Abstract
Whether red cell distribution width (RDW) can be a potential indicator for diabetic nephropathy (DN) is unknown. A total of 809 type 2 diabetes mellitus (T2D) patients were divided into 4 groups according to the quartiles (Q) of the RDW (%): Q1 ≤ 12.4 (n = 229), 12.4 < Q2 ≤ 12.9 (n = 202), 12.9 < Q3 < 13.5 (n = 168), Q4 ≥ 13.5 (n = 210). Results showed that the levels in Q4 group was higher in age, disease duration, systolic blood pressure, blood urea nitrogen, creatinine, uric acid and proteinuria but lower in hemoglobin, serum albumin and glycosylated hemoglobin compared to Q1 group. Furthermore, the incidences of DN, diabetic peripheral neuropathy, hypertension and coronary heart disease in the Q3 or Q4 group were higher compared to Q1 group. Medications including calcium channel blockers and antiplatelet therapy also showed higher frequencies in Q3 or Q4 group compared to Q1. Logistic regression indicated that the antiplatelet therapy (OR = 2.065), hypertension (OR = 2.819), creatinine (OR = 4.473) and proteinuria (OR = 2.085) were positively associated with level of Q4 group, but higher hemoglobin (OR = 0.021) and serum Ca2+ (OR = 0.178) were negatively associated with Q4. This data suggest that high level of RDW in T2D patients indicates a higher risk and a poor prognosis for DN.
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Introduction
Diabetes mellitus (DM) is a metabolic disorder caused either by the insufficient production of insulin in islet cells of the pancreas or by resistance against secreted insulin in tissues, leading to an elevation in the glucose concentration in the blood. In China, a national survey in 2010 showed a DM prevalence of 9.65%, with the total number of DM patients up to 92.4 million and accounting for a quarter of worldwide DM patients in the population aged 20 to 79 years. Several studies have shown that plasma cholesterol levels, blood pressure, microalbuminuria and hyperglycemia are closely associated with the progression of DM1, 2. Additionally, recent studies have revealed serum uric acid level, carboxy-terminal propeptide and retinal venular diameter as significant indicators of diabetic complications such as diabetic nephropathy or retinopathy3,4,5. RDW, defined as the heterogeneity of circulating erythrocytes (anisocytosis), was used to distinguish the variable pathogenesis of anemia together with the MPV6. Malnutrition, including Fe deficiency and lack of vitamin B12 and folic acid, generates elevated RDW7. Recent studies have demonstrated that RDW may also be an effective predictor of morbidity and mortality in various diseases such as PH8, 9, NAFLD10, CHD11,12,13,14,15, stoke16, atherosclerosis17, prevalent dementia18, IBD19, ESRD20 and heart failure21. A study following 8175 adults for up to 6 years showed that the measurement of RDW may be used to predict mortality in CVD, cancer and other diseases22 in the early stages. Moreover, in a 5.5-year follow-up of 13039 patients diagnosed with PAD, the 1% increase of RDW was accompanied by the increased 10% in all-cause mortality. Also, RDW was considered as a prognostic marker in PAD patients23, which was extraordinarily higher in metabolic syndrome (MS) patients compared to those without MS24.
Recently, some studies have shown that increased RDW is associated with the incidence of DM6, 25,26,27. However, the explicit relationship between RDW and the basic indexes, drug treatment and related complications (such as chronic heart disease and diabetic retinopathy) remains implicit in T2D patients. Here, we study the characteristics of RDW and its association with distributions of clinical indexes in T2D patients.
Results
General characteristics of Q1 to Q4 in type 2 diabetic patients
As shown in Table 1 and in Fig. 1a–f, group Q4 showed the higher average age (62.10 ± 11.88 years) compared to Q1 group (56.33 ± 12.59 years), Fig. 1a. The male ratio was lower in the Q3 group (48.2%) compared to that of the Q1 group (62.0%), Fig. 1d. Moreover, compared to the Q1 group (7.63 ± 6.58), T2D patients of Q4, Q3 and Q2 group showed all the longer average duration years (10.65 ± 7.77, 10.24 ± 6.80, 10.03 ± 7.37), Fig. 1b, and higher average SBP (144.56 ± 24.18), Fig. 1c, and the proportion of smokers and drinkers (Fig. 1e, Fig. 1f). There were no significant differences in the distributions of DBP, BMI among the Q1 to Q4 groups.
