Expression of Serum GRP78 and CHOP in Endoplasmic Reticulum Stress Pathways of Chinese Type 2 Diabetic Kidney Disease Patients

Expression of Serum GRP78 and CHOP in Endoplasmic Reticulum Stress Pathways of Chinese Type 2 Diabetic Kidney Disease Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Expression of Serum GRP78 and CHOP in Endoplasmic Reticulum Stress Pathways of Chinese Type 2 Diabetic Kidney Disease Patients Ning Ma, Ning Xu, Dong Yin, Ping Zheng, Weiwei Liu, Guofeng Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-41272/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The kidney has a rich endoplasmic reticulum system. A close relationship exists between endoplasmic reticulum stress (ERS) and diabetic kidney disease (DKD). The current study aimed to investigate serum glucose-regulated protein 78 (GRP78) as well as CCAAT/enhancer binding protein homologous protein (CHOP) concentrations in type 2 diabetes mellitus (T2DM) Chinese patients, especially those with microalbuminuria. Methods: We evaluated the relationships between serum GRP78 or CHOP levels and DKD. We recruited 67 patients with T2DM and 63 control subjects. We determined serum GRP78 and CHOP concentrations by ELISA, collected anthropometric data, and measured biochemical parameters in a clinical laboratory. Results: Compared with control groups, Chinese T2DM patients showed decreased serum levels of GRP78 [0.21 (0.16–0.24) vs. 0.16 (0.16–0.19) ng/mL, p < 0.01] and CHOP [3.8 (3.0–5.5) vs. 5.5 (3.7–7.9) ng/mL, p < 0.01]. Reduction in GRP78 and CHOP serum levels was more pronounced in patients with more severe categories of microalbuminuria. Amounts of serum GRP78 correlated directly with serum fasting c-peptide, cystatin-c (cys-c), creatinine (Cr), blood urea nitrogen (BUN), and uric acid, and inversely with glomerular filtration rates. Serum CHOP level was positively correlated with age, Cr, BUN, cys-c, urinary microalbumin/creatinine (UmALB/Cr), and eGFR. Serum GRP78 was predicted independently by Cr, BUN, serum uric acid, eGFR, and cys-c, while CHOP depended on age, Cr, BUN, serum uric acid, eGFR, UmALB/Cr, and cys-c. After controlling for confounding factors, GRP78 and CHOP expression was significantly associated with DKD (binary logistic regression, p < 0.01). Conclusions: T2DM patients showed increased serum GRP78 and CHOP concentrations. Receiver operating characteristic (ROC) areas under the curve for predicting DKD based on GRP78 and CHOP were 0.686 [95% CI: 0.558–0.813] and 0.670[0.524–0.816], respectively. Urology & Nephrology Type 2 diabetes diabetic kidney disease (DKD) endoplasmic reticulum stress (ERS) glucose-regulated protein (GRP) 78 CCAAT/enhancer binding protein homology protein (CHOP) Figures Figure 1 Figure 2 Background Diabetic kidney disease (DKD) represents an important health problem worldwide with millions of people affected. As a microvascular complication of diabetes mellitus (DM), it is responsible for substantial proportion of end-stage kidney disease cases. It is estimated that there are approximately 120 million people with chronic kidney disease in China [[1]]. DKD accounts for 30–47% of cases of end-stage renal disorders (ESRD) and is a major cause of death in DM patients [[2]]. However, the underlying pathophysiological mechanisms of DKD remain incompletely understood, hampering the development of new therapeutic approaches. Over the years, numerous basic and clinical studies have confirmed that advanced glycation end products (AGEs), oxidative stress, inflammation, as well as activation of protein kinases C, renin-angiotensin aldosterone system, and others have made valuable contributions to the pathogenesis and development of DKD. Among these, it is believed that increased production of reactive oxygen species (ROS) and subsequent oxidative stress contributes significantly to DKD development [[3]]. In attempt to counteract numerous environmental stressors and preserve normal cell function, kidney cells in DM patients developed delicate signaling systems, such as a homeostatic pathway for regulating membrane structure and secretory activity of endoplasmic reticulum (ER) (unfolded protein response, UPR). It was reported that activation of UPR in kidneys of DM patients contributes to ER’s functional restoration and preserved cell viability [[4]]. Growing evidence now suggests that endoplasmic reticulum stress (ERS) plays critical roles in the pathophysiological mechanisms of DKD. Studies have shown that changes in ER regulation of protein folding pathways cause ROS imbalance and increase their production, indirectly interfering with ER and redox balance [[5]]. ER’s main function in normal conditions is related to folding, modification and degradation of secretory binding proteins of the plasma membrane [[6, 7]]. We know that disturbed homeostasis in DM due to various factors, such as ROS, high serum glucose, free fatty acids, etc. lead to ERS reflected in ER accumulation of unfolded proteins [[8]]. Earlier studies showed that ERS plays a key role in diabetes [[9–13]]. By identifying disease-causing mutations in epithelial-restricted genes, recent studies have indicated the importance of severe or prolonged ERS for multiple organs’ degenerative diseases and fibrosis, including the pro-fibrosis role of UPR signaling in different cell types [[14]]. Many stressors encountered upon kidney injury may trigger ERS. In the kidney, despite ERS involvement in both acute and chronic histological damage, it may have nephroprotective effects and promote cellular adaptation [[15]]. Yet, pathological ERS activation may lead to inflammatory response, cell apoptosis, and alteration in protective processes, such as autophagy and mammalian target of rapamycin complex (mTORC) activation [[4]]. However, the way ERS participates in the generation and development of DKD is still not fully understood. Glucose-regulated protein 78 (GRP78) belongs to the family of heat-shock proteins (HSP70 family). It is also known as the immunoglobulin heavy chain binding protein (BiP). It is an ER lumen protein whose expression is induced during ERS and plays a novel protective role in preventing ERS-induced cell death [[16]]. As an ER chaperone, GRP78 modulates the UPR signaling network. In conditions of ERS, it dissociates from protein kinase R-like ER kinase (PERK) and binds to unfolded or misfolded proteins [[17]]. CCAAT/enhancer binding protein (C/EBP) homologous protein (CHOP) is another essential player in ERS-induced apoptotic cell death [[18]]. CHOP contains a C-terminal alkaline zinc finger (bZIP) domain and an N-terminal transcriptional activation threshold. The expression of CHOP can significantly affect cell survival. It is also known as growth arrest and DNA damage inducible gene 153 (GADD153) [[19]]. Each of the three ERS pathways can induce C/EBP source protein CHOP – is a translocation factor unique to ERS [[19]]. CHOP mainly exists in the cytoplasm, and its expression level is very low. When ERS is induced, the expression of CHOP increases substantially and is activated and translocated into the nucleus [[20]]. Overexpressed CHOP promotes cell cycle stagnation or apoptosis [[21]], but CHOP can also protect cells from apoptosis [[22]]. Acute kidney injury leads to perturbations of kidney cell pathways, which causes ER accumulation of unfolded/misfolded proteins and subsequent UPR or ERS. The cell fate in ERS depends on the balance between the UPR adaptive and apoptotic pathway [[23]]. The involvement of serum GRP78 and/or CHOP in the development of DKD through ERS pathway has not yet been elucidated. Although some observations have been made in animal studies, few have yet explored ERS in humans with DKD. Therefore, here we investigated the relationships of serum concentrations of GRP78 and CHOP in T2DM patients from China, particularly in patients with different severity categories of microalbuminuria, to test the hypothesis that ERS potently affects the pathophysiological mechanisms of DKD. Methods Subjects We enrolled 67 T2DM patients hospitalized at Lianyungang No. 1 People’s Hospital and 63 healthy patients from a medical examination center that were included as controls. T2DM was diagnosed as per American Diabetes Association diagnostic criteria from 2014 [[24]]: fasting glucose of 7.0 mmol/L or higher, glycosylated hemoglobin of 6.5% or higher, or oral glucose tolerance test (OGTT) showing plasma glucose of 11.1 mmol/L or higher at 2 hours after glucose load. Based on cystatin-c levels, two groups were defined, including Group A (Cys-c ≤ 1.03) and Group B (Cys-c > 1.03). Standard OGTT was also conducted in control group to confirm normal glucose tolerance. Histories of disease, smoking, and alcohol consumption were collected via a detailed questionnaire. The following were considered as exclusion: type 1 DM, secondary diabetes, pregnancy, thyroid diseases, endogenous or exogenous corticosteroid excess, acute or chronic virus hepatitis, malignant tumor, failure of major organs (such as heart, liver, kidneys), infection or inflammation. The study was performed in accordance with the Helsinki Declaration, and the Ethics Committee of our hospital approved the study. Each subject provided a written informed consent after understanding the study details. We followed the methods of Xing-bo Cheng et al 2017 [25]. Anthropometric data collection Based on hospital case files, the data on body height, body weight, waist circumference (WC), and hip circumference (HC) were obtained. We calculated waist-to-hip ratio (WHR) by dividing WC by HC. After resting in a sitting position for 10 minutes, before blood pressure was measured using Omron electronic sphygmomanometer. The average of three measurements