Systematic Discordance Between HbA1c and Continuous Glucose Monitoring-Derived Glycaemic Metrics in Paediatric Diabetes: Implications for Treatment Decisions

Systematic Discordance Between HbA1c and Continuous Glucose Monitoring-Derived Glycaemic Metrics in Paediatric Diabetes: Implications for Treatment Decisions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systematic Discordance Between HbA1c and Continuous Glucose Monitoring-Derived Glycaemic Metrics in Paediatric Diabetes: Implications for Treatment Decisions Ajmina Hasan Flabe, Jing Luo, Abigail Foulds, Kamrul Huda, Nowroj Kobir Prottoy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8356318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: HbA1c guides paediatric diabetes treatment, but biological factors like erythrocyte lifespan cause differences between HbA1c and CGM measures, risking overtreatment, undertreatment, and misclassification. This study quantifies HbA1c–GMI discordance in children, exploring clinical and biological predictors, including residual beta cell function via C-peptide. Methods: In this prospective cross-sectional study at a paediatric diabetes centre in Dhaka, Bangladesh, 120 youths aged 5–25 years with type 1 or type 2 diabetes (duration ≥ 6 months) were enrolled between March 2024 and November 2025. Participants underwent 7–14 days of blinded CGM with the Abbott FreeStyle Libre Pro iQ system, and concurrent laboratory HbA1c; fasting C-peptide was measured in a subset, and demographic, clinical, and treatment data were abstracted from medical records. The primary outcome was HbA1c–GMI discordance, defined “a priori” as an absolute difference greater than 0.5 percentage points; agreement was assessed with Bland–Altman analysis, and multivariable linear regression examined associations between discordance magnitude and diabetes duration, C-peptide, total daily insulin dose, and diabetes type. Results: Out of 120 enrolled patients, 97 with concurrent HbA1c and sufficient CGM data were included in the analysis (mean age 14.8 years; 57.7% female; 70.1% with type 1 diabetes). HbA1c and GMI showed only a modest correlation, and a significant proportion of participants met the predefined discordance threshold. Bland–Altman plots revealed a systematic positive bias, with HbA1c generally exceeding GMI. The mean cohort HbA1c was notably higher than the mean GMI, suggesting that many youths would be classified as poorly controlled and considered for intensification based on HbA1c alone, despite CGM indicating closer-to-target glycaemia; lower C-peptide levels were independently associated with greater discordance magnitude after adjusting for clinical covariates. Conclusions: In this paediatric diabetes cohort, HbA1c often overestimated glycaemic burden compared to CGM-derived GMI, especially among young people with reduced residual beta cell function. This challenges the assumption that these metrics are interchangeable. Using CGM measures alongside HbA1c and considering discordance patterns—possibly guided by C-peptide—can prevent unnecessary treatment increases and promote more personalised, physiology-informed management. Figures Figure 1 Figure 2 Figure 3 Introduction Glycated haemoglobin (HbA1c) remains the gold standard for monitoring glycaemic control in diabetes, supported by pivotal trials and incorporated into major guidelines, including targets below 7.0% for most children and adolescents.( 1 – 4 ) However, HbA1c is subject to biological limitations. Factors such as haemoglobinopathies, haemolytic anaemia, kidney disease, and, notably, variations in erythrocyte lifespan can cause significant discrepancies independent of actual glucose levels, potentially leading to systematic over- or underestimation of glycaemic control.( 5 – 9 ) The introduction of continuous glucose monitoring (CGM) has revolutionised diabetes care by enabling real-time assessment of glucose levels and enhancing clinical outcomes, particularly in paediatric patients.( 10 – 14 ) The Glucose Management Indicator (GMI), calculated from CGM-derived average glucose, was developed to align with laboratory HbA1c and is now endorsed as a key glycaemic metric in consensus guidelines, on the assumption that it is largely interchangeable with HbA1c when data are sufficient.( 15 – 17 ) However, recent studies in adults have questioned this assumption, showing 30–50% discordance between HbA1c and GMI and indicating that HbA1c may systematically overestimate the actual glycaemic burden.( 9 , 18 – 20 ) Despite these findings, the discrepancy between HbA1c and GMI in paediatric diabetes remains poorly understood. Most existing research is limited and focuses on homogeneous, resource-rich groups. ( 21 – 23 ) This lack of comprehensive paediatric data has significant clinical implications. Systematic overestimation of HbA1c might lead to unnecessary treatment escalation and a higher risk of hypoglycaemia, or it could mask inadequate glycaemic control. ( 18 , 20 , 24 – 26 ) Residual beta-cell function, as indicated by C-peptide levels, may contribute to this discrepancy, given its known associations with reduced glycaemic variability and improved time-in-range ( 27 – 29 ). However, this connection has not been thoroughly explored in children. ( 30 – 31 ) This prospective cross-sectional study aimed to assess the prevalence and factors influencing significant HbA1c-GMI discordance in a diverse group of paediatric diabetes patients. We hypothesised that such discordance would be frequent and that residual beta cell function, measured by C-peptide, would serve as an independent predictor even after accounting for other variables. Understanding these mechanisms is expected to enhance diabetes management techniques and guide future monitoring guidelines. Methods Study Design and Setting This prospective cross-sectional study was conducted at BADAS (Bangladesh Diabetic Association) Paediatric Diabetes Care & Research Centre, Dhaka, Bangladesh, from March 2024 to November 2025. The study received approval from the institutional ethics committee of the Diabetic Association of Bangladesh (Protocol BADAS-ERC/EC/24/16), and written