Laboratory results of blood and urine in type 2 diabetic patients
Compared to the Q1 group, in the Q4 group, the T-test or the non-parametric Kruskal-Wallis test showed that Hb (g/dL), Alb (g/L) and HbA1c (%) levels were significantly lower, but BUN (mmol/L), serum Ca2+ (mmol/L), Cr (μmmol/L), UA (mmol/L), Upro (mg/L) and HbA1c (%) levels were significantly higher (Table 2 and Fig. 2a–h) (P < 0.01). For the distributions of WCC (*109), MPV (fl), PCT (%), P-LCR (%), PDW(%), TG (mmol/L), TC (mmol/L), HDL (mmol/L), LDL (mmol/L), serum phosphorus (mmol/L), and FBG (mmol/L), no significant differences were observed among the four groups.
Complications and medications of the Q1 to Q4 groups in type 2 diabetic patients
Compared to the Q1 group, the patients in the higher Q3 or Q4 groups had increasing morbidities of DR, HTN and using CCB. The Q3 group had a higher rate of DPN compared to the Q1 group (61.9% vs 46.7%, P < 0.05). Moreover, groups Q2 and Q4 had more morbidities of CHD compared to the Q1 group (P < 0.05), and Q4 group used less OHA than Q1 group (P < 0.05). (Table 3 and Fig. 3a–f). No significant differences were found in either category of patient treatment, with or without ACE-I or ARB, β-Blocker, lipid-lowering agents, or insulin (P > 0.05).
Binary Logistic regression models for RDW
As shown in Table 4, Q4 and Q1 of RDW were defined as the dependent variables in the Binary Logistic regression model. After adjustments for age and gender as confounding factors, the results showed a positive association between Q4 of RDW and antiplatelet therapy (OR = 2.065, 95% CI: 1.14–3.75), HTN (OR = 2.819, 95% CI: 1.49–5.35), Cr (OR = 4.473, 95% CI: 1.80–11.10), Upro (OR = 2.085, 95% CI: 1.19–3.67) and disease duration ≥10 years (OR = 3.189, 95% CI: 1.35–7.53). However, Hb ≥110 g/dL (OR = 0.021, 95% CI: 0.01–0.10) and the levels of serum Ca2+ ≥2.03 mmol/L (OR = 0.178, 95% CI: 0.08–0.39) were negatively associated with Q4 of RDW. For the Logistic regression model of Q3 vs Q1 of RDW, the number of significant variables in the model decreased from 7 to 4, including antiplatelet therapy (OR = 1.855), Cr (OR = 3.756), Upro (OR = 1.814) and disease duration ≥10 years (OR = 1.996). Moreover, in the Logistic regression model of Q2 vs Q1 of RDW, the results showed that the least significant variables were antiplatelet therapy (OR = 1.775) and Upro (OR = 1.569).
Discussion
Recently, the increasing prevalence of DM has become a global health problem. Disclosed risk factors include age, family history of diabetes, obesity, hypertension and high triglycerides28. In China, the crude and age-standardized prevalence of DM are 12.19% and 6.98%, respectively29. Several studies have focused on prognostic biomarkers that could indicate the incidences of coronary artery spasm (CAS), acute ischemic stroke (AIS), cardiovascular disease (CVD) and diabetic nephropathy (DN) in DM patients. Microalbuminuria is a remarkable biomarker for the diagnosis of DN30. Inflammatory biomarkers including WBC, TNF-α, matrix metalloproteinases (MMT) and dysglycemia are expressed concurrently to fit with the early stages of CVD in patients with diabetes31. Low high-sensitivity C-reactive protein (hs-CRP) levels in patients with DM are associated with a high risk of CAS32. The higher copeptin levels in the upper inter-quartile group (Q4 > 17.1 pmol/L) were associated with a higher death risk in short-term stroke prognosis in patients with T2D and stroke33. Currently, the significant indicators involved in the development or the prognosis of DM are still not fully understood.
