|Year : 2016 | Volume
| Issue : 1 | Page : 167-173
Study of toll-like receptor 4 in type 2 diabetic patients with or without nephropathy
Waleed M Fathy1, Mohamed A Soliman1, Ahmed Ragheb2, Ghada H Al Ashram3
1 Department of Clinical Pathology, Faculty of Medicine, Menoufia University, Menoufia, Egypt
2 Department of Internal Medicine, Faculty of Medicine, Menoufia University, Menoufia, Egypt
3 Clinical Pathology Department, Benha Teaching Hospital, Benha, Egypt
|Date of Submission||30-Nov-2014|
|Date of Acceptance||01-Feb-2015|
|Date of Web Publication||18-Mar-2016|
Ghada H Al Ashram
MBBCH, 3 Moheeb St, Shibin El-Kom, 32521 Menoufia
Source of Support: None, Conflict of Interest: None
The aim of this work was to study the role of toll-like receptor 4 (TLR4) in the development of type 2 diabetes mellitus (T2DM) as well as its relation to the occurrence of diabetic nephropathy.
Chronic kidney disease is one of the major complications of T2DM and is the leading cause of end-stage renal disease. There is growing evidence indicating that chronic low-grade inflammatory response is a recognized factor in the pathogenesis and progression of diabetic renal injury.
Patients and methods
A total of 50 T2DM patients were divided into three groups according to urinary albumin excretion: those with normoalbuminuria, those with microalbuminuria, and those with macroalbuminuria. In addition, 10 apparently healthy individuals were included as a control group. Fasting blood glucose, glycated hemoglobin, blood urea, and serum creatinine were measured in all patients. Urinary albumin excretion was measured using a morning spot urine sample and the urinary albumin/creatinine ratio was calculated. Quantification of CD14 and TLR4 expression on monocyte subsets was performed by means of flow cytometry.
Levels of CD14 were found to be significantly increased in patients with macroalbuminuria, whereas TLR4 levels were increased in T2DM patients, with further elevation in patients with macroalbuminuria. Both markers showed significant positive correlations with the duration of diabetes, glycated hemoglobin, serum creatinine, and urinary albumin/creatinine ratio and significant negative correlations with estimated glomerular filtration rate. Multivariate regression analysis demonstrated that CD14 and TLR4 are independent predictors of the occurrence of microalbuminuria in T2DM patients.
TLR4 levels were higher in T2DM patients compared with normal individuals. These observations significantly add to the emerging role of TLRs in T2DM development. TLR4 was also found to correlate well with the severity of albuminuria in T2DM and to be a good predictor of microalbuminuria, suggesting its possible role in the pathogenesis and progression of diabetic nephropathy.
Keywords: CD14, diabetes mellitus, diabetic nephropathy, toll-like receptor 4
|How to cite this article:|
Fathy WM, Soliman MA, Ragheb A, Al Ashram GH. Study of toll-like receptor 4 in type 2 diabetic patients with or without nephropathy. Menoufia Med J 2016;29:167-73
|How to cite this URL:|
Fathy WM, Soliman MA, Ragheb A, Al Ashram GH. Study of toll-like receptor 4 in type 2 diabetic patients with or without nephropathy. Menoufia Med J [serial online] 2016 [cited 2020 Feb 28];29:167-73. Available from: http://www.mmj.eg.net/text.asp?2016/29/1/167/179009
| Introduction|| |
The global diabetes burden is predicted to rise to 366 million by 2030 and would present itself as a major health challenge . Chronic kidney disease is one of the major complications of type 2 diabetes mellitus (T2DM) and is the leading cause of end-stage renal disease . Diabetic kidney disease is associated with enhanced morbidity and mortality, particularly with accelerated cardiovascular disease . The earliest clinical evidence of nephropathy is elevated urine albumin level more than 30 mg/24 h (i.e. microalbuminuria) . Microalbuminuria is generally considered the earliest noninvasive marker for the development of diabetic nephropathy (DN) .
