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Abstract

Background

Prognosticating disease progression in patients with diabetic kidney disease (DKD) is challenging, especially in the early stages of kidney disease. Anemia can occur in the early stages of kidney disease in diabetes. We therefore postulated that serum hemoglobin (Hb) concentration, as a reflection of incipient renal tubulointerstitial impairment, can be used as a marker to predict DKD progression.

Methods

Drawing on nationally representative data of patients with biopsy-proven DKD, 246 patients who had an estimated glomerular filtration rate (eGFR)  ≥60 mL/min/1.73 m2 at renal biopsy were identified: age 56 (45–63) years; 62.6% men; Hb 13.3 (12.0–14.5) g/dL; eGFR 76.2 (66.6–88.6) mL/min/1.73 m2; urine albumin-to-creatinine ratio 534 (100–1480) mg/g Crea. Serum Hb concentration was divided into quartiles: ≤12, 12.1–13.3, 13.4–14.5 and ≥14.6 g/dL. The association between serum Hb concentration and the severity of renal pathological lesions was explored. A multivariable Cox regression model was used to estimate the risk of DKD progression (new onset of end-stage kidney disease, 50% reduction of eGFR or doubling of serum creatinine). The incremental prognostic value of DKD progression by adding serum Hb concentration to the known risk factors of DKD was assessed.

Results

Serum Hb levels negatively correlated with all renal pathological features, especially with the severity of interstitial fibrosis (ρ = −0.52; P < 0.001). During a median follow-up of 4.1 years, 95 developed DKD progression. Adjusting for known risk factors of DKD progression, the hazard ratio in the first, second and third quartile (the fourth quartile was reference) were 2.74 [95% confidence interval (CI) 1.26–5.97], 2.33 (95% CI 1.07–5.75) and 1.46 (95% CI 0.71–3.64), respectively. Addition of the serum Hb concentration to the known risk factors of DKD progression improved the prognostic value of DKD progression (the global Chi-statistics increased from 55.1 to 60.8; P < 0.001).

Conclusions

Serum Hb concentration, which reflects incipient renal fibrosis, can be useful for predicting DKD progression in the early stages of kidney disease.

Graphical Abstract
Graphical Abstract
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anemia, diabetic kidney disease, interstitial fibrosis, renal biopsy, renal decline

KEY LEARNING POINTS

What is already known about this subject?

  • prognosticating disease progression in patients with diabetic kidney disease (DKD) is challenging, especially in the early stages of kidney disease; and

  • anemia, which reflects interstitial fibrosis, can occur in the early stages of kidney disease in diabetes.

What this study adds?

  • serum hemoglobin (Hb) concentration negatively correlated with all renal pathological features, especially with the severity of interstitial fibrosis;

  • serum Hb concentration was associated with renal decline even after adjusting for known risk factors of DKD progression; and

  • addition of the serum Hb concentration to the known risk factors of DKD progression improved the prognostic value of DKD progression.

What impact this may have on practice or policy?

  • serum Hb concentration, which is a widely available, routinely measured marker and which reflects incipient renal fibrosis, can be useful for predicting DKD progression in the early stages of kidney disease.

INTRODUCTION

Diabetic kidney disease (DKD) is not just the most prevalent form of chronic kidney disease (CKD) but also the leading cause of end-stage kidney disease (ESKD) worldwide [1–3]. It also accounts for an increase in cardiovascular disease (CVD) and mortality [4]. Since morbidity and mortality increase as the renal function declines [5], the early identification of patients at high risk of CKD progression is important so that clinicians can provide intensive treatment that may ultimately alter the prognosis of DKD. Although some novel biomarkers have shown to be of prognostic value in patients with diabetes in the early stages of CKD [6], it is too laborious for routine use. This predicament highlights the need for widely available, routinely measured markers that reflect early kidney damage for prognosticating the clinical course of DKD, especially in the earlier stages of CKD.

Anemia is common among patients with CKD [7] and it is associated with the risk of progression of kidney disease, CVD and mortality [8–11]. Although possible causes of anemia in CKD include iron and vitamin deficiency, the major cause of anemia in CKD is renal tubulointerstitial impairment that interferes with erythropoietin production [12, 13]. Therefore, anemia is more common and severe in the later stages of CKD [7]. Meanwhile, anemia occurs in the earlier stages of CKD in patients with diabetes [14]. However, data on the association between anemia, tubulointerstitial lesions and renal prognosis are lacking in the early stages of CKD in patients with diabetes due to the fact that kidney biopsy is not always applicable for them.

Fortunately, the Ministry of Health, Labor and Welfare in Japan has currently established a nationwide biopsy-proven cohort of DKD, including patients in the early stages of CKD [15, 16]. We therefore postulate that anemia in the early stages of CKD reflects incipient kidney injury due to tubulointerstitial damage, and thus serum hemoglobin (Hb) concentration in the early stages of CKD could be useful for prognosticating the clinical course of DKD. The objectives of our study were to: (i) evaluate the association between serum Hb concentration and tubulointerstitial lesions in kidney biopsy specimens and (ii) quantify the risk for progression of CKD, according to the serum Hb concentration, in patients with Type 2 diabetes and biopsy-proven DKD in the early stages of CKD.

MATERIALS AND METHODS

Source of data and study population

We used the data of the nationwide, biopsy-based cohort study of DKD, provided by the Ministry of Health, Labour and Welfare and the Japan Agency for Medical Research and Development. The rationale and study designs are available elsewhere [15, 16]. Briefly, the source population for this study included 895 patients with Type 2 diabetes aged 30–82 years who underwent clinical kidney biopsy at 18 main hospitals across Japan between 1985 and 2016. The study population had a pathological diagnosis with DKD as the only kidney disease diagnosis. The majority of study population was under the care of each hospital or its satellite clinics every 3 months and was followed up from the date of biopsy until the earliest date of: (i) CKD progression [defined as initiation of hemodialysis, peritoneal dialysis, kidney transplantation, death from uremia, a doubling of serum creatinine or a decrease of estimated glomerular filtration rate (eGFR) by ≥50%]; (ii) all-cause death; or (iii) censoring (censoring for loss to follow-up or administrative censoring occurring on the end of December 2019).

Since our primary focus was to evaluate the association of serum Hb concentration with tubulointerstitial lesions in kidney biopsy specimens and the risk for progression of CKD, in the early stages of CKD, we included patients who had an eGFR ≥60 mL/min/1.73 m2 at kidney biopsy. The nationwide, biopsy-based cohort study of DKD was approved by the institutional review boards of each hospital and conducted in accordance with the declaration of Helsinki.