In this study, we demonstrated that changes in RDW are associated with T2D. Disease duration and SBP showed positive associations with the higher Q4 group of RDW, but other clinical indexes, including serum Ca2+, Hb or HDL, were negatively associated with the Q4 group of RDW. One retrospective study22 indicated that RDW is an age-associated biomarker in people >45 years old. Here, we also found that elderly patients had significantly higher Q4 values of RDW than younger patients, P < 0.05 (Fig. 1a). Furthermore, the regression model showed that age was positively associated with RDW (Table 4). One possible explanation involves the greater potential of seniors to be in a state of inflammation, nutritional deficiency and other complications. Another retrospective study6 that included 260 T2D patients and 44 healthy control subjects found that RDW was correlated with BMI. However, our study showed that the changes of RDW value were not significantly associated with the BMI levels in diabetic patients. Additionally, in the present study, SBP in the Q4 group was obviously higher compared to the Q1 group (Fig. 1c) and HTN was positively associated with RDW as detected by the regression model (Table 4), which was consistent with reports by Dada, O. A. et al.34. However, Malandrino N’s study indicated that the duration of DM had no significant association with RDW, which contradicts our study that indicates that the higher levels of RDW are positively associated with the longer duration in T2D patients (Table 4 and Fig. 1b)35.
The levels of RDW were measured in 26709 non-diabetic subjects over a 14-year follow-up period in a report by Engstrom G, showing that RDW was positively correlated to HbA1c, indicating that the HbA1c would increase by 0.10% per Standard Deviation (SD) elevation in RDW26. In our study, the HbA1c in the Q4 group showed a significant decrease compared to Q1 group (Fig. 2h). Recently, Lippi G et al.36 showed a negative correlation between RDW and Hb in 4874 outpatients, which was consistent with our results (Fig. 2a). Additionally, a negative association was identified between RDW and HDL-cholesterol in the multivariate Logistic regression after adjustment for age, Hb, and MCV, which was different from our study, which showed no obvious discrepancy between HDL and RDW. The reason may be related to the fact that the patients in our study were receiving appropriate treatment, including control of lipidemia, because all indicators such as TG, TC and LDL in lipidemia showed no differences (P > 0.05). On the other hand, we also concluded that serum Ca2+ was negatively associated RDW since the lower Q1 of RDW showed the higher level of serum Ca2+ (Table 2). A possible explanation is that higher serum Ca2+ may increase the deformability of red cells, leading to reduced RDW.
For the relationship between RDW and proteinuria, the results of several studies showed the consistence with our study. For example, Zhang M et al.27 assessed 320 patients who were newly diagnosed with T2D and indicated that RDW was a risk factor for microalbuminuria (MAU), with a value of 0.79 for the area under the curve as compared to a healthy group. In addition, Caroline J et al.37 collected 196 T2D patients with DN (57%), diabetic neuropathy (46%) and peripheral arterial disease (26%) and found that RDW level is a high risk factor in DN (OR: 1.64, 95% CI: 1.15–2.35). Similarly, our results indicate that RDW is positively associated with proteinuria (Table 4) after adjustment of the potential confounders such as age and gender.
Furthermore, one study of 786 older women suggested that a higher quartile of RDW tended to be associated with a higher interleukin-6 level38. It was also reported that TNF-α and interleukin-6, which reflect pro-inflammatory conditions in DM patients, have significantly close relationships with proteinuria39. Moreover, chronic inflammatory cytokines displayed a key role in damaging and increasing the permeability of glomerular endothelial cells, resulting in proteinuria40. Therefore, proteinuria could be used to reflect the level of inflammation, providing a reasonable explanation for the close association between RDW and proteinuria. Interestingly, proteinuria was significantly related to oxidative stress, which is involved in the oxidation of the LDL fatty acids, and it was also associated with RBC fragments41, 42 that give rise to incremental increases in RDW43.