of blood pressure was calculated. Biochemical measurements Patients had fasted overnight not less than 10 hours before venous blood samples were taken. Blood samples were taken at 07:00–08:00 in the morning, and centrifuged. Blood was tested for the following fasting parameters: fasting glucose, fasting c-peptide, fasting insulin (FIns), glycosylated hemoglobin (HbA1c), serum uric acid, CA19-9, carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), neuron-specific enolase (NSE), total homocysteine (tHcy), D-dimer. Serum lipidogram included total cholesterol (TC) and cholesterol fractions (high-density- and low-density lipoprotein cholesterol - HDL-C and LDL-C, respectively) and level of triglycerides (TG). For measuring insulin concentration we used an automated immunoassay analyzer (Beckman Coulter AU5800). Indices of insulin secretion and insulin sensitivity/resistance Insulin resistance status was assessed based on homeostasis model assessment of insulin resistance index (HOMA-IR) which was calculated as a product of fasting glucose (mmol/L) and fasting insulin (mIU/L), divided by 22.5 [[26]]. The following formulate was used to calculate insulin secretion index HOMA-β [[26, 27]]: HOMA-β = fasting insulin (mIU/L) × 20 / [fasting glucose (mmol/L) − 3.5]. Insulin sensitivity index - quantitative insulin check index (QUICKI) was determined as follows [[27]]: QUICKI = 1 / [log 10 fasting glucose (mg/dL) + log 10 fasting insulin (mIU/L)]. Different severity category of renal function Urinary micro albumin/creatinine (UmALB/Cr), blood urea nitrogen (BUN), creatinine (Cr), and cys-c were determined using a Beckman coulter AU5800 analyzer (Beckman coulter, Inc, USA). estimated glomerular filtration rate(eGFR)was used to evaluate the status of renal function by CKD-EPI formula [[28]]. Measurements of serum GRP78 and CHOP After centrifuging of blood samples, serum samples were preserved at − 80 °C for further analyses. Commercial ELISA kits (Cloud-Clone Corp., Wuhan, China) were used to determine serum concentrations of GRP78 and CHOP proteins, strictly complying with the instruction manual. The detection ranges of the GRP78 and CHOP assays were 0.312–20 ng/mL and 0.156–10 ng/mL, respectively, while minimum detectable doses were typically lower than 0.129 ng/mL (GRP78) and lower than 0.065 ng/mL (CHOP). The interassay and intraassay coefficients of variation were < 12% and 10% for both proteins. Statistical analysis Statistical analyses were conducted by means of SPSS v22.0 (SPSS Inc., Chicago, IL, USA). To test data distribution, Kolmogorov–Smirnov test was used. Mean with standard deviation (SD) was used for presenting normally distributed data, while median with interquartile range (IQR, 25th–75th) was used for non-normally distributed data (skewed distribution). Comparisons among categorical variables were conducted using Chi-square test. Differences in continuous variables between two groups were done using Kruskal–Wallis H test or one-way analysis of variance (ANOVA). For multiple comparisons among groups, Bonferroni correction was used after one way ANOVA or Kruskal–Wallis H test. To analyze correlations between GRP78, CHOP, and other variables, we used bivariate correlations. For identification of factors independently associated with GRP78 and CHOP and control for covariates, we performed multiple stepwise regression. Data not fitting to normal distribution underwent log-transformation (log- GRP78, CHOP) before correlation and regression analyses. A receiver operating characteristic (ROC) curve analysis was applied to determine the area under curve and cutoff value for potential of serum GRP78 and CHOP levels as biomarkers for DKD. A two-tailed p value below 0.05 was considered as significant. In addition, the power analysis showed that the effect size for GRP78 concentrations was 0.686 [95% CI 0.558–0.813] and for CHOP concentrations was 0.670 [95% CI 0.524–0.816]. Results Characteristics of study participants Table 1 shows the clinical parameters of the 67 T2DM and 63 health control patients. The groups did not differ in age, sex, and BMI. Serum GRP78 and CHOP concentrations were significantly lower ( p < 0.01) in T2DM than in control group. Table 1 General clinical and laboratory parameters of study participants Variable Normal control group T2DM group P value N 63 67 Sex (M/F) 34/29 37/30 0.886 Age (y) a 54.76 ± 18.77 59.34 ± 12.94 0.110 BMI (kg/m 2 ) b 26.27 ± 3.85 24.54 ± 4.32 0.018 SBP (mmHg) a 127.83 ± 21.294 147.45 ± 23.92 0.000 DBP (mmHg) a 72.05 ± 14.463 83.97 ± 12.691 0.000 Fasting glucose (mmol/L) a 5.27 ± 0.45 10.57 ± 5.30 0.000 HbA1c (%) a 5.48 ± 0.50 9.09 ± 2.24 0.000 Creatinine b 66.00 (53.00–73.00) 60.60 (50.20–85.60) 0.939 Blood urea nitrogen b 5.00 (4.00–7.00) 6.34 (4.96–8.79) 0.002 TC (mmol/L) a 4.25 ± 0.88 4.93 ± 1.68 0.004 TG (mmol/L) b 1.00 (1.00–1.00) 1.63 (1.17–2.94) 0.000 LDL-C (mmol/L) b 2.00(2.00–3.00) 2.87(2.07–3.15) 0.041 HDL-C (mmol/L) a 1.37 ± 0.49 1.10 ± 0.34 0.000 Serum uric acid a 294.11 ± 69.12 349.09 ± 138.05 0.005 CA19-9 a 8.86 ± 5.96 21.82 ± 13.16 0.000 AFP a 1.63 ± 0.79 3.25 ± 1.73 0.000 CEA b 1.00 (1.00–2.00) 2.89 (2.14–4.61) 0.000 GRP78 b 0.21(0.16–0.24) 0.16 (0.16–0.19) 0.000 CHOP a 0.29 ± 0.02 0.27 ± 0.03 0.000 BMI body mass index; WC waist circumference; WHR waist–hip ratio; SBP systolic blood pressure; DBP diastolic blood pressure; QUICKI: Quantitative Insulin Check Index. The enumeration data were compared with χ 2 test. a: Data normally distributed are shown as mean ± SD. Independent sample T test was performed. b: Data with skewed distributions are shown as median (IQR, 25th–75th). Mann–Whitney U test was performed. Serum GRP78 and CHOP concentrations As shown in Fig. 1 a, according to the cys-c, serum GRP78 level was significantly higher in DKD ( p = 0.008). As shown in Fig. 1 b, serum CHOP concentrations also showed significant differences ( p = 0.011). The biochemical and clinical parameters and of patients with DKD are shown in Table 2 . Table 2 General clinical and laboratory parameters in patients with DKD Variable Group A Group B p value N 42 25 Sex (M/F) 23/14 19/11 0.921 Age (y) a 56.93 ± 11.39 63.4 ± 14.53 0.064 BMI (kg/m2) a 24.75 ± 4.96 24.20 ± 3.06 0.620 WC(cm) a 93.15 ± 10.84 91.25 ± 6.81 0.477 WHR a 0.95 ± 0.07 0.95 ± 0.04 0.009 SBP(mmHg) a 143.83 ± 21.38 153.52 ± 27.05 0.109 DBP(mmHg) a 83.71 ± 12.26 84.40 ± 13.63 0.833 Duration of DM(month) a 113.63 ± 83.33 190.56 ± 115.43 0.003 Fasting glucose(mmol/l) a 10.66 ± 3.89 10.43 ± 7.16 0.882 Fasting insulin(mIU/l) b 8.41(5.12–13.12) 5.00(3.86–15.63) 0.422 Fasting c-peptide(pmol/l) b 553.45(366.75–812.38) 766.30(547.79–1182.00) 0.086 HbA1c(%) a 9.54 ± 2.17 8.33 ± 2.19 0.032 Creatinine b 52.45(47.83–60.30) 140.10(75.80–164.00) 0.000 Blood urea nitrogen b 5.35(4.68–6.40) 9.80(7.07–12.92) 0.000 UmALB/Cr b 52.05(6.80–183.20) 665.00(175.15–834.80) 0.000 TC(mmol/l) a 5.02 ± 1.84 4.79 ± 1.40 0.591 TG(mmol/l) b 1.60(1.08–3.02) 1.76(1.18–2.29) 1.000 LDL-C(mmol/l) b 2.86(2.27–3.14) 2.89(2.00–3.15) 0.932 HDL-C(mmol/l) a 1.11 ± 0.32 1.07 ± 0.37 0.672 THcy a 7.20 ± 4.65 13.27 ± 7.77 0.000 Serum uric acid a 304.46 ± 81.56 420.51 ± 177.12 0.001 AFP a 3.47 ± 1.63 2.90 ± 1.87 0.202 CEA b 3.46 ± 2.3 3.99 ± 1.96 0.343 CA199 a 21.78 ± 12.3 23.79 ± 13.7 0.553 NSE a 12.66 ± 2.69 13.00 ± 5.40 0.760 D-Dimer b 62.00(32.00–87.50) 175.00(93.50–292.25) 0.000 eGFR a 107.76 ± 11.54 75.13 ± 14.54 0.000 HOMA-IR b 3.50(2.00–6.00) 3.00(2.00–8.00) 0.487 HOMA-β b 12.50(7.00–24.00) 14.00(6.25–33.50) 0.650 QUICKI b 0.52(0.47–0.59) 0.56(0.44–0.64) 0.394 GRP78 b 0.16(0.15–0.17) 0.17(0.16–0.19) 0.011 CHOP a 0.28 ± 0.02 0.25 ± 0.036 0.008 Group A (T2DM group, Cystatin-C ≤ 1.03) Group B (T2DM group, Cystatin-C > 1.03) BMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; QUICKI: Quantitative Insulin Check Index. The enumeration data were compared with χ 2 test. a: Data normally distributed are shown as mean ± SD. Independent sample T test was performed. b:Data with skewed distribution are shown as median (IQR, 25th–75th). Mann-Whitney U test was performed. Correlations and regression analysis between serum GRP78 and CHOP concentrations and clinical parameters Serum GRP78 level was negatively correlated with eGFR and positively correlated with fasting c-peptide, Cr, BUN, cys-c, and serum uric acid. Serum CHOP level was positively correlated with age, Cr, BUN, cys-c, UmALB/Cr, and eGFR (Tables 3 – 6 ). Table 3 Bivariate correlation between GRP78 levels and other variables. GRP78 r P Fasting c-peptide(pmol/l) 0.258 * 0.045 Creatinine 0.401 ** 0.001 Blood urea nitrogen 0.244 * 0.047 Cys-c 0.426 ** 0.000 Serum uric acid 0.360 ** 0.003 eGFR −0.319 ** 0.009 CHOP −0.256 * 0.037 Pearson correlation analysis was used. P value < 0.05 was considered significant. ** significant differences ( p < 0.01). Table 4 Bivariate correlation between CHOP levels and other variables. CHOP r P Age (y) −0.309 * 0.011 Creatinine −0.282 * 0.021 Blood urea nitrogen −0.383 ** 0.001 Cys-c −0.462 ** 0.000 UmALB/Cr −0.319 ** 0.008 eGFR 0.451 ** 0.000 GRP78 −0.256 * 0.037 Pearson correlation analysis was used. P value < 0.05 was considered significant. ** significant differences ( p < 0.01) Table 5 Multiple stepwise regression analysis: independent factors associated with GRP78 levels in patients with T2DM. Independent factors β (unstandardized coefficient) Std. error t P value Fasting c-peptide(pmol/l) < 0.001 0.000 2.053 0.045 Creatinine 0.000 0.000 3.532 0.001 Blood urea nitrogen 0.002 0.001 2.024 0.047 Cys-c 0.018 0.005 3.792 0.000 Serum uric acid < 0.001 0.000 3.063 0.003 eGFR 0.000 0.000 −2.709 0.009 CHOP −0.243 0.114 −2.132 0.037 Table 6 Multiple stepwise regression analysis: independent factors associated with CHOP levels in patients