informed consent or assent was obtained from all participants and their legal guardians. The study adhered to Good Clinical Practice guidelines and the Declaration of Helsinki. Participants Patients were randomly selected from routine paediatric diabetes clinic visits. Eligible participants were children and adolescents aged 5–25 years with established Type 1 or Type 2 diabetes mellitus (diagnosed ≥ 6 months) receiving diabetes treatment. Exclusion criteria included active diabetic ketoacidosis, known hemoglobinopathy or chronic haemolytic anaemia, severe chronic kidney disease, pregnancy, CGM device malfunction (data capture < 70%), or absence of concurrent HbA1c measurement within 30 days of CGM monitoring. Of 120 patients enrolled, 23 were excluded due to the absence of concurrent HbA1c measurements, resulting in a final analysis cohort of 97 patients (68 with Type 1 diabetes, 29 with Type 2 diabetes). Data Collection Baseline demographic, anthropometric, and clinical data were extracted from paper medical records. Height and weight were measured using calibrated equipment; body mass index was calculated as weight (kg) divided by height squared (m²). Information on diabetes duration, treatment regimen, total daily insulin dose, and recent HbA1c and fasting C-peptide measurements (when available) was recorded. All participants wore the Abbott Freestyle Libre Pro iQ blinded continuous glucose monitoring (CGM) system for 7–14 days. The device measures interstitial glucose every 15 minutes using a subcutaneous sensor. CGM-derived metrics were taken from downloaded data Glucose Management Indicator (GMI, estimated HbA1c from mean glucose using the formula GMI = 3.31 + 0.02392 × [mean glucose in mg/dL]), Time-in-Range (TIR, percentage of time 70–180 mg/dL), Time-above-Range (TAR, > 180 mg/dL), Time-below-Range (TBR, < 70 mg/dL), and coefficient of variation (CV, SD/mean × 100). HbA1c was measured using high-performance liquid chromatography certified by the National Health Guidelines. C-peptide was measured using a chemiluminescent immunoassay (normal range 0.5-3.0 ng/mL) in a subset of participants. Outcomes The primary outcome was HbA1c-GMI discordance, defined as an absolute difference greater than 0.5% between laboratory HbA1c and CGM-derived GMI. This threshold was set beforehand based on the minimal clinically important difference for HbA1c. Discordance magnitude was calculated as HbA1c (%) minus GMI (%), with positive values indicating HbA1c overestimation. Statistical Analysis: Although a formal sample size calculation was not conducted for this exploratory analysis, post hoc power analysis showed sufficient power (> 80%) to identify medium-to-large effect sizes. Continuous variables are presented as mean (SD) or median (interquartile range) where appropriate, and categorical variables as number (percentage). Baseline characteristics were compared between diabetes types using t tests, Mann-Whitney U tests, or χ² tests as suitable. Agreement between HbA1c and GMI was assessed using Bland-Altman methodology, calculating mean bias and 95% limits of agreement. We tested for proportional bias by examining the correlation between mean and difference of measurements. In univariable analyses, we assessed associations between potential predictors and discordance magnitude using Mann-Whitney U tests and Spearman correlations. Variables with P < 0.20 were considered for multivariable modelling. The multivariable linear regression initially included age, body mass index, Time-in-Range, and glucose variability, but significant multicollinearity was observed (variance inflation factors > 20). We thus built a streamlined model including four predictors with acceptable collinearity (VIF < 6): diabetes duration, C-peptide, total daily insulin dose, and diabetes type. Model assumptions were verified using diagnostic plots, and fit was evaluated with R² and adjusted R². Our analytical plan involved prespecified subgroup analyses based on diabetes type, testing for interactions related to C-peptide effects, and conducting three sensitivity analyses: one restricting to patients with at least 90% CGM adherence, another excluding those with less than 1 year of diabetes, and a third using a discordance threshold greater than 1.0%. Additionally, exploratory analyses focused on C-peptide tertiles and identified paradoxical patterns where HbA1c remained ≥ 8.0% despite Time-in-Range being ≥ 60%. These exploratory results were not adjusted for multiple comparisons and are intended to generate hypotheses. Missing data were minimal ( 0.70), indicating missingness was random. All tests were two-sided with P < 0.05 considered significant. Analyses utilised Python version 3.11.0 (scipy, statsmodels, scikit-learn packages). The study adhered to STROBE guidelines; a completed checklist is provided in the Supplement. Detailed statistical methods are in eMethods. Results Study Population From March 2024 to November 2025, a total of 120 paediatric diabetes patients underwent continuous glucose monitoring (CGM) as part of routine care. After excluding 23 patients lacking concurrent HbA1c data, 97 patients were analysed. The average age was 14.8 years (SD, 5.7), with 56 (57.7%) female participants. Among them, 68 (70.1%) had Type 1 diabetes, and 29 (29.9%) had Type 2 diabetes. A high adherence rate was observed, with 93 patients (95.9%) monitoring with CGM for at least 7 days. Table 1 shows the baseline characteristics. Patients with Type 2 diabetes were older (average age of 19.0 vs. 13.1 years; p<0.001), had a higher body mass index (average BMI of 26.7 vs. 19.0 kg/m²; p<0.001), and exhibited greater residual beta cell function (average C-peptide of 2.3 vs. 0.4 ng/mL; p<0.001) compared to patients with Type 1 diabetes. Table:1 Baseline Characteristics of Study Cohort (n=97) Characteristic Total (n=97) Type 1 DM (n=68) Type 2 DM (n=29) Demographics Age, years 14.8 ± 5.7 13.1 ± 5.3 19.0 ± 4.2 Female sex, n (%) 56 (57.7) 38 (55.9) 18 (62.1) Anthropometric Measures BMI, kg/m² 21.4 ± 7.7 19.0 ± 4.2 26.7 ± 10.7 Clinical Characteristics Diabetes duration, years 5.8 ± 4.0 5.4 ± 3.7 6.6 ± 4.5 C-peptide, ng/mL 1.0 ± 1.4 0.4 ± 0.4 2.3 ± 1.9 Total daily dose, units 45.1 ± 27.2 42.1 ± 23.0 52.6 ± 35.0 Glycemic Control Metrics HbA1c, % 8.5 ± 1.7 8.6 ± 1.7 8.3 ± 1.5 GMI, % 6.9 ± 1.1 7.2 ± 0.9 6.3 ± 1.4 HbA1c-GMI difference, % 1.5 ± 1.7 1.4 ± 1.7 2.0 ± 1.7 CGM-Derived Metrics Time-in-range, % 57.8 ± 16.7 52.5 ± 13.8 70.3 ± 16.5 Time-above-range, % 31.5 ± 19.7 36.6 ± 18.2 19.6 ± 18.3 Time-below-range, % 10.7 ± 10.0 10.9 ± 8.9 10.1 ± 12.3 Coefficient of variation, % 39.4 ± 9.2 41.9 ± 8.1 33.3 ± 8.9 CGM adherence ≥7 days, n (%) 93 (95.9) 66 (97.1) 27 (93.1) HbA1c-GMI Discordance Discordance >0.5%, n (%) 81 (83.5) 56 (82.4) 25 (86.2) Primary Outcome: HbA1c-GMI Discordance The average HbA1c was notably higher than the Glucose Management Indicator (8.5% [SD, 1.7%] vs 6.9% [SD, 1.1%]; mean difference, 1.55% [SD, 1.69%]; p 0.5%. HbA1c consistently overestimated glycaemic control compared to CGM-derived metrics (see Fig. 1). The bar chart illustrates the mean HbA1c (8.5 ± 1.7%) versus mean GMI (6.9 ± 1.1%), with error bars indicating standard deviation. This demonstrates a systematic overestimation by HbA1c. This prevalence substantially exceeds prior reports in adults with Type 1 diabetes (30–40%), suggesting age-related or population-specific factors may influence HbA1c-GMI agreement. The magnitude of discordance was similar between diabetes types (Type 2: mean, 2.0% [SD, 1.7%] vs Type 1: mean, 1.4% [SD, 1.7%]; difference, 0.6% [95% CI, -0.2% to 1.4%]; p = 0.08), though the prevalence of discordance was uniformly high in both groups (Type 2: 25 of 29 [86.2%]; Type 1: 56 of 68 [82.4%]; p = 0.63). Agreement Analysis Bland-Altman analysis demonstrated a consistent positive bias, with a mean difference of + 1.55% and 95% limits of agreement ranging from − 1.77% to + 4.87% (see Fig. 2). This bias was stable across different mean glucose levels, showing no proportional bias (r = 0.09; p = 0.38). In cases where patients were discordant, HbA1c tended to overestimate glycemic burden in 78 out of 81 cases (96.3%), while only 3 patients (3.7%) experienced underestimation. The 6.64% width of the 95% limits of agreement (calculated as + 4.87% minus − 1.77%) highlights poor agreement between the measurement methods and underscores the importance of independent assessment rather than using them interchangeably. Scatter plot with x-axis showing mean of (HbA1c + GMI)/2 and y-axis showing difference (HbA1c-GMI). Horizontal lines indicate mean bias (+ 1.55%) and 95% limits of agreement (-1.77% to + 4.87%). Points colored by diabetes type (blue = Type 1, orange = Type 2). Demonstrates systematic positive bias with wide limits of agreement. CGM-Derived Glycaemic Metrics The Mean Time-in-Range was 57.8% (SD, 16.7%), with only 26 of 97 patients (26.8%) reaching the guideline-recommended TIR ≥ 70%. Patients with Type 2 diabetes showed better glycaemic control than those with Type 1 across several CGM metrics: Time-in-Range (average 70.3% vs 52.5%; difference, 17.8% [95% CI, 10.4%-25.2%]; p < 0.001), Time-above-Range (average 19.6% vs 36.6%; difference, -17.0% [95% CI, -24.8% to -9.2%]; p < 0.001), and coefficient of variation (average 33.3% vs 41.9%; difference, -8.6% [95% CI, -12.5% to -4.7%]; p < 0.001). Time-below-Range was similar between groups (average 10.1% vs 10.9%; difference, -0.8% [95% CI, -5.5% to 3.9%]; p = 0.73). Univariable Predictors of Discordance In univariable analyses, none of the demographic, anthropometric, or clinical variables showed significant links with the level of discordance (all p > 0.10) (see Table 2). However, C-peptide levels indicated a trend toward an inverse relationship with discordance; patients with discordance had a mean C-peptide of 0.96 [SD, 1.49] ng/mL, compared to 1.36 [SD, 0.99] ng/mL in those without discordance (Cohen d, -0.31; P = 0.14). Table:2 Univariable and Multi variable Associations Between Clinical Variables and HbA1c-GMI Discordance Magnitude Predictor Discordant (Mean±SD) Non-discordant (Mean±SD) p-value Cohen's d Current Age 14.86±5.45 14.62±6.80 0.953 0.04 Sex 1.58±0.50 1.56±0.51 0.901 0.04 BMI 21.41±8.12 21.03±4.82 0.646 0.06 Duration_DM 5.60±3.70 6.62±5.35 0.685 -0.22 C_peptide 0.96±1.49 1.36±0.99 0.140 -0.31 TDD 46.59±28.38 38.00±19.65 0.349 0.35 TIR 58.47±16.02 54.38±20.13 0.400 0.23 TAR 30.52±19.24 36.44±21.91 0.283 -0.29 TBR 10.97±10.25 9.19±8.66 0.587 0.19 Variability 39.25±9.34 39.89±8.80 0.752 -0.07 Average_glucose 8.42±2.07 9.06±2.52 0.338 -0.28 Predictor β Coefficient 95% CI SE p-value Intercept 0.659 (-0.673, 1.992) 0.833 0.324 Duration_DM 0.000 (-0.099, 0.099) 0.050 0.997 C_peptide -0.344 (-0.679, -0.008) 0.170 0.045 TDD 0.009 (-0.006, 0.023) 0.007 0.233 Type_of_Diabetes 0.820 (-0.227, 1.868) 0.530 0.122 Among CGM-derived metrics, patients with discordance paradoxically demonstrated higher Time-in-Range compared with concordant patients (mean, 58.5% [SD, 16.0%] vs 54.4% [SD, 20.1%]; Cohen d, 0.23; p = 0.40), though this difference did not reach statistical significance. Multivariable Predictors of Discordance In a multivariable linear regression that accounted for diabetes duration, total daily insulin dose, and diabetes type, C-peptide level was an independent predictor of discordance magnitude (adjusted β coefficient of -0.344 per ng/mL increase [95% CI, -0.679 to -0.008]; P = 0.045) (Table 3). A 1 ng/mL increase in C-peptide correlated with a 0.34% decrease in HbA1c-GMI discordance, indicating a meaningful reduction of HbA1c overestimation among patients with preserved residual beta cell function. No other variables showed independent associations (all P > 0.10). The model explained 13.0% of variance in discordance magnitude (R²=0.130; adjusted R²=0.050), indicating modest predictive value. While the overall F-test did not reach conventional significance (F = 1.64; P = 0.18), the individual C-peptide effect remained robust, reflecting biological plausibility despite limited overall variance explained by measured predictors. Multicollinearity diagnostics showed acceptable variance inflation factors for all predictors (Duration_DM: VIF = 2.40; C-peptide: VIF = 2.09; TDD: VIF = 3.72; Type_of_Diabetes: VIF = 5.63), indicating minimal collinearity