Measurements and definitions

Clinical characteristics, medication and laboratory data were gathered from the medical records at the time of kidney biopsy. The obtained data included age, gender, body mass index (BMI), known duration of diabetes, Hb A1c, eGFR (computed using the Modification of Diet in Renal Disease study equation for Japanese [17]), urine albumin-to-creatinine ratio (UACR), systolic blood pressure (BP), diastolic BP, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, medication usage including renin–angiotensin system (RAS) blocker, glucose-lowering agent (oral hypoglycemics and/or insulin) and statin, medical history of CVD and history of smoking. Type 2 diabetes was defined as having diabetes onset after the age of 30 years. Diabetes duration was defined as the time from the date of diabetes onset to the date of kidney biopsy.

Pathological findings were evaluated according to the Renal Pathology Society Diabetic Nephropathy Classification [18]. In brief, glomerular lesions were classified as follows: (i) Class I was glomerular basement thickening and only mild, non-specific changes on light microscopy; (ii) Class II was mild (IIa) or severe (IIb) mesangial expansion without either nodular lesions or global sclerosis in >50% of the glomeruli; (iii) Class III was nodular lesions without global sclerosis in >50% of the glomeruli; and (iv) Class IV was global sclerosis in >50% of the glomeruli. Pathological findings other than glomerulus were also evaluated as follows: interstitial lesions [interstitial fibrosis and tubular atrophy (Grades 0–3) and interstitial inflammation (Grades 0–2)] and vascular lesions [arteriolar hyalinosis (Grades 0–2) and large vessels arteriosclerosis (Grades 0–2)]. These procedures were conducted by three pathologists.

The outcome of interest was progression of DKD defined as a composite of new onset ESKD (dialysis, kidney transplantation or death from renal cause), a doubling of serum creatinine or a decrease of eGFR by ≥50%. The occurrence of DKD progression was ascertained by the medical records.

Statistical analysis

The study population was divided into four groups according to baseline (at the time of kidney biopsy) quartiles of serum Hb concentration: first quartile group, ≤12.0 g/dL; second quartile group, 12.1–13.3 g/dL; third quartile group, 13.4–14.5 g/dL; and fourth quartile group, ≥14.6 g/dL. Baseline clinical and pathological characteristics were quantified using median and interquartile range for continuous variables and percentages for categorical variables. Then, these characteristics were compared across serum Hb concentration quartiles, with P-values for trend calculated by a nonparametric test for trend across ordered groups developed by Cuzick [19]. Spearman rank correlation was performed to examine intercorrelations between serum Hb concentration quartiles and pathological lesions. A Cox proportional hazards regression model that was stratified according to the quartiles of baseline serum Hb concentration and that was adjusted for the baseline covariates was used to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) for the outcome. The fourth quartile group was designated as the reference quartile group to compare with other quartile groups. Global Chi-squared likelihood ratio, Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the incremental prognostic value of serum Hb concentration over known risk factors of DKD progression (age, gender, BMI, known duration of diabetes, systolic BP, Hb A1c, eGFR, UACR and RAS blocker use [20]).

We conducted subgroup analyses to assess the robustness of our findings. First, we calculated the cumulative incidence rates of DKD progression by gender, across serum Hb concentration quartiles, because the gender difference in serum Hb concentration has been reported in healthy adults [21]. Second, we calculated the cumulative incidence rates of DKD progression in patients with normo (UACR <30 mg/g Crea) and microalbuminuria (UACR 30–300 mg/g Crea), across serum Hb concentration quartiles.

For all descriptive analysis, we used the Stata version 15.1 (StataCorp, College Station, TX, USA). All statistical tests were two sided, and we considered P ˂ 0.05 to be statistically significant.

RESULTS

Baseline clinical and pathological characteristics of the patients, overall and stratified by quartiles of serum Hb concentration

Among 895 patients in the overall cohort, 246 patients who had a eGFR ≥60 mL/min/1.73 m2 at kidney biopsy were included in this analyses. The study flow and selection of study population were summarized in Supplementary data, Figure S1.

Table 1 shows the baseline characteristics of the patients, overall and stratified by the quartile of serum Hb concentration. The characteristics of overall cohort were as follows: median (interquartile range) age of 56 (45–63) years old, men 62.6%, known duration of diabetes 10 (5–17) years, BMI 22.9 (20.9–25.6), systolic BP 136 (124–150) mmHg, diastolic BP 78 (70–87) mmHg, HbA1c 7.9% (6.6–9.6), total cholesterol 210 (177–238) mg/dL, eGFR 76.2 (66.6–86.6) mL/min/1.73 m2 and UACR 534 (100–1480) mg/g Crea. There were no patients who had been received erythropoiesis-stimulating agents or iron supplementation before and at the time of kidney biopsy. Percentage of males, BMI and eGFR increased by increasing serum Hb concentration quartiles, whereas age and UACR decreased by increasing serum Hb concentration quartiles.

Table 1.
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Baseline clinical and pathological characteristics of the patients, overall and stratified by the quartiles of serum Hb concentration

 

