http://informahealthcare.com/rnf ISSN: 0886-022X (print), 1525-6049 (electronic) Ren Fail, Early Online: 1–5 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/0886022X.2015.1010938
CLINICAL STUDY
Association between red blood cell distribution and renal function in patients with untreated type 2 diabetes mellitus Min Zhang1, Yan Zhang1, Che Li2, and Linhua He3
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1
Department of Pathology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China, 2Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China, and 3Department of Implementation, Hunan Judicial Police Vocational College, Changsha, Hunan Province, China Abstract
Keywords
We assessed whether red cell distribution width (RDW) is associated with microalbuminuria (MAU) in a group of 320 patients with newly diagnosed type 2 diabetes mellitus (T2DM), recruited in Zhengzhou. Patients were divided into normal group and MAU group. Compared with the normal group, the patients with MAU had higher red blood cell count (p ¼ 0.005) and RDW (p50.001). The multiple logistic regression indicated that RDW (OR ¼ 3.89, 95% CI: 1.98– 7.66, p50.001) was an independent risk factor of MAU in newly diagnosed T2DM. Other factors include smoking (OR ¼ 4.44, 95% CI: 2.90–8.72, p50.001), higher waist index (OR ¼ 2.17, 95% CI: 1.89–5.26, p ¼ 0.002), FBG level (OR ¼ 2.05, 95% CI: 1.21–3.84, p ¼ 0.008) and uric acid level (OR ¼ 2.18, 95% CI: 1.05–4.52, p ¼ 0.037). The receiver operating characteristic (ROC) curves explored the relationship between MAU and RDW. The area under the curve was 0.79 (95% CI: 0.74–0.84; p50.001). Using a cut-off point of 12.8, the RDW predicted MAU in the T2DM patients with a sensitivity of 71.3% and specificity of 66.9%. RDW may be independently associated with MAU in patients with newly diagnosed T2DM. RDW may be treated as effective predictive index in the evaluation of diabetes nephropathy or diabetes-associated complications.
Chronic kidney disease, diabetes mellitus, microalbuminuria, red cell distribution width, renal function
Introduction Diabetes mellitus (DM) is the most common chronic endocrine disease. It was estimated that there are 382 million DM patients around the world in 2013, and the number of DM is almost certain to hit 592 million patients in 2035.1 The prevalence of DM is increasing so rapidly that it has been becoming a major world-wide public health issue.2 With the number of DM, particularly type-2 DM (T2DM) increasing, diabetic-associated complications have also increased, such as diabetic nephropathy. DM is the most important risk factor for chronic kidney disease (CKD), and the risk of CKD attributed to DM continues to rise around the world.3 It is reported that DM nephropathy in T2DM patients has prevalence of 30–40% and has become the leading cause of end-stage renal disease in Western countries.4 Several lines of evidences have suggested that DM patients with renal function damage are at higher risk of cardiovascular disease events.5–7 Hence, early identification of those patients is of great importance, as renal function damage might be risk factors of future adverse cardiovascular events, especially for DM patients.
History Received 27 October 2014 Revised 28 December 2014 Accepted 4 January 2015 Published online 16 February 2015
Red width cell distribution width (RDW) is an index for evaluating the variation of red blood cell volume. Increased RDW has been treated as a measure of all kinds of anemia such as iron deficiency anemia, vitamin B12 deficiency anemia.8 Recently, RDW not only has been suggested to be related with cardiovascular disease mortality in the general population9,10, but also has been treated as an independent predictor of mortality in patients with heart failure,11 stroke,12 pulmonary arterial hypertension13,14 and acute kidney injury.15 Tsuboi et al. also found that RDW was significantly associated with increased all-cause mortality in DM patients.16 It is known that DM is a chronic condition with increased oxidative stress and vascular inflammation while RDW is also found to be associated with some inflammatory cytokines, and oxidative stress has been known to play a major role in the pathogenesis of renal function damage through some inflammatory markers.17,18 However, there are few studies assessing the association between RDW and renal function in untreated T2DM. The purpose of the present study is to assess whether RDW is associated with microalbuminuria (MAU) in patients with newly diagnosed T2DM.
