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Home , Carboxyhemoglobin, Hyperglycemia, Red blood cell indices

European Review for Medical and Pharmacological Sciences 2019; 23: 2139-2150 effect on red cells indices

B.N. ALAMRI1, A. BAHABRI2, A.A. ALDEREIHIM3, M. ALABDULJABBAR4, M.M. ALSUBAIE5, D. ALNAQEB6, E. ALMOGBEL7, N.S. METIAS6, O.A. ALOTAIBI6, K. AL-RUBEAAN6

1Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada 2Department of Pediatrics, King Saud and McMaster University, Riyadh, Saudi Arabia 3Radiology Resident in King Fahad Medical City, Riyadh, Saudi Arabia 4Ophthalmology Department, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia 5Department of Emergency Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia 6University Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia 7College of Medicine, Qassim University, Saudi Arabia

Abstract. – OBJECTIVE: Hyperglycemia has sulting from the glycation of different . an effect on all body tissues; one of them is the Bone marrow is one of the body tissues with bone marrow. This effect is related to a high proliferation rate that produces all the glycation and other chemical and physiological different types of blood cells on a daily basis, changes of red blood cells (RBCs). The aim of one of which is red blood cells through the this study was to assess the effect of hypergly- 1 cemia on different RBCs indices along with eval- system . The persistent elevation uating these changes in the normal physiology of glycosylated as a result of diabe- and chronic diabetes pathology. tes-related hyperglycemia is associated with the PATIENTS AND METHODS: This is a cross- structural and functional changes in hemoglobin sectional hospital-based study of 1000 type 2 (Hb) molecule, the osmotic disturbance and the Saudi diabetic patients without any hematologi- cytoplasmic viscosity within each cell. All these cal diseases. Patients were fully evaluated clini- cally and biochemically with full blood hematolog- changes could have an imposing effect on any ical parameters assessment. The studied cohort of the indices, which include the matched the general characteristics of Saudi type red blood cells (RBCs) count, (Hct), 2 diabetic patients. (MCV), mean cor- RESULTS: This study shows that hyperglyce- puscular hemoglobin (MCH), mean corpuscular mia increases the red blood cells count, mean hemoglobin concentration (MCHC) and the cell corpuscular volume (MCV), mean corpuscular shape and deformability represented by red blood hemoglobin (MCH), mean corpuscular hemoglo- 2 2,3 bin concentration (MCHC). Red blood cell dis- cell distribution (RDW) . Recently, researchers tribution width (RDW) was negatively correlat- demonstrated the effect of different levels of gly- ed with poor glycemic control. Concurrently, the cemia on hematological parameters, wherein it presence of micro and macroangiopathies with has been reported that hyperglycemia with insu- hyperglycemia shortens the lifespan of RBCs. CONCLUSIONS: lin resistance has an imposing effect on red blood We conclude that hypergly- cell and hematocrit (Hct). On the other hand, a cemia has an imposing effect on RBCs count and its physiological function, which can be nor- high value of glycosylated hemoglobin was re- malized effectively with good glycemic control. ported to correlate with decreased deformability of erythrocytes4. Additionally, RBCs count was Key Words: positively associated with hyperglycemia and Red blood cells indices, Red blood cell distribution was reflected by higher HbA1c, as shown in both width (RDW), , Diabetes mellitus. diabetes and state5. The effect of hyperglycemia on the hematological parameters does not manifest any pathological phenomena, Introduction but it could well be the reason behind different abnormal observations among diabetic patients Overwhelming data demonstrated the effect including delayed healing and a defect in of hyperglycemia on different body tissues re- the normal physiology of hematopoietic system6.

Corresponding Author: Khalid Al-Rubeaan, MD; e-mail: [email protected] 2139 B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al.

These changes may also contribute to chronic analyzer (Beckman Coulter, Fullerton, CA, USA) diabetes complications, wherein, the RDW, Hct machine. Simultaneously, another sample was and RBCs count have been found to be associated collected in a plain tube for metabolic markers with microvascular and macrovascular compli- including glycemic and lipid markers in . cations7,8. Since the data is scarce related to the Blood assessment was performed us- association of different erythropoietic parameters ing the glucose oxidase-peroxidase methodology. with hyperglycemia and diabetes complications, The serum assessment was performed the main aim of the current work was to evaluate using the cholesterol oxidase-peroxidase meth- the correlation between glycemic markers and odology and the HDL, LDL and different erythropoietic parameters among type assessments were performed using direct and 2 diabetic patients along with their association in glycerokinase oxidase-peroxidase methodology. the presence of diabetes chronic complications. A third sample was also collected in potassium EDTA tube for HbA1c measurement based on the Randox Daytona (United Kingdom) latex aggluti- Patients and Methods nation inhibition assay.