with T2DM. Independent factors β (unstandardized coefficient) Std. error t P value Age (y) −0.001 0.000 0.000 0.011 Creatinine 0.000 0.000 −2.372 0.021 Blood urea nitrogen −0.003 0.001 −3.341 0.001 UmALB/Cr <-0.001 0.000 −2.717 0.008 Cys-c −0.020 0.005 −4.198 0.000 AFP 0.004 0.002 1.957 0.055 eGFR 0.001 0.000 4.075 0.000 GRP78 −0.268 0.126 −2.132 0.037 T Serum GRP78 and CHOP concentrations and DKD As shown in Fig. 2 , the area under the curve of GRP78 for DKD prediction was 0.686 [95% CI 0.558–0.813], and that of CHOP was 0.670 [95% CI 0.524–0.816]. Discussion Recent studies suggest that development of diabetic nephropathy (DN) is partly caused by ER dysfunction [[29]]. High glucose may induce ERS in podocytes. ERS upregulates GRP78 expression, activates the CHOP pathway and caspase-12 pathway, and causes apoptosis of mouse podocytes, which may be related to the development process of DKD [[30]]. There is already evidence for the involvement of ERS-mediated apoptosis in development of diabetic complications in eyes and kidneys, but also in pathogenesis of non-diabetic neurodegenerative changes. For instance, a study in hippocampal neurons of diabetic mice induced by streptozocin (STZ) showed a reduced expression of GRP78 along with higher expression of the UPR-associated pro-apoptotic regulator CHOP [[31]]. Wu et al. have shown that GRP78 levels in renal tissue are higher than CHOP, JUK, and the caspase-12 pathway. The parallel relationship between expression and transcription of death signals suggests that excessive ERS promotes progressive damage of DKD by increasing apoptosis [[18]]. Expression of nuclear transcription factor rBp65, CHOP, and GRP78 were increased in DN rats with myocardial infarction compared with control rats with myocardial infarction. In addition, the degree of podocyte damage caused by high-glucose-mediated ERS was more severe, which deformed the structure and function of the glomerulus [[32]].Cao et al.. induced a DN model by unilateral nephrectomy combined with single STZ (65 mg/kg) injection intraperitoneally in rats. GRP78 was found by histochemical staining in diabetic rats compared with controls, and the expression levels of renal glomerular and tubular epithelial cells were upregulated[[33]]. Lindenmeye and colleagues confirmed that, compared with mild diabetes, mRNA expression of GRP78, oxyregulatory protein 150, and transcription molecule X-box binding protein-1(Xbp-1) of diabetic patients increased in the kidneys, indicating that ERS was stimulated in human DN [[34]]. These studies suggest that ERS is a central link in the development of a variety of systemic chronic metabolic diseases including T2DM, and it is also coupled with inflammatory responses, oxidative stress, autophagy, apoptosis, and other signaling pathways [[35]]. In this study, the classic proteins of ERS, GRP78, and CHOP, were measured and compared with cys-c, urinary microalbumin, eGFR, and other indicators for prediction of DKD. We found higher serum concentrations of GRP78 and CHOP in T2DM group than in controls ( p < 0.01). GRP78 and CHOP concentrations were significantly increased during DKD (GRP78: p = 0.008; CHOP: p = 0.011). Urinary micro albumin creatinine ratio (UACR) or eGFR are usually chosen as a standard, but in this study cystatin-c (Cys-c) was used as a grouping indicator. Increased UACR and decreased eGFR are closely related to higher risk of adverse cardiovascular events and death. UmALB/Cr is usually used as the evaluation index of DKD, but UACR measures the influence of various factors, e.g., hypertension, heart failure, infection, hyperglycemia. Microalbuminuria as a marker of DKD progression has been challenged [[36, 37]].Early DKD is often associated with eGFR, a phenomenon known as high glomerular hyperfiltration. A cross-sectional survey showed that some diabetic patients did not have abnormal urinary albumin excretion but had decreased eGFR [[38, 39]]. Calculation of eGFR requires information on patient age and sex, as well as serum Cr level. When a patient’s eGFR < 60 mL, a decrease in eGFR can be diagnosed. However, the eGFR value may fluctuate and should be reviewed when a decrease occurs to determine the DKD stage. eGFR decline is closely associated with a higher cardiovascular risk and risk of death. Recent studies from China have shown that even mild eGFR decline can increase cardiovascular risk [[40]]. Cys-c, a low molecular weight protein that can be produced by all nucleated cells in the body, was not glycosylated, and its production rate is constant and is not affected by the patient’s age, gender, etc. Therefore, serum Cys-c level mainly depends on the filtration rate of the glomerulus, which, together with urine α-microglobulin, IgG or IgM, and IV collagen, are sensitive indicators for early diagnosis of DKD [[41]]. In this study, we therefore used Cys-c as a grouping indicator. Hitherto, the mechanisms behind lower levels in DKD have not been clarified. Notably, GRP78 and CHOP are closely related to DKD as animal studies have shown; herein, we demonstrated that GRP78 and CHOP in human serum correlated with DKD. Hence, we assumed a possible variation in levels of GRP78 between the subgroups divided by cys-c. Together with previous studies, the results reported herein suggest that GRP78 and CHOP levels may have potential to be used as biomarkers of the DKD risk. Nevertheless, this study had a few limitations. Based on cross-sectional design of the study the potential influence of increased GRP78 and CHOP levels on the development of T2DM could not be evaluated, and further studies are warranted to further clarify this issue. The strength of our conclusions and wide extrapolation to the general population is limited by a relatively small sample size and single center study design. In addition, the study encompassed single measurements of fasting serum GRP78 and CHOP levels. That approach was based on limited funds and does not reflect any time-dependent fluctuations in GRP78 and CHOP levels, which is of particular interest after macronutrient consumption. Therefore, further studies are still necessary. In summary, GRP78 and CHOP serum levels are increased in T2DM patients from China. Furthermore, DKD patients had greater reductions in GRP78 and CHOP levels. Conclusions Here we showed evidence for the importance of ERS as well as associations of GRP78 and CHOP with DKD, which may lead to new therapeutic directions for renal complications of diabetes. With consideration of the roles of GRP78 and CHOP and involvement of ERS in other diabetic microvascular complications, it will be needed to further analyze the exact roles of ERS/UPR in DM-related complications, as well as evaluate interactions of ERS and biochemical parameters and their relationship with DKD. Our data highlight the possibility of using serum indicators of ERS as biomarkers of DKD. Therefore, with further study to elucidate the underlying mechanisms behind these effects, there may be a chance to improve treatment of DKD through improved regulation of ERS. Abbreviations ERS:endoplasmic reticulum stress;diabetic kidney disease:DKD;glucose-regulated protein 78:GRP78;CCAAT/enhancer binding protein homologous protein:CHOP;type 2 diabetes mellitus:T2DM;cystatin-c:Cys-c;creatinine:Cr;blood urea nitrogen:BUN;urinary microalbumin/creatinine:UmALB/Cr;receiver operating characteristic:ROC; diabetes mellitus:DM;cases of end-stage renal disorders:ESRD;advanced glycation end products:AGEs;endoplasmic reticulum:ER;unfolded protein response:UPR;mammalian target of rapamycin complex:mTORC;immunoglobulin heavy chain binding protein:BiP;CCAAT/enhancer binding protein:C/EBP;growth arrest and DNA damage inducible gene 153:GADD153;oral glucose tolerance test:OGTT;waist circumference:WC;hip circumference:HC;waist-to-hip ratio:WHR;fasting insulin:Fins;glycosylated hemoglobin:HbA1c;carcinoembryonic antigen:CEA;alpha-fetoprotein:AFP;neuron-specific enolase:NSE;total homocysteine:tHcy;total cholesterol:TC;high-density lipoprotein cholesterol:HDL-C;low-density lipoprotein cholesterol:LDL-C;triglycerides:TG;homeostasis model assessment of insulin resistance index:HOMA-IR;homeostasis model assessment of insulin secretion index:HOMA-β;quantitative insulin check index:QUICKI; estimated glomerular filtration rate:eGFR;diabetic nephropathy:DN;streptozocin:STZ;X-box binding protein-1:Xbp-1;micro albumin creatinine ratio:UACR Declarations Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration. All participants gave their written informed consent prior to their participation in our study. The study was approved by the Ethics Committee of Lianyungang No1 People’s Hospital (Protocol number: 2018–0522). Consent for publication The consent for publication is not required since no personal or identifying information of participants are contained within the manuscript or supplementary materials. Availability of data and material The serum expression data of GRP78 and CHOP of Chinese Type 2 Diabetic Kidney Disease patients used to support the findings of this study are restricted by the Ethics Committee of the First People’s Hospital of Lianyungang in order to protect patient privacy. Data are available from Ning Ma, [email protected] for researchers who meet the criteria for access to confidential data. Competing interests The authors declare that there is no conflict of interest regarding the publication of this paper. Funding This work was supported by Jiangsu Provincial Commission of Health and Family Planning (Grant NO.Z2018021) and Lianyungang Commission Health Foundation (Grant NO.zd1802). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Authors’ contributions All the authors engaged in the study. XBC and NM designed this article. WWL,PZ,NM,GFW and YH acquired and collected the data. NX,NM,DY,GJH and CHY organized all the data. NM,PZ and WWL analyzed all the information. NM,CXB,WWL and NX drafted the manuscript. XBC, NM and NX revised the article critically. All authors read and approved the final manuscript. Acknowledgments We express our sincere thanks to all the volunteers and nurses who offered help in the study. Author details 1 Department of Endocrinology and Metabolism, First Affiliated Hospital of Soochow University, 188 Shizi Road, Suzhou, Jiangsu, 215006, China. 2 Department of Endocrinology and Metabolism, Lianyungang No1 People’s Hospital, 6 Zhenghua Road, Lianyungang, Jiangsu, 222002, China References Zhang L, Wang F, Wang L, Wang W, Liu B, Liu J, Chen M, He Q, Liao Y, Yu X et al : Prevalence of chronic kidney disease in China: a cross-sectional survey . Lancet 2012, 379 (9818):815-822. Sharma D, Bhattacharya P, Kalia K, Tiwari V: Diabetic nephropathy: New insights into established therapeutic paradigms and novel molecular targets . Diabetes Res Clin Pract 2017, 128 :91-108. Badal SS, Danesh FR: New insights into molecular mechanisms of diabetic kidney disease . Am J Kidney Dis 2014, 63 (2 Suppl 2):S63-83. Cunard R: Endoplasmic Reticulum Stress in the Diabetic Kidney, the Good, the Bad and the Ugly . J Clin Med 2015, 4 (4):715-740. Hasanain M, Bhattacharjee A, Pandey P, Ashraf R, Singh N, Sharma S, Vishwakarma AL, Datta D, Mitra K, Sarkar J: α-Solanine induces ROS-mediated autophagy through activation of endoplasmic reticulum stress and inhibition of Akt/mTOR pathway . Cell Death Dis 2015, 6 :e1860. Hetz C, Mollereau B: Disturbance of endoplasmic reticulum proteostasis in neurodegenerative diseases . Nat Rev Neurosci 2014, 15 (4):233-249. Wang M, Kaufman RJ: Protein misfolding in the endoplasmic reticulum as a conduit to human disease . Nature 2016, 529 (7586):326-335. Chen Y, Gui D, Chen J, He D, Luo Y, Wang N: Down-regulation of PERK-ATF4-CHOP pathway by Astragaloside IV is associated with the inhibition of endoplasmic reticulum stress-induced podocyte apoptosis in diabetic rats . Cell Physiol Biochem 2014, 33 (6):1975-1987. Eizirik DL, Cardozo AK, Cnop M: The role for endoplasmic reticulum stress in diabetes mellitus . Endocr Rev 2008, 29 (1):42-61. Hotamisligil GS: Endoplasmic reticulum stress and the inflammatory basis of metabolic disease . Cell 2010, 140 (6):900-917. Fonseca SG, Lipson KL, Urano F: Endoplasmic reticulum stress signaling in pancreatic beta-cells . Antioxid Redox Signal 2007, 9 (12):2335-2344. Maris M, Overbergh L, Gysemans C, Waget A, Cardozo AK, Verdrengh E, Cunha JP, Gotoh T, Cnop M, Eizirik DL et al : Deletion of C/EBP homologous protein (Chop) in C57Bl/6 mice dissociates obesity from insulin resistance . Diabetologia 2012, 55 (4):1167-1178. Kars M, Yang L, Gregor MF, Mohammed BS, Pietka TA, Finck BN, Patterson BW, Horton JD, Mittendorfer B, Hotamisligil GS et al : Tauroursodeoxycholic Acid may improve liver and muscle but not adipose tissue insulin sensitivity in obese men and women . Diabetes 2010, 59 (8):1899-1905. Kropski JA, Blackwell TS: Endoplasmic reticulum stress in the pathogenesis of fibrotic disease . J Clin Invest 2018, 128 (1):64-73. Gallazzini M, Pallet N: Endoplasmic reticulum stress and kidney dysfunction . Biol Cell 2018, 110 (9):205-216. Rao RV, Peel A, Logvinova A, del Rio G, Hermel E, Yokota T, Goldsmith PC, Ellerby LM, Ellerby HM, Bredesen DE: Coupling endoplasmic reticulum stress to the cell death program: role of the ER chaperone GRP78 . FEBS Lett 2002, 514 (2-3):122-128. Cybulsky AV: Endoplasmic reticulum stress, the unfolded protein response and autophagy in kidney diseases . Nat Rev Nephrol 2017, 13 (11):681-696. Wu X, He Y, Jing Y, Li K, Zhang J: Albumin overload induces apoptosis in renal tubular epithelial cells through a CHOP-dependent pathway . OMICS 2010, 14 (1):61-73. Oyadomari S, Mori M: Roles of CHOP/GADD153 in endoplasmic reticulum stress . Cell Death Differ 2004, 11 (4):381-389. Ron D, Habener JF: CHOP, a novel developmentally regulated nuclear protein that dimerizes with transcription factors C/EBP and LAP and functions as a dominant-negative inhibitor of gene transcription . Genes Dev 1992, 6 (3):439-453. McCullough KD, Martindale JL, Klotz LO, Aw TY, Holbrook NJ: Gadd153 sensitizes cells to endoplasmic reticulum stress by down-regulating Bcl2 and perturbing the cellular redox state . Mol Cell Biol 2001, 21 (4):1249-1259. Oyadomari S, Koizumi A, Takeda K, Gotoh T, Akira S, Araki E, Mori M: Targeted disruption of the Chop gene delays endoplasmic reticulum stress-mediated diabetes . J Clin Invest 2002, 109 (4):525-532. Yan M, Shu S, Guo C, Tang C, Dong Z: Endoplasmic reticulum stress in ischemic and nephrotoxic acute kidney injury . Ann Med 2018, 50 (5):381-390. Association AD: Standards of medical care in diabetes--2014 . Diabetes Care 2014, 37 Suppl 1 :S14-80. Zhang L, Chen C, Zhou N, Fu Y, Cheng X: Circulating asprosin concentrations are increased in type 2 diabetes mellitus and independently associated with fasting glucose and triglyceride . Clin Chim Acta 2019, 489 :183-188. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man . Diabetologia 1985, 28 (7):412-419. Seltzer HS, Allen EW, Herron AL, Brennan MT: Insulin secretion in response to glycemic stimulus: relation of delayed initial release to carbohydrate intolerance in mild diabetes mellitus . J Clin Invest 1967, 46 (3):323-335. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T et al : A new equation to estimate glomerular filtration rate . Ann Intern Med 2009, 150 (9):604-612. Cunard R, Sharma K: The endoplasmic reticulum stress response and diabetic kidney disease . Am J Physiol Renal Physiol 2011, 300 (5):F1054-1061. Cao Y, Hao Y, Li H, Liu Q, Gao F, Liu W, Duan H: Role of endoplasmic reticulum stress in apoptosis of differentiated mouse podocytes induced by high glucose . Int J Mol Med 2014, 33 (4):809-816. Zhao Y, Yan Y, Zhao Z, Li S, Yin J: The dynamic changes of endoplasmic reticulum stress pathway markers GRP78 and CHOP in the hippocampus of diabetic mice . Brain Res Bull 2015, 111 :27-35. Dong Z, Wu P, Li Y, Shen Y, Xin P, Li S, Wang Z, Dai X, Zhu W, Wei M: Myocardial infarction worsens glomerular injury and microalbuminuria in rats with pre-existing renal impairment accompanied by the activation of ER stress and inflammation . Mol Biol Rep 2014, 41 (12):7911-7921. Cao YP, Hao YM, Liu QJ, Wang J, Li H, Duan HJ: [The relationship between endoplasmic reticulum stress and its particular apoptosis way caspase-12 and apoptosis in renal cortex of diabetic rats] . Zhongguo Ying Yong Sheng Li Xue Za Zhi 2011, 27 (2):236-240. Lindenmeyer MT, Rastaldi MP, Ikehata M, Neusser MA, Kretzler M, Cohen CD, Schlöndorff D: Proteinuria and hyperglycemia induce endoplasmic reticulum stress . J Am Soc Nephrol 2008, 19 (11):2225-2236. Ozcan L, Tabas I: Role of endoplasmic reticulum stress in metabolic disease and other disorders . Annu Rev Med 2012, 63 :317-328. Lee SY, Choi ME: Urinary biomarkers for early diabetic nephropathy: beyond albuminuria . Pediatr Nephrol 2015, 30 (7):1063-1075. Marshall SM: Natural history and clinical characteristics of CKD in type 1 and type 2 diabetes mellitus . Adv Chronic Kidney Dis 2014, 21 (3):267-272. de Boer IH, Rue TC, Hall YN, Heagerty PJ, Weiss NS, Himmelfarb J: Temporal trends in the prevalence of diabetic kidney disease in the United States . JAMA 2011, 305 (24):2532-2539. Dwyer JP, Parving HH, Hunsicker LG, Ravid M, Remuzzi G, Lewis JB: Renal Dysfunction in the Presence of Normoalbuminuria in Type 2 Diabetes: Results from the DEMAND Study . Cardiorenal Med 2012, 2 (1):1-10. Lu J, Mu Y, Su Q, Shi L, Liu C, Zhao J, Chen L, Li Q, Yang T, Yan L et al : Reduced Kidney Function Is Associated With Cardiometabolic Risk Factors, Prevalent and Predicted Risk of Cardiovascular Disease in Chinese Adults: Results From the REACTION Study . J Am Heart Assoc 2016, 5 (7). Lin CH, Chang YC, Chuang LM: Early detection of diabetic kidney disease: Present limitations and future perspectives . World J Diabetes 2016, 7 (14):290-301. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-41272","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":806261,"identity":"e455561e-ad38-44d4-92eb-a0c0fb0a1895","order_by":0,"name":"Ning Ma","email":"","orcid":"","institution":"Lianyungang No 1 People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Ma","suffix":""},{"id":806262,"identity":"3b24f737-9357-49f2-9b5b-891ac262397e","order_by":1,"name":"Ning Xu","email":"","orcid":"","institution":"Lianyungang No 1 People's 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15:43:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-41272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-41272/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1555818,"identity":"dd161ebb-fe66-4371-b21f-7fef37591476","added_by":"auto","created_at":"2020-07-14 18:02:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24719,"visible":true,"origin":"","legend":"(a) Median (IQR) plasma GRP78 levels in Chinese type 2 diabetic patients.\n(b) Mean ± SD plasma CHOP levels in Chinese type 2 diabetic patients.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-41272/v1/1.jpg"},{"id":1555819,"identity":"f3f07509-001c-49d9-a75a-b3d1a47d058c","added_by":"auto","created_at":"2020-07-14 18:02:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34640,"visible":true,"origin":"","legend":"(a)The crude AUC of the ROC curve of plasma GRP78 levels in Chinese type 2 diabetic patients for predicting the presence of DKD.\n(b)The crude AUC of the ROC curve of plasma CHOP levels in Chinese type 2 diabetic patients for predicting the presence of DKD.