Patient characteristicsSerum Hb concentration
AllQuartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Clinical characteristics at baseline
Serum Hb concentration, g/dL13.3 (12.0–14.5)11.1 (9.8–11.7)12.8 (12.3–13.1)13.8 (13.6–14.2)15.4 (15.1–16.1)<0.001
Number of patients24664616160
Age, years56 (45–63)57 (49–66)58 (49–64)57 (46–63)51 (41–58)<0.001
Male, %62.642.252.563.993.3<0.001
Diabetes duration, years10 (5–17)13 (8–18)10 (5–20)11 (6–17)7 (3–14)0.027
BMI, kg/m222.9 (20.9–25.6)21.8 (20.3–24.0)22.8 (20.3–25.4)22.8 (20.8–25.4)25.0 (22.1–27.7)<0.001
HbA1c, %7.9 (6.6–9.6)7.4 (6.2–8.3)8.8 (7.2–10.2)8.5 (6.9–10.2)7.7 (6.5–8.9)0.718
eGFR, mL/min/1.73 m276.2 (66.6–88.6)70.3 (65.8–79.4)76.3 (65.9–86.9)77.4 (67.6–86.6)81.9 (68.4–95.7)0.002
UACR, mg/g creatinine534 (100–1480)1510 (241–2700)693 (153–1545)360 (50–1121)252 (120–650)<0.001
Systolic BP, mmHg136 (124–150)133 (125–153)138 (126–156)140 (127–152)130 (117–140)0.046
Diastolic BP, mmHg78 (70–87)76 (68–84)77 (70–86)80 (70–89)78 (70–88)0.391
Total cholesterol, mg/dL210 (177–238)212 (185–252)209 (170–243)202 (175–232)212 (181–235)0.236
Low-density lipoprotein cholesterol, mg/dL131 (106–158)121 (105–172)129 (92–161)129 (109–150)136 (109–158)0.743
High-density lipoprotein cholesterol, mg/dL45 (37–56)50 (34–63)52 (40–70)42 (34–51)45 (37–49)0.239
Triglycerides, mg/dL141 (97–200)134 (85–189)107 (90–158)155 (103–192)168 (112–253)0.024
Medication usage, %
  RAS blocker50.756.450.056.738.70.110
  Glucose-lowering medication52.246.261.166.735.50.577
  Lipid-lowering medication16.820.717.226.10.00.166
Ever having smoked, %53.833.347.459.166.70.033
History of CVD, %16.17.917.121.417.10.232
Pathological characteristics at baseline
Renal Pathology Society Diabetic Nephropathy Classification
Glomerular lesions, %<0.001
  Class I36.720.031.046.043.2
  Class IIa20.34.0010.324.320.3
  Class IIb18.016.034.513.510.8
  Class III23.460.020.716.28.1
  Class IV1.60.03.50.02.7
Interstitial lesions
  IFTA, %<0.001
   023.212.514.827.938.3
  141.932.847.542.626.2
  224.826.731.227.910.0
  310.125.06.61.66.7
  Interstitial inflammation, %<0.001
   031.715.627.936.148.3
   153.760.959.050.843.3
   214.623.513.113.18.4
Vascular lesions
 Arteriolar hyalinosis, %0.019
  015.99.411.714.828.3
  156.765.655.055.750.0
  227.425.033.329.521.7
 Large vessels arteriosclerosis, %)0.059
  027.728.116.731.035.0
  147.542.256.740.051.7
  226.826.726.629.013.3
Patient characteristicsSerum Hb concentration
AllQuartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Clinical characteristics at baseline
Serum Hb concentration, g/dL13.3 (12.0–14.5)11.1 (9.8–11.7)12.8 (12.3–13.1)13.8 (13.6–14.2)15.4 (15.1–16.1)<0.001
Number of patients24664616160
Age, years56 (45–63)57 (49–66)58 (49–64)57 (46–63)51 (41–58)<0.001
Male, %62.642.252.563.993.3<0.001
Diabetes duration, years10 (5–17)13 (8–18)10 (5–20)11 (6–17)7 (3–14)0.027
BMI, kg/m222.9 (20.9–25.6)21.8 (20.3–24.0)22.8 (20.3–25.4)22.8 (20.8–25.4)25.0 (22.1–27.7)<0.001
HbA1c, %7.9 (6.6–9.6)7.4 (6.2–8.3)8.8 (7.2–10.2)8.5 (6.9–10.2)7.7 (6.5–8.9)0.718
eGFR, mL/min/1.73 m276.2 (66.6–88.6)70.3 (65.8–79.4)76.3 (65.9–86.9)77.4 (67.6–86.6)81.9 (68.4–95.7)0.002
UACR, mg/g creatinine534 (100–1480)1510 (241–2700)693 (153–1545)360 (50–1121)252 (120–650)<0.001
Systolic BP, mmHg136 (124–150)133 (125–153)138 (126–156)140 (127–152)130 (117–140)0.046
Diastolic BP, mmHg78 (70–87)76 (68–84)77 (70–86)80 (70–89)78 (70–88)0.391
Total cholesterol, mg/dL210 (177–238)212 (185–252)209 (170–243)202 (175–232)212 (181–235)0.236
Low-density lipoprotein cholesterol, mg/dL131 (106–158)121 (105–172)129 (92–161)129 (109–150)136 (109–158)0.743
High-density lipoprotein cholesterol, mg/dL45 (37–56)50 (34–63)52 (40–70)42 (34–51)45 (37–49)0.239
Triglycerides, mg/dL141 (97–200)134 (85–189)107 (90–158)155 (103–192)168 (112–253)0.024
Medication usage, %
  RAS blocker50.756.450.056.738.70.110
  Glucose-lowering medication52.246.261.166.735.50.577
  Lipid-lowering medication16.820.717.226.10.00.166
Ever having smoked, %53.833.347.459.166.70.033
History of CVD, %16.17.917.121.417.10.232
Pathological characteristics at baseline
Renal Pathology Society Diabetic Nephropathy Classification
Glomerular lesions, %<0.001
  Class I36.720.031.046.043.2
  Class IIa20.34.0010.324.320.3
  Class IIb18.016.034.513.510.8
  Class III23.460.020.716.28.1
  Class IV1.60.03.50.02.7
Interstitial lesions
  IFTA, %<0.001
   023.212.514.827.938.3
  141.932.847.542.626.2
  224.826.731.227.910.0
  310.125.06.61.66.7
  Interstitial inflammation, %<0.001
   031.715.627.936.148.3
   153.760.959.050.843.3
   214.623.513.113.18.4
Vascular lesions
 Arteriolar hyalinosis, %0.019
  015.99.411.714.828.3
  156.765.655.055.750.0
  227.425.033.329.521.7
 Large vessels arteriosclerosis, %)0.059
  027.728.116.731.035.0
  147.542.256.740.051.7
  226.826.726.629.013.3

Data are expressed as the median (25–75th percentiles) or percentage.

Table 1.
Open in new tab

Baseline clinical and pathological characteristics of the patients, overall and stratified by the quartiles of serum Hb concentration

 