issues and valid coefficient estimates. The inverse association implies that a progressive loss of endogenous insulin secretion could contribute to the HbA1c-GMI discordance, possibly through mechanisms related to glycaemic variability. Correlation Analyses C-peptide demonstrated strong correlations with CGM-derived glycaemic stability metrics (Time-in-Range: r = 0.549, p < 0.001; coefficient of variation: r=-0.575, p < 0.001). Notably, laboratory-measured HbA1c and CGM-derived GMI exhibited only a modest correlation (r = 0.393, p < 0.001), supporting the hypothesis of systematic measurement discordance rather than random error(Fig:3) HbA1c-GMI discordance magnitude correlated most strongly with absolute HbA1c level (r = 0.839, p < 0.001), suggesting that patients with higher HbA1c experience proportionally greater overestimation of glycaemic burden. Discordance showed weak inverse correlation with C-peptide (r=-0.193, p = 0.06) and no significant correlation with age, BMI, diabetes duration, or treatment intensity (all |r| 0.15). Subgroup and Sensitivity Analyses The prevalence of discordance was similar between Type 1 (56 of 68 [82.4%]) and Type 2 diabetes (25 of 29 [86.2%; P = 0.63]). However, Type 2 patients exhibited a greater magnitude of discordance (mean, 2.0% versus 1.4%; difference, 0.6% [95% CI, -0.2% to 1.4%]; P = 0.08). No significant interaction between C-peptide and diabetes type was observed (P = 0.48), suggesting that the protective effect of residual beta cell function functions similarly across diabetes types. Results remained consistent across three prespecified sensitivity analyses: (1) patients with ≥ 90% CGM adherence (n = 85; discordance 84.7%; C-peptide β=-0.35, P = 0.04); (2) excluding diabetes duration 1.0% threshold (n = 61; discordance 62.9%; C-peptide β=-0.38, P = 0.04). In exploratory analyses, C-peptide stratification revealed dose-response relationships with both discordance magnitude (P for trend = 0.02) and Time-in-Range (P for trend < 0.001). Thirty-one patients (39.7% of those with HbA1c overestimation) demonstrated paradoxical patterns (HbA1c ≥ 8.0% despite Time-in-Range ≥ 60%), with significantly lower C-peptide levels (mean, 0.52 vs 1.24 ng/mL; P = 0.02). Discussion In this prospective cross-sectional study of 97 children and adolescents with diabetes, we found that clinically significant HbA1c-GMI discordance was present in 83.5% of patients, with HbA1c systematically overestimating glycaemic control by a mean of 1.55 percentage points. This prevalence substantially exceeds prior reports from adult cohorts, where discordance rates of 30–50% have been documented.( 9 , 18 – 20 , 32 ) Residual beta cell function, measured by C-peptide, emerged as the sole independent predictor of discordance magnitude, with each 1 ng/mL increase associated with 0.34% reduction in HbA1c overestimation. Bland-Altman analysis confirmed systematic positive bias with wide limits of agreement, indicating that HbA1c and GMI cannot be considered interchangeable measures in paediatric diabetes. These findings have important implications for clinical decision-making and guideline development( 27 , 29 – 30 , 33 ). Our observed prevalence of discordance is significantly higher than what has been reported in adult populations. Perlman and colleagues found HbA1c-GMI differences over 0.5% in only 50% of mostly adult patients with type 1 diabetes, while Fellinger and colleagues reported an average absolute discordance of 0.6% in adults using intermittent scanning CGM. The elevated discordance rate in our paediatric group might result from age-related variations in erythrocyte turnover, haemoglobin glycation rates, or metabolic factors unique to children and adolescents. ( 6 , 18 , 34 ) Consistent with previous studies, we found a systematic overestimation of HbA1c, with a bias toward higher HbA1c values in 96.3% of discordant cases. This trend is especially notable in patients with chronic kidney disease. ( 9 , 19 ) Notably, the average HbA1c in our group (8.5%) generally warrants treatment intensification per current guidelines ( 35 , 36 ), but the average GMI of 6.9% suggests that actual glycaemic control may be significantly better than HbA1c alone indicates( 9 ). Multiple mechanisms might account for the consistent disagreement observed. First, differences in erythrocyte lifespan are a key biological factor contributing to HbA1c variability, regardless of glycaemic exposure. ( 37 ) Second, the negative correlation between C-peptide levels and the degree of discordance indicates that residual beta cell function influences the relationship between mean glucose and haemoglobin glycation. Patients maintaining endogenous insulin production tend to show lower glycaemic variability and spend more time in the target range, both of which are strongly linked to C-peptide levels in our data.( 38 , 39 ) Reduced fluctuations in blood glucose may lessen the impact of post-meal spikes on HbA1c, while the GMI calculation treats all glucose readings equally.( 15 ) Our findings support this, as patients with very low beta cell activity (C-peptide 1.0 ng/mL). Third, the GMI equation, primarily calibrated for adult type 1 diabetes populations, may not fully capture physiological differences in paediatric or type 2 diabetes populations.( 15 ) Our observation of a trend toward more discordance in type 2 diabetes (2.0% versus 1.4%, P = 0.08) supports reports that identify type 2 diabetes as a predictor of HbA1c-GMI deviation. ( 34 ) The clinical importance is high. Notably, 39.7% of patients showed paradoxical discordance patterns—having HbA1c ≥ 8.0% despite good CGM control (time-in-range ≥ 60%). These patients risk unnecessary treatment escalation. Increasing therapy based only on high HbA1c can raise hypoglycaemia risk and treatment complexity without significant glycaemic benefit ( 40 ). Conversely, clinicians who assume that HbA1c equals GMI may miss opportunities for timely intervention in the smaller group in which HbA1c underestimates the glycaemic burden.