Methods Address correspondence to Linhua He, MD, Department of implementation, Hunan Judicial Police Vocational College, Second Yuanda Road, Rurong District, Changsha City, Hunan Province 410131, China. Tel: +86 0731 82693064; Fax: +86 0731 82693064; E-mail: [email protected]
Study population Using a cross-sectional design, 410 consecutive patients with newly diagnosed untreated T2DM were enrolled in the second
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affiliated hospital of Zhengzhou University from October 2013 to February 2014. Among 410 patients who underwent physical examination and laboratory tests, 320 patients’ data were collected according to exclusion criteria. The exclusion criteria are: malignant neoplasms, serious liver diseases, persistent urinary tract infection and chronic kidney disease (estimated glomerular filtration rate (eGFR)560 mL/min) and patients with any of these were excluded from the study. Patients who were receiving drug treatment were also excluded. We sub-divided the patients into normal group and MAU group according to albuminuria creatinine ratio (ACR). MAU was defined as an ACR more between 30 mg/mg creatinine and 300 mg/mg creatinine.19 Diabetes was defined as a self-reported diabetes with a validated history or newly diagnostic diabetes by oral glucose tolerance test (WHO: fasting plasma glucose (FPG) 7.0 mmol/L, or 2-h postprandial glucose (2hPG) 11.1 mmol/L20). Metabolic syndrome was defined according to the International Diabetes Federation criteria.21 The patients’ demographic characteristics including age, sex and smoking habits were collected through a standardized questionnaire. Patients who had smoked at least on cigarette daily for 1 year were treated as smokers. This study protocol was approved by the institutional review board of Second affiliated hospital of Zhengzhou university, and written informed consent was obtained from all participants. Laboratory examination Hematologic testing was conducted on the Beckman Coulter LH-750 Hematology Analyzer (Beckman Coulter, Inc., Fullerton, CA), automated hematology analyzer, which measures hemoglobin photometrically, including white blood cell (WBC) counts, platelet counts, hemoglobin, erythrocyte sedimentation rate (ESR), mean platelet volume (MVP), mean corpuscular volume and RDW, optical laser light scattering for cell enumeration, flow cytometer and laser diffraction for red blood cell (RBC) counts. Serum creatinine and blood urea nitrogen (BUN) were measured on a Roche/ Hitachi Modular System P (Roche Diagnostics GmbH, Mannheim, Germany) by creatinine Jaffe’, rate blanked and compensated assay. Urine concentrations of albumin were measured by immunoturbidimetric method. In addition, fasting blood glucose level (FBG), creatinine level and fasting serum lipid status including triglycerides (TG), low density lipoprotein cholesterol (LDH-C), high density lipoprotein cholesterol (HDL-C), total cholesterol and C-reactive protein (CRP) levels were also recorded. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m2). Waist index was calculated as waist circumference (cm) divided by 94 for men and 80 for women.22 The eGFR was calculated from the calculation formula defined as: 186 SCr1.154 age in years0.203 1.210 (if black) 0.742 (if female),23 the definition of CKD (low GFR) was an eGFR560 mL/min/1.73 m2. Statistical analysis The Kolmogorov–Smirnov test was conducted to test for a normal distribution of continuous variables. Data with normal distribution were expressed as mean ± standard deviation. Parameters without normal distribution were expressed as
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median with range. Differences between qualitative variables were tested by Student’s t-test. Differences between quantitative variables were tested using 2 test. Stepwise multiple logistic regression analysis was used to calculate the odds ratio of independent variables. Logistic regression model includes the following variables: age, sex, BMI, smoking status, waist index, hypertension, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR. Statistical analysis was performed using SPSS version 18.0 (SPSS Inc., Chicago, IL), and the level of statistical significance was defined as p50.05.
Results General characteristics of the subjects A total of 320 newly diagnosed T2DM patients were recruited in the present study. None of the patients studied had evidence of autoimmune disease. On the basis of the results of ACR calculation, study population were divided into two groups: 202 T2DM patients with normal urinary albumin (mean age: 46.3 ± 7.5 years, 72 females and 130 males) and 118 MAU group (mean age: 47.1 ± 8.2 year, 54 females and 64 males). Table 1 shows the baseline demographic and clinical characteristics of the two groups. Two groups were similar with respect to age, sex, BMI and prevalence of metabolic syndrome and hypertension except the waist index. Compared with the normal group, the MAU group tends to be smoking (30.5% vs. 5.4%, p50.001), higher waist index (1.3 ± 0.2 vs. 1.2 ± 0.2, p50.001), FBG level (10.8 ± 3.5 mmol/L vs. 9.9 ± 3.1 mmol/L, p ¼ 0.0118), ACR (80.6 ± 60.5 mg/g vs. 15.1 ± 8.8 mg/g, p50.001), uric acid (353.7 ± 44.6 mmol/L vs. 340.7 ± 47.5 mmol/L, p ¼ 0.016), RDW (13.3 ± 0.9 vs. 12.4 ± 0.6, p50.001), ESR (34(13.0–43.7) vs. 19.6(13.1– 30.0), p50.001), lower RBC (4.8 ± 0.4 vs. 5.0 ± 40.4, p ¼ 0.005), MPV (10.5 ± 0.8 vs. 11.2 ± 0.9, p50.001), hemoglobin level (143.9 ± 14.8 g/L vs. 152.1 ± 14.3 g/L, p50.001) and ALT (26.6 ± 17.7 U/L vs. 33.6 ± 28.2 U/L, p ¼ 0.017). No significant difference was observed for other variables between the two groups (Table 1). Multiple logistic regression analysis for MAU Defining MAU status (normal group vs. MAU group) as the dependent variable and all other factors were considered as covariates (age, sex, BMI, smoking status, waist index, hypertension, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR) in the stepwise logistic regression model. The multiple logistic regression indicated that RDW (OR ¼ 3.89, 95% CI: 1.98– 7.66, p50.001) was an independent risk factor of MAU in newly diagnosed T2DM (Table 2). Other factors include smoking (OR ¼ 4.44, 95% CI: 2.90–8.72, p50.001), higher waist index (OR ¼ 2.17, 95% CI: 1.89–5.26, p ¼ 0.002), FBG level (OR ¼ 2.05, 95% CI: 1.21–3.84, p ¼ 0.008) and uric acid level (OR ¼ 2.18, 95% CI: 1.05–4.52, p ¼ 0.037). The ROC curves explored the relationship between MAU and RDW. The area under the curve was 0.79 (95% CI: 0.74–0.84; p50.001). Using a cut-off point of 12.8, the RDW predicted
Red blood cell distribution and renal function
DOI: 10.3109/0886022X.2015.1010938
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Table 1. Clinical characteristics of T2DM patients with and without MAU.