Diabetes mellitus plays a pivotal role in recurrent atherothrombotic events, especially in patients with acute coronary syndrome (ACS) that underwent percutaneous coronary intervention (PCI)44 due to risk factors including hyperglycemia, insulin deficiency, and metabolic dysregulation resulting in platelet dysfunction45. In addition, “prolonged” antiplatelet therapy could decrease vascular events by approximately 1/4 in diabetic and non-diabetic subjects46. However, antiplatelet therapy may be associated with the elevation of RDW. Here, we found that antiplatelet therapy, such as aspirin or clopidogrel, showed a higher proportion in Q2 or Q4 of RDW in T2D patients (Table 3 and Fig. 3a), suggesting these diabetic patients, characterized by chronic inflammation and oxidative conditions, might experience rearrangement of the cytoskeleton and loss of asymmetric lipids of the RBC47, resulting in high levels of RDW.
In conclusion, our results provide novel insight into the relationship between RDW and basic characteristics, blood and urine examinations, complications and medications in DM. First, a graded association between disease duration and RDW was obtained, showing that longer durations were correlated with increasing RDW, and that older patients had significantly higher RDW levels than younger patients. Second, serum Ca2+ and Hb may be protective in decreasing the level of RDW. Third, an increased level of RDW was accompanied by elevated levels of serum Cr and proteinuria in type 2 diabetes patients. Finally, the elevated level of RDW with antiplatelet therapy indicates that RDW might be a new biomarker for evaluating the dosage of antiplatelet drugs. However, several limitations should be mentioned in our study. For example, the present study is a cross-sectional study, and the relationship between RDW and other indexes is temporal or casual. Altogether, RDW may be a significant and accessible biomarker in T2D patients relative to clinical detection and evaluation.
Materials and Methods
Patients and study design
At the beginning of this trail, we obtained informed consents from subjects and received approval from the Human Research Ethics Committees in Second Xiangya Hospital of Central South University (approval number: 2015-SO26). The study was performed in accordance with the approved guidelines. Written informed consent was obtained from all patients enrolled in the study. 809 patients were diagnosed with type 2 diabetic disease in the Endocrinology Department of Second Xiangya Hospital of Central South University from June 2014 to November 2015. According to the statistical quartiles method of RDW (%), 809 patients were classified as Q1 ≤ 12.4 (229 patients), Q2 > 12.4 and ≤ 12.9 (202 patients), Q3 > 12.9 and < 13.5 (168 patients), or Q4 ≥ 13.5 (210 patients). Then, the association between RDW and diabetes was measured by comparing group Q1 with groups Q2, Q3 and Q4. The exclusion criteria include the following: type 1 diabetic patients or latent autoimmune diabetes in adults (LADA), patients with known inflammatory-related diseases such as rheumatoid arthritis and systematic lupus erythematosus, and pregnant diabetic women. The diagnostic criteria for type 2 diabetes were defined as follows: HbA1c level ≥6.5%; or fasting plasma glucose level ≥126 mg/dL (≥7.0 mmol/L); or 2-h plasma glucose level ≥200 mg/dL (≥11.1 mmol/L) in an oral glucose tolerance test (OGTT); or a casual plasma glucose level of ≥200 mg/dL (≥11.1 mmol/L)47. Data of demographic characteristics in diabetic patients including age, sex, and smoking and drinking habits were collected. Smoking or drinking daily for at least 1 year classified patients as smokers or drinkers, respectively. The formula for BMI was calculated as weight (kg)/height [m2]. DR was diagnosed with varying degrees of microaneurysms, hemorrhages, exudates, change of vein, new vessel formation and retinal thickening consisting of background (mild non-proliferative), preproliferative (moderate/severe non-proliferative), proliferative and advanced retinopathy. Involvement of the macula can be foca, diffuse, ischemic or mixed. The criteria for DPN were mainly based on clinical symptoms (such as sensation decreases, neuropathic-related sensory symptoms), neurologic examination and electrophysiologic investigation. The definition of HTN was based on systolic blood pressures ≥140 mmHg and/or diastolic blood pressures ≥90 mmHg measured three different but consecutive times on the condition that patients had not used antihypertensive drugs. CHD was defined as myocardial impairment due to an imbalance between coronary blood flow and myocardial requirements caused by changes in the coronary circulation, which was subdivided into acute coronary syndrome and chronic coronary artery syndrome. Dysglycemia was characterized by either fasting plasma glucose (FPG) ≥5.6 mmol/L or HbA1c ≥5.7%. Oral drug use was defined as the patients taking medications regularly for 3 months.
