Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Feb 7;317(5):507-515.
doi: 10.1001/jama.2016.21035.

Association of Sickle Cell Trait With Hemoglobin A1c in African Americans

Affiliations

Association of Sickle Cell Trait With Hemoglobin A1c in African Americans

Mary E Lacy et al. JAMA. .

Abstract

Importance: Hemoglobin A1c (HbA1c) reflects past glucose concentrations, but this relationship may differ between those with sickle cell trait (SCT) and those without it.

Objective: To evaluate the association between SCT and HbA1c for given levels of fasting or 2-hour glucose levels among African Americans.

Design, setting, and participants: Retrospective cohort study using data collected from 7938 participants in 2 community-based cohorts, the Coronary Artery Risk Development in Young Adults (CARDIA) study and the Jackson Heart Study (JHS). From the CARDIA study, 2637 patients contributed a maximum of 2 visits (2005-2011); from the JHS, 5301 participants contributed a maximum of 3 visits (2000-2013). All visits were scheduled at approximately 5-year intervals. Participants without SCT data, those without any concurrent HbA1c and glucose measurements, and those with hemoglobin variants HbSS, HbCC, or HbAC were excluded. Analysis of the primary outcome was conducted using generalized estimating equations (GEE) to examine the association of SCT with HbA1c levels, controlling for fasting or 2-hour glucose measures.

Exposures: Presence of SCT.

Main outcomes and measures: Hemoglobin A1c stratified by the presence or absence of SCT was the primary outcome measure.

Results: The analytic sample included 4620 participants (mean age, 52.3 [SD, 11.8] years; 2835 women [61.3%]; 367 [7.9%] with SCT) with 9062 concurrent measures of fasting glucose and HbA1c levels. In unadjusted GEE analyses, for a given fasting glucose, HbA1c values were statistically significantly lower in those with (5.72%) vs those without (6.01%) SCT (mean HbA1c difference, -0.29%; 95% CI, -0.35% to -0.23%). Findings were similar in models adjusted for key risk factors and in analyses using 2001 concurrent measures of 2-hour glucose and HbA1c concentration for those with SCT (mean, 5.35%) vs those without SCT (mean, 5.65%) for a mean HbA1c difference of -0.30% (95% CI, -0.39% to -0.21%). The HbA1c difference by SCT was greater at higher fasting (P = .02 for interaction) and 2-hour (P = .03) glucose concentrations. The prevalence of prediabetes and diabetes was statistically significantly lower among participants with SCT when defined using HbA1c values (29.2% vs 48.6% for prediabetes and 3.8% vs 7.3% for diabetes in 572 observations from participants with SCT and 6877 observations from participants without SCT; P<.001 for both comparisons).

Conclusions and relevance: Among African Americans from 2 large, well-established cohorts, participants with SCT had lower levels of HbA1c at any given concentration of fasting or 2-hour glucose compared with participants without SCT. These findings suggest that HbA1c may systematically underestimate past glycemia in black patients with SCT and may require further evaluation.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Figures