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Parameters
Normal group (n ¼ 202)
MAU group (n ¼ 118)
p
46.3 ± 7.5 72/130 11 (5.4%) 26.4 ± 3.7 1.2 ± 0.2 131 (64.9%) 61 (30.2%) 9.9 ± 3.1 8.6 (7.3–9.6) 1.8 ± 0.5 1.1 ± 0.2 3.1 ± 0.9 5.1 ± 0.9 25.4 ± 10.7 33.6 ± 28.2 15.1 ± 8.8 67.9 ± 13.1 340.7 ± 47.5 4.7 ± 1.2 2.5 ± 5.3 108.2 ± 27.0 6.4 ± 1.5 5.0 ± 40.4 12.4 ± 0.6 19.6 (13.1–30.0) 11.2 ± 0.9 242.7 ± 54.6 152.1 ± 14.3
47.1 ± 8.2 54/64 36 (30.5%) 25.7 ± 3.2 1.3 ± 0.2 78 (66.1%) 37 (31.4%) 10.8 ± 3.5 8.4 (7.8–9.7) 1.8 ± 0.6 1.1 ± 0.2 3.3 ± 0.8 5.2 ± 0.8 24.6 ± 12.0 26.6 ± 17.7 80.6 ± 60.5 70.0 ± 13.2 353.7 ± 44.6 4.8 ± 0.4 2.4 ± 4.6 105.4 ± 26.7 6.6 ± 2.0 4.8 ± 0.4 13.3 ± 0.9 34 (13.0–43.7) 10.5 ± 0.8 239.5 ± 57.4 143.9 ± 14.8
0.415 0.074 50.001 0.118 50.001 0.821 0.828 0.018 0.298 0.781 0.371 0.069 0.109 0.057 0.017 50.001 0.187 0.016 0.199 0.861 0.359 0.446 0.005 50.001 50.001 50.001 0.677 50.001
Age, year Sex (F/M) Smoking, yes Body mass index (kg/m2) Waist index Metabolic syndrome Hypertension (%) FBG (mmol/L) HbA1c (%) Triglyceride (mmol/dL L) HDL-cholesterol (mmol/dL) LDL-cholesterol (mmol/dL) Total cholesterol (mmol/dL) AST (U/L) ALT (U/L) ACR (mg/g) Serum creatine (mmol/dL) Uric acid (mmol/L) BUN (mmol/L) Hs-CRP (mg/dL) eGFR (mL/min/1.72 m2) White blood count (109) Red blood cell (1012/L) Red cell distribution width (%) ESR (mm 1st hour) MPV (fL) Platelet count (109) Hemoglobin (g/L)
Table 2. Multiple logistic regression for MAU in patients with untreated T2DM.
Variables Smoking Waist index FBG RDW Uric acid Constant
B
S.E.
Wald
p Value
Odds ratio
95% CI
2.671 0.785 0.719 1.359 0.778 15.231
0.554 0.647 0.269 0.346 0.373 5.156
23.212 16.354 7.147 15.463 4.358 8.727
50.001 0.002 0.008 50.001 0.037 0.003
4.44 2.17 2.05 3.89 2.18 –
2.90–8.72 1.89–5.26 1.21–3.48 1.98–7.66 1.05–4.52 –
MAU in the T2DM patients with a sensitivity of 71.3%, specificity of 66.9%, positive predictive value of 70.0% and negative predictive values of 83.0% (Figure 1).
Discussion The present study demonstrated that RDW was higher in MAU patients with untreated T2DM than the normal group independently of potential confounding factors. RDW412.8 measured in hypertensive patients had a 71.3% sensitivity and 66.9% specificity in MAU status omicron patients with untreated T2DM. This value is very close to that from published study that RDW was associated with MAU in hypertension patients without treatment.24 Bulum et al. found significant associations between RBC and renal function parameters in type 1 diabetic patients even with normal renal function (eGFR460 mL/min and urinary albumin excretion rate530 mg/24 h), suggesting that the interplay between RBC and renal function exists in the absence of nephropathy in type 1 diabetes. Those patients in the highest quartile of
Figure 1. Receiver operating characteristic curve of RDW for MAU status T2DM patients.
albuminuria in normoalbuminuric range had significantly higher Hb and Hct suggesting that it can be even prognostic factor for MAU.25 Magri et al. ever found that RDW was strongly associated with diabetic nephropathy in patients with T2DM.26 But obvious differences exist between Magri’s study and the present study. One important difference is study patients. Our study results were obtained from T2DM patients without receiving treatment, and latter was from population
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with long duration of diabetes (mean duration was 18.3 years). Other, our study include larger sample size than previous study, and we want to assess the association between RDW and early renal function damage in T2DM patients. The possible reason of association between RDW and MAU status is the presence of chronic inflammation. Lippi et al.27 found that a strong, graded and independent association existed between RDW and CRP levels. Lappe et al. demonstrated that RDW was associated with mortality in patients with coronary artery disease.28 Semba et al. also found the patients with higher RDW level tended to have a higher interleukin-6 level.29 Forhecz et al. found that RDW was related with inflammation in patients with chronic heart failure, and serum antioxidants and inflammation can predict RDW level in older women.30 Besides, RDW is an index reflecting variability of red blood cells and platelets. Elevated RDW may be associated with circulation disturbance. Malandrino et al. found that RDW level significantly rose in CKD but not in retinopathy patients. In other words, RDW was strongly associated with diabetes nephropathy but not with diabetes retinopathy.31 It is known that diabetes nephropathy is a result of micro- and macro-vascular combination while retinopathy is a kind of microcirculation disease. Moreover, inflammation of retinopathy disease mainly occurred in region instead of systemic inflammatory response.32 This can partly explain that RDW could be treated as a marker of early renal function damage in T2DM. Semba et al. measured RDW at baseline, 12 months and 24 months in 786 moderately to severely disabled women aged more than 65 years old and found serum selenium was an underlying predictor of RDW, which suggests that oxidative stress may be a potential biological mechanism for increased RDW.33 This could be another important determinant of adverse outcomes in T2DM patients. The present study has several limitations. First, one of the important limitations is its cross-sectional nature, which does allow us to further evaluate the specific mechanism of the association. Second, it is noticed that subjects in this study is from Asian patients, with normal renal function and free of cardiovascular diseases. Therefore, the conclusions of our study cannot be extrapolated to non-Asian populations and caution should be needed when applying the results of our findings to other clinical settings. Third, RDW may be influenced by some other factors such as serum iron, vitamin B12 and folate status. But these factors need to be further confirmed in the future study, and mean corpuscular volume is not reported in this study. It is possible that MCV could be associated with MAU just like RDW. Fourth, MAU of patients with a level of plasma glucose 410 mmol/L, which is the threshold for glucosuria. This probably led to the high frequency of MAU in the study population that includes only newly diagnosed type 2 diabetes. Maybe another group patients with hyperglycemia but not T2DM was further studied. Finally that would be better if a cohort study was conducted to confirm the present findings. Our main strength is that T2DM patients receiving drug treatment were excluded in this study, which could eliminate the influence of treatment effect on association between RDW and MAU. In conclusion, the present study suggested that the relationship of RDW with MAU status exists in T2DM
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patients without receiving drug treatment. Our results also show this association is independent of many potential confound factors (age, sex, BMI, smoking status, waist index, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR). RDW could be treated as effective predictive index in the evaluation of diabetes nephropathy or diabetes-associated complications. Cohort studies or with more large sample size are needed to further establish the important role of RDW in incidence of MAU among T2DM patients.