Patient characteristicsSerum Hb concentration
AllQuartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Clinical characteristics at baseline
Serum Hb concentration, g/dL13.3 (12.0–14.5)11.1 (9.8–11.7)12.8 (12.3–13.1)13.8 (13.6–14.2)15.4 (15.1–16.1)<0.001
Number of patients24664616160
Age, years56 (45–63)57 (49–66)58 (49–64)57 (46–63)51 (41–58)<0.001
Male, %62.642.252.563.993.3<0.001
Diabetes duration, years10 (5–17)13 (8–18)10 (5–20)11 (6–17)7 (3–14)0.027
BMI, kg/m222.9 (20.9–25.6)21.8 (20.3–24.0)22.8 (20.3–25.4)22.8 (20.8–25.4)25.0 (22.1–27.7)<0.001
HbA1c, %7.9 (6.6–9.6)7.4 (6.2–8.3)8.8 (7.2–10.2)8.5 (6.9–10.2)7.7 (6.5–8.9)0.718
eGFR, mL/min/1.73 m276.2 (66.6–88.6)70.3 (65.8–79.4)76.3 (65.9–86.9)77.4 (67.6–86.6)81.9 (68.4–95.7)0.002
UACR, mg/g creatinine534 (100–1480)1510 (241–2700)693 (153–1545)360 (50–1121)252 (120–650)<0.001
Systolic BP, mmHg136 (124–150)133 (125–153)138 (126–156)140 (127–152)130 (117–140)0.046
Diastolic BP, mmHg78 (70–87)76 (68–84)77 (70–86)80 (70–89)78 (70–88)0.391
Total cholesterol, mg/dL210 (177–238)212 (185–252)209 (170–243)202 (175–232)212 (181–235)0.236
Low-density lipoprotein cholesterol, mg/dL131 (106–158)121 (105–172)129 (92–161)129 (109–150)136 (109–158)0.743
High-density lipoprotein cholesterol, mg/dL45 (37–56)50 (34–63)52 (40–70)42 (34–51)45 (37–49)0.239
Triglycerides, mg/dL141 (97–200)134 (85–189)107 (90–158)155 (103–192)168 (112–253)0.024
Medication usage, %
  RAS blocker50.756.450.056.738.70.110
  Glucose-lowering medication52.246.261.166.735.50.577
  Lipid-lowering medication16.820.717.226.10.00.166
Ever having smoked, %53.833.347.459.166.70.033
History of CVD, %16.17.917.121.417.10.232
Pathological characteristics at baseline
Renal Pathology Society Diabetic Nephropathy Classification
Glomerular lesions, %<0.001
  Class I36.720.031.046.043.2
  Class IIa20.34.0010.324.320.3
  Class IIb18.016.034.513.510.8
  Class III23.460.020.716.28.1
  Class IV1.60.03.50.02.7
Interstitial lesions
  IFTA, %<0.001
   023.212.514.827.938.3
  141.932.847.542.626.2
  224.826.731.227.910.0
  310.125.06.61.66.7
  Interstitial inflammation, %<0.001
   031.715.627.936.148.3
   153.760.959.050.843.3
   214.623.513.113.18.4
Vascular lesions
 Arteriolar hyalinosis, %0.019
  015.99.411.714.828.3
  156.765.655.055.750.0
  227.425.033.329.521.7
 Large vessels arteriosclerosis, %)0.059
  027.728.116.731.035.0
  147.542.256.740.051.7
  226.826.726.629.013.3
Patient characteristicsSerum Hb concentration
AllQuartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Clinical characteristics at baseline
Serum Hb concentration, g/dL13.3 (12.0–14.5)11.1 (9.8–11.7)12.8 (12.3–13.1)13.8 (13.6–14.2)15.4 (15.1–16.1)<0.001
Number of patients24664616160
Age, years56 (45–63)57 (49–66)58 (49–64)57 (46–63)51 (41–58)<0.001
Male, %62.642.252.563.993.3<0.001
Diabetes duration, years10 (5–17)13 (8–18)10 (5–20)11 (6–17)7 (3–14)0.027
BMI, kg/m222.9 (20.9–25.6)21.8 (20.3–24.0)22.8 (20.3–25.4)22.8 (20.8–25.4)25.0 (22.1–27.7)<0.001
HbA1c, %7.9 (6.6–9.6)7.4 (6.2–8.3)8.8 (7.2–10.2)8.5 (6.9–10.2)7.7 (6.5–8.9)0.718
eGFR, mL/min/1.73 m276.2 (66.6–88.6)70.3 (65.8–79.4)76.3 (65.9–86.9)77.4 (67.6–86.6)81.9 (68.4–95.7)0.002
UACR, mg/g creatinine534 (100–1480)1510 (241–2700)693 (153–1545)360 (50–1121)252 (120–650)<0.001
Systolic BP, mmHg136 (124–150)133 (125–153)138 (126–156)140 (127–152)130 (117–140)0.046
Diastolic BP, mmHg78 (70–87)76 (68–84)77 (70–86)80 (70–89)78 (70–88)0.391
Total cholesterol, mg/dL210 (177–238)212 (185–252)209 (170–243)202 (175–232)212 (181–235)0.236
Low-density lipoprotein cholesterol, mg/dL131 (106–158)121 (105–172)129 (92–161)129 (109–150)136 (109–158)0.743
High-density lipoprotein cholesterol, mg/dL45 (37–56)50 (34–63)52 (40–70)42 (34–51)45 (37–49)0.239
Triglycerides, mg/dL141 (97–200)134 (85–189)107 (90–158)155 (103–192)168 (112–253)0.024
Medication usage, %
  RAS blocker50.756.450.056.738.70.110
  Glucose-lowering medication52.246.261.166.735.50.577
  Lipid-lowering medication16.820.717.226.10.00.166
Ever having smoked, %53.833.347.459.166.70.033
History of CVD, %16.17.917.121.417.10.232
Pathological characteristics at baseline
Renal Pathology Society Diabetic Nephropathy Classification
Glomerular lesions, %<0.001
  Class I36.720.031.046.043.2
  Class IIa20.34.0010.324.320.3
  Class IIb18.016.034.513.510.8
  Class III23.460.020.716.28.1
  Class IV1.60.03.50.02.7
Interstitial lesions
  IFTA, %<0.001
   023.212.514.827.938.3
  141.932.847.542.626.2
  224.826.731.227.910.0
  310.125.06.61.66.7
  Interstitial inflammation, %<0.001
   031.715.627.936.148.3
   153.760.959.050.843.3
   214.623.513.113.18.4
Vascular lesions
 Arteriolar hyalinosis, %0.019
  015.99.411.714.828.3
  156.765.655.055.750.0
  227.425.033.329.521.7
 Large vessels arteriosclerosis, %)0.059
  027.728.116.731.035.0
  147.542.256.740.051.7
  226.826.726.629.013.3

Data are expressed as the median (25–75th percentiles) or percentage.

Associations of quartiles of serum Hb concentration with clinical and pathological characteristics

Table 2 shows the results of matrix correlation of serum Hb concentration quartiles and clinical and pathological characteristics. Serum Hb concentration quartiles were significantly correlated with eGFR, UACR and with all scores of renal lesions including interstitial lesions, especially with scores of interstitial fibrosis and tubular atrophy (IFTA) (ρ = −0.52, P < 0.001).

Table 2.
Open in new tab

Correlations between serum Hb concentration and baseline clinical and pathological characteristics

Patient characteristicsClinical characteristics
Pathological characteristics
RPS Diabetic Nephropathy ClassificationInterstitial lesions
Vascular lesions
eGFRUACRSystolic BPHbA1cGlomerular classificationIFTAInterstitial inflammationArteriolar hyalinosisLarge vessels arteriosclerosis
Hb0.54***−0.30***−0.24***0.31***−0.47***−0.52***−0.26***−0.26***−0.21**
eGFR–−0.40***−0.29***0.31***−0.49***−0.60***−0.27***−0.20***−0.39**
UACR––0.39***−0.17***0.39***0.43***0.13***0.18***0.20**
Systolic BP−0.09*0.33***0.30***0.13***0.18**0.16*
HbA1c––––−0.14*−0.20***−0.02*0.03−0.05*
Glomerular classification––––0.50***0.20***0.35***0.31***
IFTA–––––0.50***0.39***0.39***
Interstitial inflammation––––––0.43***0.11***
Arteriolar hyalinosis–––––––0.13***
Patient characteristicsClinical characteristics
Pathological characteristics
RPS Diabetic Nephropathy ClassificationInterstitial lesions
Vascular lesions
eGFRUACRSystolic BPHbA1cGlomerular classificationIFTAInterstitial inflammationArteriolar hyalinosisLarge vessels arteriosclerosis
Hb0.54***−0.30***−0.24***0.31***−0.47***−0.52***−0.26***−0.26***−0.21**
eGFR–−0.40***−0.29***0.31***−0.49***−0.60***−0.27***−0.20***−0.39**
UACR––0.39***−0.17***0.39***0.43***0.13***0.18***0.20**
Systolic BP−0.09*0.33***0.30***0.13***0.18**0.16*
HbA1c––––−0.14*−0.20***−0.02*0.03−0.05*
Glomerular classification––––0.50***0.20***0.35***0.31***
IFTA–––––0.50***0.39***0.39***
Interstitial inflammation––––––0.43***0.11***
Arteriolar hyalinosis–––––––0.13***

Spearman’s correlation coefficients between serum Hb concentration and clinical and pathological characteristics were obtained using Spearman’s correlation rank test.