Laboratory examination
Hematologic testing was performed with the ADVIA 2120 automated hematology analyzer (Siemens Healthcare Diagnostics, Germany), measuring hemoglobin, white cell count, RDW, mean platelet volume, platelet crit, platelet large cell ratio, and platelet distribution width, as described previously48. The liver and renal function parameters, including BUN, Cr, UA, Alb, TG, TC, HDL, LDL and FBG levels, were analyzed using standard automated enzymatic methods (Hitachi 912 automated analyzer), as previously described49. Other biochemical examinations including CRP, serum Ca2+, and P were detected on the C8000 Abbott ARCHITECT Clinical Chemistry Analyzers (Abbott Diagnostics, USA). In addition, proteinuria was defined as a urine albumin excretion rate (UAER) of greater than 30 mg/24 h50. Urine concentrations of albumin were measured by the immunoturbidimetric method as described previously48, 49. In addition, HbA1c evaluations were performed with automatic high-performance liquid chromatography (HPLC) (VARIANT-II Hemoglobin Testing System; Bio-Rad Laboratories, Hercules, CA)51.
Statistical analysis
Quantitative (such as Hb, SBP, DBP, Upro) and qualitative (such as sex, smoking) variables are described as the mean ± standard deviation and percentages, respectively, and the differences between groups Q4 and Q1 were compared using the T-test or the χ2 test or by the non-parametric Kruskal-Wallis test when the P value of the 1-sample Shapiro-Wilk test <0.05. Then, the significant variables were used to evaluate the risks associated with RDW by the Logistic regression method. For the requirement of Logistic regression, these quantitative variables were classified as positive or negative by a cut off value, which is antiplatelet drugs: “1” refers to patients not using antiplatelet drugs, “2” refers to patients using antiplatelet drugs; HTN: “1” refers to patients with DM alone, “2” refers to patients with DM and HTN. Age (years): “1” refers to “<51”, “2” refers to “≥51” but “<60”, “3” refers to “≥60” but “<68”, and “4” refers to “≥68”; Hb (g/dL): “1” refers to “60–90”, “2” refers to “91–109”, and “3” refers to “≥110”, which is consistent with the clinical classification. Serum Cr (µmmol/L): “1” refers to “<133”, “2” refers to “≥133” consistent with the critical value in our hospital. Serum Ca2+ (mmol/L): “1” refers to “<2.03”, “2” refers to “≥2.03” but “≤2.54”, and “3” refers to “>2.54”; urinary protein excretion (Upro): “1” was defined as proteinuria less than 30 mg/L, and “2” was defined as proteinuria more than 30 mg/L; Duration (years): “1” refers to “<3”, “2” refers to “≥3” but “<9”, “3” refers to “≥9” but “<14”, and “4” refers to “≥14”. The data were analyzed using SPSS 19.0 (SPSS Inc, USA), and P value of <0.05 was regarded as statistically significant.
References
Schmitz, A. Microalbuminuria, blood pressure, metabolic control, and renal involvement: longitudinal studies in white non-insulin-dependent diabetic patients. Am J Hypertens. 10, 189S–197S (1997).
Ravid, M., Brosh, D., Ravid-Safran, D., Levy, Z. & Rachmani, R. Main risk factors for nephropathy in type 2 diabetes mellitus are plasma cholesterol levels, mean blood pressure, and hyperglycemia. Arch Intern Med. 158, 998–1004 (1998).
Chang, Y. H. et al. Serum uric acid level as an indicator for CKD regression and progression in patients with type 2 diabetes mellitus-a 4.6-year cohort study. Diabetes Metab Res Rev. 32, 557–564, doi:10.1002/dmrr.2768 (2016).
Roy, M. S., Klein, R. & Janal, M. N. Retinal venular diameter as an early indicator of progression to proliferative diabetic retinopathy with and without high-risk characteristics in African Americans with type 1 diabetes mellitus. Arch Ophthalmol. 129, 8–15, doi:10.1001/archophthalmol.2010.340 (2011).
