Xie Ying; Lu Bin; Hu Renming

Research Article

Austin J Endocrinol Diabetes. 2015;2(1): 1031.

Combination of Estimated Glomerular Filtration Rate and Diabetic Retinopathy May be Useful for Diagnosing Diabetic Kidney Disease (DKD) in Patients with Type 2 Diabetes Mellitus

Xie Ying¹, Lu Bin¹ and Hu Renming¹*

¹Department of Endocrinology and Metabolism, Fudan University, China

*Corresponding author: Hu Renming, Department of Endocrinology and Metabolism, Fudan University, Shanghai, China,

Received: January 27, 2015; Accepted: March 22, 2015; Published: March 25, 2015

Abstract

Background: Estimated Glomerular Filtration Rate (eGFR) and Diabetic Retinopathy (DR) are associated with Diabetic Kidney Disease (DKD). This study investigated the use of DR and eGFR for the diagnosis of DKD in patients with type 2 diabetes mellitus (T2DM).

Methods: This cross-sectional study examined 490 inpatients with T2DM. Binary logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to evaluate the utility of using eGFR alone and using eGFR and DR together for the diagnosis of DKD.

Results: ROC analysis indicated that the area under the curve (AUC) of ROC curve for using eGFR alone for the diagnosis of DKD was 0.663 (95% CI:0.595, 0.730, p < 0.001). AUC for using DR and eGFR together was 0.860 (95% CI:0.819, 0.901, p < 0.001). Combined use of DR and eGFR with the cutoff value of 60 ml/min/1.73m² for the diagnosis of DKD had a positive predictive value of 0.720, a sensitivity of 0.221, and a specificity of 0.990. Among the 28patients diagnosed with DKD by this criterion, 5 had normo-albuminuria.

Conclusion: The combined use of eGFR and DR is useful for the diagnosis of DKD in patients with T2DM, especially in those with normo-albuminuria.

Keywords: Diabetic kidney disease; Estimated glomerular filtration rate; Diabetic retinopathy; Normo-albuminuria; Diagnosis

Introduction

Diabetic kidney disease (DKD) is the main cause of chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM). Persistent albuminuria is considered as the earliest sign of DKD and is associated with adverse cardiovascular and renal events [1-3]. According to the current clinical diagnostic criteria, persistent albuminuria is the key condition to diagnose DKD in patients with T2DM [4, 5]. Most patients with DKD also have Diabetic Retinopathy(DR) [6,7], so the presence of DR may also be useful for the diagnosis and screening of DKD[4-8] .In fact, the National Kidney Foundation Kidney Disease Outcomes Quality Initiative(NKF’s KDOQI) guidelines recommend consideration of DR in the diagnosis of DKD in patients with T2DM[4]. However, some patients with DKD had normo-albuminuria [9], so the KDOQI criteria may need further refinement. Normo-albuminuria is prevalent in patients with CKD and T2DM [10,11]. GFR less than 60ml/min/1.73m² is the main criterion used for the diagnosis of CKD. Other research reported that low estimated GFR (eGFR) correlated with more pathologically severe DKD in patients with T2DM and proteinuria [12]. In the present study, we investigated the use of eGFR alone and the combined use of DR and eGFR for the diagnosis of DKD in patients with T2DM.

Method

We conducted a cross-sectional study in Huashan Hospital affiliated with Fudan University in Shanghai, China. The inpatients who had been diagnosed as T2DM between Jan 2010 to Jan 2013 were included. Exclusion criteria included acute fever, acute infection, heart failure, other known kidney diseases, dialysis and other types of diabetes. 490 patients were enrolled in this study. The data of age, gender, duration of diabetes and hypertension history were collected from the clinical records of patients. Height and weight were measured at the admission day. The fasting serum samples were used to measure HbA1c, total Cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), uric acid (UA) and serum creatinine. The morning urinary samples were used to measure ACR. Diabetic retinopathy was detected by ophthalmoscopy or the fundus photographic imaging with a 45-degree digital nonmydriatic camera.

(i) Determinations of variables

Albuminuria includes Microalbuminuria (ACR30-300mg/g) and macroalbuminuria (ACR>300mg/g) [13].

eGFR was calculated by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula: eGFR(ml/min per1.73m²)

=141× (sCr/0.9)^-1.209×0.993^age (sCr>0.9 mg/dl, if male);

=141× (sCr/0.9)^-0.411×0.993^age(sCr<=0.9 mg/dl, if male);

=144× (sCr/0.7)^-1.209×0.993^age (sCr>0.7 mg/dl, if female);

=144× (sCr/0.7)^-0.329×0.993^age (sCr<=0.7mg/dl, if female)

(sCr: serum creatinine (mg/dl)) [14].

DR was classified by International Clinical Diabetic Retinopathy and Diabetic Macular Edema Disease Severity Scales. The patients were diagnosed as non-DR, non-proliferative diabetic retinopathy (NPDR, with microaneurysms, intraretinal hemorrhages, definite venous beading, or prominent intraretinal microvascular abnormalities) or proliferative diabetic retinopathy (PDR, with neovascularization or vitreous/preretinal hemorrhage) [15] respectively.

By the criteria of DKD for patients with T2DM in NKF-KDOQI guideline, most patients had one or two of the following conditions were considered to have DKD (NKF): (i) macroalbuminuria was present (ii) microalbuminuria was present in the presence with diabetic retinopathy [4].