( 41 ) Current paediatric guidelines emphasise HbA1c targets of < 7.0-7.5% as primary therapeutic goals. ( 35 , 42 ) Our data suggest these HbA1c-based targets require reconsideration in the CGM era. The weak correlation between HbA1c and GMI (r = 0.393) indicates these metrics capture different aspects of glycaemic control, with GMI potentially offering a more accurate reflection of continuous glucose exposure. ( 17 , 32 , 43 ) In practice, our results support the regular assessment of both HbA1c and CGM metrics. When significant disagreement occurs, it's essential to investigate potential causes of HbA1c inaccuracy, such as haemoglobinopathies, anaemia, or haemolysis. In cases of discordance, CGM data should be considered the main reference. Measuring C-peptide can help identify patients most at risk for discrepancies, although the cost-effectiveness of routine testing still needs evaluation. Strengths include blinded, high-adherence CGM, robust methods, and inclusion of both diabetes types across a wide paediatric age range, with sensitivity analyses supporting main findings. Limitations are the cross-sectional design, preventing evaluation of discordance over time, and a CGM period shorter than the HbA1c timeframe, though prior evidence supports our duration. C-peptide was measured in only half the cohort, but results were consistent in sensitivity analyses. Hemoglobinopathies and erythrocyte turnover markers, which may influence HbA1c, were not assessed. Finally, generalizability is limited by the single-centre design. Future directions involve investigating whether discordance predicts complications, whether CGM-based treatment intensification enhances outcomes, and assessing the value of tailored GMI equations or the cost-effectiveness of broader CGM use. Conclusions In this paediatric cohort, HbA1c often overestimates glycaemic control compared to CGM, particularly with lower beta cell function, which questions the equivalence of these measures. Paediatric diabetes management should progressively focus more on CGM-based metrics, and guidelines should be revised to address these discrepancies. Declarations Ethics approval and consent to participate: This study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Ethics approval was granted by the Diabetic Association of Bangladesh Ethics Review Committee (protocol BADAS-ERC/EC/24/16) on 5 March 2024. Written informed consent was obtained from parents or legal guardians of all participants, and assent was secured from children and adolescents in accordance with local regulations. Consent for publication: Not Applicable Availability of data and materials: The datasets used and analyzed during the current study are not publicly available due to institutional policy but are available from the corresponding author on request. Competing interests: The authors declare that they have no competing interests. Funding: This work did not receive any specific funding dedicated to the conduct, analysis, or publication of this study. All authors were employed within the HumAn-1 clinical trial, which is supported by a grant from The Leona M. and Harry B. Helmsley Charitable Trust to Jing Luo at the University of Pittsburgh. As part of this trial, A.H. Flabe was granted permission to use additional continuous glucose monitoring (CGM) sensors for this independent study; the CGM devices used were therefore provided through the HumAn-1 trial. No other funds were provided for this secondary analysis, and the funder had no role in the study design, data collection specific to this analysis, data analysis, interpretation of results, decision to submit the manuscript, or preparation of the manuscript. Authors’ contributions: AHF conceived and designed the study, performed the statistical analyses, interpreted the data, and drafted the manuscript. BZ supervised the project, provided clinical oversight, and validated the analyses. NKP and SA collected and curated the clinical and CGM data. JL provided methodological input, resources, and overall guidance on study design and analysis. KH and AF provided administrative and logistical support for study implementation. All authors critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work. Acknowledgements: We acknowledge the University of Pittsburgh for approving the use of additional continuous glucose monitoring (CGM) devices and resources from the HumAn-1 trial (https://doi.org/10.1136/bmjopen-2024-092432) for this study. We also thank Life for a Child for supporting patient care and providing insulin assistance. The author is grateful to the Foreign, Commonwealth & Development Office (FCDO) of the UK Government for awarding the Chevening Scholarship, which enriched her research knowledge and skills in the United Kingdom. Authors’ information: AHF is a physician and clinical researcher who served as the clinical trial coordinator for the HumAn-1 trial at the BADAS Paediatric Diabetes Care & Research Centre (PDRC), Dhaka, Bangladesh, with a focus on continuous glucose monitoring and the management of type 1 diabetes in youth. In parallel with this role, AHF developed this independent study under formal institutional approval, reflecting her interest in improving the interpretation of CGM metrics and HbA1c in paediatric diabetes care. During the later stages of this work, AHF commenced an MSc in Advanced Paediatrics and Child Health at University College London as a recipient of the Chevening Scholarship, continuing to work on this manuscript alongside her studies as a demonstration of her long-term commitment to children and young people living with diabetes. Generative AI disclosure: AI tools helped with language editing, abstract formatting, and manuscript organization. The authors remain fully responsible for study design, data collection, statistical analysis, interpretation of results, and all scientific and clinical conclusions. AI tools were not used for data analysis, interpreting findings, or clinical decision-making. References Hypoglycemia in the Diabetes Control and Complications Trial. 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Diabetes Technol Ther. 2023;25(S3):S–74. de Bock M, Codner E, Craig ME, Huynh T, Maahs DM, Mahmud FH, et al. ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes. Pediatr Diabetes. 