Acta Diabetologica

, Volume 55, Issue 11, pp 1143–1150 | Cite as

Urinary tubular biomarkers as predictors of kidney function decline, cardiovascular events and mortality in microalbuminuric type 2 diabetic patients

  • Viktor Rotbain Curovic
  • Tine W. Hansen
  • Mie K. Eickhoff
  • Bernt Johan von Scholten
  • Henrik Reinhard
  • Peter Karl Jacobsen
  • Frederik Persson
  • Hans-Henrik Parving
  • Peter Rossing
Original Article
Part of the following topical collections:
  1. Diabetic Nephropathy



Urinary levels of kidney injury molecule 1 (u-KIM-1) and neutrophil gelatinase-associated lipocalin (u-NGAL) reflect proximal tubular pathophysiology and have been proposed as risk markers for development of complications in patients with type 2 diabetes (T2D). We clarify the predictive value of u-KIM-1 and u-NGAL for decline in eGFR, cardiovascular events (CVE) and all-cause mortality in patients with T2D and persistent microalbuminuria without clinical cardiovascular disease.


This is a prospective study that included 200 patients. u-KIM-1 and u-NGAL were measured at baseline and were available in 192 patients. Endpoints comprised: decline in eGFR > 30%, a composite of fatal and nonfatal CVE consisting of: cardiovascular mortality, myocardial infarction, stroke, ischemic heart disease and heart failure based on national hospital discharge registries, and all-cause mortality. Adjusted Cox models included traditional risk factors, including eGFR. Hazard ratios (HR) are provided per 1 standard deviation (SD) increment of log2-transformed values. Relative integrated discrimination improvement (rIDI) was calculated.


During the 6.1 years’ follow-up, higher u-KIM-1 was a predictor of eGFR decline (n = 29), CVE (n = 34) and all-cause mortality (n = 29) in adjusted models: HR (95% CI) 1.68 (1.04–2.71), p = 0.034; 2.26 (1.24–4.15), p = 0.008; and 1.52 (1.00–2.31), p = 0.049. u-KIM-1 contributed significantly to risk prediction for all-cause mortality evaluated by rIDI (63.1%, p = 0.001). u-NGAL was not a predictor of any of the outcomes after adjustment.


In patients with T2D and persistent microalbuminuria, u-KIM-1, but not u-NGAL, was an independent risk factor for decline in eGFR, CVE and all-cause mortality, and contributed significant discrimination for all-cause mortality, beyond traditional risk factors.


Diabetic kidney disease Diabetic nephropathy Cardiovascular Albuminuria Biomarkers Type 2 diabetes 


Despite innovation and progress in the treatment of persons with type 2 diabetes (T2D), this disease is still a considerable medical problem and a substantial risk factor for development of future disease. Mainly, cardiovascular disease and diabetic nephropathy are sizable causes of mortality and reduced quality of life, and early identification and prevention of these complications are of key importance to guarantee survival and improvement of patient satisfaction [1, 2, 3, 4]. In regards to this, kidney injury marker 1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL) have been proposed as novel biomarkers and can possibly be used in both risk assessment and prediction of acute as well as chronic kidney disease (CKD) to help achieve this goal [5, 6, 7, 8, 9, 10].

KIM-1 is a surface receptor protein in epithelial and lymphoid/myeloid cells, known also as HAVCR1 or TIM-1 [11], while NGAL is a circulating protein expressed in abundance in kidney tissue and in other tissues as well [12].

The level of NGAL in urine, blood or kidney has been proposed to specifically express active, ongoing damage to renal tubules, as described by Mori et Nakao in the “forest fire” theory [13] and has shown to be a specific marker of ongoing damage to nephrons in acute kidney injury [14].

Previous studies have demonstrated a positive correlation between KIM-1 levels and albuminuria, and KIM-1 has been determined as a possible, predictive factor of acute kidney injury and CKD [6, 7, 8]. For example, higher plasma KIM-1 has been independently correlated with risk of renal outcomes in patients with both early and late diabetic kidney disease (DKD) [15], evaluating the Action to Control Cardiovascular Risk in Diabetes [16] and The Veterans Affairs Nephropathy in Diabetes [17] cohorts.

Both higher urinary and serum levels of NGAL have been associated with risk of all-cause and cardiovascular mortality in a Swedish study including 597 non-diabetic men [18]. Elevated levels of u-NGAL and s-NGAL at baseline correlated significantly with higher incidence of cardiovascular and all-cause mortality; however s-NGAL lost significance after adjustment for risk factors. Moreover, a higher urinary level of NGAL has been associated with risk of CKD stage 3 [19] and risk of all-cause mortality in non-diabetic subjects [20]. Due to the inconclusiveness in existing literature, no satisfactory evidence has been provided on these biomarkers’ role in the discovery of latent or future disease, or their general usefulness as potential risk markers.

