and inflammatory response of mesangial cells cultured with high glucose. Blood samples of all clinical subjects were collected for RT-qPCR analysis to detect serum miR-25-3p levels. Human mesangial cells (HMCs) cultured with high glucose were used to construct DN model in vitro. MTT assay, flow cytometry and ELISA were used to evaluate the effects of miR-25-3p on the proliferation, apoptosis, and inflammatory response of DN cell models. Serum miR-25-3p was decreased in both T2DM group and DN group, but more in DN group. Serum miR-25-3p was positively correlated with eGFR and negatively correlated with UAER. The expression of miR-25-3p was reduced in HMCs induced by high glucose. Transfection of miR-25-3p mimic could significantly up-regulate the miR-25-3p level in HMCs. Besides, high glucose culture resulted in abnormal proliferation of HMCs, reduced apoptotic cells, and increased inflammation. The addition of miR-25-3p mimic significantly inhibited cell proliferation and promoted cell apoptosis and reduced the production of inflammatory factors. The abnormal reduction of serum miR-25-3p in DN indicates that it may be a potential biomarker for clinical diagnosis of DN. In in vitro experiments, miR-25-3p was involved in the progression of DN by regulating cell proliferation, apoptosis, and inflammatory response.

To explore the value of serum cystatin C (CysC) levels in evaluating renal prognosis in IgA nephropathy (IgAN) patients.

We retrospectively collected the clinical data of IgAN patients diagnosed by renal biopsy at Guangdong Provincial People's Hospital from January, 2014 to December, 2018. Based on baseline serum CysC levels, the patients were divided into high serum CysC (>1.03 mg/L) group and normal serum CysC (≤1.03 mg/L) group. The composite endpoint for poor renal prognosis was defined as ≥50% decline in estimated glomerular filtration rate (eGFR) and/or progression to end-stage renal disease (ESRD). Lasso regression, multivariate Cox regression and Kaplan-Meier survival analysis were used to identify the risk factors and compare renal survival rates between the two groups. Smooth curves fitting and threshold effect analysis were used to explore the relationship between serum CysC levels and the outcomes. A nomogram model was constructed and its predictive performance was evaluated using concordance index, calibration curve, receiver operating characteristic (ROC) curve and the area under curve (AUC).

A total of 356 IgAN patients were enrolled, who were followed up for 4.65±0.93 years. The composite endpoint occurred in 74 patients. High serum CysC was identified as an independent risk factor for poor renal prognosis in IgAN (HR=2.142, 95% CI 1.222 to 3.755), and the patients with high serum CysC levels had a lower renal survival rate (Log-rank χ2=47.970, P<0.001). In patients with serum CysC below 2.12 mg/L, a higher CysC level was associated with an increased risk of poor renal prognosis (β=3.487, 95% CI: 2.561-4.413, P<0.001), while above this level, the increase of the risk was not significant (β=0.676, 95% CI: -0.642-1.995, P=0.315). The nomogram model based on serum CysC and 3 other independent risk factors demonstrated good internal validity with a concordance index of 0.873 (95% CI: 0.839-0.907) and an AUC of 0.909 (95% CI: 0.873-0.945).

Serum CysC levels are associated with renal prognosis in IgAN patients, and high serum CysC an independent risk factor for poor renal prognosis.

Advancements in early detection have demonstrated the significance of biomarkers as indicators of health and disease. Traditional detection methods often face limitations, such as low sensitivity and time consumption. Fluorescence-based techniques are considered promising approaches because of their noninvasiveness and rapid response. However, these conventional methods have some drawbacks, such as low quantum yield, photobleaching, and aggregation-caused quenching. Recently, aggregation-induced emission (AIE) has emerged as a potential alternative, characterized by luminous emission upon aggregation, thus improving detection sensitivity and stability. This review explores the recent advancements in AIE luminogens (AIEgens) in biomedical engineering, with a particular focus on their application in biomarker detection. Here, we discuss the different types of AIE mechanisms and their advantages in disease diagnosis and imaging. In addition, we summarize the development of various AIEgen-based probes for the detection of diverse biomarkers. Finally, we address the remaining challenges and future directions for AIE materials in modern biomedical engineering, emphasizing the potential of AIEgens in biomarker detection and disease diagnosis strategies.