The exact mechanisms leading to the development and progression of renal damage in diabetes are not yet completely known. Growing evidence indicates that activation of innate immunity with the development of a chronic low-grade inflammatory response is a recognized factor in the pathogenesis of this disease . Activation of the innate immune system via toll-like receptors (TLRs) is implicated in the pathogenesis of insulin resistance, diabetes, and atherosclerosis ,,. Complementary genetic studies link toll-like receptor 4 (TLR4) polymorphisms to T2DM, suggesting a casual relationship between TLR function and diabetes and its complications .
TLRs are evolutionarily preserved pattern-recognition receptors  expressed on several cell types including monocytes, which are predominant cells of the innate immune system that are pivotal in diabetes and atherogenesis . TLRs play an important role in the activation and regulation of the innate immune system and inflammation . TLRs in these cells efficiently transduce the inflammatory signals . Each TLR family member recognizes a specific pathogen component and, upon activation, triggers a signaling cascade leading to cytokine production and adaptive immune response . Among the TLRs, TLR4 plays a critical role in the pathogenesis of insulin resistance, diabetes, and atherosclerosis in both clinical and experimental conditions ,,,. Ligands for TLR4 include high-mobility group B1 protein (HMGB1), heat shock protein 60, heat shock protein 70, endotoxin, hyaluronan, advanced glycation end products, and extracellular matrix components . However, TLR4 does not interact directly with the most potent inflammatory signals. Upstream to TLR4 is the multifunctional lipopolysaccharide (LPS) receptor CD14. CD14 is a 55-kDa protein that is expressed in two forms: glycosylphosphatidylinositol-anchored membrane protein (mCDI4) and a soluble serum protein (sCD14) lacking the glycosylphosphatidylinositol anchor [16-18]. Both circulating and cellular CD14 receptors interact with the inflammatory signals; LPS is one of the most potent stimuli known. An excess of circulating sCD14 is known to buffer these signals, avoiding their exposure with cell (macrophage)-anchored CD14 . CD14 is in close interaction with TLR4, and LPS induces physical proximity between TLR4 and CD14 before nuclear translocation of nuclear factor-κB and triggering of the inflammatory cascade .
| Patients and methods|| |
The protocol for this study followed ethical standards and was approved by the ethical committee of our institution. All participants gave informed consent to participate in this study. This study was carried out on 50 T2DM patients (26 male and 24 female patients). In addition, 10 healthy individuals (five males and five females) of matched age and sex were included as a control group. Patients were divided according to the urinary albumin/creatinine ratio (UACR), which was measured using an early morning spot urine sample, into three groups: diabetic without microalbuminuria (UACR<30 mg/g; n = 10), diabetic with microalbuminuria (UACR between 30 and 299 mg/g; n = 14), and diabetic with macroalbuminuria (UACR ≥300 mg/g; n = 16). All participants underwent full history taking and clinical examination, including measurements of blood pressure, weight, and height. Mean arterial pressure (MAP) was calculated as: [(2× diastolic blood pressure (mmHg)+systolic blood pressure (mmHg)]/3. BMI was calculated as: weight (kg)/height (m 2 ). Glomerular filtration rate was estimated using the modification of diet in renal disease abbreviated equation: GFR = 186× serum creatinine−1.154× age−0.203× (0.742 if female) × (1.210 if African American) .
Blood samples were collected by means of sterile venipuncture and divided into two sets; the first set of samples was collected in K-EDTA tubes for evaluating glycated hemoglobin (HbA1c) and for flow cytometry. The second set was collected in plain tubes from which serum was separated through centrifugation at 3000 rpm for 10 min. This set was used for assessment of blood urea and serum creatinine and fasting blood glucose (FBG). Early morning 10-20 ml of midstream urine was collected for measurement of urinary albumin and creatinine for measurement of albumin creatinine ratio (ACR). ACR was calculated using the following equation: ACR = albumin (mg/dl)/creatinine (g/dl). HbA1c was measured using quantitative colorimetric measurement of HbA1c as a percentage of total hemoglobin using kits supplied by Stanbio Laboratory (San Antonio, Texas, USA). Albumin in urine was estimated with the Beckman's microalbumin test kit (Beckman Coulter Inc., California, USA) using a Synchron CX9 Autoanalyser (Beckman Coulter Inc., California, USA). Urine creatinine was measured using the modified Jaffe method.