We have previously demonstrated an association between higher level of urinary KIM-1 and NGAL and a larger a decline in eGFR in both type 1 and type 2 diabetes; however, due to a modest follow-up time, the association was lost after adjustment for known progression promoters [21, 22].

The aim of the present study was to further clarify the predictive value of urinary KIM-1 and NGAL for decline in renal function, incident cardiovascular disease and all-cause mortality in persons with T2D and persistent albuminuria, but with preserved kidney function and no known cardiac disease.

Materials and methods


A total of 200 patients, all of Caucasian origin, were recruited from the outpatient clinic at Steno Diabetes Center, Copenhagen, in a prospective observational follow-up study. The details of the study have previously been described [21]. In brief patients were eligible if they fulfilled the WHO criteria of T2D, had a urinary albumin excretion rate (UAER) > 30 mg/24 h in two out of three consecutive measurements, kidney function defined as estimated glomerular filtration rate (eGFR) > 50 ml/min/1.73 m2 and without known cardiac disease.

The study complied with the Declaration of Helsinki, the research protocol was approved by the local ethics committee and all participants gave written informed consent.

Biochemical and clinical analyses at baseline

u-NGAL, u-KIM-1 (ELISA, Roche, Switzerland) and urinary creatinine (Vitros, Raritan, NJ, USA) were measured at baseline in one urine sample collected over 24 h. Subsequently, u-NGAL and u-KIM-1 were standardized to urinary creatinine. Quantification of u-NGAL and u-KIM-1 was available for 192 participants.

HbA1c, plasma creatinine, and serum cholesterol were determined from venous blood samples using standard clinical laboratory assays. eGFR was calculated with the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [23].

Brachial blood pressure was measured after 10 min of rest, with the patient sitting in an upright position (A&D Medical UA-767 PC) using an appropriate cuff-size.


The endpoints have previously been described in detail [24, 25]. In brief, annual measurement of p-creatinine was performed after baseline in 170 of the 192 participants, and the renal endpoint was defined as an eGFR decline of > 30% from baseline, based on the latest available measurement. We defined a combined cardiovascular event (CVE) endpoint consisting of: cardiovascular mortality, non-fatal myocardial infarction, stroke, ischemic heart disease, and heart failure based on hospital discharge registries. Only the first event was included in patients experiencing multiple events. Lastly, information on all-cause mortality was collected.

All participants were identified through the Danish National Death and Health Registries on January 1, 2014, and none were lost to follow-up.

Statistical analyses

u-KIM-1, u-NGAL and UAER were non-normally distributed and are presented as median with interquartile range (IQR), and log-transformed in all analyses to achieve normal distribution. The normally distributed variables are given as mean ± standard deviation (SD) and categorical variables as total numbers with corresponding percentages. Baseline clinical characteristics were compared across the median of u-KIM-1 and u-NGAL using the t test and χ2 test for continuous and categorical variables, respectively.

Cox proportional hazard models were used to calculate hazard ratios (HR) for u-KIM-1 and u-NGAL for all three endpoints and are presented per 1 SD increment with 95% confidence interval (CI). Adjustment included traditional risk factors: sex, age, LDL cholesterol, smoking, HbA1c, p-creatinine, systolic blood pressure and UAER. Furthermore, we mutually included KIM-1 and NGAL in the respective adjusted models. The assumptions of linearity and proportional hazard were tested.

We further employed Kaplan–Meier functions and the log-rank test to compare risks based on median levels of u-KIM-1 and u-NGAL, respectively.

Finally, we evaluated the added prognostic impact to the traditional risk prediction methods for the adjusted model with significant findings. We calculated (1) receiver operating characteristic (ROC) curves and utilized C-statistics for area under curve (AUC) analysis; and (2) the relative integrated discrimination improvement (rIDI) statistic, which has been proposed as a robust method to assess added value of new biomarkers for risk prediction [26].

Significance was a two-tailed α level of 0.05 or less. Statistical analysis was performed using SAS software (v.9.4, SAS Institute, Cary, NC, USA).


Baseline characteristics

Of the 200 participants, 192 had baseline measurement of u-KIM-1 and u-NGAL. The median (IQR) of u-KIM-1 was 1.64 (0.53–3.4) pg/ml/g creatinine and of u-NGAL 101.4 (54.8–228.2) pg/ml/g creatinine. Baseline characteristics stratified according to the median of u-KIM-1 and u-NGAL, respectively, are shown in Table 1. Characteristics were generally comparable in low versus high levels of the markers; however, noteworthy was that participants with level of u-NGAL above the median had a higher HbA1c and were more frequently women. The level of UAER was also higher in participants with high levels of both u-KIM-1 and u-NGAL, whereas there was no difference in eGFR. The mean eGFR was 89 ± 17 ml/min/1.73 m2, and only eight participants had an eGFR below 60 ml/min/1.73 m2.