2022;23(8):1270–6. Wang D, Wang D, Rooney MR, Allison MA, Coresh J, Nisha Aurora R et al. Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes. 2023 Feb 4 [cited 2025 Jul 9];69(4):422–8. Available from: https://academic.oup.com/clinchem/article-abstract/69/4/422/7026054 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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01:13:29","extension":"xml","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":122893,"visible":true,"origin":"","legend":"","description":"","filename":"bcd03c29da67484bbfabd857dc881cc31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/75e68658ef70210248e52bb0.xml"},{"id":99259796,"identity":"f34a7006-b566-4201-a575-bad4dc791384","added_by":"auto","created_at":"2025-12-31 01:13:29","extension":"html","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136370,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/9631951f1eef269319c79b9b.html"},{"id":99319545,"identity":"8439991e-f21e-4abc-bd6a-927fe46f6164","added_by":"auto","created_at":"2025-12-31 16:37:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136630,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Laboratory-Measured HbA1c and Continuous Glucose Monitoring-Derived Glucose Management Indicator (GMI) in Pediatric Diabetes\u003c/p\u003e","description":"","filename":"Figure1HbA1cGMIDiscordanceEvidenceforCGMMetricsSuperiorAccuracyn97.png","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/d495dae1fb3bd3d36256b973.png"},{"id":99319768,"identity":"4ecb13e5-5985-4eca-bef3-772661ba52ac","added_by":"auto","created_at":"2025-12-31 16:37:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":197852,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman Plot: Agreement Between HbA1c and Glucose Management Indicator\u003cbr\u003e\nScatter plot with x-axis showing mean of (HbA1c+GMI)/2 and y-axis showing difference (HbA1c-GMI). Horizontal lines indicate mean bias (+1.55%) and 95% limits of agreement (-1.77% to +4.87%). Points colored by diabetes type (blue=Type 1, orange=Type 2). Demonstrates systematic positive bias with wide limits of agreement.\u003c/p\u003e","description":"","filename":"Fig2BlandAltmanPlotHbA1cvsGMIAgreementAnalysisn97patients.png","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/6e10d7bfd56ee2c7d9330007.png"},{"id":99259767,"identity":"54004295-39f9-4090-8ba9-e65a392d5964","added_by":"auto","created_at":"2025-12-31 01:13:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":455133,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Matrix: Relationships Between Clinical, Anthropometric, and Glycaemic Variables Heatmap showing Pearson correlations.\u003c/p\u003e","description":"","filename":"Fig3CorrelationMatrixRelationshipsBetweenClinicalandGlycemicVariablesn97.png","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/d4c2d68e771d21bf27bf5b07.png"},{"id":103507256,"identity":"1cdca2ae-01ff-41c4-9ff9-45e418973a61","added_by":"auto","created_at":"2026-02-26 13:40:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1337074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/330d5832-bf75-4acb-b6b2-54371dad1761.pdf"},{"id":99318469,"identity":"1e8e9763-32fb-4146-904a-a8f575a4032d","added_by":"auto","created_at":"2025-12-31 16:33:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8356318/v1/daa05896a818eaf88bae1f02.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSystematic Discordance Between HbA1c and Continuous Glucose Monitoring-Derived Glycaemic Metrics in Paediatric Diabetes: Implications for Treatment Decisions\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e Glycated haemoglobin (HbA1c) remains the gold standard for monitoring glycaemic control in diabetes, supported by pivotal trials and incorporated into major guidelines, including targets below 7.0% for most children and adolescents.(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) However, HbA1c is subject to biological limitations. Factors such as haemoglobinopathies, haemolytic anaemia, kidney disease, and, notably, variations in erythrocyte lifespan can cause significant discrepancies independent of actual glucose levels, potentially leading to systematic over- or underestimation of glycaemic control.(\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe introduction of continuous glucose monitoring (CGM) has revolutionised diabetes care by enabling real-time assessment of glucose levels and enhancing clinical outcomes, particularly in paediatric patients.(\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) The Glucose Management Indicator (GMI), calculated from CGM-derived average glucose, was developed to align with laboratory HbA1c and is now endorsed as a key glycaemic metric in consensus guidelines, on the assumption that it is largely interchangeable with HbA1c when data are sufficient.(\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, recent studies in adults have questioned this assumption, showing 30\u0026ndash;50% discordance between HbA1c and GMI and indicating that HbA1c may systematically overestimate the actual glycaemic burden.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eDespite these findings, the discrepancy between HbA1c and GMI in paediatric diabetes remains poorly understood. Most existing research is limited and focuses on homogeneous, resource-rich groups. (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) This lack of comprehensive paediatric data has significant clinical implications. Systematic overestimation of HbA1c might lead to unnecessary treatment escalation and a higher risk of hypoglycaemia, or it could mask inadequate glycaemic control. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Residual beta-cell function, as indicated by C-peptide levels, may contribute to this discrepancy, given its known associations with reduced glycaemic variability and improved time-in-range (\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, this connection has not been thoroughly explored in children. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThis prospective cross-sectional study aimed to assess the prevalence and factors influencing significant HbA1c-GMI discordance in a diverse group of paediatric diabetes patients. We hypothesised that such discordance would be frequent and that residual beta cell function, measured by C-peptide, would serve as an independent predictor even after accounting for other variables. Understanding these mechanisms is expected to enhance diabetes management techniques and guide future monitoring guidelines.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis prospective cross-sectional study was conducted at BADAS (Bangladesh Diabetic Association) Paediatric Diabetes Care \u0026amp; Research Centre, Dhaka, Bangladesh, from March 2024 to November 2025. The study received approval from the institutional ethics committee of the Diabetic Association of Bangladesh (Protocol BADAS-ERC/EC/24/16), and written informed consent or assent was obtained from all participants and their legal guardians. The study adhered to Good Clinical Practice guidelines and the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003ePatients were randomly selected from routine paediatric diabetes clinic visits. Eligible participants were children and adolescents aged 5\u0026ndash;25 years with established Type 1 or Type 2 diabetes mellitus (diagnosed\u0026thinsp;\u0026ge;\u0026thinsp;6 months) receiving diabetes treatment. Exclusion criteria included active diabetic ketoacidosis, known hemoglobinopathy or chronic haemolytic anaemia, severe chronic kidney disease, pregnancy, CGM device malfunction (data capture\u0026thinsp;\u0026lt;\u0026thinsp;70%), or absence of concurrent HbA1c measurement within 30 days of CGM monitoring.\u003c/p\u003e \u003cp\u003eOf 120 patients enrolled, 23 were excluded due to the absence of concurrent HbA1c measurements, resulting in a final analysis cohort of 97 patients (68 with Type 1 diabetes, 29 with Type 2 diabetes).\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eBaseline demographic, anthropometric, and clinical data were extracted from paper medical records. Height and weight were measured using calibrated equipment; body mass index was calculated as weight (kg) divided by height squared (m\u0026sup2;). Information on diabetes duration, treatment regimen, total daily insulin dose, and recent HbA1c and fasting C-peptide measurements (when available) was recorded.\u003c/p\u003e \u003cp\u003eAll participants wore the Abbott Freestyle Libre Pro iQ blinded continuous glucose monitoring (CGM) system for 7\u0026ndash;14 days. The device measures interstitial glucose every 15 minutes using a subcutaneous sensor. CGM-derived metrics were taken from downloaded data Glucose Management Indicator (GMI, estimated HbA1c from mean glucose using the formula GMI\u0026thinsp;=\u0026thinsp;3.31\u0026thinsp;+\u0026thinsp;0.02392 \u0026times; [mean glucose in mg/dL]), Time-in-Range (TIR, percentage of time 70\u0026ndash;180 mg/dL), Time-above-Range (TAR, \u0026gt;\u0026thinsp;180 mg/dL), Time-below-Range (TBR, \u0026lt;\u0026thinsp;70 mg/dL), and coefficient of variation (CV, SD/mean \u0026times; 100).\u003c/p\u003e \u003cp\u003e HbA1c was measured using high-performance liquid chromatography certified by the National Health Guidelines. C-peptide was measured using a chemiluminescent immunoassay (normal range 0.5-3.0 ng/mL) in a subset of participants.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was HbA1c-GMI discordance, defined as an absolute difference greater than 0.5% between laboratory HbA1c and CGM-derived GMI. This threshold was set beforehand based on the minimal clinically important difference for HbA1c. Discordance magnitude was calculated as HbA1c (%) minus GMI (%), with positive values indicating HbA1c overestimation.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eAlthough a formal sample size calculation was not conducted for this exploratory analysis, post hoc power analysis showed sufficient power (\u0026gt;\u0026thinsp;80%) to identify medium-to-large effect sizes. Continuous variables are presented as mean (SD) or median (interquartile range) where appropriate, and categorical variables as number (percentage). Baseline characteristics were compared between diabetes types using t tests, Mann-Whitney U tests, or χ\u0026sup2; tests as suitable.\u003c/p\u003e \u003cp\u003eAgreement between HbA1c and GMI was assessed using Bland-Altman methodology, calculating mean bias and 95% limits of agreement. We tested for proportional bias by examining the correlation between mean and difference of measurements.\u003c/p\u003e \u003cp\u003eIn univariable analyses, we assessed associations between potential predictors and discordance magnitude using Mann-Whitney U tests and Spearman correlations. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.20 were considered for multivariable modelling. The multivariable linear regression initially included age, body mass index, Time-in-Range, and glucose variability, but significant multicollinearity was observed (variance inflation factors\u0026thinsp;\u0026gt;\u0026thinsp;20). We thus built a streamlined model including four predictors with acceptable collinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;6): diabetes duration, C-peptide, total daily insulin dose, and diabetes type. Model assumptions were verified using diagnostic plots, and fit was evaluated with R\u0026sup2; and adjusted R\u0026sup2;.\u003c/p\u003e \u003cp\u003eOur analytical plan involved prespecified subgroup analyses based on diabetes type, testing for interactions related to C-peptide effects, and conducting three sensitivity analyses: one restricting to patients with at least 90% CGM adherence, another excluding those with less than 1 year of diabetes, and a third using a discordance threshold greater than 1.0%. Additionally, exploratory analyses focused on C-peptide tertiles and identified paradoxical patterns where HbA1c remained\u0026thinsp;\u0026ge;\u0026thinsp;8.0% despite Time-in-Range being \u0026ge;\u0026thinsp;60%. These exploratory results were not adjusted for multiple comparisons and are intended to generate hypotheses.