Anemia is a global health problem which has negative impacts on the quality of life . Anemia leads to fatigue, reduced physical fitness, anorexia, depression, impaired cognitive function and decreased libido. These effects increase the cardiac risk and impair the quality of life . Diabetes patients who have anemia should be treated immediately with careful follow up of response to treatment. Anemia in this group of population contributes to the development and progression of cardiovascular, retinal and renal complications of diabetes. Complete blood count should be part of routine blood tests for patients with diabetes to avoid the higher risk of development of diabetes associated complications .
Bosman et al. identified increased cardiovascular and end stage kidney disease in diabetes patients who had anemia . Keane and Lyle’ study , anemia was significantly associated with increased risk of hospitalization and premature death. In cardiac diseases, one gram reduction of hemoglobin was considered as an independent predictor of morbidity and mortality .
Iron deficiency anemia is the most common form of malnutrition anemia in both developed and developing countries   . It results from inadequate iron intake, malabsorption, increased iron requirement or chronic blood loss   . Iron deficiency leads to declined red cell production and microcytic hypochromic anemia  .
Worldwide prevalence of anemia is about 25%     with 50% of anemic individuals having iron deficiency  . The WHO recommends investigation of anemia when the Hb concentration is <12 g/dl in women and <13 g/dl in men .
Some studies suggest that anemia is two times more common in patients with diabetes compared with people without diabetes . However, 25% of diabetes patients have unrecognized anemia .
Most studies focused on the prevalence of anemia in patients with diabetes and renal insufficiency. Patients with renal impairment or chronic kidney disease due to diabetes mellitus develop more reduction of hemoglobin level and more serious anemia when compared to other patients with the same degree of renal impairment but due to causes other than diabetes    .
In the present study we investigated the prevalence of anemia in type 2 diabetes mellitus patients with normal kidney function. We also tried to find out the clinical predictors that necessitate testing blood for anemia and iron deficiency in patients with type 2 DM.
2. Patients and Methods
This is a cross-sectional retrospective study that included 237 patients who were regularly visiting a single endocrine clinic at Zulekha Hospital, Sharjah, UAE. Patients visited the endocrine clinic from May 2018 to May 2019.
The study proposal has been reviewed and approved by the MOHAP Research Ethics Committee, Sharjah (Research Approval Reference No. MOHAP/DXB-REC/OON/No. 42 2019). All methods were performed in accordance with the relevant guidelines and regulations of Zulekha Hospital, Sharjah (ZHS). The ethics committee waived the need to obtain informed consent for this study.
Data, including age, sex, body weight, BMI, type and duration of diabetes were traced from patient records. All patients had T2DM, age more than 18 years and eGFR more than 90 ml/min/1.73m2.
Exclusion criteria included pregnancy, hospitalization, hemoglobinopathies, hemolytic anemia, hypothyroidism, abnormal renal function test (high serum creatinine, eGFR < 90/ml/1.73 m2). Patients with micro or macroalbuminuria were also excluded from the study. Normoalbuminuria was defined by two of three urine albumin/creatinine ratio < 30 mg/gm .
Serum iron was determined by quantitative method on Roche/Hitachi cobas c systems is by a colorimetric assay in which liberation of Fe3+ ions from the transferrin complex under acidic conditions takes place followed by reduction by ascorbate of Fe3+ ions to Fe2+ ions which then react with FerroZine to give a colored complex. Iron concentration is measured photometrically (For cobas c 311/501 analyzers: IRON2: ACN 661).
Quantitative measurement of serum Unsaturated Iron-Binding Capacity (UIBC) was done on Roche/Hitachi cobas c systems is by direct determination with FerroZine. The color intensity is directly proportionate to the unbound excess iron concentration and indirectly proportionate to the unsaturated iron binding capacity. It is determined by measuring the increase in absorbance photometrically. The sum of the serum iron and UIBC represents total iron-binding capacity (TIBC). TIBC is a measurement for the maximum iron concentration that transferrin can bind. (For cobas c 311/501 analyzers: UIBCI: ACN 779)
Serum ferritin was measured by the electrochemiluminescence immunoassay “ECLIA” on Elecsys and cobas e immunoassay analyzers. This is done by a sandwich principle. First incubation: the sample, a biotinylated monoclonal ferritin-specific antibody, and a monoclonal ferritin-specific antibody labeled with a ruthenium complex form a sandwich complex. Second incubation: after addition of streptavidin-coated microparticles, the complex becomes solid phase-bound through an interaction between biotin and streptavidin. The resulting mixture is aspirated into the measuring cell where the microparticles are magnetically captured onto the surface of the electrode. ProCell/ProCell M removes the unbound substances. A voltage is applied to the electrode with induction of chemiluminescent emission that can be measured by a photomultiplier (Ferritin elecsys cobas e 411).