Patient Selection and Data Collection Statistical Analysis This is a cross-sectional study and the cohort Data were analyzed using Statistical Package was selected from the University Diabetes Center for social science (SPSS software version 21, (UDC), King Saud University from January to IBM, Armonk, NY, USA). The t-test was used August 2016. A total of 1000 patients with type to measure mean±SD and both descriptive and 2 diabetes who were recently referred to UDC frequency measurement. Pearson’s correlation, and aged ≥18 years with complete erythropoiet- ANOVA and regression analysis were used to ic parameters, namely RBCs count, Hct, MCV assess the correlation between hematological pa- and MCHC, RDW and erythrocyte sedimentation rameters and glycemic control. Least Significant rate (ESR) were recruited in a convenience series Difference (LSD) test was used as a post-hoc test manner. These parameters were then correlated to validate ANOVA. Odds ratio (OR) and its 95% with simultaneous glycemic control parameters, confidence intervals (CI) were used to express namely HbA1c, blood glucose (FBG), 2h different risks. p-value of < 0.05 was considered postprandial and fasting lipids, which include statistically significant. total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cho- lesterol and . Patients with hemato- Results logical diseases like and my- eloproliferative disorders or chronic diseases like The mean age of the total sample was 51.1 ± cancer, liver cirrhosis and renal diseases were 12.7 years, which was identical to the median excluded. This exclusion also included patients with a range between 18-95 years. The mean with , inflammatory bowel disease, hy- duration of diabetes exceeded 8 years and the pothyroidism and all those patients on drugs mean HbA1c was 8.9 ± 2.0% with a median of which may suppress bone marrow activity. The 8.5% ranging between 5.2 and 14.6%. The val- clinical data collected from patients’ charts in- ues for erythropoietic parameters including Hb, cluded age, gender, diabetes duration, and history Hct, RBC, MCV, MCH, MCHC, RDW, and ESR of smoking. Weight, height, both systolic (SBP) demonstrated normal distribution based on their and diastolic blood pressures (DBP) were collect- mean, median and range values, as shown in ed during the same visit when the biochemical Appendix 1. Table I demonstrates the metabol- and hematological evaluations were performed. ic and erythropoietic parameters in relation to clinical categories, wherein older patients show a Laboratory Analysis significant increase of RBC count, Hb, Hct, and A venous blood sample was collected from the ESR. Men had significantly higher RBCs count, median cubital vein using a Becton Dickinson Hb, and Hct, while lower ESR when compared vacutainer heparinized tube and was then trans- to women. Diabetic patients showed a lower ferred to the central laboratory for analysis. All mean value of RBCs count and Hct with diabetes the erythropoietic parameters were measured and duration > 10 years. The results show a marked analyzed by a COULTER LH 500 hematology increase in RBCs count among smokers, Hb and

2140 Hyperglycemia effect on red blood cells indices

Appendix I. Measures for central tendency for clinical and metabolic and erythropoietic markers.

Mean (± SD) Median Range

Age (years) 51.1 ± 12.7 51.0 18.0-95.0 Diabetes duration (years) 8.4 ± 7.8 6.0 1.0-47.0 SBP (mmHg) 132.4 ± 17.8 132.0 82.0-181.0 DBP (mmHg) 76.5 ± 9.8 77.0 51.0-105.0 BMI (kg/m2) 31.2 ± 6.2 30.5 15.5-63.6 HbA1c (%) 8.9 ± 2.0 8.5 5.2-14.6 FBG (mg/dL) 9.4 ± 3.4 8.8 3.4-18.6 2 hr PC 13.9 ± 5.4 13.3 4.3- 29.0 Hemoglobin g/dL 13.8 ± 1.7 13.8 9.2-17.5 Hct % 40.7 ± 4.7 41.0 27.8-53.2 RBC (1012/L) 4.7 ± 0.5 4.7 3.3-6.2 MCV(fL) 86.5 ± 4.6 86.8 74.3-98.3 MCH (pg) 29.3 ± 1.9 29.4 23.8-33.9 MCHC (g/dL) 33.8 ± 0.1 33.8 31.0-35.8 RDW (%) 13.6 ± 0.1 13.5 11.5-16.4 ESR (mm/h) 20.6 ± 15.8 18.0 0.0-65.0 Cholesterol (mg/dL) 4.8 ± 1.1 4.7 2.4-7.9 LDL (mg/dL) 2.8 ± 0.9 2.7 0.7-5.9 HDL (mg/dL) 1.2 ± 0.4 1.2 0.6-2.5 Triglyceride (mg/dL) 1.8 ± 0.1 1.5 0.5-7.5

Hct, but lower ESR. SBP ≥ 140 mmHg did not shows the significant decrease in the mean val- have any effect on those parameters, while DBP ues of RBCs count, Hb and Hct among patients ≥ 80 mmHg showed a remarkable increase in with , retinopathy, nephropa- the mean value of RBC’s count, Hb, Hct, but de- thy and vasculopathy. This study has also shown creased ESR. Patients with BMI ≥ 30 kg/m2 had an increase in RDW, but a decrease in the mean significantly lower mean values of Hb and Hct but values of MCV and MCH among patients with increased mean ESR value. The mean values for vasculopathy. When looking for different quartile MCV, MCH, and MCHC were markedly lower in levels of the hematopoietic parameters in relation women and patients with high mean BMI values, to chronic complications, the only significant except for MCHC. Both MCV and MCH were ones were higher quartile for ESR among patients significantly higher among older patients and with nephropathy and MCV with retinopathy, smokers. RDW was remarkably increased among while lower quartile for Hct and RBCs count for women and subjects with higher mean values of neuropathy and retinopathy, as shown in Appen- BMI, but markedly lower with high DBP. Patients dix 2. Pearson correlation for erythropoietic pa- with poor glycemic control represented by HbA1c rameters demonstrated a positive correlation with > 7% or FBG > 130 mg/dL or 2-h postprandial RBCs count, Hb and Hct, while it demonstrated a glucose of > 180 mg/dL showed a significant negative correlation with RDW in relation to poor increase in their mean values of RBCs count, glycemic control presented by higher HbA1c, Hb and Hct. Patients with total cholesterol > 4.0 FBG and 2-hour postprandial blood glucose. On mg/dL, LDL > 2 mg/dL and triglycerides > 1.7 the other hand, both MCV and MCH had a nega- mg/dL showed markedly higher mean values of tive correlation with higher HbA1c values. High RBCs count, Hb, and Hct, while the reverse was FBG had a negative correlation with ESR, but observed with a high value of HDL. The mean a positive correlation with MCHC, as shown in values of ESR were remarkably lower in patients Table IV and Figure 1A and 1B. with higher FBG and 2-h postprandial glucose. None of the metabolic parameters with the ex- ception of higher FBG and lower HDL had any Discussion significant effect on MCV, MCH and MCHC. RDW was remarkably higher among patients The collected study sample represents the nor- with good glycemic control, but not with any lipid mal distribution for the Saudi diabetic population parameters values, as shown in Table II. Table III based on data from the Saudi National Diabetes