Table 1

Clinical characteristics of the study population at baseline categorized according to urinary kidney injury molecule 1 (u-KIM-1) and urinary neutrophil gelatinase-associated lipocalin (u-NGAL) below or above the median


u-KIM-1 (pg/ml/g creatinine)

u-NGAL (pg/ml/g creatinine)

< 1.64 (n = 96)

≥ 1.64 (n = 96)


< 101.4 (n = 96)

≥ 101.4 (n = 96)


Male, n (%)

78 (81)

67 (70)


86 (90)

59 (61)

< 0.001

Age (years)

58.2 ± 8.5

59.2 ± 9.1


59.0 ± 8.9

58.4 ± 8.7


Duration of diabetes (years)

11.9 ± 7.8

13.3 ± 7.1


12.6 ± 7.6

12.7 ± 7.3


Body mass index (kg/m2)

32.5 ± 5.2

32.7 ± 6.4


32.4 ± 5.7

32.8 ± 5.9


HbA1c (%)

7.86 ± 1.14

7.82 ± 1.52


7.60 ± 1.00

8.08 ± 1.57


HbA1c (mmol/mol)

62 ± 12.5

62 ± 16.6


60 ± 10.9

65 ± 17.2


UAER (mg/24-h)

71.0 (31.0–147.0)

149.6 (63.5–510.5)

< 0.001

69.0 (32.0–191.0)

128.0 (71.0–377.7)


P-creatinine (µmol/l)

76.6 ± 17.6

76.8 ± 19.4


78.8 ± 15.6

74.6 ± 20.9


eGFR (ml/min/1.73 m2)

90.5 ± 17.2

88.0 ± 18.1


89.1 ± 16.5

89.4 ± 18.7


LDL cholesterol (mmol/l)

1.89 ± 0.80

1.82 ± 0.78


1.84 ± 0.75

1.86 ± 0.82


Systolic blood pressure (mmHg)

130 ± 17

130 ± 16


130 ± 16

129 ± 16


Current smoker, n (%)

31 (32)

25 (26)


27 (28)

29 (30)


Oral antidiabetic use, n (%)

80 (83)

83 (86)


82 (85)

81 (84)


Insulin use, n (%)

64 (67)

55 (57)


64 (67)

55 (57)


Antihypertensive drug use, n (%)

95 (99)

96 (100)


96 (100)

95 (99)


RAASi use, n (%)

95 (99)

94 (98)


96 (100)

93 (97)


Statin use, n (%)

90 (94)

92 (96)


91 (95)

91 (95)


Aspirin use, n (%)

87 (91)

89 (93)


88 (92)

88 (92)


p values are calculated for differences between participants with u-KIM-1 and u-NGAL below or above the median

eGFR estimated glomerular filtration rate, RAASi renin–angiotensin–aldosterone system inhibitor

The vast majority of patients were treated with oral antidiabetic treatment, antihypertensive drugs (98% with a renin–angiotensin–aldosterone system inhibitor), statins and aspirin, while slightly more than half received insulin treatment.

The positive, but weak correlation between u-KIM-1 and u-NGAL is illustrated in Fig. 1 (R2 = 0.12; p < 0.0001).

Fig. 1

Scatterplot illustrating the significant correlation between log2-transformed urinary kidney injury molecule 1 (u-KIM-1) and urinary neutrophil gelatinase-associated lipocalin (u-NGAL) concentrations

u-KIM-1 and u-NGAL in continuous analyses

Table 2 demonstrates the HR of u-KIM-1 and u-NGAL for decline in eGFR, CVE and all-cause mortality, respectively. Higher u-KIM-1 was a strong predictor of all-cause mortality even after adjustment for traditional risk factors (HR 2.26; p = 0.008), and also showed significance for predicting decline in eGFR (HR 1.68; p = 0.034) and CVE (HR 1.52; p = 0.049). u-NGAL showed no significance in the continuous models after adjustment, however, higher level was associated with higher risk of decline in eGFR in the unadjusted model (HR 1.41; p = 0.013).