Complete Blood count was done using UniCel DxH 800 Coulter Cellular Analysis System. It is an automated analyzer with five part differential. RBCs, WBCs & platelets were measured by the Coulter Principle, Hemoglobin is measured photometrically, and Hematocrit, MCH & MCHC are calculated (Beckman Coulter).
Blood glucose, C Reactive Protein (CRP), urine albumin and urine creatinine were determined on the same instrument by enzymatic hexokinase, turbidimetric, immunoturbidimetric & kinetic Jaffe methods; respectively  .
HbA1c was measured by turbidimetric inhibition immunoassay (TINIA) using COBAS INTEGTRA 400 plus machine; Roche Diagnostics. HbA1c percent was calculated as per the equation: HbA1c (%) = (HbA1c/Hb) × 91.5 + 2.15 . Fasting plasma glucose was measured on Roche Hitachi P800/917 chemistry analyzer, Roch Diagnostics.
The latest full blood count was used for statistical analysis. The Modification of Diet in Renal Disease (MDRD) study formula was used for calculation of eGFR  . As per WHO, sex specific definition of anemia was used; Hb < 13 g/dl in men and <12 g/dl in women .
Iron studies diagnostic for iron deficiency anemia consisted of a serum ferritin concentration less than 50 μg/L, a low serum iron (<7.1 µg/L) and a high TIBC (>13.1 µmol/L)    .
Sample size: Based on a previous work by Barbieri et al. , anemia occurs in 34.2% of patients with type 2 diabetes with at least three predictors. Based on the work of Peduzzi et al.  the minimum number of cases to be included in this study can be calculated from the following equation: N = 10 k/p, where p is the smallest of the proportions of negative or positive cases in the population (in this study p = 0.342) and k represents the number of covariates or independent variables (k = 6 in this study which are age, sex, BMI, diabetes duration, diabetes control and renal function). Therefore, a minimum of 176 patients with type 2 diabetes is required.
Data were analyzed using IBM SPSS statistics 20. The data were presented as mean ± SD. A student’s t-test was applied for comparison of group means. Pearson’s coefficient of correlation was calculated to determine the correlation between the two variables. Categorical data was analyzed by χ2 test. Odds ratio and 95% confidence intervals were obtained by the use of logistic regression analyses. P value less than 0.05 was considered significant.
Binary logistic regression (multivariable) was run to ascertain the effects of old age (≥60 years), female sex and wide pulse pressure (≥60 mmHg) on the likelihood that participants with type 2 diabetes will exhibit anemia. The model was statistically significant in predicting anemia (WHO: χ2 45.89 = and P < 0.001).
Our study included 237 patients with a median age of 62 years (49.5 - 69.6) and a median duration of diabetes of 10 years (3.0 - 17). 139 patients were females (58.6% of the study sample) and 98 patients were males (41.4% of the study sample). Median HbA1c was 7.4% (6.7% - 8.55%) and median eGFR 99 ml/min/1.73m2 (90.1 - 126). Basic characteristics of the study group are shown in Table 1.
Prevalence of iron deficiency anemia, as per WHO criteria; in the study population was 36.3% with significantly higher prevalence in females than males (47.9% vs 19.6% respectively, p < 0.001). Menopausal females showed higher prevalence than premenopausal females but this did not reach statistical significance (51.8% vs 30.8% respectively, p < 0.058) (Table 2).
Table 1. Basic characteristics of the study participants (n = 237).
SBP: systolic blood pressure, DBP: diastolic blood pressure, HbA1c: glycated hemoglobin, eGFR: estimated glomerular filtration rate. RPP: rate pressure product, BMI: body mass index.
Table 2. Prevalence of iron deficiency anemia in different categories of the study sample.
