Significant association of urinary alpha‐1‐microglobulin compared to urinary neutrophil gelatinase‐associated lipocalin with renal insufficiency in patients with type 2 diabetes

A non-linear association between low-density lipoprotein cholesterol and the risk of diabetic kidney disease in patients with type 2 diabetes in China

Objective

To explore the intrinsic relationship between low-density lipoprotein cholesterol (LDL-C) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in China.

Methods

This cross-sectional study included 1,313 patients with type 2 diabetes treated at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine, located in Jinhua, China. The data were combined from two periods, 2017 and 2020-2021. Participants were categorized into groups with and without DKD. The relationship between LDL-C levels and DKD was evaluated employing logistics regression analysis and restricted cubic spline (RCS) curves.

Results

Generally, there was no statistical difference in LDL-C levels between DKD and non-DKD groups, however, a significantly non-linear relationship (Pnon-linear = 0.011) was observed between LDL-C levels and DKD prevalence after adjusting for confounding factors according to the RCS analysis. Two optimal cut-points of 2.97 and 3.61 mmol/L were selected out using random forest algorithm. With the middle LDL-C concentration (2.97-3.61 mmol/L) as the reference, the odds ratios for low (<2.97 mmol/L) and high (>3.61 mmol/L) concentrations were 1.45 (1.08-1.96) and 1.47 (1.01-2.15) respectively, after adjusting for confounding factors in the multivariate analyses. Notably, this association was more pronounced among female participants in the subgroup analyses.

Conclusion

A non-linear association was observed between LDL-C levels and the risk of DKD in patients with T2D in China. LDL-C levels below 2.97 mmol/L may elevate the risk of DKD, particularly in female patients with T2D.

Association of major candidate protein biomarkers and long-term diabetic kidney disease progression among Asians with young-onset type 2 diabetes mellitus

AIMS

We aim to determine the association of seven major candidate protein biomarkers and diabetic kidney disease (DKD) progression among Asians with young-onset type 2 diabetes mellitus (T2DM).

METHODS

824 T2DM patients (onset ≤ 40 years old) were classified as DKD progressors based on yearly estimated glomerular filtration rate (eGFR) decline of >3 ml/min/1.73 m2 or >40 % from baseline. Plasma leucine-rich α-2-glycoprotein 1 (pLRG1), tumor necrosis factor-receptor 1 (pTNF-R1), pigment epithelium-derived factor (pPEDF), urinary α-1-microglobulin (uA1M), kidney injury molecular 1 (uKIM-1), haptoglobin (uHP) and uromodulin (uUMOD) were measured using enzyme-linked immunoassays.

RESULTS

Over 5.7 years of follow-up, 25.2 % of patients were DKD progressors. Elevated levels of pLRG1, pTNF-R1, pPEDF, uA1M, uKIM-1 and uHP were associated with DKD progression. The association between pTNF-R1 levels and DKD progression persisted after adjusting for clinical covariates (OR 1.84, 95 %CI 1.44-2.34, p < 0.001). The effects of pTNF-R1 were partially mediated through hyperglycemia (8 %) and albuminuria (10 %). Inclusion of pTNF-R1 in a clinical variable-based model improved the area under the receiver operating characteristics curve for predicting DKD progression by 0.02, from 0.72 (95 %CI 0.68-0.76) to 0.74 (95 %CI 0.70-0.78), p = 0.099.

CONCLUSIONS

Among seven major candidate proteins, pTNF-R1, partially mediated through hyperglycemia and albuminuria, robustly predicted DKD progression among Asians with young-onset T2DM.

Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study

Background

Our previous cross-sectional study has demonstrated the independently non-linear relationship between fasting C-peptide with renal dysfunction odds in patients with type 2 diabetes (T2D) in China. This longitudinal observational study aims to explore the role of serum C-peptide in risk prediction of new-onset renal dysfunction, then construct a predictive model based on serum C-peptide and other clinical parameters.

Methods

The patients with T2D and normal renal function at baseline were recruited in this study. The LASSO algorithm was performed to filter potential predictors from the baseline variables. Logistic regression (LR) was performed to construct the predictive model for new-onset renal dysfunction risk. Power analysis was performed to assess the statistical power of the model.

Results

During a 2-year follow-up period, 21.08% (35/166) of subjects with T2D and normal renal function at baseline progressed to renal dysfunction. Six predictors were determined using LASSO regression, including baseline albumin-to-creatinine ratio, glycated hemoglobin, hypertension, retinol-binding protein-to-creatinine ratio, quartiles of fasting C-peptide, and quartiles of fasting C-peptide to 2h postprandial C-peptide ratio. These 6 predictors were incorporated to develop model for renal dysfunction risk prediction using LR. Finally, the LR model achieved a high efficiency, with an AUC of 0.83 (0.76 - 0.91), an accuracy of 75.80%, a sensitivity of 88.60%, and a specificity of 70.80%. According to the power analysis, the statistical power of the LR model was found to be 0.81, which was at a relatively high level. Finally, a nomogram was developed to make the model more available for individualized prediction in clinical practice.