2141 B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al. 0.147 0.179 0.631 0.580 (913) RDW < 0.001 < 0.001 13.7 ± 1.0 13.7 13.7 ± 1.0 13.7 13.6 ± 1.0 13.6 13.6 ± 1.0 13.6 13.6 ± 1.0 13.6 13.6 ± 1.0 13.6 13.5 ± 1.0 13.5 13.4 ± 0.9 13.4 13.6 ± 0.9 13.6 13.6 ± 0.9 13.6 13.5 ± 0.9 13.5 13.5 ± 0.9 13.5 13.4 ± 0.9 13.4 13.3 ± 0.8 13.3 13.5 ± 0.9 13.5 13.5 ± 0.9 13.5 13.5 ± 0.9 13.5

0.031

0.415 0.213 0.627 0.461 0.601 0.466 (957) < 0.001 MCHC ± 1.1 33.9 ± 1.0 33.7 ± 0.9 33.7 ± 0.9 33.7 ± 0.9 33.7 ± 1.0 33.8 ± 1.0 33.8 ± 1.0 33.6 ± 1.0 33.8 ± 1.0 33.8 ± 1.0 33.8 ± 0.9 33.8 ± 0.9 33.8 ± 0.9 33.8 ± 0.9 33.9 ± 0.9 33.8 ± 0.9 33.8

0.615 0.287 0.309 0.006 MCH (933) < 0.001 < 0.001 < 0.001 ± 1.7 29.9 ± 1.8 29.7 ± 1.8 29.7 ± 1.9 29.0 ± 1.8 29.4 ± 1.9 29.3 ± 1.8 29.4 ± 1.8 29.3 ± 1.9 29.2 ± 1.9 29.3 ± 1.8 29.3 ± 1.8 29.4 ± 1.8 29.2 ± 1.8 29.2 ± 1.8 30.0 ± 1.9 28.8 ± 1.8 28.8

0.101 0.992 0.548 MCV (927) < 0.001 < 0.001 < 0.001 < 0.001 ± 4.8 85.1 ± 4.4 87.5 ± 4.6 85.8 ± 4.5 87.5 ± 4.3 88.1 86.2 ± 4.7 ± 4.6 86.4 ± 4.5 86.9 ± 4.6 86.4 ± 4.4 86.9 ± 4.4 86.4 ± 4.5 85.3 ± 4.5 86.4 ± 4.5 86.6 86.2 ± 4.6 ± 4.3 86.8 88.2 ± 4.5

ESR 0.123 0.569 0.008 (894) < 0.001 < 0.001 < 0.001 < 0.001 ± 16.1 21.7 ± 15.7 21.9 ± 14.9 17.5 ± 15.9 21.6 ± 15.5 27.7 ± 15.5 18.7 ± 14.7 18.5 ± 15.9 20.7 ± 14.5 16.4 ± 16.3 21.2 ± 15.5 19.5 ± 13.2 15.4 ± 13.0 14.2 ± 16.0 23.5 ± 16.9 23.9 ± 15.6 20.0 ± 16.5 22.1

HCT 0.451 0.036 0.001 0.002 (966) < 0.001 < 0.001 < 0.001 ± 4.6 41.7 ± 4.6 41.7 ± 3.6 37.8 ± 4.8 41.3 ± 4.6 41.3 ± 4.5 41.3 ± 4.7 40.9 ± 5.1 40.0 ± 4.7 40.6 40.3 ± 4.7 ± 4.0 43.5 ± 4.5 43.5 ± 4.1 40.4 ± 4.6 40.0 ± 4.9 40.8 ± 4.5 40.0 40.2 ± 4.4

0.291 0.055 0.001 HGB (966) <0.001 <0.001 < 0.001 < 0.001 ± 1.7 13.7 ± 1.7 14.0 ± 1.6 14.1 ± 1.6 14.1 ± 1.4 14.7 ± 1.7 13.8 ± 1.7 13.5 ± 1.6 14.6 ± 1.6 13.6 ± 1.6 13.9 ± 1.6 14.0 ± 1.4 13.6 ± 1.5 13.6 ± 1.6 13.5 ± 1.8 13.8 ± 1.3 12.7 ± 1.8 13.5

RBC 0.867 0.063 (967) <0.001 <0.001 < 0.001 < 0.001 < 0.001 ± 5.0 4.7 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.7 ± 5.0 4.6 ± 0.5 4.7 ± 0.5 5.0 ± 0.5 5.0 ± 0.5 4.8 ± 5.2 4.8 ± 0.5 4.8 ± 0.4 4.5 ± 0.5 4.6 ± 0.5 4.8

0.030 0.284 (830) < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 2hrspp ± 5.0 13.1 ± 5.4 13.7 ± 5.1 14.8 ± 5.4 13.7 ± 6.0 15.7 ± 5.3 14.1 ± 5.1 15.4 ± 5.5 14.7 ± 5.2 13.1 ± 5.6 14.2 ± 5.4 14.8 ± 5.4 14.8 ± 5.2 15.6 ± 5.5 13.4 ± 5.1 12.5 ± 5.3 14.3 ± 5.4 12.6

FBG 0.108 0.015 0.033 0.081 0.001 0.002 (970) < 0.001 ± 3.5 9.7 ± 3.3 9.7 ± 3.3 9.7 ± 3.6 9.8 ± 3.4 9.4 ± 3.4 9.3 ± 3.2 9.9 ± 3.4 9.2 ± 3.1 8.6 ± 3.5 9.8 ± 3.2 9.2 ± 3.3 9.8 ± 3.3 9.5 ± 3.5 9.2 ± 3.5 8.9 ± 3.4 10.4 ± 3.5 10.0

