Back

Impact of Iron Deficiency on HbA1c Accuracy in Monitoring Glycaemic Control in Non-Anaemic Type 2 Diabetes individuals: A prospective longitudinal study

Mukasa, R. J.; Mubiru, N.; Sekitoleko, I.; Makanga, R.; Nkabura, H.; Ongaria, T.; Mugamba, V.; Nakanga, W.; Nyirenda, M. J.; Niwaha, A. J.

2025-04-29 endocrinology
10.1101/2025.04.28.25326576 medRxiv
Show abstract

PurposeResults from a few studies have been conflicting as to whether iron deficiency alters HbA1c reliability and the mechanisms on how iron might affect HbA1c reliability are not fully known. We aimed to compare the relationship between HbA1c and mean glucose concentrations measured by continuous glucose monitoring in iron replete and iron deplete states among non-anaemic type 2 diabetes mellitus (T2DM) patients. MethodsWe compared the differences in HbA1c between iron replete and deplete groups using the Chi-square test for categorical data and the Mann-Whitney U test for continuous data. We also evaluated the correlation between HbA1c and mean plasma glucose for both iron-replete and iron-deplete individuals using Pearsons correlation and linear regression. ResultsA total of 146 of the 213 participants screened had complete data and were considered for final analysis. 43/146 (29.5%) had iron deficiency and 103 were iron replete. No significant difference was observed in HbA1c levels between iron-replete and iron-deplete individuals: 69 [51.0, 85.0] vs 62 [46.0, 83.0] mmol/mol, P 0.291). There was a strong positive correlation between HbA1c and mean plasma glucose concentration for both iron-replete and iron-deplete individuals (Pearson Correlation coefficient: 0.83 (0.76 - 0.91) and 0.93 (0.89 - 0.98), respectively). ConclusionsHbA1c correlates well with mean blood glucose even in the iron deplete state amongst non-anaemic T2DM individuals. However, larger studies are needed to confirm these findings, particularly at screening and diagnostic thresholds.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.