Acknowledgments We thank all our colleagues working in the Second Affiliated Hospital of Zhengzhou University.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
References 1. International Diabetes Federation. IDF Diabetes Atlas. 6th ed. Brussels, Belgium: International Diabetes Federation; 2013. Available from: http://www.idf.org/diabetesatlas. 2. Gill G, Nayak AU, Wilkins J, et al. Challenges of emerging adulthood-transition from paediatric to adult diabetes. World J Diabetes. 2014;5:630–635. 3. Ioannidis I. Diabetes treatment in patients with renal disease: Is the landscape clear enough. World J Diabetes. 2014;5:651–658. 4. Adler AI, Stevens RJ, Manley SE, et al. Development and progression of nephropathy in type 2 diabetes: The United Kingdom Prospective Diabetes Study (UKPDS 64). Kid Int. 2003;63:225–232. 5. Sasso FC, De Nicola L, Carbonara O, et al. Cardiovascular risk factors and disease management in type 2 diabetic patients with diabetic nephropathy. Diabetes Care. 2006;29:498–503. 6. Silva AP, Fragoso A, Silva C, et al. What is the role of apelin regarding cardiovascular risk and progression of renal disease in type 2 diabetic patients with diabetic nephropathy? Biomed Res Int. 2013;2013:247649. 7. Aso Y. Cardiovascular disease in patients with diabetic nephropathy. Curr Mol Med. 2008;8:533–543. 8. Evans TC, Jehle D. The red blood cell distribution width. J Emerg Med. 1991;9(Suppl. 1):71–74. 9. Patel KV, Ferrucci L, Ershler WB, Longo DL, Guralnik JM. Red blood cell distribution width and the risk of death in middle-aged and older adults. Arch Intern Med. 2009;169:515–523. 10. Perlstein TS, Weuve J, Pfeffer MA, Beckman JA. Red blood cell distribution width and mortality risk in a community-based prospective cohort. Arch Intern Med. 2009;169:588–594. 11. Felker GM, Allen LA, Pocock SJ, 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. 2007;50:40–47. 12. Ani C, Ovbiagele B. Elevated red blood cell distribution width predicts mortality in persons with known stroke. J Neurol Sci. 2009; 277:103–108. 13. Rhodes CJ, Wharton J, Howard LS, et al. Red cell distribution width outperforms other potential circulating biomarkers in predicting survival in idiopathic pulmonary arterial hypertension. Heart. 2011;97:1054–1060. 14. Hampole CV, Mehrotra AK, Thenappan T, et al. Usefulness of red cell distribution width as a prognostic marker in pulmonary hypertension. Am J Cardiol. 2009;104:868–872. 15. Oh HJ, Park JT, Kim JK, et al. Red blood cell distribution width is an independent predictor of mortality in acute kidney injury patients treated with continuous renal replacement therapy. Nephrol Dial Transplant. 2012;24:589–594.
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16. Tsuboi S, Miyauchi K, Kasai T, et al. Impact of red blood cell distribution width on long-term mortality in diabetic patients after percutaneous coronary intervention. Circ J. 2013;77(2):456–461. 17. Levine B, Kalman J, Mayer L, Fillit HM, Packer M. Elevated circulating levels of tumor necrosis factor in severe chronic heart failure. N Engl J Med. 1990;323:236–241. 18. Marinkovic D, Zhang X, Yalcin S, et al. Foxo3 is required for the regulation of oxidative stress in erythropoiesis. J Clin Invest. 2007; 117:2133–2144. 19. Assadi F. Quantitation of microalbuminuria using random urine samples. Pediatr Nephrol. 2002;17:107–110. 20. Kuzuya T, Nakagawa S, Satoh J, et al. Report of the Committee on the classification and diagnostic criteria of diabetes mellitus. Diabetes Res Clin Pract. 2002;55:65–85. 21. Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Concensus Group . The metabolic syndrome – A new worldwide definition. Lancet. 2005;366:1059e62. 22. Ka¨rvestedt L, Martensson E, Grill V, et al. Peripheral sensory neuropathy associates with micro- or macroangiopathy: Results from a population-based study of type 2 diabetic patients in Sweden. Diabetes Care. 2009;32:317e22. 23. NKF-K/DOQI clinical practice guidelines for chronic kidney disease. Am J Kidney Dis. 2002;39:S76. 24. Li ZZ, Chen L, Yuan H, et al. Relationship between red blood cell distribution width and early-stage renal function damage in patients with essential hypertension. J Hypertens. 2014;3212:2450–2456. 25. Bulum T, Prkacin I, Blaslov K, et al. Association between red blood cell count and renal function exist in type 1 diabetic patients in the absence of nephropathy. Coll Antropol. 2013;373:777–782.
