original article Wien Klin Wochenschr DOI 10.1007/s00508-014-0679-1

Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes Yehong Yang · Shuo Zhang · Bin Lu · Wei Gong · Xuehong Dong · Xiaoyan Song · Weiwei Zhao · Jiefeng Cui · Yinkun Liu · Renming Hu

Received: 16 November 2012 / Accepted: 8 November 2014 © Springer-Verlag Wien 2014

Summary Purpose  The purpose of this work is to examine the serum proteomic profiles associated with the subsequent development of diabetic nephropathy (DN) in patients with type 2 diabetes and to develop and validate a decision tree based on the profiles to predict the risk of DN in advance by albuminuria. Methods  Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry was used to obtain the proteomic profiles from baseline serum samples of 84 patients with type 2 diabetes with normal albuminuria, including 42 case subjects who developed DN after 4 years and 42 control subjects who remained normoalbuminuric over the same 4 years. From signatures of protein mass, a decision tree was established for predicting DN. Results  At baseline, urinary albumin/creatinine ratio was similar between the case and control groups. The intensities of 5 peaks detected by CM10 chips appeared up-regulated, whereas 18 peaks were down-regulated more than twofold in the case group than compared with the control group in the training set. An optimum discriminatory decision tree for case subjects created with

Yehong Yang and Shuo Zhang contributed equally to this work. R. Hu () 12 Middle Wurumuqi Road, Shanghai 200040, P.R. China e-mail: [email protected] Y. Yang · S. Zhang · B. Lu · W. Gong · X. Dong · X. Song · W. Zhao Institute of Endocrinology and Diabetology, Department of Endocrinology, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China J. Cui · Y. Liu Proteome research section, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, P.R. China

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four nodes using four distinct masses was challenged with testing set. The positive predictive value was 77.8 % (7/9), and the negative predictive value was 72.7 % (8/11). Conclusions  We developed and validated a decision tree to predict DN in patients with type 2 diabetes. Keywords  Diabetic nephropathy · Proteomic profiling · Prediction  · Surface-enhanced laser desorption/ionization · Decision tree

Vorhersage einer diabetischen Nephropathie durch Bestimmung des Proteomprofils bei Patienten mit Typ 2 Diabetes mellitus Zusammenfassung Ziel der Studie war es zu untersuchen, ob das Serum Proteomprofil mit der Entwicklung einer diabetischen Nephropathie (DN) bei Patienten mit Diabetes mellitus. Typ 2 im Zusammenhang steht. Ein weiteres Ziel war es, einen Entscheidungsbaum, der auf dem Proteomprofil basiert, zu entwickeln und zu validieren. Dieser sollte das Risiko einer DN durch Albuminurie voraussagen. Methoden  Die Surface-enhanced Laser desorption/ ionization time-of-flight Massenspektrometrie (SELDITOF-MS) wurde verwendet, um Proteomprofile in den Sera von 84 Patienten mit Typ 2 Diabetes mit normaler Albuminurie zu bestimmen. 42 dieser Patienten entwickelten eine DN innerhalb der nächsten 4 Jahre, die anderen 42 nicht. Aus den Signaturen der Proteinmasse wurde ein Entscheidungsbaum zur Vorhersage einer DN erstellt. Ergebnisse  Zu Beginn waren die Albumin/Kreatinin Quotienten im Harn bei beiden Gruppen ähnlich. Die Intensität von 5 durch CM10 Chips entdeckten Peaks schienen hoch reguliert, während 18 Peaks in der Gruppe mit DN im Vergleich mit der Kontrolle mehr als zweifach down-reguliert waren. Unter Verwendung von 4 Knoten

Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes  

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mit 4 unterschiedlichen Massen wurde zur optimalen Unterscheidung ein Entscheidungsbaum für die Patienten mit DN geschaffen und getestet. Der positive Vorhersagewert lag bei 77,8 % (7/9), der negative Vorhersagewert bei 72,7 % (8/11). Schlussfolgerungen  Wir entwickelten und validierten einen Entscheidungsbaum, um das Auftreten einer DN bei Patienten mit Typ 2 Diabetes mellitus vorherzusagen. Schlüsselwörter  Diabetische Nephropathie  · Proteom Profil Erstellung · SELDI-TOF-MS · Entscheidungsbaum