Diabetic nephropathy (DN) also known as diabetic kidney disease, is a major microvascular complication of diabetes and a leading cause of end‑stage renal disease (ESRD), which affects the morbidity and mortality of patients with diabetes. Despite advancements in diabetes care, current diagnostic methods, such as the determination of albuminuria and the estimated glomerular filtration rate, are limited in sensitivity and specificity, often only identifying kidney damage after considerable morphological changes. The present review discusses the potential of metabolomics as an approach for the early detection and management of DN. Metabolomics is the study of metabolites, the small molecules produced by cellular processes, and may provide a more sensitive and specific diagnostic tool compared with traditional methods. For the purposes of this review, a systematic search was conducted on PubMed and Google Scholar for recent human studies published between 2011 and 2023 that used metabolomics in the diagnosis of DN. Metabolomics has demonstrated potential in identifying metabolic biomarkers specific to DN. The ability to detect a broad spectrum of metabolites with high sensitivity and specificity may allow for earlier diagnosis and better management of patients with DN, potentially reducing the progression to ESRD. Furthermore, metabolomics pathway analysis assesses the pathophysiological mechanisms underlying DN. On the whole, metabolomics is a potential tool in the diagnosis and management of DN. By providing a more in‑depth understanding of metabolic alterations associated with DN, metabolomics could significantly improve early detection, enable timely interventions and reduce the healthcare burdens associated with this condition.

Coronary heart disease (CHD) and heart failure (HF) are the major causes of morbidity and mortality worldwide. Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis. However, conventional diagnostic methods such as electrocardiography, echocardiography, and cardiac biomarkers have certain limitations, such as low sensitivity, specificity, availability, and cost-effectiveness. Therefore, there is a need for simple, noninvasive, and reliable biomarkers to diagnose CHD and HF.

To investigate serum cystatin C (Cys-C), monocyte/high-density lipoprotein cholesterol ratio (MHR), and uric acid (UA) diagnostic values for CHD and HF.

We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023. The patients were divided into CHD (n = 20), HF (n = 20), CHD + HF (n = 20), and control groups (n = 20). The serum levels of Cys-C, MHR, and UA were measured using immunonephelometry and an enzymatic method, respectively, and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic (ROC) curve analysis.

Serum levels of Cys-C, MHR, and UA were significantly higher in the CHD, HF, and CHD + HF groups than those in the control group. The serum levels of Cys-C, MHR, and UA were significantly higher in the CHD + HF group than those in the CHD or HF group. The ROC curve analysis showed that serum Cys-C, MHR, and UA had good diagnostic performance for CHD and HF, with areas under the curve ranging from 0.78 to 0.93. The optimal cutoff values of serum Cys-C, MHR, and UA for diagnosing CHD, HF, and CHD+HF were 1.2 mg/L, 0.9 × 109, and 389 µmol/L; 1.4 mg/L, 1.0 × 109, and 449 µmol/L; and 1.6 mg/L, 1.1 × 109, and 508 µmol/L, respectively.

Serum Cys-C, MHR, and UA are useful biomarkers for diagnosing CHD and HF, and CHD+HF. These can provide information for decision-making and risk stratification in patients with CHD and HF.

Identification of non-diabetic renal disease (NDRD) in patients with type 2 diabetes mellitus (T2DM) may help tailor treatment. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising tool to evaluate renal function but its potential role in the clinical differentiation between diabetic nephropathy (DN) and NDRD remains unclear.

To investigate the added role of IVIM-DWI in the differential diagnosis between DN and NDRD in patients with T2DM.

Prospective.

Sixty-three patients with T2DM (ages: 22-69 years, 17 females) confirmed by renal biopsy divided into two subgroups (28 DN and 35 NDRD).

3 T/ T2 weighted imaging (T2WI), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

The parameters derived from IVIM-DWI (true diffusion coefficient [D], pseudo-diffusion coefficient [D*], and pseudo-diffusion fraction [f]) were calculated for the cortex and medulla, respectively. The clinical indexes related to renal function (eg cystatin C, etc.) and diabetes (eg diabetic retinopathy [DR], fasting blood glucose, etc.) were measured and calculated within 1 week before MRI scanning. The clinical model based on clinical indexes and the IVIM-based model based on IVIM parameters and clinical indexes were established and evaluated, respectively.

Student's t-test; Mann-Whitney U test; Fisher's exact test; Chi-squared test; Intraclass correlation coefficient; Receiver operating characteristic analysis; Hosmer-Lemeshow test; DeLong's test. P < 0.05 was considered statistically significant.

The cortex D*, DR, and cystatin C values were identified as independent predictors of NDRD in multivariable analysis. The IVIM-based model, comprising DR, cystatin C, and cortex D*, significantly outperformed the clinical model containing only DR, and cystatin C (AUC = 0.934, 0.845, respectively).

The IVIM parameters, especially the renal cortex D* value, might serve as novel indicators in the differential diagnosis between DN and NDRD in patients with T2DM.

2 TECHNICAL EFFICACY: Stage 2.

Diabetic kidney disease (DKD) is an important microvascular complication of diabetes mellitus (DM).

This study aimed to develop predictive nomograms to estimate the risk of DKD in patients with type 2 diabetes mellitus (T2DM).

The medical records of patients with T2DM in our hospital from March 2022 to March 2023 were retrospectively reviewed. The enrolled patients were randomly selected for training and validation sets in a 7:3 ratio. The models for predicting risk of DKD were virtualized by the nomograms using logistic regression analysis.

