Skip to main content
Download PDF

Urinary orosomucoid and retinol binding protein levels as early diagnostic markers for diabetic kidney Disease

BMC Nephrology volume 24, Article number: 357 (2023) Cite this article

Abstract

Background

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, which has been a major cause of end-stage renal failure. Diagnosing diabetic kidney disease is important to prevent long-term kidney damage and determine the prognosis of patients with diabetes. In this study, we investigated the clinical significance of combined detection of urine orosomucoid and retinol-binding protein for early diagnosis of diabetic kidney disease.

Methods

We recruited 72 newly diagnosed patients with type 2 diabetes and 34 healthy persons from August 2016 to July 2018 at the First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital). Using the Mogensen grading criteria, participants were classified as having diabetes or diabetic kidney disease, and healthy persons constituted the control group. Urine orosomucoid and retinol-binding protein levels were measured and correlated with other variables.

Results

With the aggravation of renal damage, the level of urinary mucoid protein gradually increased. Urinary retinol-binding protein and microalbumin levels were significantly higher in the diabetes group than in control and nephropathy groups. Orosomucoid and retinol-binding protein might be independent risk factors for diabetes and diabetic kidney disease. Urinary orosomucoid significantly correlated with retinol-binding protein and microalbumin levels in the diabetic kidney disease group.

Conclusion

Elevated urine orosomucoid and retinol-binding protein levels can be detected in the early stages of type 2 diabetic kidney disease. Both of these markers are important for diabetic kidney disease detection and early treatment.

Peer Review reports

Introduction

Diabetic kidney disease (DKD) is the most common and severe chronic vascular complication among patients with type 2 diabetes mellitus (T2DM) [1]. It leads to chronic renal failure and is the leading cause of death due to diabetes. However, DKD patients have no obvious symptoms in the early stage. Common diagnostic indicators of DKD include 24-hour urine microalbumin (MAL), urea nitrogen, and serum creatinine levels. However, they can be affected by many factors, such as urinary tract or systemic infections, strenuous exercise, bleeding, or drugs that affect the kidneys [2]. The accuracy and specificity of these indicators are not high, and they have limitations [3]; thus, more research is needed to identify newer, more accurate, and specific early diagnostic markers of DN. Presently, preliminary progress has been made in kidney disease research using proteomics technology. Diabetic urine proteome research has shown that some protein markers have a predictive effect on early DKD [4, 5]. Screening for differentially expressed proteins in the urine of DKD patients, performing mass spectrometry and bioinformatics analysis of the differential proteins, and selecting and obtaining six differential proteins such as orosomucoid and retinol-binding protein (RBP) for research will help in the early diagnosis of DKD. El-Beblawy Nagham MS assessed serum and urinary orosomucoid levels in children and adolescents with type 1 diabetes and suggested that orosomucoid is a significant independent factor for diabetic microvascular complications and can be considered as an early marker of renal injury [6]. Wang evaluated the value of urinary orosomucoid in early renal impairment screening in T2DM patients and found orosomucoid level had high diagnostic efficiency to aid in the early detection of renal impairment in T2DM patients [7]. Mahfouz suggested that the RBP4 marker may serve as a tool to follow-up on the development and progression of DKD [8]. In this study, the urine orosomucoid and RBP levels were measured in healthy people, patients with T2DM, and patients with early DKD. The differences between the three groups were compared. We also assessed their clinical significance in the diagnosis of early type 2 DKD and their clinical value in the progression of nephropathy.