Table 2

Cox regression analyses: urinary biomarkers in relation to risk of fatal and nonfatal cardiovascular events, all-cause mortality and decline in eGFR > 30% in 192 patients, number of events given for each endpoint



Decline in eGFR > 30% (n = 39)


Cardiovascular events (n = 39)


All-cause mortality (n = 24)




1.86 (1.24–2.80)


1.44 (1.01–2.05)


1.95 (1.19–3.20)



1.68 (1.04–2.71)


1.52 (1.00–2.31)


2.26 (1.24–4.15)


Adjusted + u-NGAL

1.62 (1.00–2.63)


1.53 (1.00–2.34)


2.24 (1.19–4.20)


rIDI (%)







< 0.001



1.41 (1.08–1.86)


1.04 (0.77–1.41)


1.10 (0.75–1.62)



1.29 (0.89–1.86)


1.07 (0.73–1.55)


1.18 (0.74–1.91)


Adjusted + u-KIM-1

1.19 (0.80–1.77)


0.99 (0.66–1.48)


1.04 (0.59–1.83)


Values are hazard ratios with 95% confidence intervals, and represent a doubling of the biomarkers. Adjustment included sex, age, LDL cholesterol, smoking, HbA1c, plasma creatinine, systolic blood pressure and urinary albumin excretion ratio

u-KIM-1 urinary kidney injury molecule 1, u-NGAL urinary neutrophil gelatinase-associated lipocalin, eGFR estimated glomerular filtration rate, rIDI relative integrated discrimination improvement

After inclusion of both biomarkers in the adjusted model, only higher u-KIM-1 remained significantly associated with all-cause mortality (HR 2.24; p = 0.012), but not with renal decline or CVE.

We calculated ROC-curves and AUC for all endpoints in relation to u-KIM-1 compared to the adjusted model without u-KIM-1, which in turn, were all non-significant (eGFR decline: p = 0.48; CVE: p = 0.41; all-cause mortality: p = 0.46). However, rIDI analysis shows a significant discrimination slope contribution of 63.1% (p = 0.007) in predicting all-cause mortality, whilst the contribution to eGFR decline (27.9%) and CVE (8.94%) prediction was not significant (p = 0.09 and p = 0.21, respectively).

u-KIM-1 and u-NGAL analyzed according to the median

As can be seen in Fig. 2, Kaplan–Meier function plots for u-KIM-1 divided according to the median showed a significant higher risk for decline in eGFR (p = 0.004) and CVE (p = 0.015) at levels above the median, however no association for all-cause mortality was found (p = 0.059). Similar plots for u-NGAL (not pictured) were significant for decline in eGFR (p = 0.019), but not for CVE or all-cause mortality (p ≥ 0.16). After adjustment for the previously described risk factors in a Cox regression model, u-KIM-1 above the median remained associated with risk of CVE [HR (95% CI) 2.62 (1.25–5.53); p = 0.011], while there was no association for decline in eGFR (p = 0.059) or all-cause mortality (p = 0.11). The association between elevated NGAL and decline in eGFR was also lost (p = 0.070).

Fig. 2

Kaplan–Meier functions for time to a 30% decline in estimated glomerular filtration rate; b cardiovascular events; c death, in 192 type 2 diabetic patients stratified by urinary kidney injury molecule 1 according to the median (1.64 pg/ml/g creatinine). The log-rank test was used to calculate p values


The present study serves to investigate the predictive qualities of the biomarkers KIM-1 and NGAL measured in urine. In our cohort of 192 T2D patients with persistent microalbuminuria, higher u-KIM-1 seems to be an independent predictor of all-cause mortality, as well as a promising marker of eGFR decline and CVE, while u-NGAL failed to show significance for any of the endpoints after adjustment. Regarding the possible clinical use of u-KIM-1 these results are promising, while the results concerning u-NGAL seems to confirm u-NGAL being a weaker marker pertaining long term prediction of progression of DKD, CVE and mortality [27, 28]. A combination of the two markers was not helpful in predicting the endpoints. Put in relation to our previous study on the same cohort [21] where we failed to show significance for yearly kidney function decline after adjustment, we now have a much stronger foundation to build on with a longer follow-up and more robust endpoints.

The current approach for the stratification of risk for DKD, cardiovascular disease and mortality is based on a variety of factors in the diabetic patient [2, 4, 29, 30, 31]. In urine specifically, albuminuria is the primary measure to assess microvascular dysfunction in the glomeruli and the general vasculature, and when combined with eGFR these markers are used to determine DKD, as well as playing a part in identifying the risk of other diabetic complications [4, 31].

The biomarkers analyzed in our study both reflect damage to the proximal tubular cells in the kidneys as opposed to albuminuria which is considered a marker of glomerular damage. It has been shown that the biomarkers are expressed specifically in damaged tissue, and are mostly non-present in healthy kidneys [5, 32]; and when present they can be measured in urine as well as in circulation [10]. Additionally, they are considered specific markers of active damage to tubuli [13, 33] and therefore the possible predictive effect derived from damaged tissue releasing these markers, may materialize ahead of any kidney function decline measured using conventional methods. This theory leads to the question of urinary versus circulatory quantification of the biomarkers and it has been hypothesized [34], but not confirmed, that measurement in urine is more representative of acute production in the tubuli, while, when measured in circulation, it rather supports the idea of a more systematic, microvascular syndrome facilitating leakage from the kidney.