*

P < 0.05,

**

P < 0.01,

***

P < 0.001.

RPS, Renal Pathology Society.

Table 2.
Open in new tab

Correlations between serum Hb concentration and baseline clinical and pathological characteristics

Patient characteristicsClinical characteristics
Pathological characteristics
RPS Diabetic Nephropathy ClassificationInterstitial lesions
Vascular lesions
eGFRUACRSystolic BPHbA1cGlomerular classificationIFTAInterstitial inflammationArteriolar hyalinosisLarge vessels arteriosclerosis
Hb0.54***−0.30***−0.24***0.31***−0.47***−0.52***−0.26***−0.26***−0.21**
eGFR–−0.40***−0.29***0.31***−0.49***−0.60***−0.27***−0.20***−0.39**
UACR––0.39***−0.17***0.39***0.43***0.13***0.18***0.20**
Systolic BP−0.09*0.33***0.30***0.13***0.18**0.16*
HbA1c––––−0.14*−0.20***−0.02*0.03−0.05*
Glomerular classification––––0.50***0.20***0.35***0.31***
IFTA–––––0.50***0.39***0.39***
Interstitial inflammation––––––0.43***0.11***
Arteriolar hyalinosis–––––––0.13***
Patient characteristicsClinical characteristics
Pathological characteristics
RPS Diabetic Nephropathy ClassificationInterstitial lesions
Vascular lesions
eGFRUACRSystolic BPHbA1cGlomerular classificationIFTAInterstitial inflammationArteriolar hyalinosisLarge vessels arteriosclerosis
Hb0.54***−0.30***−0.24***0.31***−0.47***−0.52***−0.26***−0.26***−0.21**
eGFR–−0.40***−0.29***0.31***−0.49***−0.60***−0.27***−0.20***−0.39**
UACR––0.39***−0.17***0.39***0.43***0.13***0.18***0.20**
Systolic BP−0.09*0.33***0.30***0.13***0.18**0.16*
HbA1c––––−0.14*−0.20***−0.02*0.03−0.05*
Glomerular classification––––0.50***0.20***0.35***0.31***
IFTA–––––0.50***0.39***0.39***
Interstitial inflammation––––––0.43***0.11***
Arteriolar hyalinosis–––––––0.13***

Spearman’s correlation coefficients between serum Hb concentration and clinical and pathological characteristics were obtained using Spearman’s correlation rank test.

*

P < 0.05,

**

P < 0.01,

***

P < 0.001.

RPS, Renal Pathology Society.

Quartiles of serum Hb concentration and rate of DKD progression

During the median follow-up duration of 4.1 years after the date of kidney biopsy, 95 (38.6%) developed DKD progression. The cumulative incidence of DKD progression according to the quartiles of serum Hb concentration is shown in Figure 1. The cumulative incidence of DKD progression increased significantly with decreasing serum Hb concentration (P for trend <0.01).

Cumulative incidence of DKD progression according to quartiles of serum Hb concentration. DKD progression was defined as a composite of new onset ESKD (dialysis, kidney transplantation or death from renal cause), a doubling of serum creatinine or a decrease of eGFR by ≥50%.
FIGURE 1

Cumulative incidence of DKD progression according to quartiles of serum Hb concentration. DKD progression was defined as a composite of new onset ESKD (dialysis, kidney transplantation or death from renal cause), a doubling of serum creatinine or a decrease of eGFR by ≥50%.

Open in new tabDownload slide

In the subgroup analysis of male (n = 154) and female (n = 92) patients, the cumulative incidence of DKD progression was similar to that in the overall cohort (Supplementary data, Figure S2A and B). In the subgroup analysis of patients with normo- and microalbuminuria (n = 93; n = 39 in normoalbuminuria, n = 54 in microalbuminuria), the cumulative incidence of DKD progression was also similar to that in the overall cohort, although there was a marginal significant trend across serum Hb groups (Supplementary data, Figure S3).

Table 3 presents HRs and 95% CI for the association between the quartiles of serum Hb concentration and incident DKD progression. Compared with the fourth quartile, the unadjusted HRs (95% CI) of DKD progression were 1.48 (0.78–2.80) for the third quartile, 2.34 (1.29–4.24) for the second quartile and 3.81 (2.14–6.80) for the first quartile, respectively. After adjusting for known risk factors of DKD progression at baseline, the HRs of DKD progression remain unchanged [the HRs in the third, second and first quartile were 1.46 (0.71–3.64), 2.33 (1.07–5.75) and 2.74 (1.26–5.97), respectively].

Table 3.
Open in new tab

Crude and adjusted risk of DKD progression, according to the quartiles of serum Hb concentration

ModelsSerum Hb concentration
HR (95% CI)Quartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Model13.81 (2.14–6.80)2.34 (1.29–4.24)1.48 (0.78–2.80)Ref.<0.001
Model 24.37 (2.28–8.37)2.64 (1.38–5.04)1.61 (0.81–3.18)Ref.<0.001
Model 32.74 (1.26–5.97)2.33 (1.07–5.75)1.46 (0.71–3.64)Ref.<0.001
ModelsSerum Hb concentration
HR (95% CI)Quartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Model13.81 (2.14–6.80)2.34 (1.29–4.24)1.48 (0.78–2.80)Ref.<0.001
Model 24.37 (2.28–8.37)2.64 (1.38–5.04)1.61 (0.81–3.18)Ref.<0.001
Model 32.74 (1.26–5.97)2.33 (1.07–5.75)1.46 (0.71–3.64)Ref.<0.001

HR (95% CI) and P-values were determined for demographic and laboratory characteristics by univariable and multivariable Cox proportional models.

Model 1: univariable.

Model 2: adjusted for age and gender.

Model 3: adjusted for known risk factors of DKD progression (age, gender, BMI, diabetic duration, systolic BP, Hb A1c, eGFR, UACR and RAS blocker use).