Introduction Diabetic nephropathy (DN) is the leading cause of chronic kidney disease in patients starting renal replacement therapy [1, 2]. Microalbuminuria is the earliest clinical sign of DN, and patients with type 2 diabetes with microalbuminuria have elevated risks of cardiovascular disease [3]. Thus, timely prediction of progression to microalbuminuria is of major importance. Prediction of DN by proteomic profiling is yet to be studied extensively [4]. In this study, we used a protein biochip surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) [5] coupled with an artificial intelligence learning algorithm to differentiate the patients with type 2 diabetes who were normoalbuminuric at the baseline and progressed to DN within 4 years from the controls who were also normoalbuminuric at baseline but remained normoalbuminuric after 4 years. Then, we developed and validated a decision tree for predicting the risk of progression to DN in patients with type 2 diabetes. This study demonstrated the possibility of predicting DN based on serum proteomic profiling in patients with type 2 diabetes.

Methods We compared serum proteomic profiles among adult Chinese with type 2 diabetes and those with normal urinary albumin excretion, which were followed for 4 years, for the development of DN. This study was approved by the Ethics Committee of Huashan Hospital. All subjects gave informed consent. A total of 42 case subjects and 42 contemporaneous control subjects matched for age (± 5 years), sex, history of hypertension, duration of diabetes (± 5 years), and body mass index (BMI) (± 5  kg/m2) were studied. The training set consisted of the profiles of 32 case subjects (group A) and 32 matched control subjects (group B), while the testing set consisted of the 10 other case subjects (group C) and 10 matched control subjects (group D). The case subjects included were Chinese patients with type 2 diabetes who were normoalbuminuric (albumin-to-creatinine ratio (ACR) less than 30 mg/g), had a normal serum creatinine concentration at the baseline, and progressed to DN within 4 years. The control subjects included were type 2 diabetic Chi-

nese who were also normoalbuminuric and had a normal serum creatinine concentration at the baseline, but remained normoalbuminuric after 4 years. Absence of other clinical renal diseases was confirmed based on the clinical history, while normal urinary sediment and lack of detectable renal tract lesions were confirmed based on ultrasound examination. Patients receiving insulin, angiotensin-converting enzyme (ACE) inhibitors, or angiotensin receptor blockers (ARB) were excluded. All patients were residents of the JiangNing community in the JingAn district of Shanghai who had participated in a screening study of chronic diabetic complications. Details of the screening study have been published elsewhere [6]. Type 2 diabetes was defined by the World Health Organization criteria (1999). DN was defined by having at least two out of three measurements of the ACR during a 3-month period being elevated. Demographic information of the subjects was listed in Table 1. Age, duration of diabetes, weight, BMI, waist-tohip ratio, blood pressure, lipid profile, kidney function, and blood glucose control were all comparable (p > 0.05) between case subjects and control individuals. Urinary creatinine (Sarcosine Oxidase-PAP method, S708; Shanghai Kehua Dongling Diagnostic Products, Shanghai, China) was measured using Hitachi 7600020. The Albumin Radioimmunoassay kit was purchased from Beijing Atom High-tech Co. Ltd. Total protein con-

Table 1  Clinical characteristics of the subjects at baseline Control subjects

Case subjects

n

42

42

Gender (Male/Female)

24/18

24/18

Age (years)

64.2 ± 12.3

64.4 ± 10.5

Duration of diabetes (years)

6.5 ± 4.9

6.7 ± 7.5

Weight (kg)

65.1 ± 10.0

66.4 ± 12.3

Body mass index (BMI) (kg/m2)

25.2 ± 2.9

25.8 ± 4.0

Waist-to-hip ratio (WHR)

0.86 ± 0.05

0.87 ± 0.06

Systolic blood pressure (SBP) (mmHg)

135.7 ± 19.7

138.9 ± 20.8

Diastolic blood pressure (DBP) (mmHg)

83.1 ± 10.5

80.6 ± 12.3

Cholesterol (mmol/L)

5.19 ± 1.02

5.09 ± 0.91

Triglyceride (mmol/L)

1.92 ± 1.00

2.00 ± 1.16

High-density lipoprotein (HDL) (mmol/L)

1.25 ± 0.35

1.28 ± 0.35

Low-density lipoprotein (LDL) (mmol/L)

2.87 ± 0.70

2.83 ± 0.69

Fasting blood glucose (FBG) (mmol/L)