Among the enrolled 597 patients, 418 were assigned to the training set, while 179 were assigned to the validation set. Using the predictors included glycated hemoglobin A1c (HbA1c), high density lipoprotein cholesterol (HDL-C), presence of diabetic retinopathy (DR) and duration of diabetes (DD), we constructed a full model (model 1) for predicting DKD. And using the laboratory indexes of HbA1c, HDL-C, and cystatin C (Cys-C), we developed a laboratory-based model (model 2). The C-indexes were 0.897 for model 1 and 0.867 for model 2, respectively. The calibration curves demonstrated a good agreement between prediction and observation in the two models. The decision curve analysis (DCA) curves showed that the two models achieved a net benefit across all threshold probabilities.

We successfully constructed two prediction models to evaluate the risk of DKD in patients with T2DM. The two models exhibited good predictive performance and could be recommended for DKD screening and early detection.

Diabetic kidney disease (DKD) is one of the most common microvascular complications of both type 1 and type 2 diabetes and the most common cause of the end-stage renal disease (ESRD). It has been evidenced that targeted interventions at an early stage of DKD can efficiently prevent or delay the progression of kidney failure and improve patient outcomes. Therefore, regular screening for DKD has become one of the fundamental principles of diabetes care. Long-established biomarkers such as serum-creatinine-based estimates of glomerular filtration rate and albuminuria are currently the cornerstone of diagnosis and risk stratification in routine clinical practice. However, their immanent biological limitations and analytical variations may influence the clinical interpretation of the results. Recently proposed new predictive equations without the variable of race, together with the evidence on better accuracy of combined serum creatinine and cystatin C equations, and both race- and sex-free cystatin C-based equation, have enabled an improvement in the detection of DKD, but also require the harmonization of the recommended laboratory tests, wider availability of cystatin C testing and specific approach in various populations. Considering the complex pathophysiology of DKD, particularly in type 2 diabetes, a panel of biomarkers is needed to classify patients in terms of the rate of disease progression and/or response to specific interventions. With a personalized approach to diagnosis and treatment, in the future, it will be possible to respond to DKD better and enable improved outcomes for numerous patients worldwide.

Metabolic-associated fatty liver disease (MAFLD) and diabetic kidney disease (DKD) share various pathophysiological factors, and epidemiological evidence suggests that these two diseases are associated. Albuminuria and the estimated glomerular filtration rate, which are conventional biomarkers of DKD, are reportedly associated with the risk or severity of MAFLD. Recently, novel DKD biomarkers reflecting renal tubular injury have been introduced to complement conventional DKD markers. In this article, we looked at previous studies that showed an association between MAFLD and DKD, and also reviewed the significance of DKD biomarkers as predictive risk factors for MAFLD.

To explore the correlation of tumor necrosis factor α (TNF-α), cystatin C (Cys C), and NLR family pyrin domain containing 3 (NLRP3) inflammasomes with venous ulcers from lower extremity varicose veins.

In this retrospective analysis, 135 patients with primary varicose veins of lower extremities were selected and divided into a varicose ulcer group (n=32) and a non-varicose ulcer group (n=103) according to clinical ulcer presence. Healthy adults with similar general information during the same period were included as a healthy controls (n=30). The levels of TNF-α, interleukin-1β (IL-1β), Cys C, and NLRP3 inflammasomes were statistically analyzed among the three groups. Logistic regression was used for analyzing the risk factors for venous ulcers in patients with varicose veins of the lower extremities. Spearman correlation was applied for correlation analysis. The area under the receiver operating characteristic (ROC) curve (AUC) was found to disclose the predictive value of TNF-α, Cys C, and NLRP3 inflammasomes for venous ulcers.

(1) Logistic regression analysis showed that TNF-α, IL-1β, and NLRP3 inflammasomes were risk factors for venous ulcers in patients with varicose veins of the lower extremity, and Cys C in ulcer wound tissue was a protective factor. (2) TNF-α was significantly correlated with IL-1β and Cys C in ulcer wound tissue, and NLRP3 in plasma (r=0.256, -0.290, 0.305; P=0.003, 0.001, <0.001). IL-1β was significantly correlated with CysC in ulcer wound tissue and plasma (r=-0.251, -0.193; P=0.003, 0.025). (3) The AUC, sensitivity, and specificity of TNF-α and NLRP3 inflammasomes for predicting varicose veins were high, with AUC of 0.881 and 0.712, sensitivity of 0.875% and 0.875%, and specificity of 0.893% and 0.738%, respectively.

TNF-α in plasma, Cys C in ulcer wound tissue and plasma, and NLRP3 inflammasomes in plasma were closely related to the occurrence of venous ulcers in patients with varicose veins of the lower and may serve as new targets for treatment.