0.111 0.127 0.125 0.004 (978) < 0.001 < 0.001 < 0.001 HbA1c ± 1.9 9.1 ± 1.8 9.6 ± 2.0 9.0 ± 1.9 8.6 ± 2.0 9.0 ± 2.0 9.0 ± 2.0 8.7 9.4 ± 2.3 9.4 ± 2.0 9.0 ± 2.0 9.0 ± 2.0 9.2 ± 2.0 9.2 ± 2.0 8.8 ± 2.0 8.8 ± 2.0 8.8 8.3 ± 2.0 8.5 ± 2.0

The mean values erythropoietic of and metabolic parameters in relation to baseline clinical categories. value value -value -value -value -value 25-30 ≥ 30 < 25 < 25 ≥ 80 < 80 < 140 ≥ 140 > 10 Yes Yes No No ≤ 5 6-10 Women Women Men ≤ 45 46-64 ≥ 65 (number) -value

BMI p- p-

DBP p SBP

Smoking p p DM duration p Gender p Age (years)Age Parameter 2142 Table I. Table Hyperglycemia effect on red blood cells indices 2143 0.141 0.310 0.760 0.350 0.523 0.873 0.886 MCHC 33.8±1.0 33.7 ± 1.0 33.7 33.7 ± 1.0 33.7 33.7 ± 0.9 33.7 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 1.0 33.8 33.8 ± 0.9 33.8 33.8 ± 0.9 33.8 338.0 ± 1.0 338.0

0.128 0.052 0.983 0.029 0.386 0.237 0.905 MCH ± 1.9 29.1 ± 1.7 29.3 ± 1.7 29.5 ± 1.9 29.3 ± 1.8 29.4 ± 2.0 29.1 ± 1.9 29.3 ± 1.9 29.2 ± 1.9 29.3 ± 1.9 29.5 ± 1.8 29.3 ± 1.8 29.3 ± 1.8 29.5 ± 2.0 29.4

0.036 0.387 0.599 0.292 0.092 0.208 0.409 MCV ± 4.4 87.0 ± 5.0 86.7 86.2 ± 4.7 ± 4.8 86.1 86.2 ± 5.0 ± 4.4 86.6 ± 4.8 86.9 ± 4.4 86.3 ± 4.8 86.4 ± 4.6 86.3 ± 4.5 86.4 ± 4.5 86.4 ± 4.5 86.4 86.5 ± 4.5

ESR 0.165 0.787 0.036 0.395 0.094 0.004 < 0.001 ± 13.6 17.1 ± 17.0 21.4 ± 15.5 19.7 ± 17.0 23.0 ± 15.6 19.9 ± 15.0 19.4 ± 15.9 21.2 ± 16.0 21.2 ± 15.1 20.9 ± 15.4 20.1 ± 15.7 22.0 20.2 ± 15.4 ± 16.0 20.5 22.8 ± 16.8

0.105 0.770 0.383 0.066 0.044 RDW < 0.001 < 0.001 ± 1.0 13.7 ± 0.9 13.7 ± 1.0 13.8 ± 1.0 13.8 ± 0.9 13.6 ± 0.9 13.6 ± 0.9 13.6 ± 0.9 13.5 ± 0.9 13.5 ± 0.9 13.5 ± 0.9 13.5 ± 0.9 13.5 ± 0.9 13.5 ± 0.9 13.5

Hct 0.006 <0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.006 ± 4.7 41.1 ± 4.7 41.1 ± 4.6 41.0 ± 4.6 41.0 ± 4.7 39.2 ± 4.6 41.4 ± 4.6 41.2 ± 4.8 41.8 ± 4.6 39.4 ± 4.6 39.6 ± 4.8 39.6 ± 4.8 40.4 ± 4.8 40.0 40.5 ± 4.6

0.001 HGB 0.002 <0.001 <0.001 < 0.001 < 0.001 < 0.004 ± 1.7 13.6 ± 1.7 13.9 ± 1.6 13.7 ± 1.7 13.9 ± 1.7 13.4 ± 1.7 14.2 ± 1.7 13.3 ± 1.7 13.5 ± 1.6 14.0 ± 1.6 13.9 ± 1.6 13.9 ± 1.6 13.4 ± 1.6 13.8 ± 1.6 13.3

RBC 0.001 <0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 ± 0.5 4.7 ± 0.5 4.7 ± 0.5 4.6 ± 0.5 4.6 ± 0.5 4.6 ± 0.5 4.6 ± 0.5 4.8 ± 0.5 4.6 ± 0.5 4.8 ± 0.5 4.8 ± 0.5 4.8 ± 0.5 4.8 ± 0.5 4.8 ± 0.5 4.8

– (%) No. (23.4) 227 75.8%) 747 (58.6%) 557 408 (41.4%) (19.8%) 194 (76.5%) 741 (69.2%) 671 (76.6%) 742 (27.5%) 228 (23.5%) 228 (30.8%) 299 (24.2%) 239 (80.2%) 784 602 (72.5%)

>2.0 -value > 1.7 < 1.7 -value -value -value <180 -value -value -value < 4.0 > 4.0 < 2.0 > 180 < 130 > 130 < 7.0% > 7.0% p p p p p p p < 1.0 men /1.2 women men /1.2 < 1.0 > 1.0 men /1.2 women men /1.2 > 1.0

(mmol/l) FBG (mg/dL) Triglyceride 2hrs PC (mg/dL)

(%) HbA1c

HDL (mmol/l)

LDL (mmol/l)LDL Cholesterol (mmol/l)

The effect of metabolic control on the studied erythropoietic parameters. of metabolic control on the studied erythropoietic The effect Table II. B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al.

Table III. The mean ± SD of the different erythropoietic parameters and presence of chronic complications.