Figure 1
Figure 1. Scatterplot of Observed Data Model of Hemoglobin A1c vs Fasting and 2-Hour Glucose Measures in Participants With or Without Sickle Cell Trait
Scatterplot of observed data points along side unadjusted and adjusted regression lines examining the association between sickle cell trait (SCT) and hemoglobin A1c (HbA1c), controlling for fasting or 2-hour glucose values was obtained using generalized estimating equations (GEE) with an exchangeable correlation matrix to account for correlation of repeated measures. All continuous covariates are centered at the population mean. The solid blue lines represent the regression line for those for who did not have SCT and the dashed orange lines for those who had SCT. BMI indicates body mass index; CARDIA, Coronary Artery Risk Development in Young Adults study. To convert glucose from mg/dL to mmol/L, multiply by 0.0555. A, Included 9062 observations, 720 from participants with SCT and 8342 from participants without SCT. The regression equation: predicted HbA1c = 6.01 + (−0.28 × SCT) + (0.03 × fasting glucose) + (−0.004 SCT fasting glucose). B, Included 8460 observations, 683 from participants with SCT and 7777 from participants without SCT. The regression equation: predicted HbA1c = 5.93 + (−0.32 × SCT) + (0.03 × fasting glucose) + (−0.005 × SCT × fasting glucose) + (0.04 × 1 if male) + (0.008 × age) + (0.01 × BMI) + (−0.0004 × ferritin) + (0.001 × estimated glomerular filtration rate) + (−0.08 × 1 if a CARDIA participant) + (0.46 × 1 if currently using diabetes medications) + (0.14 × 1 if previous diabetes diagnosis). C, Included 2001 observations, 127 from participants with SCT and 1874 from participants without SCT. Regression equation: predicted HbA1c = 5.65 + (−0.28 × SCT) + (0.01 × 2-hour glucose) + (−0.004 × SCT × 2-hour glucose). D, Included 1712 observations, 109 from participants with SCT and 1606 from participants without SCT. Regression equation: predicted HbA1c = 5.66 + (−0.36 × SCT) + (0.01 × 2-hour glucose) × (−0.004 × SCT × 2-hour glucose) + (0.24 × 1 if male) + (0.02 × age) + (0.006 × BMI) + (−0.0006 × ferritin) + (0.0003 × eGFR) + (0.07 × 1 if previous diabetes diagnosis).
Figure 2
Figure 2. Prevalence of Prediabetes and Diabetes by Sickle Cell Trait Status Among Participants Not Taking Diabetes Medications and With No Prior Diagnosis of Diabetes
Fasting glucose and hemoglobin A1c analyses included 7499 total observations (6877 observations from participants without sickle cell trait [SCT] and 572 from participants with it). Analyses for 2-hour glucose concentrations were only available from CARDIA participants and included 1869 total observations (1752 observations from participants without SCT and 117 from participants with SCT). For the definition of prediabetes and diabetes by glucose measures, see the Methods section. The prevalence of prediabetes and diabetes by fasting glucose and 2-hour glucose concentration was similar in those with and without SCT (P > .10 for all comparisons). However, the prevalence of prediabetes and diabetes as defined by hemoglobin A1c was significantly higher among participants with vs without SCT (P < .001 for both). Error bars indicate 95% CIs.
Figure 3
Figure 3. Comparison of the Diagnostic Sensitivity of Hemoglobin A1c to Identify Combined Prediabetes or Diabetes by Sickle Cell Trait Status
A, For fasting glucose of 100mg/dL or higher, the area under the receiver operating characteristic (AUROC) of hemoglobin A1c (HbA1c) was 0.77 (95% CI, 0.75–0.78) among those without sickle cell trait (SCT) and 0.70 (95% CI, 0.65–0.74) among those with SCT. An unpaired comparison of the AUROC curves indicated that the diagnostic ability of HbA1c to identify fasting glucose–defined prediabetes or diabetes was significantly lower among those with SCT than among those without it (P = .007). B, For 2-hour glucose levels of 140mg/dL or higher, the AUROC of HbA1c was 0.74 (95% CI, 0.71–0.78) among those without SCT and 0.60 (95% CI, 0.47–0.72) among those with SCT. An unpaired comparison of the AUROC curves indicated that the diagnostic ability of HbA1c to identify 2-hour glucose-defined prediabetes or diabetes was significantly lower in those with SCT than in those without it (P = .03). To convert glucose from mg/dL to mmol/L, multiply by 0.0555.

Comment in

References

    1. Nathan DM, Kuenen J, Borg R, Zheng H, Schoenfeld D, Heine RJ A1c-Derived Average Glucose Study Group. Translating the A1c assay into estimated average glucose values. Diabetes Care. 2008;31(8):1473–1478. - PMC - PubMed
    1. Bonora E, Tuomilehto J. The pros and cons of diagnosing diabetes with A1c. Diabetes Care. 2011;34(S2 suppl 2):S184–S190. - PMC - PubMed
    1. Sacks DB. A1c versus glucose testing: a comparison. Diabetes Care. 2011;34(2):518–523. - PMC - PubMed
    1. Selvin E, Crainiceanu CM, Brancati FL, Coresh J. Short-term variability in measures of glycemia and implications for the classification of diabetes. Arch Intern Med. 2007;167(14):1545–1551. - PubMed
    1. International Expert Committee. International Expert Committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care. 2009;32(7):1327–1334. - PMC - PubMed

Publication types

LinkOut - more resources