Laboratory quantification of toll-like receptor 4 expression on monocyte subsets
Principle of the test
Flow cytometry measures the optical and fluorescence characteristics of a single cell (or of any other particle, including nuclei, microorganisms chromosome preparations, and latex beads). Physical properties such as size (represented by forward-angle light scatter) and internal complexity (represented by right-angle scatter) can identify certain cell populations. Fluorescent dyes may bind or intercalate with different cellular components such as DNA or RNA .
- PBS, provided by Sigma, (3050 Spruce Street, Saint Louis, MO 63103, USA) stored at 4°C.
- Flow cytometry fixation buffer (or equivalent solution containing 1-4% paraformaldehyde).
- Flow cytometry permeabilization buffer/wash buffer.
- Monoclonal antibodies:
- Fluorescein isothiocyanate-conjugated mouse monoclonal anti-human antibodies against TLR4 (clone 76B357.1, mouse anti-human IgG2b; Abcam, Cambridge, Massachusetts, USA). This antibody was developed against apportion of amino acid 100-200 of TLR4 (human).
- Phycoerythrin mouse monoclonal anti-human antibodies against CD14 [clone 61D3, mouse anti-human immunoglobulin G (IgG1); Abcam]. Monoclonal antibodies are designed to quantitatively determine the percentage of cells bearing TLR4 within a population and qualitatively determine the density of TLR4 on monocyte cell surfaces by flow cytometry.
- Isolation of peripheral blood mononuclear cells: Blood was collected in sterile collection tubes containing EDTA. Two milliliters of Ficoll were placed in a centrifuge tube and a layer of 1 ml of blood was placed on top very carefully ensuring that the blood and Ficoll did not mix; white blood cells were isolated by Ficoll (Sigma, 3050 Spruce Street, Saint Louis, MO 63103, USA) gradient and centrifugation was performed at 1800 rpm for 20 min. The cell suspension was washed three times in PBS and centrifuged for 5 min at 3200 rpm.
- Staining : A volume of 100 μl of cell suspension in PBS was incubated with 10 μl of phycoerythrin-conjugated anti-CD14 antibodies for labeling of monocytes; peripheral blood mononuclear cells were costained with fluorescein isothiocyanate-conjugated anti-TLR4.
- Flow cytometric analysis: Data were acquired on a FACS caliber flow cytometer (Becton Dickinson Immune Cytometry Systems; Becton Dickinson, San Jose, California, USA). The instrument setup was checked weekly using QC windows beads (Flow Cytometry Standards Corporation, San Juan, Puerto Rico, USA).
Results were expressed as percentages of monocytes expressing TLR4 marker [22,23].
We used the statistical package for the social sciences (SPSS, version 16; SPSS Inc., Chicago, Illinois, USA) to perform analyses. Categorical data were presented as number and percentages and continuous variables as means ± SD. One-way analysis of variance or the Kruskal-Wallis test was used as appropriate for comparison of quantitative variables between more than two independent groups. Intergroup comparisons were performed using the ν2 -test, t-test, and Mann-Whitney U-test as appropriate. Pearson's correlation coefficient (r) was used to assess the relationship between CD14 and TLR4 and other variables in the three patient groups (i.e. 1, 2, and 3). Multivariate regression analysis was performed to identify variables that were independently associated with microalbuminuria. The values of odds ratio (OR) and 95% confidence interval (CI) are summarized. P values less than 0.05 were considered significant.
| Results|| |
This study included 50 T2DM patients and 10 healthy controls. The diabetic patients were divided into three groups according to the UACR. Thus, the whole cohort was divided into four groups: group 1, the control group consisting of 10 healthy individuals, which included five (50%) males and five (50%) females; group 2, which included diabetic patients with normoalbuminuria (UACR <30 mg/g) and consisted of 10 patients [five (50%) males and five (50%) females]; group 3, which included diabetic patients with microalbuminuria (UACR between 30 and 299 mg/g) and included 14 patients [six (42.9%) males and eight (57.1%) females]; group 4, which included diabetic patients with macroalbuminuria (UACR ≥300 mg/g) and consisted of 25 patients [14 (53.8%) males and 12 (46.2%) females].