Chronic Number complications (%) RBC HGB Hct RDW ESR MCV MCH MCHC

Neuropathy Absent 672 (67.2%) 4.8 ± 0.5 13.9 ± 1.6 41.0 ± 4.6 13.5 ± 0.9 20.2 ± 15.6 86.5 ± 4.6 29.3 ± 1.9 33.8 ± 0.9 Present 328 (32.8%) 4.7 ± 0.6 13.5 ± 1.7 40.1 ± 4.9 13.7 ± 0.1 21.4 ± 16.1 86.5 ± 4.6 29.3 ± 1.8 33.8 ± 0.1 p-value 0.001 0.002 0.004 0.076 0.319 0.848 0.804 0.663 Retinopathy Absent 794 (79.4%) 4.8 ± 0.5 13.8 ± 1.7 40.9 ± 4.7 13.6 ± 0.1 20.1 ± 15.8 86.4 ± 4.6 29.3 ± 1.9 33.8 ± 0. 1 Present 206 (20.6%) 4.6 ± 0.5 13.5 ± 1.6 39.99 ± 4.7 13.5 ± 0.1 22.4 ± 15.9 86.8 ± 4.4 29.4 ± 1.8 33.9 ± 0.9 p-value < 0.001 0.020 0.012 0.439 0.092 0.277 0.257 0.209 Nephropathy Absent 915 (91.5%) 4.8 ± 0.5 13.8 ± 1.6 40.9 ± 4.6 13.6 ± 0.1 20.3 ± 15.7 86.5 ± 4.6 29.3 ± 1.9 33.8 ± 0.1 Present 85 (8.5%) 4.6 ± 0.6 13.3 ± 1.9 39.4 ± 5.7 13.7 ± 0.9 24.3 ± 16.7 86.0 ± 4.7 29.0 ± 1.8 33.8 ± 0.1 p-value 0.015 0.010 0.022 0.367 0.050 0.414 0.248 0.905 Vasculopathy Absent 879 (87.9%) 4.8 ± 0.5 13.8 ± 1.7 40.9 ± 4.7 13.6 ± 0.1 20.3 ± 15.7 86.6 ± 4.6 29.3 ± 1.9 33.8 ± 0.1 Present 121 (12.1%) 4.7 ± 0.6 13.4 ± 1.7 39.8 ± 4.8 13.8 ± 0.1 23.0 ± 16.1 85.6 ± 4.6 29.0 ± 1.9 33.6 ± 0.1 p-value 0.083 0.030 0.027 0.012 0.103 0.028 0.049 0.057

Registry (SNDR) in terms of important charac- older patients and men14. Smoking among the teristics, which include mean age, duration of studied diabetic patients was associated with diabetes and BMI. In this work, we focused on increased RBCs count as expected due to the the effect of hyperglycemia, hyperlipidemia and effect of carboxyhemoglobin (HbCO), which chronic diabetes complications on erythropoiesis. would stimulate the production of RBCs as its During erythropoiesis, RBCs structure or chem- hypoxic effect would eventually lead to the istry will be affected by hyperglycemic state development of secondary polycythemia15. The among diabetic patients when compared with the association between increased RBCs counts normal subjects9. with higher DBP and lipid parameters namely cholesterol, triglycerides and LDL, could also Red Blood Cells (RBCs) Count be explained by the resistance effect, Among diabetic patients, chronic hypergly- since such conditions are associated with met- cemia will lead to non-enzymatic glycation of abolic syndrome16, as reported by Hosseini et RBC membrane proteins that would accelerate al17. The RBCs count in this study has shown to RBCs aging as a result of reduced negative sur- be reduced with microvascular complications, face electrical charge10. Our study has demon- especially with longer diabetes duration and strated an increase in RBCs count among older was also reported by other researchers8. The patients, especially men, although there was a decreased RBCs count could be a consequence significant reduction in RBCs count for those of erythropoietin deficiency observed among with diabetes duration of more than 10 years. patients with or as a result This increase in RBCs count observed during of the increased RBCs destruction, secondary to hyperglycemia is similar to the observation of macroangiopathic or microangiopathic chang- other studies and could be explained by the es18. The decreased negative charge of RBCs effect of , especially when it among patients with hyperglycemia would also has been demonstrated that insulin regulates result in reduced average lifespan of RBCs or erythropoiesis in vitro11. The increased microviscosity and aggregation or ad- effect on erythropoiesis has been well explained hesion of RBCs and consequently would reduce by different mechanisms, since the presence of RBCs count19. insulin receptors in human erythropoietin cells during different developmental stages, suggests Hemoglobin (Hb) the role of insulin as a co-factor in erythropoie- The part of hemoglobin is the protein sis12. Polycythaemia observed in newborn babies that is subjected to glycation and is affected by of diabetic mothers give another clue for hyper- the duration and level of hyperglycemia20. In insulinemia’s relationship with erythropoiesis13. this work, patients with were excluded to The insulin resistance effect on erythropoiesis eliminate the effect of other hematological fac- could explain the increase in RBCs count in tors that may affect the concentration of Hb in

2144 Hyperglycemia effect on red blood cells indices 2145 0.119 0.135 0.132 0.756 0.180 0.375 0.381 0.784 0.861 0.933 0.033 0.430 0.945 0.370 0.078 0.428 0.973 0.850 0.093 -value 0.069 0.299 0.246 0.064 p

3.14 3.14 3.10 3.15 1.81 1.33 2.15 1.93 3.47 1.52 1.98 1.05 1.97 3.07 2.47 2.55 3.54 4.02 2.03 2.69 3.64 2.58 2.09 0.146

1.18 0.75 0.71 0.91 0.81 0.47 1.06 0.55 0.67 0.49 0.39 0.49 0.69 0.68 0.94 0.96 0.86 0.86 0.88 0.56 0.29 0.34 0.44 0.44 Lower Upper

Vasculopathy CI 95% 1.10 1.77 1.31 0.71 0.72 1.52 1.65 1.59 1.82 1.36 1.07 1.87 1.30 0.55 1.34 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.64 1.64 0.89 0.97 0.97 0.94 0.99 0.34 2.06