Methods

Study subjects

Thirty-four healthy people who underwent physical examination at the First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital) between August 2016 and July 2018 were categorized as the normal control (NC) group, including 18 males and 16 females, with an average age of 47.9 ± 14.2 years. Seventy-seven patients with T2DM who were hospitalized at the same time were assessed according to the Mogensen classification criteria for the degree of kidney damage. We recorded the medical history and complications of all patients. Complications of diabetes include DKD and diabetic retinopathy (DR). These patients were categorized as those with T2DM (T2DM group; n = 38; microalbumin (MAL) < 30 mg/24 h), which included 21 males and 17 females, with an average age of 48.7 ± 13.6 years, and patients with early DKD and combined DR (T2DKD group; n = 34; MAL 30–300 mg/24 h), which included 19 males and 15 females, with an average age of 49.1 ± 14.4 years. Diabetes was diagnosed and classified according to the 1999 diagnostic criteria of the World Health Organization [9]. The exclusion criteria were diabetic ketosis, hyperglycemia and osmotic pressure syndrome, combined fever and infection, acute cardiocerebrovascular diseases and urinary system diseases (kidney stones, acute and chronic nephritis, and nephrotic syndrome), non-diabetic congestive heart failure, liver dysfunction, rheumatic diseases, hematological diseases, pregnancy, tumors, fractures, primary hyperparathyroidism, a history of kidney transplant and intake of glucocorticoids, history of immunosuppressant and nephrotoxic drugs use, history of renal damage caused by strenuous exercise, and severe hypertension.

Data collection

We recorded the medical history and complications of all patients and measured their diastolic blood pressure (DBP), systolic blood pressure (SBP), height (cm), and weight (kg). The levels of total cholesterol (CHOL), low-density lipoprotein cholesterol (LDL), triglycerides (TG), fasting blood glucose (FBG), 2-hour blood glucose, serum creatinine (SCr) and urine orosomucoid, and RBP were measured using Applied blood biochemical detector (Hitachi 7600, Hitachi, Japan). HLC-723 HbA1c Analyzer (Japan Toshiba, Tokyo, Japan), i.e., high-pressure liquid phase ion-exchange chromatography was used to determine glycosylated hemoglobin (HbA1c) levels. The urine specimen was collected between 10 pm and 6 am, and the total urine volume (mL) was recorded after mixing. IMMAGE 800 protein chemistry analyzer (Beckman Coulter, USA) was used to detect urine MAL. The urine orosomucoid level was determined using the rate scattering immunoturbidimetric method performed on the Array 360 System (Beckman Coulter, USA). Orosomucoid reagents, calibration cards, and calibration were provided by the manufacturer. The detection temperature was 37℃, while the measurement wavelength of the instrument was 340 nm; the immunoturbidimetric measurement was performed after calibration. CV within and between batches was less than 5%. The CKD-EPI equation [10] was used to estimate the glomerular filtration rate (eGFR).

Statistical analysis

For normally distributed data determined using the Shapiro–Wilk’s test, the indicators in each group were expressed as the mean ± standard deviation. The chi-square test was used to compare quantitative data between groups. The mean values for each of the three groups were compared using one-way analysis of variance. If there were significant differences between the groups, intra-group comparisons were performed using the least significant difference. A binary logistic regression model was used to determine the factors associated with T2DM and T2DKD, and correlation analyses were performed using Spearman’s rank correlation. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic points and diagnostic value of orosomucoid and RBP in DKD. All statistical analyses were performed using SPSS version 23.0 (IBM Corp., Armonk, NY, USA). A two-tailed test with P < 0.05 was considered significant.

Results

There were no significant differences in age and gender among the three groups (P > 0.05). There were no significant differences in DBP, BMI, CHOL, TG, and LDL levels among the three groups (P > 0.05) (Table 1).

Table 1 Comparison of general clinical data among the three groups
Full size table

There were significant differences in SBP, HbA1c, FBG between the NC and the other two groups (P < 0.05). However, there was no significant difference in the general clinical data between the T2DM and T2DKD groups (P > 0.05). As renal damage increased in patients, urine orosomucoid levels gradually increased as well (P < 0.05) (Table 1).

Urine RBP and MAL levels in the T2DKD group were significantly higher than those in the NC and T2DKD groups (P < 0.001). The eGFR levels in the T2DKD group were significantly lower than those in the NC and T2DM groups (P < 0.001). There were no significant differences in RBP, eGFR, and MAL levels between the NC and T2DM groups (P > 0.05) (Table 2).