Baseline characteristics and comparison between studied groups are shown in [Table 1]. All groups were matched for age, sex, and BMI. There were significant differences among the studied groups regarding serum CD14 and TLR4 (P < 0.05). Analysis between two groups regarding the serum CD14 level showed that there was a significant difference between group 4 (diabetes with macroalbuminuria) and each of the other three groups, whereas the first three groups did not show significant differences among themselves. In contrast, analysis of TLR4 between each of two groups showed that there were significant differences between group 1 (control group) and each group separately, but there was no significant difference between group 2 (diabetes with normoalbuminuria) and group 3 (diabetes with microalbuminuria). Finally, TLR4 levels were significantly elevated in group 4 (diabetes with macroalbuminuria) compared with group 2 (diabetes with normoalbuminuria) and group 3 (diabetes with microalbuminuria).
Pearson's correlation between CD14 and the other variables showed significant positive correlations with MAP, duration of diabetes mellitus (DM), FBG, HbA1c, blood urea, serum creatinine, and UACR and significant negative correlation with estimated glomerular filtration rate (eGFR) in the three patient groups (i.e. groups 2, 3, and 4) [Table 2] and [Figure 1]. Similarly, TLR4 showed significant positive correlations with MAP, duration of DM, FBG, HbA1c, blood urea, serum creatinine, and UACR and significant negative correlation with eGFR in the three patient groups (i.e. groups 2, 3, and 4) [Table 3] and [Figure 2]. A multivariate regression model including age, BMI, MAP, FBG, HbA1c, duration of diabetes, eGFR, CD14, and TLR4 showed that both CD14 and TLR4 were independently associated with the occurrence of microalbuminuria in T2DM patients (OR 1.8 and 95% CI 0.56-7.6; and OR 2.1 and 95% CI 0.88-5.8, respectively; P < 0.05) [Table 4].
|Figure 1: Pearson's correlation between serum CD14 and estimated glomerular filtration rate (eGFR) and urinary albumin/creatinine ratio.|
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|Figure 2: Pearson's correlation between serum toll-like receptor 4 (TLR4) and estimated glomerular filtration rate (eGFR) and urinary albumin/ creatinine ratio (UACR).|
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|Table 2: Pearson's correlation between serum CD14 and other variables in type 2 diabetes mellitus patients (groups 2, 3, and 4)|
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|Table 3: Pearson's correlation between serum toll-like receptor 4 and other variables in type 2 diabetes mellitus patients (groups 2, 3, and 4)|
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|Table 4: Multivarint regression analysis for independent risk factors for the occurrence of microalbuminurea in type 2|
diabetes mellitus patients
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| Discussion|| |
DN is one of the major microvascular complications of type 1 diabetes mellitus (T1DM) and T2DM and the leading cause of end-stage renal disease. It was thought to be a result from interactions between hemodynamic and metabolic factors; however, research over the past 10 years has provided insight into the etiology of DN at the cellular and molecular level, and inflammation has emerged as a key pathophysiological mechanism . In the present work we aimed to analyze the relationship between the inflammatory markers CD14 and TLR4 and UACR as a marker of DN in T2DM patients.
In the current study there were significant differences between the four studied groups regarding both CD14 and TLR4. When two groups were compared, CD14 levels were found to be significantly elevated only in T2DM patients with macroalbuminuria. In contrast, TLR4 levels were significantly elevated in T2DM patients compared with the control group and in T2DM patients with macroalbuminuria compared with both normoalbuminuric and microalbuminuric T2DM patients. TLRs have been considered as activators of inflammation under hyperglycemia and insulin resistance [7, 9, 25, 26]. Dasu and colleagues measured the TLR mRNA, protein expression, TLR ligands, and TLR signaling in freshly isolated monocytes from 23 healthy human control participants and 23 T2DM patients using real-time PCR, western blot, and flow cytometric assays. Consistent with our results they found that T2DM patients had significantly increased TLR2, TLR4 mRNA, and protein in monocytes compared with control participants. They concluded that TLR2 and TLR4 expression and their ligands, as well as their signaling and functional activation, are increased in recently diagnosed T2DM and contribute to the proinflammatory state, which they deemed a novel observation . Shi et al.  and Wong et al.  also reported consistent observations confirming the implication of TLR4 in the pathogenesis of insulin resistance and T2DM. Similar results, but in T1DM, were obtained by Devaraj et al.  who examined TLR2 and TLR4 expression in monocytes from 31 T1DM patients and 31 controls. They found that TLR2 and TLR4 surface expression and mRNA were significantly increased in T1DM monocytes compared with controls .