Table 3.
Open in new tab

Crude and adjusted risk of DKD progression, according to the quartiles of serum Hb concentration

ModelsSerum Hb concentration
HR (95% CI)Quartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Model13.81 (2.14–6.80)2.34 (1.29–4.24)1.48 (0.78–2.80)Ref.<0.001
Model 24.37 (2.28–8.37)2.64 (1.38–5.04)1.61 (0.81–3.18)Ref.<0.001
Model 32.74 (1.26–5.97)2.33 (1.07–5.75)1.46 (0.71–3.64)Ref.<0.001
ModelsSerum Hb concentration
HR (95% CI)Quartile 1Quartile 2Quartile 3Quartile 4P for trend
≤12.0 g/dL12.1–13.3 g/dL13.4–14.5 g/dL≥14.6 g/dL
Model13.81 (2.14–6.80)2.34 (1.29–4.24)1.48 (0.78–2.80)Ref.<0.001
Model 24.37 (2.28–8.37)2.64 (1.38–5.04)1.61 (0.81–3.18)Ref.<0.001
Model 32.74 (1.26–5.97)2.33 (1.07–5.75)1.46 (0.71–3.64)Ref.<0.001

HR (95% CI) and P-values were determined for demographic and laboratory characteristics by univariable and multivariable Cox proportional models.

Model 1: univariable.

Model 2: adjusted for age and gender.

Model 3: adjusted for known risk factors of DKD progression (age, gender, BMI, diabetic duration, systolic BP, Hb A1c, eGFR, UACR and RAS blocker use).

Incremental prognostic value of serum Hb concentration over known risk factors of DKD progression

Figure 2 shows the statistics showing the incremental prognostic value of serum Hb concentration over standard established clinical risk factors of DKD progression. The global Chi-squared likelihood ratio increased from 55.1 to 60.8 (P < 0.001) with addition of serum Hb concentration to the clinical model alone, which showed an equivalent value of adding IFTA scores to the clinical model alone (global Chi-squared likelihood ratio increased from 55.1 to 62.4; P < 0.001). Likewise, addition of serum Hb concentration to the clinical model alone improved the AIC and BIC values, which is also an improvement equivalent to the clinical model alone.

Incremental prognostic value of DKD. Incremental value of serum Hb concentration and IFTA over standard clinical assessment of established risk factors of DKD progression were shown with global Chi-squared likelihood ratio, AIC and BIC. Model 1: standard clinical assessment of established risk factors of DKD progression (age, gender, BMI, known duration of diabetes, systolic BP, Hb A1c, eGFR, UACR and RAS blocker use). Model 2: Model 1 + serum Hb concentration. Model 3: Model 1 + IFTA.
FIGURE 2

Incremental prognostic value of DKD. Incremental value of serum Hb concentration and IFTA over standard clinical assessment of established risk factors of DKD progression were shown with global Chi-squared likelihood ratio, AIC and BIC. Model 1: standard clinical assessment of established risk factors of DKD progression (age, gender, BMI, known duration of diabetes, systolic BP, Hb A1c, eGFR, UACR and RAS blocker use). Model 2: Model 1 + serum Hb concentration. Model 3: Model 1 + IFTA.

Open in new tabDownload slide

DISCUSSION

In this study, we have demonstrated that serum Hb concentration, which is a widely available, routinely measured marker, is associated with renal pathological changes (especially with interstitial fibrosis) and DKD progression in patients with Type 2 diabetes in the early stages of CKD. The association between serum Hb concentration and DKD progression remained significant when adjusting for the known risk factors for DKD progression, including eGFR and UACR. Addition of the serum Hb concentration to the known risk factors of DKD progression improved the prognostic value of DKD progression, which suggests that serum Hb concentration can be useful for predicting DKD progression.

Biomarkers to predict DKD progression currently rely exclusively on eGFR and UACR [22]. However, using these markers for prognosticating DKD progression is challenging in early stages of CKD, where eGFR has not yet declined and UACR has not severely increased. In addition, previous morphological and autopsy studies have demonstrated that some patients with diabetes and preserved renal function have already undergone glomerular and tubulointerstitial damage [23–25], which highlights the need for biomarkers that relate to both incipient pathological changes and renal prognosis in patients with early stages of CKD. Recently, a number of novel biomarkers have shown to be of prognostic value in patients with diabetes in the early stages of CKD [6]; however, they are too laborious for routine use. We therefore performed a thorough search of widely available, routinely measured, clinical applicable markers that were strongly associated with pathological changes and found that serum Hb concentration negatively correlated with all scores of renal lesions, especially with glomerular damage and scores of interstitial lesions, all of which are major predictors of DKD progression [16, 26–28]. Among pathological lesions, it is well known that interstitial lesions rather than glomerular lesions correlate better with renal function loss both in DKD and in other glomerular diseases [29, 30]. Recent studies proposed potential biomarkers that predict DKD progression including serum and urinary biomarkers of tubulointerstitial injury such as kidney injury molecule-1, liver-type fatty acid-binding protein and N-acetyle-β-d-glucosaminidase [31]. Again, these biomarkers are favorable markers, but are not ready for clinical use.

We found that serum Hb concentration is a candidate marker that is associated with tubulointerstitial injury. Though many factors may explain the presence of anemia in patients with CKD, accumulating evidence suggests that renal fibrosis, which interferes with erythropoietin production, is the main reason for anemia [12, 13]. However, studies investigating the association between tubulointerstitial injury and anemia in humans are scarce [32], since kidney biopsy is not always applicable for patients with DKD, especially with early stages of CKD. Our data showed a concrete relationship between tubulointerstitial fibrosis and serum Hb concentration.

It is known that there is a gender difference in serum Hb concentration, and females have been reported to have mean serum Hb concentration lower than males in both healthy adults and adults with CKD [33, 34]. Since the mean circulating erythropoietin level does not differ between males and females, or between pre- and post-menopausal females, the difference in serum Hb concentration has been reported to be mainly due to estrogen hormone, which inhibits the production of red blood cells in the bone marrow [21]. Therefore, we conducted the subgroup analysis separately for males and females to see whether there is a difference in results between genders. We found that despite the fact that females have a median serum Hb concentration lower than that of males (12.3 g/dL for females versus 13.8 g/dL for males), both the quartiles of serum Hb concentration for males and females stratified the cumulative incidence of DKD progression (Supplementary data, Figure S2A and B). However, it appeared that serum Hb concentration is much more discriminatory between the quartiles in males than females (P for trend: <0.001 for males versus 0.015 for females). This finding may be attributed to the fact that the age of the female population ranged from 30 to 82 years, which indicates that the study population includes both pre- and post-menopausal females, suggesting that miscellaneous estrogen levels may have somehow distorted the discriminatory power of serum Hb concentration on the incidence of DKD progression. In addition, iron deplete caused by menopause may also contribute to the change of serum Hb concentration. Unfortunately, data on estrogen and iron levels were not available in this study.

The strengths of our study are the use of study population from a real-world cohort of DKD across Japan with a long follow-up time that allows us to observe the relationship between serum Hb concentration and DKD progression, well evaluated pathological lesions and the precise ascertainment of renal outcome, all of which enabled robust analysis of the association between serum Hb concentration and renal pathological findings and DKD progression.