Noteworthy, there was a higher ratio of females in the group with value above the median level for both biomarkers, albeit a non-significant difference for u-KIM-1. This is interesting considering the discrepancy of CKD and ESRD exhibited between males and females in the general population, where males generally have a higher rate of renal function deterioration than females [35]. Sex related differences of these biomarkers have been evaluated before, where u-NGAL, but not u-KIM-1, showed statistically significant sex effect after adjustment for urinary creatinine [36].


As described above, analysis with Kaplan–Meier functions based on the median level of u-KIM-1 exhibit significance, after adjustment, only for CVE. However, our continuous data clearly show that higher u-KIM-1 is a predictor of eGFR decline and CVE, in addition to all-cause mortality. Furthermore, rIDI calculations demonstrated a significant added value of u-KIM-1 to existing parameters for the prediction of all-cause mortality, which further strengthens the possible usefulness of KIM-1. Searching the literature, u-KIM-1 has been proposed as a predictor of various complications in patients with diabetes [20, 37], but its efficiency is still under scrutiny. Studies have suggested several different points of usage, without yet conclusively determining its role in the general setting. There are implications of it being a robust marker of future decline in eGFR [21, 34]; but not necessarily an independent risk factor [38], and other studies have demonstrated a role in the prediction of CVE [20, 39] and mortality [20, 40, 41, 42] as well. Furthermore, it has been shown to be modifiable by inhibition of the renin–angiotensin–aldosterone system, in both humans [43] and mice [44, 45], but not with empagliflozine [46]. There are, however, great variances in the selection of subjects in the studies, as well as difference in the interpretation of the biomarker. Our results shed some clarity on the role KIM-1 could have if applied in a clinical setting, as we can establish a clear correlation between higher u-KIM-1 and the respective endpoints. However, there is need for further studies, especially regarding its modifiability in relation to existing antihypertensive and antidiabetic treatment, if KIM-1 is to become an effective and useful biomarker in a clinical setting.


Concerning NGAL, higher levels have also shown promising associations with impaired kidney function in vivo and in vitro [13, 14], mainly focusing on acute kidney injury. Recently, it has also been proposed as a predictor of CKD [47], as well as DKD and CVE in patients with T2D [48]. Our data do not support u-NGAL as a reliable risk marker for decline in eGFR, CVE or mortality. The reason could be that we included patients with persistent microalbuminuria but preserved renal function at baseline. This fact may well be a significant factor to take into account when using u-NGAL for risk stratification, as it is a marker of active, ongoing damage to the proximal tubuli in the kidney [32] and as such may not be present in the same magnitude in patients with established kidney disease, receiving ongoing treatment. Furthermore, compared to u-KIM-1, the level of u-NGAL has not been shown to predict chronic diseases or long term events convincingly, as positive results in the literature are more prominent in the prediction of acute kidney injury [7].


There are relevant limitations to this study which should be taken into account. A relatively small sample size of patients from a single center limits both the number of events recorded and the generalization of the results. Moreover, all of our patients were diagnosed with DKD (microalbuminuria) and this may, as discussed above, influence the predictive strength of the markers, and further diminish generalization. Lastly, as we have not distinguished the anti-diabetic treatment of the patients, some drugs can influence our results; such as glucagon-like peptide 1 receptor analogues or dipeptidyl peptidase-4 inhibitors that have a known effect on the kidney [49]. Sodium-glucose co-transporter-2 inhibitors, which have a potent effect on urinary biomarkers [50], were not on the market when initiating this cohort.


This study of patients with T2D and persistent microalbuminuria demonstrates that higher level of u-KIM-1, but not of NGAL, is an independent risk factor for decline in renal function, cardiovascular events and all-cause mortality, and contributes significant discrimination, beyond traditional risk factors, for the risk of all-cause mortality.



We thank all participants and acknowledge the work of study nurse Lone Jelstrup and lab technicians Anne G. Lundgaard, Berit R. Jensen, Tina R. Juhl, and Jessie A. Hermann, employees at Steno Diabetes Center, Copenhagen.

Author contributions

VRC, TWH, MKE, BJvS, HR, PJ, FP, H-HP, and PR conceived and designed the research; VRC, TWH, MKE, BJvS, FP and PR analyzed and interpreted the data; TWH performed the statistical analysis; VRC, wrote the manuscript; TWH, MKE, BJvS, HR, PJ, FP, H-HP, and PR critically revised the manuscript for key intellectual content; PR obtained funding and supervised the study. All authors approved the final version of the manuscript. VRC is responsible for the integrity of the work as a whole.


European Foundation for the Study of Diabetes (EFSD), clinical research grant in Type 2 Diabetes. Internal funding was provided by Steno Diabetes Center Copenhagen, Gentofte, Denmark.