In the current study, although the CD14 levels were elevated in normoalbuminuric T2DM patients compared with controls, this elevation was not statistically significant. Fernαndez-Real et al.  stated that systemic CD14 expression might play a role in obesity and inflammation-induced insulin resistance. The presence of two forms of CD14 as well as the possible presence of other cofactors for TLR4 activation could be the reason why a significant elevation was seen in TLR4 but not in CD14 levels in T2DM patients [16-19].
Although TLRs are increased in diabetic patients and have been suggested to play a role in DN, the relation between TLR4 and the pathogenesis of DN has not been studied extensively . In our study both CD14 and TLR4 expressions were significantly elevated in patients with macroalbuminuria in comparison with normoalbuminuric and microalbuminuric T2DM patients as well as control participants. Similar findings, but in vitro, were observed by Kaur and coworkers who hypothesized that the expression of TLRs in the mesangium might be an important factor contributing to mesangial expansion and nephropathy. It is postulated that progression of DN involves altered mesangial cell function with an expansion of the mesangial matrix. Hence, they evaluated the effect of high glucose on TLR2 and TLR4 expression in mouse mesangial cells (MMC) in vitro. They found that exposure of MMC to 25 mmol/l glucose for 24 h resulted in increased TLR4 mRNA and cell surface receptor expression compared with 5.5 mmol/l glucose. They concluded that hyperglycemia activates TLR4 expression and activity in MMC and could contribute to DN . Concordant results were observed by Verzola et al.  in 2014. They studied the TLR4 gene and protein expression and TLR4 downward signaling in kidney biopsies of 12 patients with T2DM and microalbuminuria, and compared them with 11 patients with overt DN, 10 patients with minimal change disease, and control kidneys from 13 patients undergoing surgery for a small renal mass. They found that both in microalbuminuria and in overt DN, TLR4 mRNA and protein were overexpressed 4-10-fold in the glomeruli and tubules compared with the control kidney and in minimal change disease . Unlike our results, they observed significant differences in TLR4 levels in patients with both microalbuminuria and macroalbuminuria, whereas significant differences were seen only in patients with macroalbuminuria in our study. This could be explained by the fact that Verzola and colleagues measured TLR4 gene and protein expression in kidney biopsies, whereas we used flow cytometric analysis of TLR expression on peripheral monocytes. The former is most probably more accurate in detecting minor changes at the level of kidney tissues.
Furthermore, the current study demonstrated that both CD14 and TLR4 had significant positive correlations with each of MAP, duration of DM, FBG, HbA1c, blood urea, serum creatinine, and UACR and significant negative correlations with eGFR in T2DM patients with or without albuminuria. Consistent with our results, Dasu et al.  found that increased TLR2 and TLR4 expression correlated with BMI, FBG, and HbA1c in addition to homeostasis model assessment - insulin resistance (HOMA-IR), Ne-(carboxymethyl)lysine, and free fatty acid. In contrast, Lorenzen et al.  found a significant positive correlation between MAP and both CD14 and TLR4 in 191 patients with chronic kidney disease stage V receiving hemodialysis therapy.
Microalbuminuria is generally considered the earliest noninvasive marker for the development of DN . In our study, a multivariate regression analysis of all parameters in the studied groups was performed to detect the independent risk factors for the occurrence of microalbuminuria (i.e. in groups 3 and 4). It demonstrated that CD14 and TLR4 were independent risk factors for the occurrence of microalbuminuria in T2DM patients.
| Conclusion|| |
In conclusion, we found that CD14 and TLR4 levels were higher in T2DM patients compared with normal participants. These observations significantly add to the emerging role of TLRs in T2DM development. CD14 and TLR4 were also found to correlate well with the severity of albuminuria in T2DM and to be good predictors of microalbuminuria, suggesting their possible role in the pathogenesis and progression of DN.
| Acknowledgements|| |
Conflicts of interest
There are no conflicts interest.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4]