\n","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-41272/v1/2.jpg"},{"id":13551108,"identity":"e19dd8b4-02f1-420f-bfb0-a132127a2d1f","added_by":"auto","created_at":"2021-09-17 02:27:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1353322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-41272/v1/93a14011-be36-4e2c-bac0-68306efd67b1.pdf"}],"financialInterests":"","formattedTitle":"Expression of Serum GRP78 and CHOP in Endoplasmic Reticulum Stress Pathways of Chinese Type 2 Diabetic Kidney Disease Patients","fulltext":[{"header":"Background","content":" \u003cp\u003eDiabetic kidney disease (DKD) represents an important health problem worldwide with millions of people affected. As a microvascular complication of diabetes mellitus (DM), it is responsible for substantial proportion of end-stage kidney disease cases. It is estimated that there are approximately 120\u0026nbsp;million people with chronic kidney disease in China [[1]]. DKD accounts for 30\u0026ndash;47% of cases of end-stage renal disorders (ESRD) and is a major cause of death in DM patients [[2]]. However, the underlying pathophysiological mechanisms of DKD remain incompletely understood, hampering the development of new therapeutic approaches. Over the years, numerous basic and clinical studies have confirmed that advanced glycation end products (AGEs), oxidative stress, inflammation, as well as activation of protein kinases C, renin-angiotensin aldosterone system, and others have made valuable contributions to the pathogenesis and development of DKD. Among these, it is believed that increased production of reactive oxygen species (ROS) and subsequent oxidative stress contributes significantly to DKD development [[3]].\u003c/p\u003e \u003cp\u003eIn attempt to counteract numerous environmental stressors and preserve normal cell function, kidney cells in DM patients developed delicate signaling systems, such as a homeostatic pathway for regulating membrane structure and secretory activity of endoplasmic reticulum (ER) (unfolded protein response, UPR). It was reported that activation of UPR in kidneys of DM patients contributes to ER\u0026rsquo;s functional restoration and preserved cell viability [[4]]. Growing evidence now suggests that endoplasmic reticulum stress (ERS) plays critical roles in the pathophysiological mechanisms of DKD. Studies have shown that changes in ER regulation of protein folding pathways cause ROS imbalance and increase their production, indirectly interfering with ER and redox balance [[5]].\u003c/p\u003e \u003cp\u003eER\u0026rsquo;s main function in normal conditions is related to folding, modification and degradation of secretory binding proteins of the plasma membrane [[6, 7]]. We know that disturbed homeostasis in DM due to various factors, such as ROS, high serum glucose, free fatty acids, etc. lead to ERS reflected in ER accumulation of unfolded proteins [[8]]. Earlier studies showed that ERS plays a key role in diabetes [[9\u0026ndash;13]]. By identifying disease-causing mutations in epithelial-restricted genes, recent studies have indicated the importance of severe or prolonged ERS for multiple organs\u0026rsquo; degenerative diseases and fibrosis, including the pro-fibrosis role of UPR signaling in different cell types [[14]]. Many stressors encountered upon kidney injury may trigger ERS. In the kidney, despite ERS involvement in both acute and chronic histological damage, it may have nephroprotective effects and promote cellular adaptation [[15]]. Yet, pathological ERS activation may lead to inflammatory response, cell apoptosis, and alteration in protective processes, such as autophagy and mammalian target of rapamycin complex (mTORC) activation [[4]]. However, the way ERS participates in the generation and development of DKD is still not fully understood.\u003c/p\u003e \u003cp\u003eGlucose-regulated protein 78 (GRP78) belongs to the family of heat-shock proteins (HSP70 family). It is also known as the immunoglobulin heavy chain binding protein (BiP). It is an ER lumen protein whose expression is induced during ERS and plays a novel protective role in preventing ERS-induced cell death [[16]]. As an ER chaperone, GRP78 modulates the UPR signaling network. In conditions of ERS, it dissociates from protein kinase R-like ER kinase (PERK) and binds to unfolded or misfolded proteins [[17]].\u003c/p\u003e \u003cp\u003eCCAAT/enhancer binding protein (C/EBP) homologous protein (CHOP) is another essential player in ERS-induced apoptotic cell death [[18]]. CHOP contains a C-terminal alkaline zinc finger (bZIP) domain and an N-terminal transcriptional activation threshold. The expression of CHOP can significantly affect cell survival. It is also known as growth arrest and DNA damage inducible gene 153 (GADD153) [[19]]. Each of the three ERS pathways can induce C/EBP source protein CHOP \u0026ndash; is a translocation factor unique to ERS [[19]]. CHOP mainly exists in the cytoplasm, and its expression level is very low. When ERS is induced, the expression of CHOP increases substantially and is activated and translocated into the nucleus [[20]]. Overexpressed CHOP promotes cell cycle stagnation or apoptosis [[21]], but CHOP can also protect cells from apoptosis [[22]].\u003c/p\u003e \u003cp\u003eAcute kidney injury leads to perturbations of kidney cell pathways, which causes ER accumulation of unfolded/misfolded proteins and subsequent UPR or ERS. The cell fate in ERS depends on the balance between the UPR adaptive and apoptotic pathway [[23]]. The involvement of serum GRP78 and/or CHOP in the development of DKD through ERS pathway has not yet been elucidated. Although some observations have been made in animal studies, few have yet explored ERS in humans with DKD. Therefore, here we investigated the relationships of serum concentrations of GRP78 and CHOP in T2DM patients from China, particularly in patients with different severity categories of microalbuminuria, to test the hypothesis that ERS potently affects the pathophysiological mechanisms of DKD.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eWe enrolled 67 T2DM patients hospitalized at Lianyungang No. 1 People\u0026rsquo;s Hospital and 63 healthy patients from a medical examination center that were included as controls. T2DM was diagnosed as per American Diabetes Association diagnostic criteria from 2014 [[24]]: fasting glucose of 7.0\u0026nbsp;mmol/L or higher, glycosylated hemoglobin of 6.5% or higher, or oral glucose tolerance test (OGTT) showing plasma glucose of 11.1\u0026nbsp;mmol/L or higher at 2 hours after glucose load. Based on cystatin-c levels, two groups were defined, including Group A (Cys-c\u0026thinsp;\u0026le;\u0026thinsp;1.03) and Group B (Cys-c\u0026thinsp;\u0026gt;\u0026thinsp;1.03). Standard OGTT was also conducted in control group to confirm normal glucose tolerance. Histories of disease, smoking, and alcohol consumption were collected via a detailed questionnaire. The following were considered as exclusion: type 1 DM, secondary diabetes, pregnancy, thyroid diseases, endogenous or exogenous corticosteroid excess, acute or chronic virus hepatitis, malignant tumor, failure of major organs (such as heart, liver, kidneys), infection or inflammation. The study was performed in accordance with the Helsinki Declaration, and the Ethics Committee of our hospital approved the study. Each subject provided a written informed consent after understanding the study details. We followed the methods of Xing-bo Cheng et al 2017 [25].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric data collection\u003c/h2\u003e \u003cp\u003eBased on hospital case files, the data on body height, body weight, waist circumference (WC), and hip circumference (HC) were obtained. We calculated waist-to-hip ratio (WHR) by dividing WC by HC. After resting in a sitting position for 10 minutes, before blood pressure was measured using Omron electronic sphygmomanometer. The average of three measurements of blood pressure was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical measurements\u003c/h2\u003e \u003cp\u003ePatients had fasted overnight not less than 10 hours before venous blood samples were taken. Blood samples were taken at 07:00\u0026ndash;08:00 in the morning, and centrifuged. Blood was tested for the following fasting parameters: fasting glucose, fasting c-peptide, fasting insulin (FIns), glycosylated hemoglobin (HbA1c), serum uric acid, CA19-9, carcinoembryonic antigen (CEA), alpha-fetoprotein (AFP), neuron-specific enolase (NSE), total homocysteine (tHcy), D-dimer. Serum lipidogram included total cholesterol (TC) and cholesterol fractions (high-density- and low-density lipoprotein cholesterol - HDL-C and LDL-C, respectively) and level of triglycerides (TG). For measuring insulin concentration we used an automated immunoassay analyzer (Beckman Coulter AU5800).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIndices of insulin secretion and insulin sensitivity/resistance\u003c/h2\u003e \u003cp\u003eInsulin resistance status was assessed based on homeostasis model assessment of insulin resistance index (HOMA-IR) which was calculated as a product of fasting glucose (mmol/L) and fasting insulin (mIU/L), divided by 22.5 [[26]]. The following formulate was used to calculate insulin secretion index HOMA-β [[26, 27]]: HOMA-β\u0026thinsp;=\u0026thinsp;fasting insulin (mIU/L)\u0026thinsp;\u0026times;\u0026thinsp;20 / [fasting glucose (mmol/L)\u0026thinsp;\u0026minus;\u0026thinsp;3.5]. Insulin sensitivity index - quantitative insulin check index (QUICKI) was determined as follows [[27]]: QUICKI\u0026thinsp;=\u0026thinsp;1 / [log\u003csub\u003e10\u003c/sub\u003e fasting glucose (mg/dL)\u0026thinsp;+\u0026thinsp;log\u003csub\u003e10\u003c/sub\u003e fasting insulin (mIU/L)].