Inukai, T., Fujiwara, Y., Tayama, K., Aso, Y. & Takemura, Y. Serum levels of carboxy-terminal propeptide of human type I procollagen are an indicator for the progression of diabetic nephropathy in patients with type 2 diabetes mellitus. Diabetes Res Clin Pract. 48, 23–28 (2000).
Nada, A. M. Red cell distribution width in type 2 diabetic patients. Diabetes Metab Syndr Obes. 8, 525–533, doi:10.2147/DMSO.S85318 (2015).
Borne, Y., Smith, J. G., Melander, O. & Engstrom, G. Red cell distribution width in relation to incidence of coronary events and case fatality rates: a population-based cohort study. Heart. 100, 1119–1124, doi:10.1136/heartjnl-2013-305028 (2014).
Hampole, C. V., Mehrotra, A. K., Thenappan, T., Gomberg-Maitland, M. & Shah, S. J. Usefulness of red cell distribution width as a prognostic marker in pulmonary hypertension. Am J Cardiol 104, 868–872, doi:10.1016/j.amjcard.2009.05.016 (2009).
Rhodes, C. J., Wharton, J., Howard, L. S., Gibbs, J. S. & Wilkins, M. R. Red cell distribution width outperforms other potential circulating biomarkers in predicting survival in idiopathic pulmonary arterial hypertension. Heart. 97, 1054–1060, doi:10.1136/hrt.2011.224857 (2011).
Kim, H. M. et al. Elevated red cell distribution width is associated with advanced fibrosis in NAFLD. Clin Mol Hepatol. 19, 258–265, doi:10.3350/cmh.2013.19.3.258 (2013).
Vaya, A. et al. Red blood cell distribution width and erythrocyte deformability in patients with acute myocardial infarction. Clin Hemorheol Microcirc. 59, 107–114, doi:10.3233/CH-131751 (2015).
Veeranna, V., Zalawadiya, S. K., Panaich, S., Patel, K. V. & Afonso, L. Comparative analysis of red cell distribution width and high sensitivity C-reactive protein for coronary heart disease mortality prediction in multi-ethnic population: findings from the 1999-2004 NHANES. Int J Cardiol. 168, 5156–5161, doi:10.1016/j.ijcard.2013.07.109 (2013).
Tonelli, M. et al. Relation Between Red Blood Cell Distribution Width and Cardiovascular Event Rate in People With Coronary Disease. Circulation. 117, 163–168, doi:10.1161/CIRCULATIONAHA.107.727545 (2008).
Cavusoglu, E. et al. Relation between red blood cell distribution width (RDW) and all-cause mortality at two years in an unselected population referred for coronary angiography. Int J Cardiol. 141, 141–146, doi:10.1016/j.ijcard.2008.11.187 (2010).
Afsar, B., Saglam, M., Yuceturk, C. & Agca, E. The relationship between red cell distribution width with erythropoietin resistance in iron replete hemodialysis patients. Eur J Intern Med. 24, e25–29, doi:10.1016/j.ejim.2012.11.017 (2013).
Soderholm, M., Borne, Y., Hedblad, B., Persson, M. & Engstrom, G. Red cell distribution width in relation to incidence of stroke and carotid atherosclerosis: a population-based cohort study. PLoS One. 10, e0124957, doi:10.1371/journal.pone.0124957 (2015).
Wonnerth, A. et al. Red cell distribution width and mortality in carotid atherosclerosis. Eur J Clin Invest 46, 198–204, doi:10.1111/eci.12584 (2016).
Weuve, J., M de Leon, C. F., Bennett, D. A., Dong, X. & Evans, D. A. The red cell distribution width and anemia in association with prevalent dementia. Alzheimer Dis Assoc Disord. 28, 99–105, doi:10.1097/WAD.0b013e318299673c (2014).
Yesil, A. et al. Red cell distribution width: a novel marker of activity in inflammatory bowel disease. Gut Liver 5, 460–467, doi:10.5009/gnl.2011.5.4.460 (2011).
Yoon, H. E. et al. Progressive rise in red blood cell distribution width predicts mortality and cardiovascular events in end-stage renal disease patients. PLoS One. 10, e0126272, doi:10.1371/journal.pone.0126272 (2015).
