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Hyperglycemia Effect on Red Blood Cells Indices

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Hyperglycemia Effect on Red Blood Cells Indices European Review for Medical and Pharmacological Sciences 2019; 23: 2139-2150 Hyperglycemia effect on red blood 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 Diabetes 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 proteins. 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 protein 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- erythropoiesis system . The persistent elevation uating these changes in the normal physiology of glycosylated hemoglobin as a result of diabe- and chronic diabetes complication 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 red blood cell indices, which include the matched the general characteristics of Saudi type red blood cells (RBCs) count, hematocrit (Hct), 2 diabetic patients. mean corpuscular volume (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), Hematology, Diabetes mellitus. diabetes and prediabetes 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 wound 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 serum. cations7,8. Since the data is scarce related to the Blood glucose assessment was performed us- association of different erythropoietic parameters ing the glucose oxidase-peroxidase methodology. with hyperglycemia and diabetes complications, The serum cholesterol 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 triglyceride 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, fasting 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 triglycerides. Patients with hemato- Results logical diseases like hemoglobinopathies 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 infections, 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 diabetic neuropathy, retinopathy, nephropa- the mean value of RBC’s count, Hb, Hct, but de- thy and vasculopathy.
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