Conclusion

Our results indicated that the baseline level of serum C-peptide had the potential role in the risk prediction of new-onset renal dysfunction. The LR model demonstrated high efficiency and had the potential to guide individualized risk assessments for renal dysfunction in clinical practice.

Non-linear association of fasting C-peptide and uric acid levels with renal dysfunction based on restricted cubic spline in patients with type 2 diabetes: A real-world study

BACKGROUND

Previous studies had showed divergent findings on the associations of C-peptide and/or uric acid (UA) with renal dysfunction odds in patients with type 2 diabetes mellitus (T2DM). We hypothesized that there were non-linear relationships between C-peptide, UA and renal dysfunction odds. This study aimed to further investigate the relationships of different stratification of C-peptide and UA with renal dysfunction in patients with T2DM.

METHOD

We conducted a cross-sectional real-world observational study of 411 patients with T2DM. The levels of fasting C-peptide, 2h postprandial C-peptide, the ratio of fasting C-peptide to 2h postprandial C-peptide (C0/C2 ratio), UA and other characteristics were recorded. Restricted cubic spline (RCS) curves was performed to evaluated the associations of stratified C-peptide and UA with renal dysfunction odds.

RESULTS

Fasting C-peptide, C0/C2 ratio and UA were independently and significantly associated with renal dysfunction in patients with T2DM as assessed by multivariate analyses (p < 0.05). In especial, non-linear relationships with threshold effects were observed among fasting C-peptide, UA and renal dysfunction according to RCS analyses. Compared with patients with 0.28 ≤ fasting C-peptide ≤ 0.56 nmol/L, patients with fasting C-peptide < 0.28 nmol/L (OR = 1.38, p = 0.246) or fasting C-peptide > 0.56 nmol/L (OR = 1.85, p = 0.021) had relatively higher renal dysfunction odds after adjusting for confounding factors. Similarly, compared with patients with 276 ≤ UA ≤ 409 μmol/L, patients with UA < 276 μmol/L (OR = 1.32, p = 0.262) or UA > 409 μmol/L (OR = 6.24, p < 0.001) had relatively higher odds of renal dysfunction.

CONCLUSION

The renal dysfunction odds in patients with T2DM was non-linearly associated with the levels of serum fasting C-peptide and UA. Fasting C-peptide and UA might have the potential role in odds stratification of renal dysfunction.

Prediction of the Short-Term Risk of New-Onset Renal Dysfunction in Patients with Type 2 Diabetes: A Longitudinal Observational Study

Background

Studies in the past decade have reported many novel biomarkers for predicting the new-onset or progression risk of renal dysfunction in patients with type 2 diabetes (T2D) based on the genomic, metabolomic, and proteomic technologies. These novel predictive markers, however, are difficult to be widely used in clinical practice over the short term due to their high technology content, instability, and high cost. This study was aimed at evaluating the associations of clinical features and six traditional renal markers with the short-term risk of new-onset renal dysfunction in patients with T2D.

Methods

This study involved 213 participants with T2D and normal renal function at baseline. The baseline levels of the albumin-to-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), alpha-1-microglobulin-to-creatinine ratio (A1MCR), neutrophil gelatinase-associated lipocalin-to-creatinine ratio, transferrin-to-creatinine ratio (UTRF/Cr), and retinol-binding protein-to-creatinine ratio (URBP/Cr) were analyzed. Multivariate logistic models were established and validated.

Results

During the two-year follow-up period, 23.01% participants progressed to renal dysfunction. The basal levels of ACR, A1MCR, UTRF/Cr, and URBP/Cr were the independent risk factors of new-onset renal dysfunction (P < 0.05). Several logistic models incorporating clinical characteristics and these renal markers were constructed for predicting the short-term risk of new-onset renal dysfunction. Comparatively, the model including age, glycated hemoglobin (HbA1c), hypertension, ACR, A1MCR, UTRF/Cr, and URBP/Cr levels at baseline had the highest potential (C − index = 0.785, P < 0.001). This model was validated using the K-fold cross-validation method; the accuracy was 0.815 ± 0.013 in training sets and 0.784 ± 0.019 in validation sets, indicating a good consistency for predicting the new-onset renal dysfunction risk. Finally, a nomogram based on this model was constructed to provide a quantitative tool to assess the individualized risk of short-term new-onset renal dysfunction.