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDifferent severity category of renal function\u003c/h2\u003e \u003cp\u003eUrinary micro albumin/creatinine (UmALB/Cr), blood urea nitrogen (BUN), creatinine (Cr), and cys-c were determined using a Beckman coulter AU5800 analyzer (Beckman coulter, Inc, USA). estimated glomerular filtration rate(eGFR)was used to evaluate the status of renal function by CKD-EPI formula [[28]].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements of serum GRP78 and CHOP\u003c/h2\u003e \u003cp\u003eAfter centrifuging of blood samples, serum samples were preserved at \u0026minus;\u0026thinsp;80\u0026nbsp;\u0026deg;C for further analyses. Commercial ELISA kits (Cloud-Clone Corp., Wuhan, China) were used to determine serum concentrations of GRP78 and CHOP proteins, strictly complying with the instruction manual. The detection ranges of the GRP78 and CHOP assays were 0.312\u0026ndash;20\u0026nbsp;ng/mL and 0.156\u0026ndash;10\u0026nbsp;ng/mL, respectively, while minimum detectable doses were typically lower than 0.129\u0026nbsp;ng/mL (GRP78) and lower than 0.065\u0026nbsp;ng/mL (CHOP). The interassay and intraassay coefficients of variation were \u0026lt;\u0026thinsp;12% and 10% for both proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted by means of SPSS v22.0 (SPSS Inc., Chicago, IL, USA). To test data distribution, Kolmogorov\u0026ndash;Smirnov test was used. Mean with standard deviation (SD) was used for presenting normally distributed data, while median with interquartile range (IQR, 25th\u0026ndash;75th) was used for non-normally distributed data (skewed distribution). Comparisons among categorical variables were conducted using Chi-square test. Differences in continuous variables between two groups were done using Kruskal\u0026ndash;Wallis H test or one-way analysis of variance (ANOVA). For multiple comparisons among groups, Bonferroni correction was used after one way ANOVA or Kruskal\u0026ndash;Wallis H test. To analyze correlations between GRP78, CHOP, and other variables, we used bivariate correlations. For identification of factors independently associated with GRP78 and CHOP and control for covariates, we performed multiple stepwise regression. Data not fitting to normal distribution underwent log-transformation (log- GRP78, CHOP) before correlation and regression analyses. A receiver operating characteristic (ROC) curve analysis was applied to determine the area under curve and cutoff value for potential of serum GRP78 and CHOP levels as biomarkers for DKD. A two-tailed \u003cem\u003ep\u003c/em\u003e value below 0.05 was considered as significant. In addition, the power analysis showed that the effect size for GRP78 concentrations was 0.686 [95% CI 0.558\u0026ndash;0.813] and for CHOP concentrations was 0.670 [95% CI 0.524\u0026ndash;0.816].\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the clinical parameters of the 67 T2DM and 63 health control patients. The groups did not differ in age, sex, and BMI. Serum GRP78 and CHOP concentrations were significantly lower (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in T2DM than in control group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral clinical and laboratory parameters of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal control group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT2DM group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.76\u0026thinsp;\u0026plusmn;\u0026thinsp;18.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.34\u0026thinsp;\u0026plusmn;\u0026thinsp;12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.27\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg) \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.83\u0026thinsp;\u0026plusmn;\u0026thinsp;21.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147.45\u0026thinsp;\u0026plusmn;\u0026thinsp;23.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.05\u0026thinsp;\u0026plusmn;\u0026thinsp;14.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.97\u0026thinsp;\u0026plusmn;\u0026thinsp;12.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mmol/L)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.00 (53.00\u0026ndash;73.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.60 (50.20\u0026ndash;85.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.00 (4.00\u0026ndash;7.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.34 (4.96\u0026ndash;8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63 (1.17\u0026ndash;2.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00(2.00\u0026ndash;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87(2.07\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum uric acid\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294.11\u0026thinsp;\u0026plusmn;\u0026thinsp;69.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e349.09\u0026thinsp;\u0026plusmn;\u0026thinsp;138.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA19-9\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.82\u0026thinsp;\u0026plusmn;\u0026thinsp;13.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89 (2.14\u0026ndash;4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRP78\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21(0.16\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16 (0.16\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBMI body mass index; WC waist circumference; WHR waist\u0026ndash;hip ratio; SBP systolic blood pressure; DBP diastolic blood pressure; QUICKI: Quantitative Insulin Check Index. The enumeration data were compared with χ\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e \u003cp\u003ea: Data normally distributed are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Independent sample T test was performed.\u003c/p\u003e \u003cp\u003eb: Data with skewed distributions are shown as median (IQR, 25th\u0026ndash;75th). Mann\u0026ndash;Whitney U test was performed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSerum GRP78 and CHOP concentrations\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, according to the cys-c, serum GRP78 level was significantly higher in DKD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, serum CHOP concentrations also showed significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). The biochemical and clinical parameters and of patients with DKD are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral clinical and laboratory parameters in patients with DKD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (M/F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23/14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.93\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.75\u0026thinsp;\u0026plusmn;\u0026thinsp;4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC(cm)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.25\u0026thinsp;\u0026plusmn;\u0026thinsp;6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP(mmHg) \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143.83\u0026thinsp;\u0026plusmn;\u0026thinsp;21.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.52\u0026thinsp;\u0026plusmn;\u0026thinsp;27.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP(mmHg) \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.71\u0026thinsp;\u0026plusmn;\u0026thinsp;12.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.40\u0026thinsp;\u0026plusmn;\u0026thinsp;13.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of DM(month) \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.63\u0026thinsp;\u0026plusmn;\u0026thinsp;83.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e190.56\u0026thinsp;\u0026plusmn;\u0026thinsp;115.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose(mmol/l) \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting insulin(mIU/l)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.41(5.12\u0026ndash;13.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.00(3.86\u0026ndash;15.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting c-peptide(pmol/l)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e553.45(366.75\u0026ndash;812.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e766.30(547.79\u0026ndash;1182.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c(%)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.45(47.83\u0026ndash;60.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.10(75.80\u0026ndash;164.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.35(4.68\u0026ndash;6.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.80(7.07\u0026ndash;12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmALB/Cr\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.05(6.80\u0026ndash;183.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665.00(175.15\u0026ndash;834.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(mmol/l)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(mmol/l)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60(1.08\u0026ndash;3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76(1.18\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/l)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.86(2.27\u0026ndash;3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89(2.00\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/l)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHcy \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.20\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum uric acid \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e304.46\u0026thinsp;\u0026plusmn;\u0026thinsp;81.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420.51\u0026thinsp;\u0026plusmn;\u0026thinsp;177.