Hemoglobin level negatively correlated with patient’s age (p < 0.001) and pulse pressure (p = 0.005). It positively correlated with body weight (p = 0.011), diastolic blood pressure (p = 0.004) and eGFR (p < 0.001) (Table 3).
Comparing anemic with non anemia groups showed significantly older age in anemic patients (p < 0.001), significantly higher prevalence in females than males (p < 0.001). No significant difference was found as regard to metabolic syndrome (IDF criteria p = 0.588, NHLBI/AHA criteria p = 0.375), body mass index (p = 0.081) or rate pressure product (p = 0.264). Diastolic blood pressure
Table 3. Comparison between those with and without anemia.
SBP: systolic blood pressure, DBP: diastolic blood pressure, PP: pulse pressure, HbA1c: glycated hemoglobin, eGFR: estimated glomerular filtration rate, BMI: body mass index.
was significantly lower in anemic patients (p = 0.008). Pulse pressure was significantly wider in anemic than non anemic individuals (p = 0.006). eGFR was significantly lower in anemic than non anemia persons 92.7 (90 - 102.9) vs 110.2 (97.7 - 128), p < 0.001. There was a tendency towards higher HbA1c in anemic patients but did not reach a statistical significance (p = 0.07) (Table 4).
In the present study, significant and independent clinical predictors of iron deficiency anemia in diabetic patients are age, female gender and wide pulse pressure ≥ 60 mmHg (Table 5).
T2DM patients with age ≥ 60 years had 4.2 times higher odds that participants will exhibit anemia. Females had 1.95 times higher odds that to exhibit anemia. T2DM patients with wide pulse pressure ≥ 60 mmHg had 2.4 times higher odds that participants will exhibit anemia.
Table 4. Correlation of hemoglobin with clinical and laboratory parameters.
SBP: systolic blood pressure, DBP: diastolic blood pressure, PP: pulse pressure, HbA1c: glycated hemoglobin, eGFR: estimated glomerular filtration rate, BMI: body mass index.
Table 5. Predictors of the likelihood of iron deficiency anemia in T2DM.
Prevalence of anemia in our study group was 36.3% as per the WHO diagnostic criteria of anemia .
In a study conducted by Shaheen et al., type 2 diabetes Egyptian population had a prevalence of 65% compared with 10% in the control group. Of the 65% anemic patients, 55.4% had microcytic hypochromic anemia .
AlDallal et al.  also used the WHO criteria for diagnosis of anemia . She found 29.7% prevalence of anemia in Kuwaiti patients with type 2 DM. Mean HbA1c in her study was 7.5%. On the other hand the prevalence reported by Sharif et al. in the same population was 63%, a much higher level . In Sherif’s study, 71.5% of patients had poorly controlled diabetes. In AlDallal’s study, 68% of the subjects had well controlled diabetes.
Studies of anemia in patients with diabetes were done in various places with different prevalence. In Taderegew’s study , a prevalence of 20.1% was reported. In Iran it was 19.6% and 30.4% in two different studies  . It was 12.3% in India , 63% in Pakistan , 31.7% and 39% in two different studies in Malaysia   and 63% in Egypt .
Variability in the prevalence can be explained by differences in ethnicity, age of the study participants and duration of DM. The level of development of the country can also affect the quality of health care delivery. Health care may also differ from one place to another in the same country with reflection on patient health   .
Similar to our study, AlDallal et al.  found that females with T2DM are at higher risk of anemia than males. This is consistent with findings of Alsayegh et al. which reported a prevalence of 35.8% in females versus 21.3% in males . In our study, iron deficiency anemia was significantly more prevalent in females than males (47.9% vs. 19.6, respectively, p < 0.001). The prevalence was also higher in menopausal than premenopausal females but this did not reach a statistical significance (51.8% vs.30.8%, p = 0.058). We also found that female gender had 1.95 times higher odds to exhibit anemia than males (Table 5).
The higher prevalence of anemia in females may be explained by malnutrition, lack of empowerment and inadequate health awareness. Education, provision of iron rich food, prescription of vitamin and iron supplements and knowledge of the diabetes associated complications can help .
In the study conducted by Kaur , the prevalence of anemia was lowest in the younger age groups 45 - 55 years (78.8%) and highest in the oldest age groups 66 - 80 years (89.7%).
