{"doi":"10.1002/jha2.161","title":"Type 2 diabetes mellitus burdens among adults with sickle cell disease: A 12‐year single health system‐based cohort analysis","abstract":"To the Editor: Recent observational studies have demonstrated similar type 2 diabetes mellitus (T2DM) prevalence in commercially insured sickle cell disease (SCD) patients compared with the general population.[1, 2] However, several important questions about T2DM in SCD remain unanswered. Past studies are limited due to lack of definitive genotype information [3-5]; it is possible that different SCD genotypes (sickle cell anemia (HbSS), sickle hemoglobin C disease (HbSC), sickle β+-thalassemia (HbSβ+), and sickle β0-thalassemia (HbSβ0)) demonstrate varying risks of T2DM and its associated complications. Additionally, important information on anthropometrics were lacking in most epidemiology studies. It is unclear whether low lean body weight and fat mass in anthropomorphic studies associated with SCD conferred protection against T2DM [6, 7]. In this single health-system based, retrospective cohort study, we extracted 12 years (2008–2019) of electronic health records (EHR) data from a large, urban, tertiary health care system. Patients who self-identified as African American (AA) were included in the analysis. SCD genotypes were ascertained through a combination of chart review and by hemoglobin electrophoresis test records extracted from EHR. Identified T2DM cases with and without SCD were then matched by exact sex and age in up to a 1:10 ratio. The Institutional Review Board of the University of Illinois at Chicago approved this study. Categorical variables were compared using the chi-square test or Fisher's exact test, while continuous measurements between groups were assessed using the nonparametric Wilcoxon rank sum test to account for nonnormal distribution of the variables such as age and body mass index (BMI). Multivariable logistic regression analysis was used to assess the association between T2DM and SCD status, after accounting for sex, age, BMI, insurance type, and household income levels. A marginal multilevel model was used, with SCD status as a between-subject effect, and individual plasma glucose values as repeated measures [8]. All analyses were conducted using SAS 9.4 (Cary, North Carolina) and a two-tailed P value of less than 0.05 was used to determine statistical significance. T2DM was identified in 89 out of the 634 patients with SCD and aged ≥ 20 years old. Among these patients, mean [median] age was greater among SCD patients with T2DM compared to SCD patients without T2DM (43.3 [44] vs. 32.6 [29] years; p < 0.01). SCD patients with T2DM were found to have higher mean [median] BMI compared to SCD patients without SCD (28.9 [27.8] vs. 25.5 [24.0]; p < 0.01). Patients with SCD had lower BMI compared to age- and sex-matched AAs with T2DM (28.9 [27.8] vs. 36.7 [35.3]; p < 0.01). Among 89 patients with SCD and T2DM, 22 (24.7%) were on treatment for diabetes (Table 1 and Table S1). The types of comorbidities differed by SCD status in the T2DM population, and SCD patients also were at a greater risk of SCD-related comorbidities. A higher proportion of SCD patients with T2DM developed diabetic nephropathy (44.9% vs. 12.0%; p < 0.01), peripheral circulatory complications (18.0% vs. 12.0%; p = 0.11), foot ulcer (12.4% vs. 3.4%; p < 0.01), as well as myocardial infarction (13.5% vs. 5.8%; p < 0.01) (Table 2). After standardization to the 2010 US Census of AA population, the prevalence rates of T2DM were 14.5%, 18.9%, and 16.8% for HbSS, HbSC, and HbSβ+ thalassemia patients (Table S2). SCD patients were found to have comparable risk for T2DM relative to non-SCD self-identified AA patients (odd ratio [OR] 1.01, 95%CI (0.79-1.27), after accounting for age, sex, BMI, and household income and insurance plan types (Table S3). SCD patients with T2DM and valid laboratory results on plasma glucose were found to have lower glucose levels compared to age- and sex-matched AA subjects with T2DM (average glucose levels, 108.6 mg/dL vs. 132.2 mg/dL, estimated difference [standard error]: −17.30 [4.38] mg/dL) (Table S4). 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Nutescu","orcid":"0000-0002-2651-0020","position":2,"is_corresponding":false},{"id":351695,"name":"Jin Han","orcid":"0000-0003-0662-006X","position":3,"is_corresponding":false},{"id":431723,"name":"Surrey M. Walton","orcid":"0000-0001-7458-6460","position":4,"is_corresponding":false},{"id":110018,"name":"Andrew Srisuwananukorn","orcid":"0000-0002-8736-8726","position":5,"is_corresponding":false},{"id":451025,"name":"William Galanter","orcid":"0000-0001-7811-5391","position":6,"is_corresponding":false},{"id":446811,"name":"Jifang Zhou","orcid":"0000-0003-2590-8667","position":0,"is_corresponding":true}],"reference_count":12,"raw_metadata":null,"created_at":"2026-07-18T23:51:53.662668Z","pmid":"35846078","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":[]}