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26. Magri CJ, Fava S. Red blood cell distribution width and diabetesassociated complications. Diabetes Metab Syndr. 2014;8:13–17. 27. Lippi G, Targher G, Montagnana M, et al. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med. 2009; 133:628–632. 28. Lappe JM, Horne BD, Shah SH, et al. Red cell distribution width, C-reactive protein, the complete blood count, and mortality in patients with coronary disease and a normal comparison population. Clin Chim Acta. 2011;412:2094–2099. 29. Semba RD, Patel KV, Ferrucci L, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: The Women’s Health and Aging Study I. Clin Nutr. 2010;29: 600–604. 30. Forhecz Z, Gombos T, Borgulya G, et al. Red cell distribution width in heart failure: Prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J. 2009;158: 659–666. 31. Malandrino N, Wu WC, Taveira TH, et al. Association between red blood cell distribution width and macrovascular and microvascular complications in diabetes. Diabetologia. 2012;55:226–235. 32. Sawicki PT, Kaiser S, Heinemann L, et al. Prevalence of renal artery stenosis in diabetes mellitus – An autopsy study. J Intern Med. 1991;229:489–492. 33. Semba RD, Patel KV, Ferrucci L, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: The Women’s Health and Aging Study I. Clin Nutr. 2010;29: 600–604.
CLINICAL STUDY
Association between red blood cell distribution and renal function in patients with untreated type 2 diabetes mellitus Min Zhang1, Yan Zhang1, Che Li2, and Linhua He3
Ren Fail Downloaded from informahealthcare.com by Chinese University of Hong Kong on 02/24/15 For personal use only.
1
Department of Pathology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China, 2Department of Epidemiology and Health Statistics, School of Public Health, Zhengzhou University, Zhengzhou, Henan Province, China, and 3Department of Implementation, Hunan Judicial Police Vocational College, Changsha, Hunan Province, China Abstract
Keywords
We assessed whether red cell distribution width (RDW) is associated with microalbuminuria (MAU) in a group of 320 patients with newly diagnosed type 2 diabetes mellitus (T2DM), recruited in Zhengzhou. Patients were divided into normal group and MAU group. Compared with the normal group, the patients with MAU had higher red blood cell count (p ¼ 0.005) and RDW (p50.001). The multiple logistic regression indicated that RDW (OR ¼ 3.89, 95% CI: 1.98– 7.66, p50.001) was an independent risk factor of MAU in newly diagnosed T2DM. Other factors include smoking (OR ¼ 4.44, 95% CI: 2.90–8.72, p50.001), higher waist index (OR ¼ 2.17, 95% CI: 1.89–5.26, p ¼ 0.002), FBG level (OR ¼ 2.05, 95% CI: 1.21–3.84, p ¼ 0.008) and uric acid level (OR ¼ 2.18, 95% CI: 1.05–4.52, p ¼ 0.037). The receiver operating characteristic (ROC) curves explored the relationship between MAU and RDW. The area under the curve was 0.79 (95% CI: 0.74–0.84; p50.001). Using a cut-off point of 12.8, the RDW predicted MAU in the T2DM patients with a sensitivity of 71.3% and specificity of 66.9%. RDW may be independently associated with MAU in patients with newly diagnosed T2DM. RDW may be treated as effective predictive index in the evaluation of diabetes nephropathy or diabetes-associated complications.
Chronic kidney disease, diabetes mellitus, microalbuminuria, red cell distribution width, renal function
Introduction Diabetes mellitus (DM) is the most common chronic endocrine disease. It was estimated that there are 382 million DM patients around the world in 2013, and the number of DM is almost certain to hit 592 million patients in 2035.1 The prevalence of DM is increasing so rapidly that it has been becoming a major world-wide public health issue.2 With the number of DM, particularly type-2 DM (T2DM) increasing, diabetic-associated complications have also increased, such as diabetic nephropathy. DM is the most important risk factor for chronic kidney disease (CKD), and the risk of CKD attributed to DM continues to rise around the world.3 It is reported that DM nephropathy in T2DM patients has prevalence of 30–40% and has become the leading cause of end-stage renal disease in Western countries.4 Several lines of evidences have suggested that DM patients with renal function damage are at higher risk of cardiovascular disease events.5–7 Hence, early identification of those patients is of great importance, as renal function damage might be risk factors of future adverse cardiovascular events, especially for DM patients.
History Received 27 October 2014 Revised 28 December 2014 Accepted 4 January 2015 Published online 16 February 2015
Red width cell distribution width (RDW) is an index for evaluating the variation of red blood cell volume. Increased RDW has been treated as a measure of all kinds of anemia such as iron deficiency anemia, vitamin B12 deficiency anemia.8 Recently, RDW not only has been suggested to be related with cardiovascular disease mortality in the general population9,10, but also has been treated as an independent predictor of mortality in patients with heart failure,11 stroke,12 pulmonary arterial hypertension13,14 and acute kidney injury.15 Tsuboi et al. also found that RDW was significantly associated with increased all-cause mortality in DM patients.16 It is known that DM is a chronic condition with increased oxidative stress and vascular inflammation while RDW is also found to be associated with some inflammatory cytokines, and oxidative stress has been known to play a major role in the pathogenesis of renal function damage through some inflammatory markers.17,18 However, there are few studies assessing the association between RDW and renal function in untreated T2DM. The purpose of the present study is to assess whether RDW is associated with microalbuminuria (MAU) in patients with newly diagnosed T2DM.