Compliance with ethical standards

Conflict of interest

F. P. reports having received research Grants from Astra Zeneca, lecture fees from Astra Zeneca, MSD, Janssen, Lily, Boehringer Ingelheim, Novo Nordisk, Novartis and being consultant/advisory board member for Astra Zeneca, Bayer, Amgen and MSD. P. R. received lecture fees from Bayer and Boehringer Ingelheim, and research Grant from Novartis, Astra Zeneca, Novo Nordisk and has served as a consultant for Bayer, Astra Zeneca, Astellas, Boehringer Ingelheim, AbbVie, Novo Nordisk (all honoraria to his institution) and having equity interest in Novo Nordisk. The results presented in this paper have not been published previously in whole or part, except in abstract format.

Statement on human rights

All procedures have been in accordance to ethical standards and ethical law, were applied, including the 1964 Declaration of Helsinki and the guidelines for Good Clinical Practice.

Informed consent

All subjects in the study gave their informed and signed consent prior to inclusion.


  1. 1.
    Afkarian M, Zelnick LR, Hall YN et al (2016) Clinical manifestations of kidney disease among US adults with diabetes, 1988–2014. JAMA 316(6):602–610CrossRefGoogle Scholar
  2. 2.
    Buse JB, Ginsberg HN, Bakris GL et al (2007) Primary prevention of cardiovascular diseases in people with diabetes mellitus: a scientific statement from the American Heart Association and the American Diabetes Association. Diabetes Care 30(1):162–172CrossRefGoogle Scholar
  3. 3.
    Gaede P, Lund-Andersen H, Parving HH et al (2008) Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 358(6):580–591CrossRefGoogle Scholar
  4. 4.
    Tuttle KR, Bakris GL, Bilous RW et al (2014) Diabetic kidney disease: a report from an ADA consensus conference. Diabetes Care 37(10):2864–2883CrossRefGoogle Scholar
  5. 5.
    Han WK, Bailly V, Abichandani R et al (2002) Kidney injury molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int 62(1):237–244CrossRefGoogle Scholar
  6. 6.
    Castillo-Rodriguez E, Fernandez-Prado R, Martin-Cleary C et al (2017) Kidney injury marker 1 and neutrophil gelatinase-associated lipocalin in chronic kidney disease. Nephron 136(4):263–267CrossRefGoogle Scholar
  7. 7.
    Schiffl H, Lang SM (2012) Update on biomarkers of acute kidney injury: moving closer to clinical impact? Mol Diagn Ther 16(4):199–207CrossRefGoogle Scholar
  8. 8.
    Ko GJ, Grigoryev DN, Linfert D et al (2010) Transcriptional analysis of kidneys during repair from AKI reveals possible roles for NGAL and KIM-1 as biomarkers of AKI-to-CKD transition. Am J Physiol Renal Physiol 298(6):F1472–F1483CrossRefGoogle Scholar
  9. 9.
    Yin C, Wang N (2016) Kidney injury molecule-1 in kidney disease. Ren Fail 38(10):1567–1573CrossRefGoogle Scholar
  10. 10.
    Wasung ME, Chawla LS, Madero M (2015) Biomarkers of renal function, which and when? Clin Chim Acta 438:350–357CrossRefGoogle Scholar
  11. 11.
    Bonventre JV (2014) Kidney injury molecule-1: a translational journey. Trans Am Clin Climatol Assoc 125:293–299 (discussion 299) PubMedPubMedCentralGoogle Scholar
  12. 12.
    Nasioudis D, Witkin SS (2015) Neutrophil gelatinase-associated lipocalin and innate immune responses to bacterial infections. Med Microbiol Immunol 204(4):471–479CrossRefGoogle Scholar
  13. 13.
    Mori K, Nakao K (2007) Neutrophil gelatinase-associated lipocalin as the real-time indicator of active kidney damage. Kidney Int 71(10):967–970CrossRefGoogle Scholar
  14. 14.
    Singer E, Marko L, Paragas N et al (2013) Neutrophil gelatinase-associated lipocalin: pathophysiology and clinical applications. Acta Physiol (Oxf) 207(4):663–672CrossRefGoogle Scholar
  15. 15.
    Coca SG, Nadkarni GN, Huang Y et al (2017) Plasma biomarkers and kidney function decline in early and established diabetic kidney disease. J Am Soc Nephrol 28(9):2786–2793CrossRefGoogle Scholar
  16. 16.
    Group AS, Buse JB, Bigger JT et al (2007) Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial: design and methods. Am J Cardiol 99(12A):21i–33iGoogle Scholar
  17. 17.