Statistical analyses were performed by the software package SPSS 16.0. Data of continuous variables were expressed as mean±standard deviation (SD). Data of continuous variables were expressed as number or percentage. The differences between groups were analyzed by ANOVE analysis for continuous variables and Chi-square tests for categorical variables. Binary logistic regression analysis was performed to obtain the predictive probabilities to predict DKD. The predictive probabilities of eGFR were saved as pre-1 and the predictive probabilities of combined use of DR and eGFR were saved as pre-2. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the efficiency of combined use of DR and eGFR to diagnose DKD. In the ROC analysis, DKD diagnosed by criteria of NKF-KDOQI guideline was set as state variable, and pre-1 and pre-2 were set as the tested variables. Significance level was set as p-value less than 0.05.

Result

Table 1 showed the baseline characteristics of the 490 enrolled patients and all of them had T2DM. The means of age and duration of diabetes were 61.04±11.44 years and 9.85±7.33 years, respectively. A total of 34.9% of patients had DR, 21.2% of patients had DKD based on NKF-KDOQI guidelines. The mean of urinary ACR was 235.01±769.44mg/g, and the mean of eGFR was 89.93±22.15ml/ min/1.73m². (Table 2) showed the coefficients of binary logistic regression analysis for combined use of eGFR and DR to diagnose DKD. Using the results in Table 2, pre-2 was calculated by the following equation:

Table 1: Characteristics of the participants.




  
    Characteristics 
    N=490 
  
  
    Age(years)
    61.04±11.44
  
  
    Male (n (%)) 
    274(55.9%) 
  
  
    Duration of T2DM(years) 
    9.85±7.33 
  
  
    Hypertension (n (%)) 
    222(45.4%) 
  
  
    BMI(kg/m²) 
    24.18±3.36 
  
  
    HbA1c (%) 
    8.60±2.14 
  
  
    TC(mmol/L) 
    4.73±1.15 
  
  
    TG(mmol/L) 
    1.95±1.73 
  
  
    HDLC(mmol/L) 
    1.09±0.31 
  
  
    LDLC(mmol/L) 
    2.79±0.88 
  
  
    UA(mmol/L) 
    0.33±0.12 
  
  
    DR (n (%)) 
    171(34.9%) 
  
  
    ACR(mg/g) 
    235.01±769.44 
  
  
    eGFR(ml/min/1.73m²) 
    89.93±22.15 
  
  
    DKD (NKF) (n (%)) 
    104(21.2%)



Table 1:  Characteristics of the participants.

Table 2: Coefficient, standard error and p-value of Binary Logistic Regression for eGFR and DR*.




  
    Covariates
    B(coefficient) 
    Standard error 
    p-value 
  
  
    eGFR 
    -0.033 
    0.006 
    <0.001 
  
  
    DR 
    2.839 
    0.300 
    <0.001 
  
  
    Constant 
    0.083 
    0.536 
    0.877



Table 2:  Coefficient, standard error and p-value of Binary Logistic Regression
for eGFR and DR*.

Predictive probability (eGFR=X,DR=1) =1/[1+e^{-(0.083- 0.033*X+2.839*1)]}=1/[1+e^{-(2.922-0.033*X)]}[1]. In the ROC curve analysis, area under the curve (AUC) of eGFR to diagnose DKD was 0.663 (95%CI 0.595 ,0.730, p<0.001) (Figure 1). AUC of combined use of DR and eGFR to diagnose DKD was 0.860 (95%CI 0.819, 0.901, p<0.001) (Figure 1).

Figure 1: ROC curve of eGFR alone and combination of DR and eGFR to diagnose DKD in patients with T2DM.

    
    
    Figure 1:  ROC curve of eGFR alone and combination of DR and eGFR to
diagnose DKD in patients with T2DM.

(Table 3) showed the clinical utility of the combined use of DR and eGFR to diagnose DKD. When the cut-off value of eGFR was set as 90 ml/min/1.73m², the predictive probability, sensitivity and specificity to diagnose DKD were 0.488, 0.462 and 0.925, respectively At this cut-off value, 77 patients were diagnosed with DKD and 29 out of these patients had normo-albuminuria. When the cut-off value was set as 60 ml/min/1.73m², the predictive probability, sensitivity and specificity to diagnose DKD were 0.720, 0.221 and 0.990, respectively. At this cut-off value, 28 patients were diagnosed with DKD and 5 of these patients had normo-albuminuria. When the cut-off value was set as 30 ml/min/1.73m², 6 patients were diagnosed with DKD and all of them had albuminuria.

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Citation: Ying X, Bin L and Renming H. Combination of Estimated Glomerular Filtration Rate and Diabetic Retinopathy May be Useful for Diagnosing Diabetic Kidney Disease (DKD) in Patients with Type 2 Diabetes Mellitus. Austin J Endocrinol Diabetes. 2015;2(1): 1031. ISSN: 2381-9200.

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Combination of Estimated Glomerular Filtration Rate and Diabetic Retinopathy May be Useful for Diagnosing Diabetic Kidney Disease (DKD) in Patients with Type 2 Diabetes Mellitus

Abstract

Introduction

Method

Result

Discussion

Conclusion

References

Cut-off value of eGFR (ml/min/1.73m²)	Predictive probability*	Sensitivity	Specificity	Diagnosed patients(n)**	Diagnosed patients with normo-albuminuria(n)
30	0.873	0.058	1.000	6	0
60	0.720	0.221	0.990	28	5
90	0.488	0.462	0.925	77	29