Table 2 Comparison of urinary orosomucoid, RBP, MAL, and eGFR levels among the three groups
Full size table

For the NC group and T2DM group, the dependent variable was whether T2DM had occurred (Yes = 1, No = 0) and the independent variables were the four variables (SBP, HbA1c, FBG, and orosomucoid) with differences between the two groups, as shown in Tables 1 and 2. A binary logistic regression model was established and used to determine the influence of these four variables on T2DM (Table 3), and all were shown to be risk factors (all OR > 1, p < 0.05).

Table 3 Classification of clinical, biochemical parameters and biomarkers (RBP, Orosomucoid) in T2DKD as per micro albumin tertiles
Full size table

For the T2DM group and T2DKD group, the dependent variable was whether T2DKD had occurred (Yes = 1, No = 0) and the independent variables were the five variables with differences between the two groups, as shown in Tables 1 and 2. A binary logistic regression model was established for analysis (Table 4).

Table 4 Binary logistic regression analysis of the factors associated with Type 2 diabetic kidney disease
Full size table

Of the five factors that were included in the regression model (p < 0.05), SBP, orosomucoid, RBP, and MAL were all determined to be risk factors (OR > 1), and eGFR was shown to be a protective factor (OR = 0.948 > 1). Correlation analysis showed that in the T2DKD group, the urinary orosomucoid level was significantly positively correlated with RBP (r = 0.489) and MAL (r = 0.513). RBP and MAL were significantly positively correlated with a correlation coefficient of 0.468. eGFR and urine orosomucoid, RBP, and MAL were significantly negatively correlated (r = -0.577, -0.474, and − 0.466, respectively).

ROC curve analysis was used to assess the diagnostic points and diagnostic value of orosomucoid and that of RBP to predict DKD. Figure 1; Table 5 show the areas under the ROC curves for orosomucoid and RBP with the respective standard error values.

Fig. 1
figure 1

ROC curve analysis was used to assess the diagnostic points and diagnostic value of orosomucoid and that of RBP to predict DKD.

Full size image
Table 5 Areas under the two ROC curves for predicting diabetic kidney disease
Full size table

The diagnostic value of DKD had improved; however, no significance was observed (Z = 0.598, P = 0.550 > 0.05). The diagnostic point of orosomucoid was 22.43, sensitivity was 0.941, specificity was 0.842, and Youden’s index was 0.783. The diagnostic point of RBP was 0.53, sensitivity was 0.942, specificity was 1.000, and Youden’s index was 0.941.

Discussion

DKD has become the leading indication for dialysis due to end-stage renal disease (ESRD) [11]. Recent findings suggest that immune-mediated inflammatory processes play a crucial role in DKD. Many pre-inflammatory cells, growth regulators, and adhesion factors interact with each other and cross-link, resulting in expanding the corresponding cascade of inflammation [12]. In recent years, the rapid development of proteomics technology has provided us with new methods and ideas for identifying early diagnostic markers of DKD. Proteomics techniques have been used to identify disease-specific biomarkers and other related proteins in urine. Differential proteins have been identified, and some protein markers were found to have predictive effects on glomerular diseases.

Orosomucoid protein is a non-specific acute phase reaction protein that is mainly synthesized and secreted by the liver; it is low in healthy body fluids but is significantly increased in a state of inflammation or during tumor growth. Orosomucoid can act in damaged areas, be released into the circulation and intercellular fluid, and become involved in the induction and regulation of body damage, immune, and inflammatory responses [13]. Elevated urine orosomucoid levels in T2DM patients have predictive effects on cardiovascular complications and mortality [14]. El-Beblawy et al. [15] pointed out that orosomucoid is an independent factor for diabetic microvascular complications and can be considered an early marker of kidney damage. Fandiño-Vaquero et al. [16] also found that orosomucoid levels increased in patients with T2DM and might mirror local endothelial dysfunction or inflammatory processes. Although there are existing studies on orosomucoid, studies on changes in orosomucoid concentration in urine during the early stage of DKD is lacking. In this study, we found that as the disease progressed, urine orosomucoid levels gradually increased (P < 0.001). Orosomucoid might be an independent risk factor for T2DM and T2DKD, and it had a significant positive correlation with MAL (r = 0.489) and a significant negative correlation with eGFR (r = -0.577). The results also revealed an increase in orosomucoid in the early stage of DKD, suggesting that this increase may promote the occurrence and development of DKD.