We acknowledge that our study has limitations. First, because of the nature of retrospective studies, we cannot conclude the causation between serum Hb concentration and DKD progression. For example, we cannot assure that the correction of serum Hb concentration to normal level reduces the rates of DKD progression, even though we found a strong association between serum Hb concentration and the risk of DKD progression. Second, we only measured serum Hb concentration at a single time point of kidney biopsy. Data on serum Hb concentration and interventions during the follow-up period were not available. Although considering them may have some advantages, we believe that the approach we adopted minimizes the risk of reverse causation in the interpretation of the results. Third, even though the study population is from a real-world cohort of biopsy-proven DKD, there may be a selection bias. There is a possibility that the population was biopsied because they were suspected to have any kind of kidney disease rather than DKD. In contrast, however, we believe that the use of biopsy-proven rather than inaccurate clinical diagnosis of DKD provides a clear picture of the clinical course of DKD. Fourth, data on erythropoietin level, ferritin level, iron level and vitamin deficiency were not available. Also, data on comorbid diseases that affect serum Hb level, such as gastrointestinal bleeding and malignancy, were not available. Fifth, the study population comprised Asian, mostly Japanese patients, and our findings may not apply to populations from different geographic origins. Sixth, we noticed a striking increase in cumulative incidence of DKD progression after Year 1 for patients in the lowest quartile. This originated from the lack of eGFR data for several patients before Year 1; these patients only had the second eGFR measurement at Year 1 since the first measurement of eGFR at renal biopsy (the reason for this is unknown). There is a possibility that they might have developed DKD progression before Year 1 if they had had eGFR measurements before Year 1. Conversely, this highlights the need for an intensive eGFR monitoring in patients with severe anemia even if they are in the early stages of DKD.

In conclusion, we have demonstrated that serum Hb concentration, which is a widely available, routinely measured, clinical applicable marker, is associated with renal pathological changes (especially with interstitial fibrosis) and DKD progression in patients with Type 2 diabetes in the early stages of CKD. Addition of serum Hb concentration to the known risk factors of DKD progression improved the prognostic value of DKD progression. These findings suggest that serum Hb concentration, as a reflection of interstitial damage, can be useful for predicting DKD progression.

SUPPLEMENTARY DATA

Supplementary data are available at ndt online.

ACKNOWLEDGEMENTS

The authors thank various people for their contribution to this project; Dr Yoko Yoshida, Ms Yuki Inoue, The Institute for Adult Disease, Asahi Life Foundation, Ms Yurina Takaishi, Ms Keiko Sahara and Ms Tokiko Hoshikawa, Toranomon Hospital, for their help in collecting the data.

FUNDING

This study was supported in part by a Ministry of Health, Labour and Welfare Grant-in-Aid for Diabetic Nephropathy and Nephrosclerosis Research (JP17ek0310003) and a grant for medical research from the Okinaka Memorial Institute for Medical Research, Tokyo, Japan. The funding source had no role in study design or execution, data analysis, manuscript writing or manuscript submission.

AUTHORS’ CONTRIBUTIONS

M.Y., K.F., J.H. and T.W. designed the study protocol, researched data, contributed to the discussion, wrote the manuscript and reviewed and edited the manuscript. T.T., M.S., Y.Yuzawa, M.O., S.K., A.H., Y.I., N.Sawa, Y.O., S.Matsuo, D.I., H.Makino, T.S., N.Sakai, Y.Yamamura, H.K., Y.Suzuki, H.S., N.U., Y.Ubara, S.N., H.Y., T.N., K.S., K.K., Y.Shibagaki, H.Mizuno, S.Matsuoka and Y.Ueda contributed to the discussion and reviewed the manuscript. T.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

CONFLICT OF INTEREST STATEMENT

No potential conflicts of interest relevant to this article were reported.

REFERENCES

1

United States Renal Data System
.
2016 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States
.
Bethesda, MD
:
National Institutes ofHealth, National Institute of Diabetes and Digestiveand Kidney Diseases
,
2016

Google Scholar

OpenURL Placeholder Text

2

ERA-EDTA Registry: ERA-EDTA Registry Annual Report 2015
. Academic Medical Center, Department of Medical Informatics. Amsterdam, The Netherlands,
2017

3

Japanese Society for Dialysis Therapy Renal Data Registry
. Annual Report 2014, JSDT Renal Data Registry (JRDR). Re Replace Ther 3:
18
.

4

Sarnak
MJ
,
Levey
AS
,
Schoolwerth
AC
 et al. ;
American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention
.
Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention
.
Circulation
2003
;
108
:
2154
–
2169

Google Scholar

Crossref
Search ADS
PubMed

 
5

Levey
AS
,
de Jong
PE
,
Coresh
J
 et al.
The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report
.
Kidney Int
2011
;
80
:
17
–
28
[Erratum in: Kidney Int 2011; 80: 1000]

Google Scholar

Crossref
Search ADS
PubMed

 
6

Nowak
N
,
Skupien
J
,
Smiles
AM
 et al.
Markers of early progressive renal decline in type 2 diabetes suggest different implications for etiological studies and prognostic tests development
.
Kidney Int
2018
;
93
:
1198
–
1206

Google Scholar

Crossref
Search ADS
PubMed

 
7

Hsu
CY
,
McCulloch
CE
,
Curhan
GC.
Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey
.
J Am Soc Nephrol
2002
;
13
:
504
–
510

Google Scholar

Crossref
Search ADS
PubMed

 
8

Astor
BC
,
Coresh
J
,
Heiss
G
 et al.
Kidney function and anemia as risk factors for coronary heart disease and mortality: the Atherosclerosis Risk in Communities (ARIC) Study
.
Am Heart J
2006
;
151
:
492
–
500

Google Scholar

Crossref
Search ADS
PubMed

 
9

Levin
A
,
Djurdjev
O
,
Duncan
J
 et al.
Haemoglobin at time of referral prior to dialysis predicts survival: an association of haemoglobin with long-term outcomes
.
Nephrol Dial Transplant
2006
;
21
:
370
–
377

Google Scholar

Crossref
Search ADS
PubMed

 
10

Mohanram
A
,
Zhang
Z
,
Shahinfar
S
 et al.
Anemia and end-stage renal disease in patients with type 2 diabetes and nephropathy
.
Kidney Int
2004
;
66
:
1131
–
1138

Google Scholar

Crossref
Search ADS
PubMed

 
11

Chang
JM
,
Chen
SC
,
Huang
JC
 et al.
Anemia and left ventricular hypertrophy with renal function decline and cardiovascular events in chronic kidney disease
.
Am J Med Sci
2014
;
347
:
183
–
189

Google Scholar

Crossref
Search ADS
PubMed

 
12

El-Achkar
TM
,
Ohmit
SE
,
McCullough
PA
 et al.
Kidney early evaluation program. Higher prevalence of anemia with diabetes mellitus in moderate kidney insufficiency: the kidney early evaluation program
.
Kidney Int
2005
;
67
:
1483
–
1488