    Fried LF, Emanuele N, Zhang JH et al (2013) Combined angiotensin inhibition for the treatment of diabetic nephropathy. N Engl J Med 369(20):1892–1903CrossRefGoogle Scholar
  18. 18.
    Helmersson-Karlqvist J, Larsson A, Carlsson AC et al (2013) Urinary neutrophil gelatinase-associated lipocalin (NGAL) is associated with mortality in a community-based cohort of older Swedish men. Atherosclerosis 227(2):408–413CrossRefGoogle Scholar
  19. 19.
    Bhavsar NA, Kottgen A, Coresh J et al (2012) Neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) as predictors of incident CKD stage 3: the Atherosclerosis Risk in Communities (ARIC) study. Am J Kidney Dis 60(2):233–240CrossRefGoogle Scholar
  20. 20.
    Park M, Hsu CY, Go AS et al (2017) Urine kidney injury biomarkers and risks of cardiovascular disease events and all-cause death: the CRIC study. Clin J Am Soc Nephrol 12(5):761–771CrossRefGoogle Scholar
  21. 21.
    Nielsen SE, Reinhard H, Zdunek D et al (2012) Tubular markers are associated with decline in kidney function in proteinuric type 2 diabetic patients. Diabetes Res Clin Pract 97(1):71–76CrossRefGoogle Scholar
  22. 22.
    Nielsen SE, Andersen S, Zdunek D et al (2011) Tubular markers do not predict the decline in glomerular filtration rate in type 1 diabetic patients with overt nephropathy. Kidney Int 79(10):1113–1118CrossRefGoogle Scholar
  23. 23.
    Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150(9):604–612CrossRefGoogle Scholar
  24. 24.
    von Scholten BJ, Reinhard H, Hansen TW et al (2015) Additive prognostic value of plasma N-terminal pro-brain natriuretic peptide and coronary artery calcification for cardiovascular events and mortality in asymptomatic patients with type 2 diabetes. Cardiovasc Diabetol 14:59CrossRefGoogle Scholar
  25. 25.
    von Scholten BJ, Reinhard H, Hansen TW et al (2016) Urinary biomarkers are associated with incident cardiovascular disease, all-cause mortality and deterioration of kidney function in type 2 diabetic patients with microalbuminuria. Diabetologia 59(7):1549–1557CrossRefGoogle Scholar
  26. 26.
    Pencina MJ, D’Agostino RB, Vasan RS (2010) Statistical methods for assessment of added usefulness of new biomarkers. Clin Chem Lab Med 48(12):1703–1711CrossRefGoogle Scholar
  27. 27.
    Liu KD, Yang W, Anderson AH et al (2013) Urine neutrophil gelatinase-associated lipocalin levels do not improve risk prediction of progressive chronic kidney disease. Kidney Int 83(5):909–914CrossRefGoogle Scholar
  28. 28.
    Liu KD, Yang W, Go AS et al (2015) Urine neutrophil gelatinase-associated lipocalin and risk of cardiovascular disease and death in CKD: results from the Chronic Renal Insufficiency Cohort (CRIC) study. Am J Kidney Dis 65(2):267–274CrossRefGoogle Scholar
  29. 29.
    Lewington S, Clarke R, Qizilbash N et al (2002) Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 360(9349):1903–1913CrossRefGoogle Scholar
  30. 30.
    Margolis KL, O’Connor PJ, Morgan TM et al (2014) Outcomes of combined cardiovascular risk factor management strategies in type 2 diabetes: the ACCORD randomized trial. Diabetes Care 37(6):1721–1728CrossRefGoogle Scholar
  31. 31.
    Levey AS, Coresh J, Balk E et al (2003) National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 139(2):137–147CrossRefGoogle Scholar
  32. 32.
    Kashani K, Cheungpasitporn W, Ronco C (2017) Biomarkers of acute kidney injury: the pathway from discovery to clinical adoption. Clin Chem Lab Med 55(8):1074–1089CrossRefGoogle Scholar
  33. 33.
    Ichimura T, Bonventre JV, Bailly V et al (1998) Kidney injury molecule-1 (KIM-1), a putative epithelial cell adhesion molecule containing a novel immunoglobulin domain, is up-regulated in renal cells after injury. J Biol Chem 273(7):4135–4142CrossRefGoogle Scholar
  34. 34.
    Nowak N, Skupien J, Niewczas MA et al (2016) Increased plasma kidney injury molecule-1 suggests early progressive renal decline in non-proteinuric patients with type 1 diabetes. Kidney Int 89(2):459–467CrossRefGoogle Scholar
  35. 35.
    Silbiger SR, Neugarten J (1995) The impact of gender on the progression of chronic renal disease. Am J Kidney Dis 25(4):515–533CrossRefGoogle Scholar