8.1 ± 2.2

8.1 ± 2.8

Postprandial blood glucose (PBG) (mmol/L)

14.1 ± 5.4

13.8 ± 5.1

HbA1c (mmol/mol)/(%)

53 ± 11/7.0 ± 1.5

50 ± 9/6.7 ± 1.2

Blood urea nitrogen (mmol/L)

5.89 ± 1.35

5.90 ± 1.34

Creatinine (umol/L)

71.2 ± 16.9

70.8 ± 18.6

Uric acid (mmol/L)

0.29 ± 0.06

0.30 ± 0.07

Data were means ± standard deviation

2   Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes

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tent was determined by the Biuret method using Hitachi 7600-020. Serum samples were also assayed by a standard automated method for hepatic function, renal function, and lipid concentrations (Hitachi 7600-020). All samples were left to clot before centrifugation, and then were aliquoted and stored at − 80 °C until the time of assay. Serum samples were applied to weak cationic exchange chip (CM10) surfaces (balanced with 50  mM NaAc pH 4.0). A 3-μl volume of serum was mixed with 6  μl of U9 buffer {9  M urea, 2 % 3[(3-cholamidopropyl) dimethylammonio] -propanesulphonic acid, 1 % dithiothreitol, 50  mM Tris-HCl, pH 9.0} for 30  min, and were vortexed on ice, followed by the addition of 108 μl 50 mM NaAc pH 4.0. One hundred microliters of the diluted samples were then applied to the chips using a bioprocessor. Following 60  min of incubation, nonspecifically bound molecules were removed by washing with the binding buffer (50 mM NaAc pH 4.0), followed by washing with high-performance liquid chromatography (HPLC)gradient H2O. A saturated solution of sinapinic acid in 50 % acetonitrile and 0.5 % (v/v) trifluoroacetic acid was applied to the chip array surface, and mass spectrometry was performed using a SELDI mass spectrometer (PBS IIC; Ciphergen Biosystems Inc.). Data were collected at a laser intensity of 185 and a sensitivity of 8 in a positive mode. Mass accuracy was calibrated daily using the all-in-one peptide molecular mass standard (Ciphergen Biosystems) before chip processing. Intra-ProteinChip Array reproducibility was checked by spotting eight different aliquots of one sample on the same array, while Inter-ProteinChip Array reproducibility was checked by spotting a given sample on every different array. The intra- and inter-ProteinChip Array coefficients of variation were then assessed for five randomly selected peaks. The means of intra- and inter-ProteinChip Array CVintensity (coefficient variations of intensity) were 11.4 and 11.1 %, respectively. The means of intra- and inter-ProteinChip Array CV mass/charge ratio (m/z) (coefficient variations of m/z) were 0.023 and 0.020 %, respectively. Weak cationic exchange protein chips CM10 and other SELDI-related materials were obtained from Ciphergen (Fremont, CA, USA). Reagents were purchased from Sigma (St Louis, MO, USA). HPLC H2O was used throughout. We used the Ciphergen Protein Chip software version 3.2.0 to label spectral peaks and normalize their intensities to the total ion current to account for variation in ionization efficiencies. Peak masses were aligned, and clustering was performed with the Biomarker Wizard software (Ciphergen Biosystems). A signal-to-noise ratio > 5 was used for the first pass, while clusters of mass setting 0.3 % were completed using a signal-to-noise ratio > 2 for the second pass and a requirement to be present in at least 30 % of the samples. The samples were analyzed for peaks within the range of 2–30 kDa. Pattern recognition and sample classification were performed with the Biomarker Pattern Software version 5.0.2 (Ciphergen Biosystems, Inc.), which was based on classification and regression tree analysis.

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We used 64 profiles for the training set and 20 profiles for the testing set. The Biomarker Pattern Software algorithm splits the training dataset into two bins based on decision rules. The rules were formed based on the peak intensities for a designated mass value. Samples that follow the rule went to the left daughter node. When splitting could no longer be performed, terminal nodes were generated and classified according to the samples in the majority. Peaks selected by this process to form the splitting rules were those that achieved the maximum reduction of cost in the two descendent nodes.

Results Comparison of urinary ACR between case subjects and control subjects At the baseline, the two groups were similar with respect to their urinary ACR. The ACR of the control and case subjects were 16.72 (2.07–28.50) and 20.60 (8.16–28.55), respectively. At follow-up, the case subjects had significantly higher urinary ACR (Table 2). The ACR of control and case subjects were 8.60 (0.17–26.29)mg/g and 70.37 (30.25–1132.68)mg/g, respectively.