In our study, age of patients negatively correlated with Hb level (p < 0.001) and was a strong independent predictor of iron deficiency anemia. Participants with age ≥ 60 had 4.2 times higher odds to exhibit anemia. The age-related decrease in hemoglobin concentrations might be due to a lower erythropoietin secretion  or a reduced hematopoietic reserve . Dietary deficiencies and diabetes associated comorbidities also increase with increasing age  .
Al Dallal  and others reported older age and less glycemic control in anemic patients in comparison with non-anemic patients  . In our study, there was a trend towards higher HbA1c in anemic participants but it did not reach a statistical significance (p = 0.072).
In the study conducted by Taderegew et al. , age > 60 years was associated with greater odds for developing anemia. Increased odds ratio for developing anemia with increasing age has also been found in previous studies conducted in California , Australia , China , Nigeria  and Finote Selam hospital .
In the study conducted by Maninder et al. , postmenopausal females experienced higher prevalence of anemia. Hemoglobin concentration showed negative correlation with age of the patients. Women with anemia had lower intake of nutrients essential for erythropoiesis such as calcium, protein and iron. Maninder considered age as a possible predictor of anemia with an odds ratio of 1.04.
Higher prevalence of anemia in post menopausal women in our study can be explained by the fact that menopausal women are older than premenopausal women. Similar to our study, several authors mentioned increasing prevalence of anemia in women with increasing age    .
Previous studies indicated nutrition as an important factor for controlling anemia. Intake of protein, vegetables, fruits, calcium and iron are protective. Thomson et al.  described inadequate nutrient intake as a significant risk factor for anemia in older women. They further elaborated that among anemic postmenopausal women enhanced access to nutrient-rich foods particularly iron and vitamin intake may be required to correct nutritional anemia.
In Merlin’s study , at all levels of eGFR, patients with diabetes were more likely to have anemia. In agreement with Shaheen’s study, we found a significant positive correlation between hemoglobin level and e GFR in our study population (p < 0.001)  (Table 4).
Also in agreement with Shaheen’s report , in our study, Hb was significantly and positively associated with body weight (p = 0.011) but no association with the duration of diabetes was found (p = 0.386). Merlin did not find an association between Hb and age, BMI or duration of diabetes .
Alap et al. , similar to our study, did not find a significant correlation between HbA1c and hemoglobin (p = 0.064) (Table 4).
According to the explanation provided by Sluiter et al., glycation of Hb is an irreversible process. Hence, HbA1 levels in the red blood cell increases with cell age. The average age of circulating red cells is increased in iron deficiency anemia due to decreased production of young cells. HbA1c can spuriously increase in patients with iron deficiency or low hemoglobin. So, iron studies are highly recommended in patients of diabetes mellitus with unexplained high HbA1c before changing their medication .
Contrary to our study, Barbieri et al. found a higher prevalence of obesity and higher mean BMI and waist circumference in anemic patients when compared with nonanemic ones . In our study, BMI was higher in non anemic patients who were overweight, grade I and II obese. However, grade III obesity was more in anemic persons.
Barbieri et al.  did not find correlation between Hb and creatinine or statistical differences in creatinine values or eGFR between anemic and non anemic individuals. In our study eGFR was significantly lower in anemic persons (p < 0.001).
Normal pulse pressure is 30 - 40 mmHg. Cardiovascular death has been shown to be closely associated with high pulse pressure. After the age of 60 years, a pulse pressure > 60 mmHg can be a predictor of cardiovascular and heart attacks . An increase of pulse pressure by 10 mmHg or more is associated with 20% increased risk of CVD .
Iron deficiency anemia is associated with hyperdynamic circulation which is characterized by wide pulse pressure . In our study, wide pulse pressure was a significant independent predictor of iron deficiency anemia. An adult with type 2 diabetes with pulse pressure ≥ 60 mmHg has 2.4 higher odds to have iron deficiency anemia (Table 5).
Limitation of our study includes the retrospective nature and absence of a placebo group. We recommend an extended prospective study which should include a placebo group with further analysis of the effect of iron replacement on the different study variables in patients with iron deficiency anemia.
In conclusion, iron deficiency anemia is common in patients with type 2 diabetes. Age ≥ 60 years, female gender and wide pulse pressure ≥ 60 mmHg are significant independent predictors of iron deficiency anemia in type 2 diabetic patients. Diabetic patients with these predictors should be investigated thoroughly for iron deficiency anemia.
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