RBP is filtered through the glomerulus and absorbed and degraded by proximal tubular epithelial cells. Therefore, it is generally stable in urine, difficult to decompose, and has a low excretion rate. An increase in RBP excretion may reflect the extent of damage [17, 18]. Studies have shown that urine RBP levels in patients with T2DM are closely related to DKD [19, 20]. Some studies have shown that urine RBP can be used to assess the degree of renal interstitial fibrosis due to various causes, progress with ESRD dialysis, and even diabetes related to an increased risk of death [21, 22]. This study found that urine RBP and MAL levels in the T2DKD group were significantly increased (P < 0.001). Therefore, RBP might be an independent risk factor for T2DKD, owing to a significant positive correlation with MAL (r = 0.468) and a significant negative correlation with eGFR (r = -0.474). Urine RBP may reflect early renal damage in DKD. In the area under the ROC curve for predicting DKD using orosomucoid and RBP, both factors had high sensitivity and specificity. Therefore, both orosomucoid and RBP can be used to diagnose DKD.

This study’s main limitation is that all the participants were residents of Henan Province, China, and the sample size was small. Further verification is needed through large sample-size studies and multicenter studies.

Conclusions

Urine orosomucoid and RBP can serve as potential markers for the early diagnosis of DKD, facilitating timely treatment. However, the underlying molecular mechanisms and the clinically important levels of these potential biomarkers need to be studied further.

Data Availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request and with the permission of The First Affiliated Hospital of Henan Polytechnic University ethics committee.

Abbreviations

AKI:

Acute Kidney Injury

BMI:

Body mass index

CHOL:

Cholesterol

CI:

Confidence interval

DBP:

Diastolic blood pressure

DKD:

diabetic kidney disease

ESRD:

End-stage renal disease

FBG:

Fasting blood glucose

LDL:

Low-density lipoprotein

MAL:

Microalbumin

NC:

Normal control

NGAL:

Neutrophil gelatinase-associated lipocalin

RBF:

Renal blood flow

RBP:

Retinol binding protein

ROC:

Receiver operating characteristic

SBP:

Systolic blood pressure

References

  1. Kapoula GV, Kontou PI, Bagos PG. Diagnostic accuracy of Neutrophil Gelatinase-Associated Lipocalin for Predicting Early Diabetic Nephropathy in patients with type 1 and type 2 Diabetes Mellitus: a systematic review and Meta-analysis. J Appl Lab Med. 2019;4(1):78–94.

    Article  PubMed  Google Scholar 

  2. Furuichi K, Shimizu M, Hara A, Toyama T, Wada T. Diabetic Nephropathy: a comparison of the clinical and pathological features between the CKD Risk classification and the classification of Diabetic Nephropathy 2014 in Japan. Intern Med. 2018;57(23):3346–50.

    Article  Google Scholar 

  3. Papadopoulou-Marketou N, Kanaka-Gantenbein C, Marketos N, Chrousos G, Papassotiriou I. Biomarkers of diabetic Nephropathy: a 2017 update. Crit Rev Clin Lab Sci. 2017;54(5):326–42.

    Article  PubMed  CAS  Google Scholar 

  4. Varghese S, Powell T, Budisavljevic M, Oates J, Raymond J, Almeida J, Arthur J. Urine biomarkers predict the cause of glomerular Disease. J Am Soc Nephrology: JASN. 2007;18(3):913–22.