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA \u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.78\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.79\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.66\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-Dimer\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.00(32.00\u0026ndash;87.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175.00(93.50\u0026ndash;292.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR \u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.13\u0026thinsp;\u0026plusmn;\u0026thinsp;14.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.50(2.00\u0026ndash;6.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00(2.00\u0026ndash;8.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-β\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.50(7.00\u0026ndash;24.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00(6.25\u0026ndash;33.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQUICKI\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52(0.47\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56(0.44\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRP78\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16(0.15\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17(0.16\u0026ndash;0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGroup A (T2DM group, Cystatin-C\u0026thinsp;\u0026le;\u0026thinsp;1.03)\u003c/p\u003e \u003cp\u003eGroup B (T2DM group, Cystatin-C\u0026thinsp;\u0026gt;\u0026thinsp;1.03)\u003c/p\u003e \u003cp\u003eBMI, body mass index; WC, waist circumference; WHR, waist-hip ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; QUICKI: Quantitative Insulin Check Index.\u003c/p\u003e \u003cp\u003eThe enumeration data were compared with χ\u003csup\u003e2\u003c/sup\u003e test.\u003c/p\u003e \u003cp\u003ea: Data normally distributed are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Independent sample T test was performed.\u003c/p\u003e \u003cp\u003eb:Data with skewed distribution are shown as median (IQR, 25th\u0026ndash;75th). Mann-Whitney U test was performed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations and regression analysis between serum GRP78 and CHOP concentrations and clinical parameters\u003c/h2\u003e \u003cp\u003eSerum GRP78 level was negatively correlated with eGFR and positively correlated with fasting c-peptide, Cr, BUN, cys-c, and serum uric acid. Serum CHOP level was positively correlated with age, Cr, BUN, cys-c, UmALB/Cr, and eGFR (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate correlation between GRP78 levels and other variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRP78\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting c-peptide(pmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.258\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.401\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCys-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.426\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum uric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.360\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.319\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ePearson correlation analysis was used. \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. ** significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate correlation between CHOP levels and other variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.309\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.282\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.383\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCys-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.462\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmALB/Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.319\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.451\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRP78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.256\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ePearson correlation analysis was used. \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. ** significant differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple stepwise regression analysis: independent factors associated with GRP78 levels in patients with T2DM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ \u003c/p\u003e \u003cp\u003e(unstandardized coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting c-peptide(pmol/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCys-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum uric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple stepwise regression analysis: independent factors associated with CHOP levels in patients with T2DM.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (unstandardized coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmALB/Cr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCys-c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;4.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRP78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eT\u003cb\u003eSerum GRP78 and CHOP concentrations and DKD\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the area under the curve of GRP78 for DKD prediction was 0.686 [95% CI 0.558\u0026ndash;0.813], and that of CHOP was 0.670 [95% CI 0.524\u0026ndash;0.816].\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eRecent studies suggest that development of diabetic nephropathy (DN) is partly caused by ER dysfunction [[29]]. High glucose may induce ERS in podocytes. ERS upregulates GRP78 expression, activates the CHOP pathway and caspase-12 pathway, and causes apoptosis of mouse podocytes, which may be related to the development process of DKD [[30]]. There is already evidence for the involvement of ERS-mediated apoptosis in development of diabetic complications in eyes and kidneys, but also in pathogenesis of non-diabetic neurodegenerative changes. For instance, a study in hippocampal neurons of diabetic mice induced by streptozocin (STZ) showed a reduced expression of GRP78 along with higher expression of the UPR-associated pro-apoptotic regulator CHOP [[31]]. Wu et al. have shown that GRP78 levels in renal tissue are higher than CHOP, JUK, and the caspase-12 pathway. The parallel relationship between expression and transcription of death signals suggests that excessive ERS promotes progressive damage of DKD by increasing apoptosis [[18]]. Expression of nuclear transcription factor rBp65, CHOP, and GRP78 were increased in DN rats with myocardial infarction compared with control rats with myocardial infarction. In addition, the degree of podocyte damage caused by high-glucose-mediated ERS was more severe, which deformed the structure and function of the glomerulus [[32]].Cao et al.. induced a DN model by unilateral nephrectomy combined with single STZ (65\u0026nbsp;mg/kg) injection intraperitoneally in rats. GRP78 was found by histochemical staining in diabetic rats compared with controls, and the expression levels of renal glomerular and tubular epithelial cells were upregulated[[33]]. Lindenmeye and colleagues confirmed that, compared with mild diabetes, mRNA expression of GRP78, oxyregulatory protein 150, and transcription molecule X-box binding protein-1(Xbp-1) of diabetic patients increased in the kidneys, indicating that ERS was stimulated in human DN [[34]]. These studies suggest that ERS is a central link in the development of a variety of systemic chronic metabolic diseases including T2DM, and it is also coupled with inflammatory responses, oxidative stress, autophagy, apoptosis, and other signaling pathways [[35]].\u003c/p\u003e \u003cp\u003eIn this study, the classic proteins of ERS, GRP78, and CHOP, were measured and compared with cys-c, urinary microalbumin, eGFR, and other indicators for prediction of DKD. We found higher serum concentrations of GRP78 and CHOP in T2DM group than in controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). GRP78 and CHOP concentrations were significantly increased during DKD (GRP78: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008; CHOP: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011). Urinary micro albumin creatinine ratio (UACR) or eGFR are usually chosen as a standard, but in this study cystatin-c (Cys-c) was used as a grouping indicator. Increased UACR and decreased eGFR are closely related to higher risk of adverse cardiovascular events and death. UmALB/Cr is usually used as the evaluation index of DKD, but UACR measures the influence of various factors, e.g., hypertension, heart failure, infection, hyperglycemia. Microalbuminuria as a marker of DKD progression has been challenged [[36, 37]].Early DKD is often associated with eGFR, a phenomenon known as high glomerular hyperfiltration. A cross-sectional survey showed that some diabetic patients did not have abnormal urinary albumin excretion but had decreased eGFR [[38, 39]]. Calculation of eGFR requires information on patient age and sex, as well as serum Cr level. When a patient\u0026rsquo;s eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60\u0026nbsp;mL, a decrease in eGFR can be diagnosed. However, the eGFR value may fluctuate and should be reviewed when a decrease occurs to determine the DKD stage. eGFR decline is closely associated with a higher cardiovascular risk and risk of death. Recent studies from China have shown that even mild eGFR decline can increase cardiovascular risk [[40]]. Cys-c, a low molecular weight protein that can be produced by all nucleated cells in the body, was not glycosylated, and its production rate is constant and is not affected by the patient\u0026rsquo;s age, gender, etc. Therefore, serum Cys-c level mainly depends on the filtration rate of the glomerulus, which, together with urine α-microglobulin, IgG or IgM, and IV collagen, are sensitive indicators for early diagnosis of DKD [[41]]. In this study, we therefore used Cys-c as a grouping indicator.