0.63 0.743 0.517 0.187 0.168 0.516 0.015 0.312 0.787 0.770 0.831 0.991 0.539 0.901 0.350 0.078 0.499 0.827 0.923 0.888 -value OR 0.605 0.648 0.209 p 0.850 1.74 1.74 1.70 1.35 1.53 1.65 1.49 1.59 2.12 1.48 1.48 1.60 1.66 1.34 1.46 1.34 1.64 1.24 1.26 2.58 2.08 2.06 2.24 0.010 Upper

1.11 0.75 0.70 0.72 0.72 0.55 0.55 0.62 0.52 0.62 0.62 0.65 0.69 0.63 0.52 0.69 0.96 0.85 0.87 0.66 0.88 0.56 0.66 2.66

Neuropathy CI 95% OR Lower 1.11 1.14 1.14 1.12 1.33 1.03 1.69 0.81 1.07 1.36 1.02 1.08 1.06 1.46 1.34 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.95 0.95 0.96 0.86 0.97 0.86 0.86 0.80

1.74 0.145 0.103 0.516 0.138 0.015 0.140 0.787 0.197 0.812 0.275 0.201 0.036 0.985 0.877 0.028 0.399 0.093 0.020 -value 0.229 0.097 0.407 0.809 0.648 p

1.15 1.19 1.13 1.70 1.70 1.72 1.55 2.76 1.33 1.85 3.32 1.34 1.64 1.20 0.94 3.25 2.02 2.94 2.56 2.34 2.54 2.28 2.64 0.050 Upper

1.11 1.14 0.75 0.79 0.72 2.73 0.43 0.62 0.42 0.49 1.04 0.65 0.69 0.39 0.58 0.92 0.92 0.92 0.88 0.56 0.60 0.34 0.46 0.84 Lower

Retinopathy CI 95% OR 1.11 1.75 0.75 0.71 1.53 1.95 0.81 1.07 1.06 1.56 1.50 1.60 1.40 1.06 0.67 1.34 1.23 0.57 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.69 1,90 0.96 0.99 0.86

1.65 0.168 0.931 0.770 0.736 0.725 0.951 0.021 0.371 0.704 0.650 0.522 0.257 0.492 0.563 0.065 0.823 0.580 -value 0.480 0.907 0.940 0.802 0.226 0.309 p

4.17 1.70 3.71 2.18 1.38 1.07 3.32 5.05 1.84 2.43 2.67 2.63 3.24 2.39 2.83 2.85 2.36 2.09 2.56 2.06 2.40 2.84 0.692 Upper

1.88 1.14 0.10 0.73 0.51 0.51 0.51 0.45 0.55 0.33 0.42 0.63 0.52 0.52 0.65 0.57 0.39 0.59 2.43 0.89 0.48 0.56 0.46 0.80 0.26 Lower

Nephropathy CI 95% OR 1.10 1.15 1.12 1.72 0.75 1.45 1.55 1.03 1.93 1.05 1.02 1.09 1.29 1.27 1.30 1.26 1.20 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.59 0.97 0.86 0.56 0.88 0.34 2.40

Association between erythropiotic parameters and macrovascular and microvascular complications.

/L)

12 (g/dL)

(%) (pg) (f L) (10 quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile quartile (mm/h) quartile quartile quartile quartile quartile quartile quartile Parameters % nd nd nd nd nd nd nd th th th th th th th rd rd rd rd rd rd rd st st st st st st st quartile quartile quartile quartile 1.16 nd th rd st 1 3 MCV 1 1 Hct 1 RBC MCH 1 MCHC RDW 2 2 2 2 4 4 4 4 4 3 3 3

1 ESR 1 2 2 4 3 3 3

2 2 3 Hemoglobin g/dL 1 4 4 Appendix II. Appendix B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al.

Table IV. Pearson correlation coefficient of each erythropbiotic parameters and glycemic control.

HbA1c FBG 2 hours postprandial

Characteristic r p r p r p

RBC 0.21 < 0.001 0.25 < 0.001 0.22 < 0.001 HGB 0.14 < 0.001 0.24 < 0.001 0.22 < 0.001 HCT 0.15 < 0.001 0.23 < 0.001 0.23 < 0.001 ESR 0.01 0.791 -0.09 0.009 -0.03 0.370 RDW -0.09 0.006 -0.21 < 0.001 -0.18 < 0.001 MCV -0.06 0.054 0.00 0.970 0.07 0.053 MCH -0.07 0.049 0.04 0.218 0.08 0.021 MCHC -0.02 0.485 0.08 0.011 0.05 0.175