    Article  PubMed  CAS  Google Scholar 

  5. Lewandowicz A, Bakun M, Kohutnicki R, Fabijanska A, Kistowski M, Imiela J, Dadlez M. Changes in urine proteome accompanying diabetic Nephropathy progression. Pol Arch Med Wewn. 2015;125(1–2):27–38.

    PubMed  Google Scholar 

  6. El-Beblawy NMS, Andrawes NG, Ismail EAR, Enany ES, Abou El-Seoud HS, Erfan MA. Serum and urinary orosomucoid in young patients with type 1 Diabetes: a link between inflammation, Microvascular Complications, and subclinical Atherosclerosis. Clin Appl Thromb Hemost. 2016;22(8):718–26.

    Article  PubMed  CAS  Google Scholar 

  7. Wang H, Bao X, Ma Y, Shan X, Huang C. Urinary orosomucoid 1 protein to creatinine ratio as a potential biomarker for early screening of kidney impairment in type-2 Diabetes patients. Nephrol (Carlton Vic). 2020;25(9):667–75.

    Article  CAS  Google Scholar 

  8. Mahfouz M, Assiri A, Mukhtar M. Assessment of Neutrophil Gelatinase-Associated Lipocalin (NGAL) and retinol-binding protein 4 (RBP4) in type 2 Diabetic patients with Nephropathy. Biomark Insights. 2016;11:31–40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Harreiter J, Roden M. [Diabetes Mellitus: definition, classification, diagnosis, screening and prevention (update 2023)]. Wiener Klinische Wochenschrift. 2023;135:7–17.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Schwandt A, Denkinger M, Fasching P, Pfeifer M, Wagner C, Weiland J, Zeyfang A, Holl R. Comparison of MDRD, CKD-EPI, and Cockcroft-Gault equation in relation to measured glomerular filtration rate among a large cohort with Diabetes. J Diabetes Complicat. 2017;31(9):1376–83.

    Article  Google Scholar 

  11. Nichols G, Vupputuri S, Lau H. Medical care costs associated with progression of diabetic Nephropathy. Diabetes Care. 2011;34(11):2374–8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Liu S, Chen J, Li Y. Clinical significance of serum interleukin-8 and soluble Tumor necrosis factor-like weak inducer of apoptosis levels in patients with diabetic Nephropathy. J Diabetes Invest. 2018;9(5):1182–8.

    Article  CAS  Google Scholar 

  13. Luo Z, Lei H, Sun Y, Liu X, Su D. Orosomucoid, an acute response protein with multiple modulating activities. J Physiol Biochem. 2015;71(2):329–40.

    Article  PubMed  CAS  Google Scholar 

  14. Lage R, Moscoso I, Fernández-Trasancos Á, Cebro M, Couselo M, Fandiño-Vaquero R, Bravo S, Sierra J, González-Juanatey J, Eiras S. Differential behaviour of epicardial adipose tissue-secretomes with high and low orosomucoid levels from patients with Cardiovascular Disease in H9C2 cells. Mol Cell Endocrinol. 2015;416:77–87.

    Article  PubMed  CAS  Google Scholar 

  15. El-Beblawy N, Andrawes N, Ismail E, Enany B, El-Seoud H, Erfan M. Serum and urinary orosomucoid in young patients with type 1 Diabetes: a link between inflammation, Microvascular Complications, and subclinical Atherosclerosis. Clin Appl thrombosis/hemostasis: Official J Int Acad Clin Appl Thrombosis/Hemostasis. 2016;22(8):718–26.

    Article  CAS  Google Scholar 

  16. Fandiño-Vaquero R, Fernández-Trasancos A, Alvarez E, Ahmad S, Batista-Oliveira A, Adrio B, Fernández A, González-Juanatey J, Eiras S. Orosomucoid secretion levels by epicardial adipose tissue as possible indicator of endothelial dysfunction in Diabetes Mellitus or inflammation in coronary artery Disease. Atherosclerosis. 2014;235(2):281–8.