Felker, G. M. et al. Red cell distribution width as a novel prognostic marker in heart failure: data from the CHARM Program and the Duke Databank. J Am Coll Cardiol. 50, 40–47, doi:10.1016/j.jacc.2007.02.067 (2007).
Patel, K. V., Ferrucci, L., Ershler, W. B., Longo, D. L. & Guralnik, J. M. Red blood cell distribution width and the risk of death in middle-aged and older adults. Arch Intern Med. 169, 515–523, doi:10.1001/archinternmed.2009.11 (2009).
Ye, Z., Smith, C. & Kullo, I. J. Usefulness of red cell distribution width to predict mortality in patients with peripheral artery disease. Am J Cardiol. 107, 1241–1245, doi:10.1016/j.amjcard.2010.12.023 (2011).
Farah, R. & Khamisy-Farah, R. Significance of MPV, RDW with the Presence and Severity of Metabolic Syndrome. Exp Clin Endocrinol Diabetes 123, 567–570, doi:10.1055/s-0035-1564072 (2015).
Solak, Y. et al. Red cell distribution width is independently related to endothelial dysfunction in patients with chronic kidney disease. Am J Med Sci. 347, 118–124, doi:10.1097/MAJ.0b013e3182996a96 (2014).
Engstrom, G. et al. Red cell distribution width, haemoglobin A1c and incidence of diabetes mellitus. J Intern Med. 276, 174–183, doi:10.1111/joim.12188 (2014).
Zhang, M., Zhang, Y., Li, C. & He, L. Association between red blood cell distribution and renal function in patients with untreated type 2 diabetes mellitus. Ren Fail. 37, 659–663, doi:10.3109/0886022X.2015.1010938 (2015).
Ning, F. et al. Risk factors associated with the dramatic increase in the prevalence of diabetes in the adult Chinese population in Qingdao, China. Diabet Med 26, 855–863, doi:10.1111/j.1464-5491.2009.02791.x (2009).
Liu, X. et al. Prevalence, awareness, treatment, control of type 2 diabetes mellitus and risk factors in Chinese rural population: the RuralDiab study. Sci Rep. 6, 31426, doi:10.1038/srep31426 (2016).
Gluhovschi, C. et al. Urinary Biomarkers in the Assessment of Early Diabetic Nephropathy. J Diabetes Res. 2016, 4626125, doi:10.1155/2016/4626125 (2016).
Zayani, Y. et al. Inflammations mediators and circulating levels of matrix metalloproteinases: Biomarkers of diabetes in Tunisians metabolic syndrome patients. Cytokine. 86, 47–52, doi:10.1016/j.cyto.2016.07.009 (2016).
Hung, M. J., Hsu, K. H., Hu, W. S., Chang, N. C. & Hung, M. Y. C-reactive protein for predicting prognosis and its gender-specific associations with diabetes mellitus and hypertension in the development of coronary artery spasm. PLoS One. 8, e77655, doi:10.1371/journal.pone.0077655 (2013).
Wang, C. B., Zong, M., Lu, S. Q. & Tian, Z. Plasma copeptin and functional outcome in patients with ischemic stroke and type 2 diabetes. J Diabetes Complications 30, 1532–1536, doi:10.1016/j.jdiacomp.2016.07.030 (2016).
Dada, O. A. et al. The relationship between red blood cell distribution width and blood pressure in patients with type 2 diabetes mellitus in Lagos, Nigeria. J Blood Med. 5, 185–189, doi:10.2147/JBM.S67989 (2014).
Malandrino, N., Wu, W. C., Taveira, T. H., Whitlatch, H. B. & Smith, R. J. Association between red blood cell distribution width and macrovascular and microvascular complications in diabetes. Diabetologia. 55, 226–235, doi:10.1007/s00125-011-2331-1 (2012).
Lippi, G., Sanchis-Gomar, F., Danese, E. & Montagnana, M. Association of red blood cell distribution width with plasma lipids in a general population of unselected outpatients. Kardiol Pol. 71, 931–936, doi:10.5603/KP.2013.0228 (2013).
