Anemia complicating type 2 diabetes: Prevalence, risk factors and prognosis

https://doi.org/10.1016/j.jdiacomp.2017.04.002Get rights and content

Abstract

Aims

To determine the prevalence, risk factors and prognosis of anemia in representative community-based patients with type 2 diabetes.

Methods

Data from the Fremantle Diabetes Study Phase II (FDS2; n = 1551, mean age 65.7 years, 51.9% males) and Busselton Diabetes Study (BDS; n = 186, mean age 70.2 years, 50.0% males) cohorts, and from 186 matched BDS participants without diabetes, were analyzed. The prevalence of anemia (hemoglobin ≤ 130 g/L males, ≤ 120 g/L females) was determined in each sample. In FDS2, associates of anemia were assessed using multiple logistic regression and Cox proportional hazards modeling identified predictors of death during 4.3 ± 1.2 years post-recruitment.

Results

The prevalence of anemia at baseline was 11.5% in FDS2 participants, 17.8% in BDS type 2 patients and 5.4% in BDS participants without diabetes. In FDS2, 163 of 178 patients with anemia (91.6%) had at least one other risk factor (serum vitamin B12 < 140 pmol/L, serum ferritin < 30 μg/L and/or transferrin saturation < 20%, serum testosterone < 10 nmol/L (males), glitazone therapy, estimated glomerular filtration rate (eGFR) < 60 mL/min 1.73 m2, malignancy, hemoglobinopathy). More anemic than non-anemic FDS2 patients died (28.7% versus 8.0%; P < 0.001). After adjustment for other independent predictors (age as time-scale, male sex, Aboriginality, marital status, smoking, eGFR), anemia was associated with a 57% increase in mortality (P = 0.015).

Conclusions

Type 2 diabetes at least doubles the risk of anemia but other mostly modifiable risk factors are usually present. Anemia is associated with an increased risk of death after adjustment for other predictors.

Introduction

Anemia has been considered a frequent and unrecognized co-morbidity in diabetes1 but estimates of its prevalence vary widely. In the Predialysis Survey on Anemia Management (PRESAM) involving patients with advanced nephropathy,2 diabetes did not increase the risk of anemia, but other studies from a variety of countries have shown that between 14% and 45% of patients with diabetes are anemic.3., 4., 5., 6., 7., 8. Notwithstanding the PRESAM data, this discrepancy may reflect differences in the proportions with renal impairment since the prevalence in outpatients with normal renal function (estimated glomerular filtration rate (eGFR) > 60 mL/min/1.73 m2) has been estimated at only 10%.9

An apparent diabetes-specific risk of anemia has been attributed to reduced responsiveness to erythropoietin (EPO).10 There are, however, other factors that could contribute to depressed erythrocyte production and accelerated destruction in diabetes including chronic inflammation, oxidative stress, advanced glycation, microangiopathy, male hypogonadism and metformin-associated depressed serum vitamin B12 concentrations.1., 11. Whatever the cause, the consequences of anemia complicating diabetes appear adverse, including evidence of increased all-cause and cardiovascular mortality.6., 12.

Evaluation of available data relating to the prevalence, causes and consequences of anemia in type 2 diabetes is complicated by several factors. First, most relevant studies have involved selected2 or referred3., 7., 8., 9., 12., 13. patients, with the possibility that more have complications such as renal impairment that would lead to an overestimation of anemia prevalence. Second, even in population-based studies,6 only a limited number of risk factors for anemia and its sequelae have been included in multivariable analyses, which may result in model specification bias and thus incorrect estimates of the influence of the independent variables, including anemia itself in the case of prognostic outcomes.

In light of these considerations, the aim of this study was to investigate the association between type 2 diabetes, anemia and mortality in well-characterized, community-based samples.

Section snippets

Patients

The prevalence of anemia was ascertained from two population-based observational studies conducted in the state of Western Australia (WA): the Fremantle Diabetes Study Phase II (FDS2)14 and the Busselton Diabetes Study (BDS).15 The FDS2 is a longitudinal study of 1732 patients including 1551 (89.5%) with type 2 diabetes (mean ± SD age 65.7 ± 11.6 years, 51.9% males) from an urban population of 157,000. Patients were recruited between 2008 and 2011 from 4639 people who were resident in the catchment

Prevalence of anemia

Of 1551 FDS2 participants, 1548 (99.8%) had a hemoglobin concentration available at baseline. All of the 186 BDS controls without diabetes and all but one of the 186 BDS patients with type 2 diabetes had a hemoglobin concentration at baseline. The prevalence of anemia in FDS2 participants was 11.5% (95% CI 10.0%–13.2%). In BDS, the prevalence of anemia was 17.8% in the sample with diabetes and 5.4% in the matched group without diabetes (see Table 1). To allow comparability between FDS2 and BDS,

Discussion

The present study shows that anemia was present in between one in six and one in nine patients with type 2 diabetes in two distinct community-based cohorts, and that diabetes doubled or tripled the risk of anemia compared to the prevalence in the general population. However, > 90% of the FDS2 patients with type 2 diabetes in whom anemia was detected had one or more recognized risk factors, most of which were treatable, and the majority of these patients had two or more risk factors. In a

Conclusions

Based on the present data, we recommend periodic (e.g. yearly) hemoglobin measurement as part of regular assessment of patients with type 2 diabetes wherever they are managed. In those who are anemic by WHO definitions, serum iron and vitamin B12 status should be established and deficiencies treated. Other laboratory tests such as serum magnesium, serum testosterone and hemoglobin electrophoresis may be appropriate and prompt intervention, depending on patient characteristics and clinical

Acknowledgments

We are grateful to FDS2 and BDS participants for their involvement, and FDS2 and BDS staff for help with collecting and recording clinical information. We thank PathWest Laboratory Medicine for performing laboratory tests. We also thank staff at the Western Australian Data Linkage Branch, the Hospital Morbidity Data System and Death Registrations for providing outcome data, and Professor Giles of the Cancer Epidemiology Centre of The Cancer Council Victoria for permission to use the Dietary

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    Conflicts of interest: The authors have no conflicts of interest to declare.

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