Correlation Between Hemoglobin A1c and High Sensitivity C-Reactive Protein in Population with Type II Diabetes Mellitus in the Semaglutide Treatment on Coronary Plaque Progression (STOP) Trial

Ahmad, Khadije; Verghese, Dhiran; Kinninger, April; Dahal, Suraj; Ghanem, Ahmed; Rezvanizadeh, Vahid; Manubolu, Venkat Sanjay; Javier, Denise Alison; Vicuna, Will; Shafter, Ahmed M; Alalawi, Luay; Aldana-Bitar, Jairo

doi:10.1016/j.ahj.2021.10.031

Cited by 1 publication

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“…In the same vein, information on other medical conditions/drugs associated with these patients that can affect red blood cell turnover is out-of-scope of this laboratory data. It has been speculated that inflammation may constitute interference in HbA1c pathophysiology, though not after adjusting for age, gender and metabolic syndrome indices [ 29 ]. Therefore, non-investigation of inflammatory markers is hereby acknowledged as another limitation in this study.…”

Section: Discussionmentioning

confidence: 99%

Impact of Anaemia on HbA1c Interpretation Among Normogylcaemic Individuals With Diabetes Mellitus: A Clinical Laboratory Observational Study

Bwititi,

Wang,

Butkowski

et al. 2023

Cureus

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Background and aim: Over the past decades, glycosylated haemoglobin (HbA1c) has been a gold standard for monitoring diabetes control over a long period, relative to blood glucose level (BGL) which measures short-term results. It is speculated that anaemia and factors that induce haemolysis may cause falsely elevated HbA1c leading to ‘false positive’ interpretations. This study aimed to investigate how anaemia impacts HbA1c. Methods: This was a pathology-based observational pilot study using archived data of diabetic subjects monitored with both BGL and HbA1c in regional New South Wales (NSW), Australia. A total of 28,487 cases of blood glucose results were pooled and those with HbA1c and anaemia results were evaluated for correlation with the BGL results. Data collection was limited to de-identified information from the laboratory information system, hence details on the ethnicity and medical history were unavailable. Descriptive frequencies and Pearson correlations were performed. Results: In the pooled data, 53.36% of individuals were females, and 50.54% had BGL ≥5.6 mmol/L. In the pilot dataset, the majority (64.86%) were males, 18.92% with BGL ≤5.6 mmol/L and 67.57% had HbA1c (≥6.5%). In the entire dataset, BGL was moderately and positively correlated with HbA1c (r = 0.6), whereas in the subset of individuals with normo-BGL and anaemia, the correlation was negative (r = -0.2). Discussion: This pilot investigation observed a pertinent issue, which is a negative correlation between glycaemia and HbA1c in patients with anaemia. HbA1c was falsely increased despite normal blood glucose levels in individuals with anaemia. This advances the speculation that anaemia falsely increases HbA1c. Therefore, caution is necessary while interpreting HbA1c results for patients with anaemia, and new methods for interpretation are required.

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Section: Discussionmentioning

confidence: 99%