Afonso, L. et al. Relationship between red cell distribution width and microalbuminuria: a population-based study of multiethnic representative US adults. Nephron Clin Pract. 119, c277–282, doi:10.1159/000328918 (2011).
Semba, R. D. et al. Serum antioxidants and inflammation predict red cell distribution width in older women: the Women’s Health and Aging Study I. Clin Nutr. 29, 600–604, doi:10.1016/j.clnu.2010.03.001 (2010).
Moriwaki, Y. et al. Elevated levels of interleukin-18 and tumor necrosis factor-alpha in serum of patients with type 2 diabetes mellitus: relationship with diabetic nephropathy. Metabolism. 52, 605–608, doi:10.1053/meta.2003.50096 (2003).
Satchell, S. C. & Tooke, J. E. What is the mechanism of microalbuminuria in diabetes: a role for the glomerular endothelium? Diabetologia. 51, 714–725, doi:10.1007/s00125-008-0961-8 (2008).
Kassab, A. et al. Homocysteine enhances LDL fatty acid peroxidation, promoting microalbuminuria in type 2 diabetes. Ann Clin Biochem. 45, 476–480, doi:10.1258/acb.2007.007125 (2008).
Paueksakon, P., Revelo, M. P., Ma, L. J., Marcantoni, C. & Fogo, A. B. Microangiopathic injury and augmented PAI-1 in human diabetic nephropathy. Kidney Int. 61, 2142–2148, doi:10.1046/j.1523-1755.2002.00384.x (2002).
Bessman, J. D. Red blood cell fragmentation. Improved detection and identification of causes. Am J Clin Pathol. 90, 268–273 (1988).
Park, Y., Franchi, F., Rollini, F. & Angiolillo, D. J. Antithrombotic Therapy for Secondary Prevention in Patients With Diabetes Mellitus and Coronary Artery Disease. Circ J. 80, 791–801, doi:10.1253/circj.CJ-16-0208 (2016).
Ferreiro, J. L. & Angiolillo, D. J. Diabetes and antiplatelet therapy in acute coronary syndrome. Circulation. 123, 798–813, doi:10.1161/CIRCULATIONAHA.109.913376 (2011).
Collaborative overview of randomised trials of antiplatelet therapy–I Prevention of death, myocardial infarction, and stroke by prolonged antiplatelet therapy in various categories of patients. Antiplatelet Trialists’ Collaboration. BMJ. 308, 81–106 (1994).
American Diabetes, A. Standards of medical care in diabetes–2013. Diabetes Care. 36 Suppl 1, S11–66, doi:10.2337/dc13-S011 (2013).
Luo, M. et al. Relationship between red cell distribution width and serum uric acid in patients with untreated essential hypertension. Scientific reports 4, 7291, doi:10.1038/srep07291 (2014).
Xu, X. et al. p66Shc: A novel biomarker of tubular oxidative injury in patients with diabetic nephropathy. Scientific reports 6, 29302, doi:10.1038/srep29302 (2016).
Kdoqi. KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Diabetes and Chronic Kidney Disease. American journal of kidney diseases: the official journal of the National Kidney Foundation. 49, S12–154, doi:10.1053/j.ajkd.2006.12.005 (2007).
Xiao, L. et al. Rap1 ameliorates renal tubular injury in diabetic nephropathy. Diabetes. 63, 1366–1380, doi:10.2337/db13-1412 (2014).
Acknowledgements
This study was sponsored by the “Big Data of Clinical kidney disease” Project of Central South University (2013–2018), the National Foundation Committee of Natural Sciences of China (81470960, 81270812, 81570658, 81300600 and 81370832) and the Hunan Province Natural Science Foundation (2016JJ6106).
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The data in the manuscript were written by X.X., X.C., X.Z., C.H., Y.H., L.Z., and Y.Y., L.X., F.L., and L.S. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
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Xiong, Xf., Yang, Y., Chen, X. et al. Red cell distribution width as a significant indicator of medication and prognosis in type 2 diabetic patients. Sci Rep 7, 2709 (2017). https://doi.org/10.1038/s41598-017-02904-9
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DOI: https://doi.org/10.1038/s41598-017-02904-9
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