A1C underestimates glycemia in HIV infection
Open accessAbstract
OBJECTIVE: The objective of this study was to determine the relationship between A1C and glycemia in HIV infection. RESEARCH DESIGN AND METHODS: We completed a prospective cross-sectional study of 100 HIV-infected adults with type 2 diabetes (77%) or fasting hyperglycemia (23%) with measured glucose, A1C, mean corpuscular volume (MCV), and fructosamine. A total of 200 HIV-uninfected type 2 diabetic subjects matched for key demographic characteristics served as control subjects. RESULTS: Relative to the control subjects, A1C underestimated glucose by 29 +/- 4 mg/dl in the HIV-infected subjects. Current nucleoside reverse transcriptase inhibitors (NRTIs), higher MCV and hemoglobin, and lower HIV RNA and haptoglobin were associated with greater A1C-glucose discordance. However, only MCV and current NTRI use, in particular abacavir, remained significant predictors in multivariate analyses. Fructosamine more closely reflected glycemia in the HIV-infected subjects. CONCLUSIONS: A1C underestimates glycemia in HIV-infected patients and is related to NRTI use. Use of abacavir and increased MCV were key correlates in multivariate analyses. Fructosamine may be more appropriate in this setting.
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A1C may not be a suitable diagnostic test in people with anemia, people treated with erythropoietin, or people undergoing hemodialysis or HIV treatment.
“A1C may not be a suitable diagnostic test in people with anemia, people treated with erythropoietin, or people undergoing hemodialysis or HIV treatment (19,20).”
From 2. Diagnosis and Classification of Diabetes: <i>Standards of Care in Diabetes—2024</i> (2024) · cites this paper as [20]
The Conclusions report that A1C underestimates glycemia in HIV-infected patients and that NRTI use is associated with the discrepancy, supporting caution during HIV treatment. The Research Design and Methods section says patients with anemia or renal disease were excluded; the paper does not establish the claim for anemia, erythropoietin treatment, or hemodialysis, nor does it directly evaluate A1C as a diagnostic test. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe A1C test may underestimate glycemia in people with HIV.
“The A1C test may underestimate glycemia in people with HIV; it is not recommended for diagnosis and may present challenges for monitoring (20).”
From 2. Diagnosis and Classification of Diabetes: <i>Standards of Care in Diabetes—2024</i> (2024) · cites this paper as [20]
The Abstract’s Conclusions state that A1C underestimates glycemia in HIV-infected patients, and the Results report that it underestimated glucose relative to controls. The Conclusions also suggest fructosamine may be more appropriate, consistent with a monitoring concern. The cited paper does not state that A1C is not recommended for diagnosis, so that part of the citing sentence is unsupported. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe A1C test may underestimate glycemia in people with HIV, so plasma glucose criteria are preferred to diagnose prediabetes and diabetes.
“As the A1C test may underestimate glycemia in people with HIV, plasma glucose criteria are preferred to diagnose prediabetes and diabetes (20).”
From 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025 (2025) · cites this paper as [20]
The Abstract Results and Conclusions report that A1C underestimated glycemia in the HIV-infected patients studied. The paper does not recommend plasma glucose criteria over A1C for diagnosing prediabetes or diabetes; its Conclusions instead suggest that fructosamine may be more appropriate for assessing glycemia in this setting. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA1C may not be a suitable diagnostic test in people with anemia, people treated with erythropoietin, or people undergoing hemodialysis or HIV treatment.
“A1C may not be a suitable diagnostic test in people with anemia, people treated with erythropoietin, or people undergoing hemodialysis or HIV treatment (1,19,20).”
From 2. Diagnosis and classification of diabetes: standards of care in diabetes-2025 (2025) · cites this paper as [20]
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- Authors
- Kim, P. S., Woods, C., Georgoff, P., Crum, D., Rosenberg, A., Smith, M., & Hadigan, C.
- Affiliations
- MedStar Washington Hospital Center (US)
- University of Pennsylvania (US)
- National Institutes of Health (US)
- Corresponding author
- Hadigan, Colleen
- Venue
- Diabetes Care
- Publisher
- American Diabetes Association
- Published
- 2009-06-05
- Volume
- 32
- Issue
- 9
- Pages
- 1591-1593
- ISSN
- 0149-5992 (ISSN-L)
- 1935-5548
- PMC
- PMC2732167
- OpenAlex
- W1976959779
- Cited by
- 145
- References
- 13
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 10 · 2023: 8 · 2022: 7 · 2021: 15 · 2020: 13 · 2019: 10 · 2018: 13 · 2017: 6 · 2016: 11 · 2015: 6 · 2014: 7 · 2013: 2 · 2012: 8
- FWCI
- 9.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Diabetes Care (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Pfizer
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Emergency Medicine › HIV-related health complications and treatments
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- HbA1c, glycemia, HIV infection, type 2 diabetes, fasting hyperglycemia, nucleoside reverse transcriptase inhibitors, abacavir, mean corpuscular volume, fructosamine, haptoglobin, hemoglobin
- MeSH terms
- Blood Glucose, Cross-Sectional Studies, Female, Glycated Hemoglobin, Humans, Male, Middle Aged, Prospective Studies, HIV Infections, Multivariate Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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