Conclusion

The model incorporating these markers and clinical features may have a high potential to predict the short-term risk of new-onset renal dysfunction.

A nomogram for predicting the risk of new-onset albuminuria based on baseline urinary ACR, orosomucoid, and HbA1c in patients with type 2 diabetes

OBJECTIVES

Numerous biomarkers have been shown to be associated with albuminuria. However, few of them are valuable separate predictors of albuminuria development. This study aimed to develop a model for predicting the short-term risk of new-onset albuminuria in normoalbuminuric patients with type 2 diabetes (T2D).

METHODS

213 patients with T2D who were normoalbuminuric at the baseline were enrolled in this study. Basal levels of clinical characteristics and renal biomarkers including urinary orosomucoid (alpha-1-acid-glycoprotein, UORM), neutrophil gelatinase-associated lipocalin, retinol-binding protein, alpha-1-microglobulin, transferrin, and albumin-to-creatinine ratio (ACR) were utilized to analyze the association with the short-term risk of new-onset albuminuria.

RESULTS

19.72% of normoalbuminuric subjects at baseline progressed to albuminuria over the 2-year follow-up period. Except for NGAL, the basal levels of the other five renal biomarkers were significantly associated with new-onset albuminuria risk in the univariate analysis. In the multivariate logistic regression analysis using Forward: LR method, a model incorporating UORM/Cr, ACR, and HbA1c was established. Comparatively, this model had a higher potential to predict new-onset albuminuria risk compared with the single use of renal markers. In the validation of this model performed by 5-fold cross-validation method, the accuracy of this model was 0.818 ± 0.008 in the training sets, 0.827 ± 0.062 in the test sets, indicating a good capability for assessing albuminuria risk. Finally, a nomogram based on this model was constructed to facilitate its use in clinical practice.

CONCLUSION

The combined analysis of UORM/Cr, ACR and HbA1c may be of potential value for predicting the short-term risk of new-onset albuminuria in such patients.

Novel Cardiovascular Risk Factors in Patients with Diabetic Kidney Disease

Patients with diabetic kidney disease (DKD) are at very high risk for cardiovascular events. Only part of this increased risk can be attributed to the presence of diabetes mellitus (DM) and to other DM-related comorbidities, including hypertension and obesity. The identification of novel risk factors that underpin the association between DKD and cardiovascular disease (CVD) is essential for risk stratification, for individualization of treatment and for identification of novel treatment targets.In the present review, we summarize the current knowledge regarding the role of emerging cardiovascular risk markers in patients with DKD. Among these biomarkers, fibroblast growth factor-23 and copeptin were studied more extensively and consistently predicted cardiovascular events in this population. Therefore, it might be useful to incorporate them in risk stratification strategies in patients with DKD to identify those who would possibly benefit from more aggressive management of cardiovascular risk factors.

Clinical and prognostic values of urinary alpha1-microglobulin as a tubular marker in acute heart failure

BACKGROUND

Although urinary alpha-1-microglobulin has been used as a marker of tubular dysfunction, its clinical and prognostic values in patients with acute heart failure have not been validated.

METHODS

We analyzed 623 patients (74 ± 13 years old, 60.0% male) with acute heart failure in whom urinary alpha-1-microglobulin (A1MG) levels were measured as tubular markers at the time of admission. The primary endpoint was all-cause mortality.

RESULTS

The median levels of urinary alpha-1-microglobulin with and without correction for urinary creatinine concentration were 8.80 (interquartile range: 4.20-17.7) mg/dL and 12.9 (5.92-30.7) mg/gCr, respectively. Urinary A1MG levels were significantly correlated with all of beta-2-microglobulin (r = 0.77), N-acetyl-β-D-glucosaminidase (r = 0.51), and estimated glomerular filtration rate (r = -0.42); however, alpha-1-microglobulin was most often predicted using beta-2-microglobulin or N-acetyl-β-D-glucosaminidase. During the 488-day (interquartile range: 185-938 days) follow-up, 141 deaths occurred. Higher A1MG levels were associated with higher mortality even after adjustment for other covariates. Only A1MG, but not beta-2-microglobulin or N-acetyl-β-D-glucosaminidase, yielded incremental prognostic information in addition to the preexisting prognostic factors (net-reclassification improvement: 0.254, P = 0.023; integrated discrimination improvement: 0.015, P = 0.028).

CONCLUSIONS

In patients hospitalized due to acute heart failure, urinary alpha-1-microglobulin was a marker of tubular dysfunction. High alpha-1-microglobulin was associated with all-cause mortality independent of glomerular function and was a better predictor of mortality than urinary beta-2-microglobulin.