Methods Address correspondence to Linhua He, MD, Department of implementation, Hunan Judicial Police Vocational College, Second Yuanda Road, Rurong District, Changsha City, Hunan Province 410131, China. Tel: +86 0731 82693064; Fax: +86 0731 82693064; E-mail: [email protected]
Study population Using a cross-sectional design, 410 consecutive patients with newly diagnosed untreated T2DM were enrolled in the second
Ren Fail Downloaded from informahealthcare.com by Chinese University of Hong Kong on 02/24/15 For personal use only.
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M. Zhang et al.
affiliated hospital of Zhengzhou University from October 2013 to February 2014. Among 410 patients who underwent physical examination and laboratory tests, 320 patients’ data were collected according to exclusion criteria. The exclusion criteria are: malignant neoplasms, serious liver diseases, persistent urinary tract infection and chronic kidney disease (estimated glomerular filtration rate (eGFR)560 mL/min) and patients with any of these were excluded from the study. Patients who were receiving drug treatment were also excluded. We sub-divided the patients into normal group and MAU group according to albuminuria creatinine ratio (ACR). MAU was defined as an ACR more between 30 mg/mg creatinine and 300 mg/mg creatinine.19 Diabetes was defined as a self-reported diabetes with a validated history or newly diagnostic diabetes by oral glucose tolerance test (WHO: fasting plasma glucose (FPG) 7.0 mmol/L, or 2-h postprandial glucose (2hPG) 11.1 mmol/L20). Metabolic syndrome was defined according to the International Diabetes Federation criteria.21 The patients’ demographic characteristics including age, sex and smoking habits were collected through a standardized questionnaire. Patients who had smoked at least on cigarette daily for 1 year were treated as smokers. This study protocol was approved by the institutional review board of Second affiliated hospital of Zhengzhou university, and written informed consent was obtained from all participants. Laboratory examination Hematologic testing was conducted on the Beckman Coulter LH-750 Hematology Analyzer (Beckman Coulter, Inc., Fullerton, CA), automated hematology analyzer, which measures hemoglobin photometrically, including white blood cell (WBC) counts, platelet counts, hemoglobin, erythrocyte sedimentation rate (ESR), mean platelet volume (MVP), mean corpuscular volume and RDW, optical laser light scattering for cell enumeration, flow cytometer and laser diffraction for red blood cell (RBC) counts. Serum creatinine and blood urea nitrogen (BUN) were measured on a Roche/ Hitachi Modular System P (Roche Diagnostics GmbH, Mannheim, Germany) by creatinine Jaffe’, rate blanked and compensated assay. Urine concentrations of albumin were measured by immunoturbidimetric method. In addition, fasting blood glucose level (FBG), creatinine level and fasting serum lipid status including triglycerides (TG), low density lipoprotein cholesterol (LDH-C), high density lipoprotein cholesterol (HDL-C), total cholesterol and C-reactive protein (CRP) levels were also recorded. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m2). Waist index was calculated as waist circumference (cm) divided by 94 for men and 80 for women.22 The eGFR was calculated from the calculation formula defined as: 186 SCr1.154 age in years0.203 1.210 (if black) 0.742 (if female),23 the definition of CKD (low GFR) was an eGFR560 mL/min/1.73 m2. Statistical analysis The Kolmogorov–Smirnov test was conducted to test for a normal distribution of continuous variables. Data with normal distribution were expressed as mean ± standard deviation. Parameters without normal distribution were expressed as
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median with range. Differences between qualitative variables were tested by Student’s t-test. Differences between quantitative variables were tested using 2 test. Stepwise multiple logistic regression analysis was used to calculate the odds ratio of independent variables. Logistic regression model includes the following variables: age, sex, BMI, smoking status, waist index, hypertension, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR. Statistical analysis was performed using SPSS version 18.0 (SPSS Inc., Chicago, IL), and the level of statistical significance was defined as p50.05.
