{"doi":"10.1111/bjh.16596","title":"Increased iron stores influence glucose metabolism in sickle cell anaemia","abstract":"Past studies suggested low prevalence of diabetes mellitus in sickle cell anaemia (SCA). Our recent studies1, 2 suggest that the prevalence may reflect the general African-American population. Inflammation, oxidative stress, and iron overload due to blood transfusions may result in pancreatic β-cell damage and decreased insulin production, promoting diabetes3. In this study we evaluated the relationship of serum ferritin, an indicator of iron stores, with fasting glucose, insulin and derived homeostatic parameters in older SCA patients. The study (Clinicaltrials.gov NCT02922296) was approved by the Institutional Review Board of the University of Illinois at Chicago (UIC) and conducted according to US Federal Policy for the Protection of Human Subjects. We recruited 46 African-American haemoglobin SS or S/β0-thalassaemia patients of >35 years as they presented to clinic, without a diabetes diagnosis and without knowledge of the ferritin. These SCA patients provided written informed consent prior to participation. Venous blood was drawn from fasting patients at steady-state (no vaso-occlusive crisis, infection, surgery or requirement for parenteral pain medication for at least two weeks). Blood transfusions administered during the previous 17 years at UIC Hospital were abstracted from the electronic medical records. Glucose was measured by glucose oxidase methodology, insulin by quantitative chemiluminescent immunoassay, and ferritin by enzyme immunoassay. The Homeostatic Model Assessments to identify insulin resistance and steady-state β-cell function (HOMA2-IR and HOMA2-β) were calculated (http://www.dtu.ox.ac.uk/homacalculator/index.php). We selected 46 African-American controls from the National Health and Nutrition Examination Survey (NHANES) 1988-2012, matched to the patients by age, sex, body mass index (BMI) (greater or less than 25 kg/m2), and ferritin (greater or less than 300 ng/ml with a minimum of 30 ng/ml). Spearman rank order was used to examine the relationship of ferritin with parameters of glucose metabolism. Comparisons between groups of subjects were made with the Kruskal-Wallis test for linear variables and the Fisher exact test for categorical variables. Analyses were carried out using Systat version 13. The median red blood cell transfusion burden in the 46 SCA patients as recorded in the electronic medical records of the UIC Hospital was 13 units (Table I); this is an underestimate because transfusions at other institutions were not captured. The median haemoglobin concentration was >3 g/dl below the reference range, reflecting the decreased oxygen delivery and increased cardiac output of chronic anaemia. The increased reticulocyte count and lactate dehydrogenase (LDH) reflected a chronic haemolytic state. The median fasting glucose and insulin concentrations were within the reference ranges, but the fasting blood sugar was >126 mg/dl in three out of the 46 (6·5%) patients, suggesting unsuspected diabetes mellitus, and between 100 and 125 mg/dl in five (10·9%) patients, suggesting pre-diabetes. The fasting insulin concentration was above the reference range in three (6·5%) patients. The ferritin was greater than the upper reference limit of 300 ng/ml in 30 (65·2%) patients and it was >2000 ng/ml in eight of these patients. The median (interquartile range) red blood cell transfusion burden was 21 (4–37) units in patients with ferritin >300 ng/ml versus five (0–9) units in those with lower ferritin levels (P = 0·0025), and it was 55 (31–66) units in patients with ferritin >2000 ng/ml versus eight (2–22) units in those with ferritin <2000 ng/ml (P = 0·0046). The US Multi-Center Study of Iron Overload Research Group defined transfusional iron overload in sickle cell disease by ferritin ≥2000 ng/ml.4 BMI, a well-established risk factor for glucose intolerance and diabetes, was positively associated with fasting insulin (ρ = 0·423, P = 0·003), HOMA2-IR (ρ = 0·426, P = 0·003) and HOMA2-β (ρ = 0·333, P = 0·022). ","journal":"British Journal of Haematology","year":2020,"id":109201,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9615,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":522627,"name":"Taif Hassan","orcid":null,"position":1,"is_corresponding":false},{"id":351694,"name":"Xu Zhang","orcid":"0000-0002-7732-1385","position":2,"is_corresponding":false},{"id":260520,"name":"Donald A. McClain","orcid":"0000-0002-3310-2359","position":3,"is_corresponding":false},{"id":269831,"name":"Victor R. Gordeuk","orcid":"0000-0003-4725-7295","position":4,"is_corresponding":false},{"id":269842,"name":"Binal N. Shah","orcid":"0000-0001-5169-7301","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-18T23:12:50.712357Z","pmid":"32232834","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}