    Article  PubMed  Google Scholar 

  17. Andreucci M, Faga T, Pisani A, Perticone M, Michael A. The ischemic/nephrotoxic acute kidney injury and the use of renal biomarkers in clinical practice. Eur J Intern Med. 2017;39:1–8.

    Article  PubMed  CAS  Google Scholar 

  18. Adiyanti S, Loho T. Acute kidney Injury (AKI) biomarker. Acta Med Indones. 2012;44(3):246–55.

    PubMed  Google Scholar 

  19. Klisic A, Kavaric N, Ninic A. Retinol-binding protein 4 versus albuminuria as predictors of estimated glomerular filtration rate decline in patients with type 2 Diabetes. J Res Med Sciences: Official J Isfahan Univ Med Sci. 2018;23:44.

    Article  Google Scholar 

  20. Ni X, Gu Y, Yu H, Wang S, Chen Y, Wang X, Yuan X, Jia W. Serum Adipocyte Fatty Acid-Binding Protein 4 Levels Are Independently Associated with Radioisotope Glomerular Filtration Rate in Type 2 Diabetic Patients with Early Diabetic Nephropathy. BioMed research international 2018, 2018:4578140.

  21. Pallet N, Chauvet S, Chassé J, Vincent M, Avillach P, Levi C, Meas-Yedid V, Olivo-Marin J, Nga-Matsogo D, Beaune P, et al. Urinary retinol binding protein is a marker of the extent of interstitial kidney fibrosis. PLoS ONE. 2014;9(1):e84708.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wu J, Shao X, Lu K, Zhou J, Ren M, Xie X, Liu J, Xu Y, Ding Y, Shen X, et al. Urinary RBP and NGAL Levels Are Associated with Nephropathy in patients with type 2 Diabetes. Cell Physiol Biochemistry: Int J Experimental Cell Physiol Biochem Pharmacol. 2017;42(2):594–602.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Jiaozuo City Medical Research Project under grant number (2017KY018), 2021 Key Scientific and Technological Project of Henan Science and Technology Department (202102310774), 2022 Key Scientific and Technological Project of Henan Science and Technology Department (222102310340), 2022 Key Scientific Research Project of Universities of Henan Education Department (22A310015), Doctoral Fund Project of Henan Polytechnic University (B2021-68), the Fundamental Research Funds for the Universities of Henan Province(NSFRF220416), 2023 Natural Science Foundation of Henan Province (232300420197) .

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

  1. Xue-hong Zhou and Shu-yan Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Endocrinology, The First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital), Jiaozuo, China

    Xue-hong Zhou, Shu-yan Liu & Yong-feng Li

  2. Department of Neurology, The First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital), Jiaozuo, China

    Bo Yang

  3. College of Medicine, Henan Polytechnic University, Jiaozuo, China

    Bao-hua Hou

  4. Central Laboratory, The First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital), Jiaozuo, China

    Bao-hua Hou

Authors
  1. Xue-hong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu-yan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yong-feng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bao-hua Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SY L analyzed and interpreted the patient data regarding diabetic kidney disease and the normal control. YF L conducted serological testing and analysis. BY, YF L and BH H screened the hospitalized patients and entered the patient data. XH Z was a major contributor in writing the manuscript. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Xue-hong Zhou or Bao-hua Hou.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the ethics committee of The First Affiliated Hospital of Henan Polytechnic University (Jiaozuo Second People’s Hospital) (IRB number: 2016010), and all patients provided written informed consent to participate in the study. All methods were carried out following relevant guidelines and regulations.

Consent for publication

Not Applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, Xh., Liu, Sy., Yang, B. et al. Urinary orosomucoid and retinol binding protein levels as early diagnostic markers for diabetic kidney Disease. BMC Nephrol 24, 357 (2023). https://doi.org/10.1186/s12882-023-03363-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12882-023-03363-3

Keywords

BMC Nephrology

ISSN: 1471-2369

Contact us