Results General characteristics of the subjects A total of 320 newly diagnosed T2DM patients were recruited in the present study. None of the patients studied had evidence of autoimmune disease. On the basis of the results of ACR calculation, study population were divided into two groups: 202 T2DM patients with normal urinary albumin (mean age: 46.3 ± 7.5 years, 72 females and 130 males) and 118 MAU group (mean age: 47.1 ± 8.2 year, 54 females and 64 males). Table 1 shows the baseline demographic and clinical characteristics of the two groups. Two groups were similar with respect to age, sex, BMI and prevalence of metabolic syndrome and hypertension except the waist index. Compared with the normal group, the MAU group tends to be smoking (30.5% vs. 5.4%, p50.001), higher waist index (1.3 ± 0.2 vs. 1.2 ± 0.2, p50.001), FBG level (10.8 ± 3.5 mmol/L vs. 9.9 ± 3.1 mmol/L, p ¼ 0.0118), ACR (80.6 ± 60.5 mg/g vs. 15.1 ± 8.8 mg/g, p50.001), uric acid (353.7 ± 44.6 mmol/L vs. 340.7 ± 47.5 mmol/L, p ¼ 0.016), RDW (13.3 ± 0.9 vs. 12.4 ± 0.6, p50.001), ESR (34(13.0–43.7) vs. 19.6(13.1– 30.0), p50.001), lower RBC (4.8 ± 0.4 vs. 5.0 ± 40.4, p ¼ 0.005), MPV (10.5 ± 0.8 vs. 11.2 ± 0.9, p50.001), hemoglobin level (143.9 ± 14.8 g/L vs. 152.1 ± 14.3 g/L, p50.001) and ALT (26.6 ± 17.7 U/L vs. 33.6 ± 28.2 U/L, p ¼ 0.017). No significant difference was observed for other variables between the two groups (Table 1). Multiple logistic regression analysis for MAU Defining MAU status (normal group vs. MAU group) as the dependent variable and all other factors were considered as covariates (age, sex, BMI, smoking status, waist index, hypertension, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR) in the stepwise logistic regression model. The multiple logistic regression indicated that RDW (OR ¼ 3.89, 95% CI: 1.98– 7.66, p50.001) was an independent risk factor of MAU in newly diagnosed T2DM (Table 2). Other factors include smoking (OR ¼ 4.44, 95% CI: 2.90–8.72, p50.001), higher waist index (OR ¼ 2.17, 95% CI: 1.89–5.26, p ¼ 0.002), FBG level (OR ¼ 2.05, 95% CI: 1.21–3.84, p ¼ 0.008) and uric acid level (OR ¼ 2.18, 95% CI: 1.05–4.52, p ¼ 0.037). The ROC curves explored the relationship between MAU and RDW. The area under the curve was 0.79 (95% CI: 0.74–0.84; p50.001). Using a cut-off point of 12.8, the RDW predicted
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Table 1. Clinical characteristics of T2DM patients with and without MAU.
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Parameters
Normal group (n ¼ 202)
MAU group (n ¼ 118)
p
46.3 ± 7.5 72/130 11 (5.4%) 26.4 ± 3.7 1.2 ± 0.2 131 (64.9%) 61 (30.2%) 9.9 ± 3.1 8.6 (7.3–9.6) 1.8 ± 0.5 1.1 ± 0.2 3.1 ± 0.9 5.1 ± 0.9 25.4 ± 10.7 33.6 ± 28.2 15.1 ± 8.8 67.9 ± 13.1 340.7 ± 47.5 4.7 ± 1.2 2.5 ± 5.3 108.2 ± 27.0 6.4 ± 1.5 5.0 ± 40.4 12.4 ± 0.6 19.6 (13.1–30.0) 11.2 ± 0.9 242.7 ± 54.6 152.1 ± 14.3
47.1 ± 8.2 54/64 36 (30.5%) 25.7 ± 3.2 1.3 ± 0.2 78 (66.1%) 37 (31.4%) 10.8 ± 3.5 8.4 (7.8–9.7) 1.8 ± 0.6 1.1 ± 0.2 3.3 ± 0.8 5.2 ± 0.8 24.6 ± 12.0 26.6 ± 17.7 80.6 ± 60.5 70.0 ± 13.2 353.7 ± 44.6 4.8 ± 0.4 2.4 ± 4.6 105.4 ± 26.7 6.6 ± 2.0 4.8 ± 0.4 13.3 ± 0.9 34 (13.0–43.7) 10.5 ± 0.8 239.5 ± 57.4 143.9 ± 14.8
0.415 0.074 50.001 0.118 50.001 0.821 0.828 0.018 0.298 0.781 0.371 0.069 0.109 0.057 0.017 50.001 0.187 0.016 0.199 0.861 0.359 0.446 0.005 50.001 50.001 50.001 0.677 50.001
Age, year Sex (F/M) Smoking, yes Body mass index (kg/m2) Waist index Metabolic syndrome Hypertension (%) FBG (mmol/L) HbA1c (%) Triglyceride (mmol/dL L) HDL-cholesterol (mmol/dL) LDL-cholesterol (mmol/dL) Total cholesterol (mmol/dL) AST (U/L) ALT (U/L) ACR (mg/g) Serum creatine (mmol/dL) Uric acid (mmol/L) BUN (mmol/L) Hs-CRP (mg/dL) eGFR (mL/min/1.72 m2) White blood count (109) Red blood cell (1012/L) Red cell distribution width (%) ESR (mm 1st hour) MPV (fL) Platelet count (109) Hemoglobin (g/L)
Table 2. Multiple logistic regression for MAU in patients with untreated T2DM.
Variables Smoking Waist index FBG RDW Uric acid Constant
B
S.E.
Wald
p Value
Odds ratio
95% CI
2.671 0.785 0.719 1.359 0.778 15.231
0.554 0.647 0.269 0.346 0.373 5.156
23.212 16.354 7.147 15.463 4.358 8.727
50.001 0.002 0.008 50.001 0.037 0.003
4.44 2.17 2.05 3.89 2.18 –
2.90–8.72 1.89–5.26 1.21–3.48 1.98–7.66 1.05–4.52 –
MAU in the T2DM patients with a sensitivity of 71.3%, specificity of 66.9%, positive predictive value of 70.0% and negative predictive values of 83.0% (Figure 1).