Comparison of proteomic profiles between case subjects and control subjects in the training set using SELDI-TOF-MS Processing the samples of the training set on a weak cationic exchange surface resolved 129 qualified protein or peptide peaks in the mass range of 2–30 kDa. The molecular mass of proteins from 0 to 2000 Da was eliminated from analysis because this area contains adducts and artefacts of the energy-absorbing molecule and possibly other chemical contaminants [7]. Peak detection involved using baseline subtraction, mass accuracy calibration, and automatic peak detection. Generation of spectra was performed at a laser intensity of 185. As shown in Fig.  1, the SELDI technology was particularly effective for resolving proteins and polypeptides with low molecular weights. The protein profiles were compared visually and statistically to identify differences. The amount of individual serum proteins was estimated from the peak intensity of the mass spectral signal and the m/z equivalent to the molecular weight of each protein. The peak intensities Table 2  Comparison of urinary albumin-to-creatinine ratio (ACR) between case subjects and control subjects n ACR (mg/g)

Control subjects

Case subjects

42

42

Baseline

16.7 (2.1–28.5)

20.6 (8.2–28.6)

Follow-up

8.6 (0.2–26.3)

70.4 (30.3–1132.7)*

Data were median (range). *p  1.006, that sample was placed in the terminal node 5 and was classified as a control. Otherwise, the sample was placed in node 2 and would be assigned to the case group (terminal node 1), if the intensity of peak at mass 6778.22 was ≤ 0.111. If the intensity of peak at mass 4096.78 was ≤ 1.932, the sample was assigned to the terminal node 2. Otherwise, the sample was placed in node 4. Then, if the

intensity of peak at mass 2039.56 was ≤ 0.517, the sample was assigned to the terminal node 3. The samples placed in terminal nodes 1, 2, and 3 were assigned to case group. The validity of the tree analysis pattern was then challenged with a blinded testing set. A summation of the classification results from the 5 terminal nodes is presented for training and testing sets in Table 3. The decision tree algorithms correctly predicted 75.0 % (15/20) of the case subjects of the test samples, with 70.0 % (7/10) of the group C samples and 80.0 % (8/10) of the group D samples being correctly determined. The positive predictive value was 77.8 % (7/9) and negative predictive value was 72.7 % (8/11).

Discussion HbA1c and urinary albumin at the baseline have consistently been shown to be important predictors of DN [8]. Considering the multifactorial nature of DN, the combination of several markers in a proteomic profile may be more predictive than individual biomarkers [9, 10]. Urinary protein signature appeared to be independently associated with the development of DN even after

4   Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes

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original article

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Table 3  Decision tree classification of the training and testing set Case Training set (n = 64)

 

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Fig. 3  Diagram of decision tree analysis pattern. Classification of case and control samples in the training set. The root node (top) and descendent nodes were shown as blue rectangles and the terminal nodes as red rectangles. The colors of bars in each terminal node represented the classes, red for case subjects and blue for control subjects. (There is an example in results section to explain the figure)

accounting for potential confounders, the most evident being HbA1c level. [11] In this study, we explored the association between serum proteomic profiling and the development of either micro- or macroalbuminuria in normoalbuminuric patients with type 2 diabetes. Furthermore, we developed and validated a decision tree from the protein profiles that can be used to predict nephropathy before the appearance of microalbuminuria in type 2 diabetes. The proteomic profile was obtained from serum specimens collected when subjects were normoalbuminuric and antedated the development of DN by 4 years. We matched baseline characteristics including HbA1c to exclude the possibility that the proteomic signature was linked to both glucose control and risk of DN. For those patients at a high risk of DN predicted by this decision tree, the blood pressure was suggested to be controlled to less than 130/80 mmHg. ACE inhibitor or ARB might be the first choice for those patients accompanied by hypertension, and further study might be needed to illustrate whether ACE inhibitor or ARB would be helpful for those high-risk patients with normal blood pressure. Meanwhile, we suggest that doctors are supposed to strengthen the management of their glucose (such as HbA1c less than 6.5 %) and lipid (such as low-density

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Control

 Case (n = 32)

27 (84.4 %)

5 (15.6 %)

 Control (n = 32)

6 (18.7 %)

