Background and Objectives: Glycated hemoglobin (HbA1c) is widely used as a marker of long-term glycemic control and metabolic risk, and represents a cornerstone in the diagnosis and management of diabetes mellitus, as endorsed by major international diabetes guidelines. However, its interpretation is typically uniform across patient populations, despite growing evidence that the biological and clinical significance of a given HbA1c value may vary depending on individual characteristics. Materials and Methods: In this cross-sectional observational study, 839 adult subjects from a real-world clinical cohort were analyzed to assess HbA1c variability and its association with cumulative diabetes-related complication burden (neuropathy, retinopathy, nephropathy, peripheral arterial disease), used here as a proxy for biological risk. Biological risk was assessed using the cumulative number of documented diabetes-related complications. To evaluate whether similar HbA1c values reflect comparable biological risk, comparisons were conducted within a predefined HbA1c stratum (6.0–6.9%). Linear regression and stratified analyses were used to explore context-dependent associations between HbA1c and cumulative complication burden. Results: HbA1c distributions showed substantial overlap across sex and obesity categories, with no marked differences in central tendency. Within the HbA1c range of 6.0–6.9%, differences in cumulative complication burden were observed across subgroups, with males and obese individuals showing a numerically higher mean number of complications despite comparable glycemic levels. Subgroup-specific regression analyses suggested heterogeneous associations between HbA1c and complication burden, indicating potential modification of HbA1c-related risk by clinical context. Conclusions: These findings demonstrate that the clinical interpretation of HbA1c should be contextualized. Identical HbA1c values may be associated with different complication profiles depending on sex and obesity status. Incorporating clinical context into HbA1c-based risk assessment may help inform more personalized approaches to metabolic risk stratification.
Popoviciu et al. (Fri,) studied this question.