the presence of metabolic abnormalities, namely Mean Corpuscular Volume (MCV), hyperglycemia and hyperlipidemia. Since Hb Mean Corpuscular Hemoglobin (MCH) levels are directly correlated with RBCs count, and Mean Corpuscular Hemoglobin all the factors as discussed above that would Concentration (MCHC) decrease or increase RBCs count, would reflect It was observed that there was an insignificant on Hb concentration. Patients with a higher BMI increase of MCV with poor glycemic control, have shown lower Hb concentrations which which could be further explained by the fact that could be related to the fact that obesity is char- membrane proteins can be non-enzymatically acterized by a state of chronic inflammation glycosylated displacing sodium and chloride ions with high circulating pro-inflammatory cyto- in the immediate environment of the cells. Such kines affecting the hematopoietic system, and displacement could explain the changes in cell may lead to the depletion of these cells1. Thus, size (increased MCV)22. The MCV, MCH and we could suggest that obesity and the associ- MCHC were reduced in obese subjects, which ated metabolic changes or inflammation may could be the result of hyposideremia that lowers affect bone marrow niches through pathways the erythrocytes indices as a result of low-calorie that have not yet been investigated before. Hy- diet, as practiced by these obese subjects23. The perlipidemia, in this study, is associated with current work has also shown that vasculopathy the increased hemoglobin mean concentration had significantly decreased MCV and MCH and and is similar to what has been earlier report- is similar to what has been observed among Tai- ed by other researchers17 thereby warranting wanese subjects24. further analysis. The presence of chronic com- plications in this work was associated with low Red Blood Cell Distribution Width Hb concentrations in non-anemic type 2 dia- (RDW) betic patients. In addition to the prevalence of Our observation correlates with the findings chronic kidney disease among diabetic patients of other studies3,19,25 that there is a significant and its association with low erythropoietin negative correlation of RDW with poor glycemic production, this hormone could be affected by control. This could result from the decreased autonomic neuropathy that decreases sympa- lifespan of erythrocytes that reduces HbA1c thetic stimulation of erythropoietin production concentration, as the average time of RBCs ex- related to renal denervation21. Reduced andro- posure to hyperglycemia is reduced. Chronic in- gens observed in diabetic patients is associated flammatory process related to diabetes may in- with decreased erythropoietin synthesis in the fluence erythropoiesis and would reduce RBCs kidney. These factors could explain the reduced half-life and deformability, thereby increasing mean hemoglobin concentration in diabetic pa- RDW. This inflammatory theory could also ex- tients with chronic complications. Another ex- plain the positive correlation between RDW and planation could well be related its resistance to obesity2,23,26. Our work also shows a positive erythropoietin as a result of the inflammatory correlation between RDW and DBP, but not process and due to the rise of cytokines that between RDW and SBP, which is similar in part would suppress cell proliferation21. to the finding of Tanindi et al27, that can be ex-

2146 Hyperglycemia effect on red blood cells indices

Figure 1. A, Pearson correlation of each erythropbiotic parameters and glycemic control. Figure continued plained on the basis of the inflammatory effect increase erythropoietin production in vivo and that might results in end-organ damage among directly stimulate the proliferation of normal patients with hypertension. The activation of early erythroid progenitors. Additionally, in- the renin-angiotensin-aldosterone system could creased sympathetic nervous system activation

2147 B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al.

Figure 1 (Continued). B, Pearson correlation of each erythropbiotic parameters and glycemic control. stimulates erythropoiesis in humans through pro-inflammatory state and oxidative that increased erythropoietin production27. The cor- impairs membrane fluidity of the erythrocytes relation of RDW with vasculopathy, which is and reduces RBCs lifespan. In addition to this, one of our observations, is further supported by it blocks and erythropoietin the fact that vasculopathy is associated with a response as a result of inflammation. Our work

2148 Hyperglycemia effect on red blood cells indices is limited by being a cross-sectional study and Funding therefore causality could not be established. This study was funded by the Strategic Center for Diabe- However, the main aim of this paper was to tes Research. determine an association rather than causality. Additionally, the results represent a single center study which might not allow us to generalize the Ethical Approval results, but the main demographic characteris- This study was reviewed and approved by the Institutional tics were matching with a large countrywide co- Review Board (IRB) at the College of Medicine, King Saud University. The data used in this publication were not con- hort of the Saudi National diabetes registry. The sented since it does not compromise anonymity, confidenti- primary strength of our work is the large sample ality or breach of the local data protection laws. Addition- size which increases the power of this study. ally, all the collected investigations were part of the rou- The secondary strength of this paper is the tine full assessment investigations for newly registered pa- exclusion of patients with anemia, to avoid any tients in the center, which did not require the patients’ con- sents to be obtained.. confounding effects on the association between the studied indices and metabolic parameters or chronic complications. Acknowledgements The authors would like to acknowledge the team of the Re- search Unit in the University Diabetes Center for their sup- Conclusions port during the data collection.

We showed that hyperglycemia affected RBCs production, its function and other phys- Authors’ Contribution ical properties. This may eventually affect the BNA: Study design, data collection, manuscript prepara- normal functional physiology of erythrocytes tion, AB: Study design, data collection, manuscript prepa- or have a direct effect on its vascular structure, ration. AAA: Study design, data collection, manuscript leading to micro or microangiopathies. At the preparation. MA: Study design, data collection, manuscript preparation. MMA: Study design, data collection, manu- same time, the presence of diabetes chronic script preparation. DA: Study design, data collection. EA: complications would affect RBCs life span, its Literature Search. NSM: Literature Search. OAA: Liter- cytoplasmic viscosity, and deformability. How- ature Search. KAR: Study design, data collection, manu- ever, these changes are not fully understood and script preparation, Literature Search, Statistical Analysis, more studies are needed to evaluate the impor- Data Interpretation. tance of such changes on the progression of di- abetes complications, especially cardiovascular. The effect of hyperglycemia on RBCs shape and its function should also be considered among References diabetic patients, especially when evaluating for 1) Benites BD, Gilli SC, Saad ST. Obesity and inflam- their hematological diseases. The findings of the mation and the effect on the hematopoietic sys- current study highlight the importance of strong tem. Rev Bras Hematol Hemoter 2014; 36: 147- glycemic control in improving the structure and 151. function of RBCs and consequently, their in- 2) Suryavanshi C, Manjula SD, Ragini B, Raghavendra dices. Additionally, diabetic patients with poor Rao K. Association of increased levels of glycated control should not be encouraged to donate hemoglobin with variations in red blood cell pa- blood. rameters in diabetes mellitus. Int J Adv Res 2015; 3: 31-37. 3) Tamariz LJ, Young JH, Pankow JS, Yeh H-C, Schmidt MI, Astor B, Brancati FL. Blood viscosity and Conflict of Interest hematocrit as risk factors for The Authors declare that they have no conflict of interests. mellitus: the atherosclerosis risk in communities (ARIC) study. Am J Epidemiol 2008; 168: 1153- 1160. 4) Cho YI, Mooney MP, Cho DJ. Hemorheological dis- Author Declaration orders in diabetes mellitus. J Diabetes Sci Tech- The authors certify that the manuscript represents valid nol 2008; 2: 1130-1138. work and neither this manuscript nor one with substantially 5) Simmons D. Increased red cell count in diabetes similar content under named authorship has been published and pre-diabetes. Diabetes Res Clin Pract 2010; or is being considered for publication elsewhere. 90: e50-53.