26 (81.3 %)

 Case (n = 10)

7 (70.0 %)

3 (30.0 %)

 Control (n = 10)

2 (20.0 %)

8 (80.0 %)

Test set (n = 20)

lipoprotein less than 2.6 mmol/L) to delay the development of chronic complications. To reduce the deviation of masses and compensate for slight shifts in mass caused by imperfections on the ProteinChip Array surface, the protein profiles were normalized. The intensities of 5 peaks detected appeared up-regulated, whereas 18 peaks were down-regulated more than twofold in the case group than compared with the control group in the training set. We applied decision-tree learning to the mass spectra of the case and control patients. The algorithm identified an optimum discriminatory pattern for the case subjects, which was defined by the amplitudes at four key m/z values 3592.46, 6778.22, 4096.78, and 2039.56. Classification trees split up a dataset into two nodes. The presence or absence and the intensity levels of one peak defined the splitting decision. This splitting process continued until the terminal nodes or leaves were produced or until further splitting provided no benefits. Classification of terminal nodes was determined by the group (“class”) of samples that represented the majority of samples in that node. In this study, the issue of the relatively small sample size is a limitation. However, this follow-up study was conducted in the same group of specific population, and the patients receiving insulin, ACE inhibitors, or angiotensin receptor blockers were excluded to reduce bias. Therefore, the population that progressed to DN was relatively small. And a couple of matching criteria such as gender or duration of history increase the limitation of sample size. As a coin has two sides, those matching criteria listed in the paper reduced the selective bias between case and control groups at meantime. Moreover, the majority of the limited samples were put into training group to raise the authenticity of the decision tree. We hope that in the future more patients of multiple centers could be included to verity the validity of this decision tree. This decision tree can help clinicians decide on more frequent check-ups for patients at a higher risk of DN and prevent long-term chronic complications. In the future, more work will be focused on the identification of the peak masses, which might contribute to the understanding of chronic complications. In conclusion, for clinical applications, decision tree is a simple, easy, and good method to predict patients at high risk of DN. Furthermore, this approach may also be useful for explaining the mechanisms of the disease and developing new therapies.

Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes  

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original article Acknowledgments The study was funded by grants to Yehong Yang from the National Nature Science Foundation of China (81000329) and the Youth Science Foundation of Shanghai Health Bureau(2008Y040). The study was also funded by grants to Renming Hu from the Shanghai Science and Technology Commission (04DZ19504) and from the Key Project of National Nature Science Foundation of China (30230380), by grants to Shuo Zhang from the National Nature Science Foundation of China(30900541), by grants to Wei Gong from the National Nature Science Foundation of China(30900695), and by grants to Bin Lu from the National Nature Science Foundation of China(30900501). Conflict of interest The authors declare that they have no conflict of interest.

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  4. Otu HH, Can H, Spentzos D, et al. Prediction of diabetic nephropathy using urine proteomic profiling 10 years prior to development of nephropathy. Diabetes Care. 2007;30:638–43.  5. Yang YH, Zhang S, Cui JF, et al. Diagnostic potential of serum protein pattern in Type 2 diabetic nephropathy. Diabet Med. 2007;24:1386–92.   6. Lu B, Song X, Dong X, et al. High prevalence of chronic kidney disease in population-based patients diagnosed with type 2 diabetes in downtown Shanghai. J Diabetes Complications. 2008;22:96–103.   7. Adam BL, Qu Y, Davis JW, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002;62:3609–14.   8. Vergouwe Y, Soedamah-Muthu SS, Zgibor J, Chaturvedi N, et al. Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule. Diabetologia. 2010;53:254–62.  9. Choudhury D, Tuncel M, Levi M. Diabetic nephropathy—a multifaceted target of new therapies. Discov Med. 2010;10:406–15. 10. Long J, Wang Y, Wang W. MicroRNA-29c is a signature microRNA under high glucose conditions that targets Sprouty homolog 1, and its in vivo knockdown prevents progression of diabetic nephropathy. J Biol Chem. 2011;286:11837–48. 11. Papale M, Di Paolo S, Magistroni R, et al. Urine proteome analysis may allow noninvasive differential diagnosis of diabetic nephropathy. Diabetes Care. 2010;33:2409–15.

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Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes.

The purpose of this work is to examine the serum proteomic profiles associated with the subsequent development of diabetic nephropathy (DN) in patient...
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