  36. 36.
    Pennemans V, Rigo JM, Faes C et al (2013) Establishment of reference values for novel urinary biomarkers for renal damage in the healthy population: are age and gender an issue? Clin Chem Lab Med 51(9):1795–1802CrossRefGoogle Scholar
  37. 37.
    Sabbisetti VS, Waikar SS, Antoine DJ et al (2014) Blood kidney injury molecule-1 is a biomarker of acute and chronic kidney injury and predicts progression to ESRD in type I diabetes. J Am Soc Nephrol 25(10):2177–2186CrossRefGoogle Scholar
  38. 38.
    Conway BR, Manoharan D, Manoharan D et al (2012) Measuring urinary tubular biomarkers in type 2 diabetes does not add prognostic value beyond established risk factors. Kidney Int 82(7):812–818CrossRefGoogle Scholar
  39. 39.
    Driver TH, Katz R, Ix JH et al (2014) Urinary kidney injury molecule 1 (KIM-1) and interleukin 18 (IL-18) as risk markers for heart failure in older adults: the Health, Aging, and Body Composition (Health ABC) study. Am J Kidney Dis 64(1):49–56CrossRefGoogle Scholar
  40. 40.
    Carlsson AC, Larsson A, Helmersson-Karlqvist J et al (2014) Urinary kidney injury molecule-1 and the risk of cardiovascular mortality in elderly men. Clin J Am Soc Nephrol 9(8):1393–1401CrossRefGoogle Scholar
  41. 41.
    Tonkonogi A, Carlsson AC, Helmersson-Karlqvist J et al (2016) Associations between urinary kidney injury biomarkers and cardiovascular mortality risk in elderly men with diabetes. Ups J Med Sci 121(3):174–178CrossRefGoogle Scholar
  42. 42.
    Sarnak MJ, Katz R, Newman A et al (2014) Association of urinary injury biomarkers with mortality and cardiovascular events. J Am Soc Nephrol 25(7):1545–1553CrossRefGoogle Scholar
  43. 43.
    Nielsen SE, Rossing K, Hess G et al (2012) The effect of RAAS blockade on markers of renal tubular damage in diabetic nephropathy: u-NGAL, u-KIM1 and u-LFABP. Scand J Clin Lab Investig 72(2):137–142CrossRefGoogle Scholar
  44. 44.
    Molina-Jijon E, Rodriguez-Munoz R, Gonzalez-Ramirez R et al (2017) Aldosterone signaling regulates the over-expression of claudin-4 and -8 at the distal nephron from type 1 diabetic rats. PLoS One 12(5):e0177362CrossRefGoogle Scholar
  45. 45.
    Su Z, Widomski D, Nikkel A et al (2018) Losartan improves renal function and pathology in obese ZSF-1 rats. J Basic Clin Physiol Pharmacol 29:281–290CrossRefGoogle Scholar
  46. 46.
    Gallo LA, Ward MS, Fotheringham AK et al (2016) Once daily administration of the SGLT2 inhibitor, empagliflozin, attenuates markers of renal fibrosis without improving albuminuria in diabetic db/db mice. Sci Rep 6:26428CrossRefGoogle Scholar
  47. 47.
    Rysz J, Gluba-Brzozka A, Franczyk B et al (2017) Novel biomarkers in the diagnosis of chronic kidney disease and the prediction of its outcome. Int J Mol Sci 18(8):1702CrossRefGoogle Scholar
  48. 48.
    Bolignano D, Lacquaniti A, Coppolino G et al (2009) Neutrophil gelatinase-associated lipocalin as an early biomarker of nephropathy in diabetic patients. Kidney Blood Press Res 32(2):91–98CrossRefGoogle Scholar
  49. 49.
    Thomson SC, Vallon V (2018) Renal effects of incretin-based diabetes therapies: pre-clinical predictions and clinical trial outcomes. Curr Diabetes Rep 18(5):28CrossRefGoogle Scholar
  50. 50.
    Dekkers CCJ, Petrykiv S, Laverman GD et al (2018) Effects of the SGLT-2 inhibitor dapagliflozin on glomerular and tubular injury markers. Diabetes Obes Metab 20:1988–1993CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  • Viktor Rotbain Curovic
    • 1
  • Tine W. Hansen
    • 1
  • Mie K. Eickhoff
    • 1
  • Bernt Johan von Scholten
    • 1
  • Henrik Reinhard
    • 1
  • Peter Karl Jacobsen
    • 2
  • Frederik Persson
    • 1
  • Hans-Henrik Parving
    • 3
  • Peter Rossing
    • 1
    • 4
  1. 1.Steno Diabetes Center CopenhagenGentofteDenmark
  2. 2.Rigshospitalet, University of CopenhagenCopenhagenDenmark
  3. 3.Department of EndocrinologyRigshospitalet, Copenhagen University HospitalCopenhagenDenmark
  4. 4.University of CopenhagenCopenhagenDenmark

Personalised recommendations