2149 B.N. Alamri, A. Bahabri, A.A. Aldereihim, M. Alabduljabbar, M.M. Alsubaie, et al.

6) Kojima H, Kim J, Chan L. Emerging roles of hema- 17) Hosseini H, Dorgalaleh A, Tabibian S, K ashiri M, topoietic cells in the pathobiology of diabetic com- Moghaddam ES, Alizadeh S, Bamedi T, Reyk ande plications. Trends Endocrinol Metab 2014; 25: SE, Dorgalaleh S. Biochezmical interfering fac- 178-187. tors and blood cells indices. Thrita 2014; 3: 7) Malandrino N, Wu WC, Taveira TH, Whitlatch HB, e15516. Smith RJ. Association between red blood cell dis- 18) Kimura T, Kaneto H, Kanda-Kimura Y, Shimoda M, Ka- tribution width and macrovascular and micro- mei S, Anno T, Kawasaki F, Hashiramoto M, Matsu- vascular complications in diabetes. Diabetologia ki M, Mune T, Kaku K. Seven-year observational 2012; 55: 226-235. study on the association between glycemic con- 8) Wang ZS, Song ZC, Bai J-H, Li F, Wu T, Qi J, Hu trol and the new onset of macroangiopathy in J. Red blood cell count as an indicator of micro- Japanese subjects with type 2 diabetes. Intern vascular complications in Chinese patients with 2016; 55: 1419-1424. type 2 diabetes mellitus. Vasc Health Risk Manag 19) Nada AM. Red cell distribution width in type 2 dia- 2013; 9: 237-243. betic patients. Diabetes Metab Syndr Obes 2015; 9) Szablewski L, Sulima A. The structural and function- 8: 525-533. al changes of blood cells and molecular compo- 20) Verma M, Paneri S, Badi P, Raman PG. Effect of in- nents in diabetes mellitus. Biol Chem 2017; 398: creasing duration of diabetes mellitus type 2 on 411-423. and insulin sensitivity. Indi- 10) Mazzanti L, Faloia E, Rabini RA, Staffolani R, Kan- an J Clin Biochem 2006; 21: 142-146. tar A, Fiorini R, Swoboda B, De Pirro R, Bertoli E. 21) Hosseini MS, Rostami Z, Saadat A, Saadatmand SM, Diabetes mellitus induces red blood cell plasma Naeimi E. Anemia and microvascular complica- membrane alterations possibly affecting the ag- tions in patients with type 2 diabetes mellitus. ing process. Clin Biochem 1992; 25: 41-46. Nephrourol Mon 2014; 6: e19976. 11) Kurtz A, Jelkmann W, Bauer C. Insulin stimu- 22) Morse EE, Kalache G, Germino W, Stockwell R. In- lates erythroid colony formation independently of creased electronic mean corpuscular volume in- erythropoietin. Br J Haematol 1983; 53: 311-316. duced by marked hyperglycemia. Ann Clin Lab 12) Aoki I, Taniyama M, Toyama K, Homori M, Ishikawa K. Sci 1981; 11: 184-187. Stimulatory effect of human insulin on erythroid 23) Vayá A, Alis R, Hernandez-Mijares A, Solá E, Cámara progenitors (CFU-E and BFU-E) in human CD34+ R, Rivera L, Romagnoli M, Laiz B. Red blood cell separated bone marrow cells and the relationship distribution width is not related with inflammatory between insulin and erythropoietin. Stem Cells parameters in morbidly obese patients. Clin Bio- 1994; 12: 329-338. chem 2014; 47: 464-466. 13) Perrine SP, Greene MF, Lee PD, Cohen RA, Faller DV. 24) Wu TJ, Chuang LM, Tai TY. Erythrocyte deformabil- Insulin stimulates cord blood erythroid progenitor ity in diabetes mellitus. Taiwan Yi Xue Hui Za Zhi growth: evidence for an aetiological role in neona- 1989; 88: 240-243. tal polycythaemia. Br J Haematol 1986; 64: 503- 25) Biadgo B, Melku M, Abebe SM, Abebe M. Hemato- 511. logical indices and their correlation with fasting 14) Karakelides H, Irving BA, Short KR, O’Brien P, Nair blood glucose level and anthropometric measure- KS. Age, obesity, and sex effects on insulin sensi- ments in type 2 diabetes mellitus patients in Gon- tivity and skeletal muscle mitochondrial function. dar, Northwest Ethiopia. Diabetes Metab Syndr Diabetes 2010; 59: 89-97. Obes 2016; 9: 91-99. 15) Ibrahim Khan M, Hussain Bukhari M. Effect of smok- 26) Demir AD, Durmaz ZH, Kılınç Ç, Güçkan R. Correla- ing on red blood cells count, hemoglobin concen- tion between red blood cell distribution width and tration and red cell indices. Pak J Med Nad Health glycated hemoglobin in diabetic and nondiabetic Sci 2014; 8: 361-364. patients. Russ Open Med J 2016; 5: e0301. 16) Barbieri M, Ragno E, Benvenuti E, Zito GA, Corsi A, 27) Tanindi A, Topal FE, Topal F, Celik B. Red cell dis- Ferrucci L, Paolisso G. New aspects of the insulin tribution width in patients with prehypertension resistance syndrome: impact on haematological and hypertension. Blood Press 2012; 21: 177- parameters. Diabetologia 2001; 44: 1232-1237. 181.

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