Discussion The present study demonstrated that RDW was higher in MAU patients with untreated T2DM than the normal group independently of potential confounding factors. RDW412.8 measured in hypertensive patients had a 71.3% sensitivity and 66.9% specificity in MAU status omicron patients with untreated T2DM. This value is very close to that from published study that RDW was associated with MAU in hypertension patients without treatment.24 Bulum et al. found significant associations between RBC and renal function parameters in type 1 diabetic patients even with normal renal function (eGFR460 mL/min and urinary albumin excretion rate530 mg/24 h), suggesting that the interplay between RBC and renal function exists in the absence of nephropathy in type 1 diabetes. Those patients in the highest quartile of
Figure 1. Receiver operating characteristic curve of RDW for MAU status T2DM patients.
albuminuria in normoalbuminuric range had significantly higher Hb and Hct suggesting that it can be even prognostic factor for MAU.25 Magri et al. ever found that RDW was strongly associated with diabetic nephropathy in patients with T2DM.26 But obvious differences exist between Magri’s study and the present study. One important difference is study patients. Our study results were obtained from T2DM patients without receiving treatment, and latter was from population
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with long duration of diabetes (mean duration was 18.3 years). Other, our study include larger sample size than previous study, and we want to assess the association between RDW and early renal function damage in T2DM patients. The possible reason of association between RDW and MAU status is the presence of chronic inflammation. Lippi et al.27 found that a strong, graded and independent association existed between RDW and CRP levels. Lappe et al. demonstrated that RDW was associated with mortality in patients with coronary artery disease.28 Semba et al. also found the patients with higher RDW level tended to have a higher interleukin-6 level.29 Forhecz et al. found that RDW was related with inflammation in patients with chronic heart failure, and serum antioxidants and inflammation can predict RDW level in older women.30 Besides, RDW is an index reflecting variability of red blood cells and platelets. Elevated RDW may be associated with circulation disturbance. Malandrino et al. found that RDW level significantly rose in CKD but not in retinopathy patients. In other words, RDW was strongly associated with diabetes nephropathy but not with diabetes retinopathy.31 It is known that diabetes nephropathy is a result of micro- and macro-vascular combination while retinopathy is a kind of microcirculation disease. Moreover, inflammation of retinopathy disease mainly occurred in region instead of systemic inflammatory response.32 This can partly explain that RDW could be treated as a marker of early renal function damage in T2DM. Semba et al. measured RDW at baseline, 12 months and 24 months in 786 moderately to severely disabled women aged more than 65 years old and found serum selenium was an underlying predictor of RDW, which suggests that oxidative stress may be a potential biological mechanism for increased RDW.33 This could be another important determinant of adverse outcomes in T2DM patients. The present study has several limitations. First, one of the important limitations is its cross-sectional nature, which does allow us to further evaluate the specific mechanism of the association. Second, it is noticed that subjects in this study is from Asian patients, with normal renal function and free of cardiovascular diseases. Therefore, the conclusions of our study cannot be extrapolated to non-Asian populations and caution should be needed when applying the results of our findings to other clinical settings. Third, RDW may be influenced by some other factors such as serum iron, vitamin B12 and folate status. But these factors need to be further confirmed in the future study, and mean corpuscular volume is not reported in this study. It is possible that MCV could be associated with MAU just like RDW. Fourth, MAU of patients with a level of plasma glucose 410 mmol/L, which is the threshold for glucosuria. This probably led to the high frequency of MAU in the study population that includes only newly diagnosed type 2 diabetes. Maybe another group patients with hyperglycemia but not T2DM was further studied. Finally that would be better if a cohort study was conducted to confirm the present findings. Our main strength is that T2DM patients receiving drug treatment were excluded in this study, which could eliminate the influence of treatment effect on association between RDW and MAU. In conclusion, the present study suggested that the relationship of RDW with MAU status exists in T2DM
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patients without receiving drug treatment. Our results also show this association is independent of many potential confound factors (age, sex, BMI, smoking status, waist index, creatinine, fasting glucose, CRP, TG, HDL-C, TC, LDL-C, ACR, ALT, AST, BUN, RDW, WBC count, Plt count, RBC count, Hb, Plt count, eGFR, MVP and ESR). RDW could be treated as effective predictive index in the evaluation of diabetes nephropathy or diabetes-associated complications. Cohort studies or with more large sample size are needed to further establish the important role of RDW in incidence of MAU among T2DM patients.
Acknowledgments We thank all our colleagues working in the Second Affiliated Hospital of Zhengzhou University.
Declaration of interest The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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26. Magri CJ, Fava S. Red blood cell distribution width and diabetesassociated complications. Diabetes Metab Syndr. 2014;8:13–17. 27. Lippi G, Targher G, Montagnana M, et al. Relation between red blood cell distribution width and inflammatory biomarkers in a large cohort of unselected outpatients. Arch Pathol Lab Med. 2009; 133:628–632. 28. Lappe JM, Horne BD, Shah SH, et al. Red cell distribution width, C-reactive protein, the complete blood count, and mortality in patients with coronary disease and a normal comparison population. Clin Chim Acta. 2011;412:2094–2099. 29. Semba RD, Patel KV, Ferrucci L, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: The Women’s Health and Aging Study I. Clin Nutr. 2010;29: 600–604. 30. Forhecz Z, Gombos T, Borgulya G, et al. Red cell distribution width in heart failure: Prediction of clinical events and relationship with markers of ineffective erythropoiesis, inflammation, renal function, and nutritional state. Am Heart J. 2009;158: 659–666. 31. Malandrino N, Wu WC, Taveira TH, et al. Association between red blood cell distribution width and macrovascular and microvascular complications in diabetes. Diabetologia. 2012;55:226–235. 32. Sawicki PT, Kaiser S, Heinemann L, et al. Prevalence of renal artery stenosis in diabetes mellitus – An autopsy study. J Intern Med. 1991;229:489–492. 33. Semba RD, Patel KV, Ferrucci L, et al. Serum antioxidants and inflammation predict red cell distribution width in older women: The Women’s Health and Aging Study I. Clin Nutr. 2010;29: 600–604.