Google Scholar

Crossref
Search ADS
PubMed

 
13

Zeisberg
M
,
Kalluri
R.
Physiology of the renal interstitium
.
Clin J Am Soc Nephrol
2015
;
10
:
1831
–
1840

Google Scholar

Crossref
Search ADS
PubMed

 
14

de Klerk
G
,
Wilmink
JM
,
Rosengarten
PC
 et al.
Serum erythropoietin (ESF) titers in anemia of chronic renal failure
.
J Lab Clin Med
1982
;
100
:
720
–
734

Google Scholar

PubMed
OpenURL Placeholder Text

 
15

Yamanouchi
M
,
Furuichi
K
,
Hoshino
J
 et al. ;
Research Group of Diabetic Nephropathy, the Ministry of Health, Labour and Welfare, and the Japan Agency for Medical Research and Development
.
Nonproteinuric versus proteinuric phenotypes in diabetic kidney disease: a propensity score-matched analysis of a nationwide
.
Diabetes Care
2019
;
42
:
891
–
902

Google Scholar

Crossref
Search ADS
PubMed

 
16

Furuichi
K
,
Yuzawa
Y
,
Shimizu
M
 et al.
Nationwide multicentre kidney biopsy study of Japanese patients with type 2 diabetes
.
Nephrol Dial Transplant
2018
;
33
:
138
–
148

Google Scholar

Crossref
Search ADS
PubMed

 
17

Imai
E
,
Horio
M
,
Nitta
K
 et al.
Estimation of glomerular filtration rate by the MDRD study equation modified for Japanese patients with chronic kidney disease
.
Clin Exp Nephrol
2007
;
11
:
41
–
50

Google Scholar

Crossref
Search ADS
PubMed

 
18

Tervaert
TW
,
Mooyaart
AL
,
Amann
K
 et al. ;
Renal Pathology Society
.
Pathologic classification of diabetic nephropathy
.
J Am Soc Nephrol
2010
;
21
:
556
–
563

Google Scholar

Crossref
Search ADS
PubMed

 
19

Cuzick
J.
A Wilcoxon-type test for trend
.
Stat Med
1985
;
4
:
87
–
90

Google Scholar

Crossref
Search ADS
PubMed

 
20

Macisaac
RJ
,
Ekinci
EI
,
Jerums
G.
Markers of and risk factors for the development and progression of diabetic kidney disease
.
Am J Kidney Dis
2014
;
63
:
S39
–S
62

Google Scholar

Crossref
Search ADS
PubMed

 
21

Murphy
WG.
The sex difference in haemoglobin levels in adults - mechanisms, causes, and consequences
.
Blood Rev
2014
;
28
:
41
–
47

Google Scholar

Crossref
Search ADS
PubMed

 
22

Berhane
AM
,
Weil
EJ
,
Knowler
WC
 et al.
Albuminuria and estimated glomerular filtration rate as predictors of diabetic end-stage renal disease and death
.
Clin J Am Soc Nephrol
2011
;
6
:
2444
–
2451

Google Scholar

Crossref
Search ADS
PubMed

 
23

Mauer
SM
,
Steffes
MW
,
Ellis
EN
 et al.
Structural-functional relationships in diabetic nephropathy
.
J Clin Invest
1984
;
74
:
1143
–
1155

Google Scholar

Crossref
Search ADS
PubMed

 
24

Caramori
ML
,
Parks
A
,
Mauer
M.
Renal lesions predict progression of diabetic nephropathy in type 1 diabetes
.
J Am Soc Nephrol
2013
;
24
:
1175
–
1181

Google Scholar

Crossref
Search ADS
PubMed

 
25

Klessens
CQ
,
Woutman
TD
,
Veraar
KA
 et al.
An autopsy study suggests that diabetic nephropathy is underdiagnosed
.
Kidney Int
2016
;
90
:
149
–
156

Google Scholar

Crossref
Search ADS
PubMed

 
26

Furuichi
K
,
Shimizu
M
,
Yuzawa
Y
 et al. ;
Research Group of Diabetic Nephropathy, Ministry of Health, Labour and Welfare of Japan, and Japan Agency for Medical Research and Development Clinicopathological analysis of biopsy-proven diabetic nephropathy based on the Japanese classification of diabetic nephropathy
.
Clin Exp Nephrol
2018
;
22
:
570
–
582

Google Scholar

Crossref
Search ADS
PubMed

 
27

Mise
K
,
Hoshino
J
,
Ubara
Y
 et al.
Renal prognosis a long time after renal biopsy on patients with diabetic nephropathy
.
Nephrol Dial Transplant
2014
;
29
:
109
–
118

Google Scholar

Crossref
Search ADS
PubMed

 
28

Mise
K
,
Hoshino
J
,
Ueno
T
 et al.
Prognostic value of tubulointerstitial lesions, urinary N-acetyl-β-d-glucosaminidase, and urinary β2-microglobulin in patients with type 2 diabetes and biopsy-proven diabetic nephropathy
.
Clin J Am Soc Nephrol
2016
;
11
:
593
–
601

Google Scholar

Crossref
Search ADS
PubMed

 
29

White
KE
,
Bilous
RW.
Type 2 diabetic patients with nephropathy show structural-functional relationships that are similar to type 1 disease
.
J Am Soc Nephrol
2000
;
11
:
1667
–
1673

Google Scholar

Crossref
Search ADS
PubMed

 
30

Nath
KA.
Tubulointerstitial changes as a major determinant in the progression of renal damage
.
Am J Kidney Dis
1992
;
20
:
1
–
17

Google Scholar

Crossref
Search ADS
PubMed

 
31

Tramonti
G
,
Kanwar
YS.
Review and discussion of tubular biomarkers in the diagnosis and management of diabetic nephropathy
.
Endocrine
2013
;
43
:
494
–
503

Google Scholar

Crossref
Search ADS
PubMed

 
32

Mise
K
,
Hoshino
J
,
Ueno
T
 et al.
Impact of tubulointerstitial lesions on anaemia in patients with biopsy-proven diabetic nephropathy
.
Diabet Med
2015
;
32
:
546
–
555

Google Scholar

Crossref
Search ADS
PubMed

 
33

Van den Bossche
J
,
Devreese
K
,
Malfait
R
 et al.
Reference intervals for a complete blood count determined on different automated haematology analysers: Abx Pentra 120 Retic, Coulter Gen-S, Sysmex SE 9500, Abbott Cell Dyn 4000 and Bayer Advia 120
.
Clin Chem Lab Med
2002
;
40
:
69
–
73

Google Scholar

PubMed
OpenURL Placeholder Text

 
34

Astor
BC
,
Muntner
P
,
Levin
A
 et al.
Association of kidney function with anemia: the Third National Health and Nutrition Examination Survey (1988–1994)
.
Arch Intern Med
2002
;
162
:
1401
–
1408

Google Scholar

Crossref
Search ADS
PubMed

 
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