\u003c/p\u003e \u003cp\u003eHitherto, the mechanisms behind lower levels in DKD have not been clarified. Notably, GRP78 and CHOP are closely related to DKD as animal studies have shown; herein, we demonstrated that GRP78 and CHOP in human serum correlated with DKD. Hence, we assumed a possible variation in levels of GRP78 between the subgroups divided by cys-c. Together with previous studies, the results reported herein suggest that GRP78 and CHOP levels may have potential to be used as biomarkers of the DKD risk.\u003c/p\u003e \u003cp\u003eNevertheless, this study had a few limitations. Based on cross-sectional design of the study the potential influence of increased GRP78 and CHOP levels on the development of T2DM could not be evaluated, and further studies are warranted to further clarify this issue. The strength of our conclusions and wide extrapolation to the general population is limited by a relatively small sample size and single center study design. In addition, the study encompassed single measurements of fasting serum GRP78 and CHOP levels. That approach was based on limited funds and does not reflect any time-dependent fluctuations in GRP78 and CHOP levels, which is of particular interest after macronutrient consumption. Therefore, further studies are still necessary. In summary, GRP78 and CHOP serum levels are increased in T2DM patients from China. Furthermore, DKD patients had greater reductions in GRP78 and CHOP levels.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eHere we showed evidence for the importance of ERS as well as associations of GRP78 and CHOP with DKD, which may lead to new therapeutic directions for renal complications of diabetes. With consideration of the roles of GRP78 and CHOP and involvement of ERS in other diabetic microvascular complications, it will be needed to further analyze the exact roles of ERS/UPR in DM-related complications, as well as evaluate interactions of ERS and biochemical parameters and their relationship with DKD. Our data highlight the possibility of using serum indicators of ERS as biomarkers of DKD. Therefore, with further study to elucidate the underlying mechanisms behind these effects, there may be a chance to improve treatment of DKD through improved regulation of ERS.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eERS:endoplasmic reticulum stress;diabetic kidney disease:DKD;glucose-regulated protein 78:GRP78;CCAAT/enhancer binding protein homologous protein:CHOP;type 2 diabetes mellitus:T2DM;cystatin-c:Cys-c;creatinine:Cr;blood urea nitrogen:BUN;urinary microalbumin/creatinine:UmALB/Cr;receiver operating characteristic:ROC; diabetes mellitus:DM;cases of end-stage renal disorders:ESRD;advanced glycation end products:AGEs;endoplasmic reticulum:ER;unfolded protein response:UPR;mammalian target of rapamycin complex:mTORC;immunoglobulin heavy chain binding protein:BiP;CCAAT/enhancer binding protein:C/EBP;growth arrest and DNA damage inducible gene 153:GADD153;oral glucose tolerance test:OGTT;waist circumference:WC;hip circumference:HC;waist-to-hip ratio:WHR;fasting insulin:Fins;glycosylated hemoglobin:HbA1c;carcinoembryonic antigen:CEA;alpha-fetoprotein:AFP;neuron-specific enolase:NSE;total homocysteine:tHcy;total cholesterol:TC;high-density lipoprotein cholesterol:HDL-C;low-density lipoprotein cholesterol:LDL-C;triglycerides:TG;homeostasis model assessment of insulin resistance index:HOMA-IR;homeostasis model assessment of insulin secretion index:HOMA-\u0026beta;;quantitative insulin check index:QUICKI; estimated glomerular filtration rate:eGFR;diabetic nephropathy:DN;streptozocin:STZ;X-box binding protein-1:Xbp-1;micro albumin creatinine ratio:UACR\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration. All participants gave their written informed consent prior to their participation in our study. The study was approved by the Ethics Committee of Lianyungang No1 People\u0026rsquo;s Hospital (Protocol number: 2018\u0026ndash;0522).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe consent for publication is not required since no personal or identifying information of participants are contained within the manuscript or supplementary materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe serum expression data of GRP78 and CHOP of Chinese Type 2 Diabetic Kidney Disease patients used to support the findings of this study are restricted by the Ethics Committee of the First People\u0026rsquo;s Hospital of Lianyungang in order to protect patient privacy. Data are available from Ning Ma, [email protected] for researchers who meet the criteria for access to confidential data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Jiangsu Provincial Commission of Health and Family Planning (Grant NO.Z2018021) and Lianyungang Commission Health Foundation (Grant NO.zd1802).\u003c/p\u003e\n\u003cp\u003eThe funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors engaged in the study. XBC and NM designed this article. WWL,PZ,NM,GFW and YH acquired and collected the data. NX,NM,DY,GJH and CHY organized all the data. NM,PZ and WWL analyzed all the information. NM,CXB,WWL and NX drafted the manuscript. XBC, NM and NX revised the article critically. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere thanks to all the volunteers and nurses who offered help in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Endocrinology and Metabolism, First Affiliated Hospital of Soochow University, 188 Shizi Road, Suzhou, Jiangsu, 215006, China.\u003csup\u003e2\u003c/sup\u003e Department of Endocrinology and Metabolism, Lianyungang No1 People\u0026rsquo;s Hospital, 6 Zhenghua Road, Lianyungang, Jiangsu, 222002, China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang L, Wang F, Wang L, Wang W, Liu B, Liu J, Chen M, He Q, Liao Y, Yu X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence of chronic kidney disease in China: a cross-sectional 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Chang YC, Chuang LM: \u003cstrong\u003eEarly detection of diabetic kidney disease: Present limitations and future perspectives\u003c/strong\u003e. \u003cem\u003eWorld J Diabetes \u003c/em\u003e2016, \u003cstrong\u003e7\u003c/strong\u003e(14):290-301.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes, diabetic kidney disease (DKD), endoplasmic reticulum stress (ERS), glucose-regulated protein (GRP) 78, CCAAT/enhancer binding protein homology protein (CHOP)","lastPublishedDoi":"10.21203/rs.3.rs-41272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-41272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe kidney has a rich endoplasmic reticulum system. A close relationship exists between endoplasmic reticulum stress (ERS) and diabetic kidney disease (DKD). The current study aimed to investigate serum glucose-regulated protein 78 (GRP78) as well as CCAAT/enhancer binding protein homologous protein (CHOP) concentrations in type 2 diabetes mellitus (T2DM) Chinese patients, especially those with microalbuminuria.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe evaluated the relationships between serum GRP78 or CHOP levels and DKD. We recruited 67 patients with T2DM and 63 control subjects. We determined serum GRP78 and CHOP concentrations by ELISA, collected anthropometric data, and measured biochemical parameters in a clinical laboratory. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCompared with control groups, Chinese T2DM patients showed decreased serum levels of GRP78 [0.21 (0.16–0.24) vs. 0.16 (0.16–0.19) ng/mL, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01] and CHOP [3.8 (3.0–5.5) vs. 5.5 (3.7–7.9) ng/mL, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01]. Reduction in GRP78 and CHOP serum levels was more pronounced in patients with more severe categories of microalbuminuria. Amounts of serum GRP78 correlated directly with serum fasting c-peptide, cystatin-c (cys-c), creatinine (Cr), blood urea nitrogen (BUN), and uric acid, and inversely with glomerular filtration rates. Serum CHOP level was positively correlated with age, Cr, BUN, cys-c, urinary microalbumin/creatinine (UmALB/Cr), and eGFR. Serum GRP78 was predicted independently by Cr, BUN, serum uric acid, eGFR, and cys-c, while CHOP depended on age, Cr, BUN, serum uric acid, eGFR, UmALB/Cr, and cys-c. After controlling for confounding factors, GRP78 and CHOP expression was significantly associated with DKD (binary logistic regression, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e T2DM patients showed increased serum GRP78 and CHOP concentrations. Receiver operating characteristic (ROC) areas under the curve for predicting DKD based on GRP78 and CHOP were 0.686 [95% CI: 0.558–0.813] and 0.670[0.524–0.816], respectively.\u003c/p\u003e","manuscriptTitle":"Expression of Serum GRP78 and CHOP in Endoplasmic Reticulum Stress Pathways of Chinese Type 2 Diabetic Kidney Disease Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-07-14 18:02:22","doi":"10.21203/rs.3.rs-41272/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5b9999bb-704f-487a-9d5d-25d0ae3f5725","owner":[],"postedDate":"July 14th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":155006,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2020-08-05T23:54:50+00:00","versionOfRecord":[],"versionCreatedAt":"2020-07-14 18:02:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-41272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-41272","identity":"rs-41272","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}