\u003c/p\u003e \u003cp\u003eMissing data were minimal (\u0026lt;\u0026thinsp;10% except C-peptide [52.5%]). We used complete-case analysis for regression models. Patients with and without C-peptide measurements did not differ in demographics or outcomes (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.70), indicating missingness was random. All tests were two-sided with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant. Analyses utilised Python version 3.11.0 (scipy, statsmodels, scikit-learn packages). The study adhered to STROBE guidelines; a completed checklist is provided in the Supplement. Detailed statistical methods are in eMethods.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eStudy Population\u003c/h2\u003e\n \u003cp\u003eFrom March 2024 to November 2025, a total of 120 paediatric diabetes patients underwent continuous glucose monitoring (CGM) as part of routine care. After excluding 23 patients lacking concurrent HbA1c data, 97 patients were analysed. The average age was 14.8 years (SD, 5.7), with 56 (57.7%) female participants. Among them, 68 (70.1%) had Type 1 diabetes, and 29 (29.9%) had Type 2 diabetes. A high adherence rate was observed, with 93 patients (95.9%) monitoring with CGM for at least 7 days.\u003c/p\u003e\n \u003cp\u003eTable 1 shows the baseline characteristics. Patients with Type 2 diabetes were older (average age of 19.0 vs. 13.1 years; p\u0026lt;0.001), had a higher body mass index (average BMI of 26.7 vs. 19.0 kg/m²; p\u0026lt;0.001), and exhibited greater residual beta cell function (average C-peptide of 2.3 vs. 0.4 ng/mL; p\u0026lt;0.001) compared to patients with Type 1 diabetes.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\u003c/table\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable:1 Baseline Characteristics of Study Cohort (n=97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (n=97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType 1 DM (n=68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eType 2 DM (n=29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.8 ± 5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.1 ± 5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.0 ± 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38 (55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnthropometric Measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m²\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.4 ± 7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.0 ± 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.7 ± 10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClinical Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiabetes duration, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.8 ± 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.4 ± 3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6 ± 4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC-peptide, ng/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0 ± 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4 ± 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.3 ± 1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal daily dose, units\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.1 ± 27.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.1 ± 23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.6 ± 35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGlycemic Control Metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.5 ± 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.6 ± 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.3 ± 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGMI, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.9 ± 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2 ± 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.3 ± 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c-GMI difference, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5 ± 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.4 ± 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0 ± 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCGM-Derived Metrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime-in-range, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.8 ± 16.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.5 ± 13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e70.3 ± 16.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime-above-range, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.5 ± 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.6 ± 18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19.6 ± 18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTime-below-range, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.7 ± 10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.9 ± 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.1 ± 12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient of variation, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.4 ± 9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.9 ± 8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.3 ± 8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCGM adherence ≥7 days, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66 (97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27 (93.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHbA1c-GMI Discordance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDiscordance \u0026gt;0.5%, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e81 (83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e56 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003ePrimary Outcome: HbA1c-GMI Discordance\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe average HbA1c was notably higher than the Glucose Management Indicator (8.5% [SD, 1.7%] vs 6.9% [SD, 1.1%]; mean difference, 1.55% [SD, 1.69%]; p \u0026lt; 0.001). A total of 81 patients (83.5%) showed clinically significant discordance, defined as an absolute HbA1c-GMI difference \u0026gt; 0.5%. HbA1c consistently overestimated glycaemic control compared to CGM-derived metrics (see Fig. 1). The bar chart illustrates the mean HbA1c (8.5 ± 1.7%) versus mean GMI (6.9 ± 1.1%), with error bars indicating standard deviation. This demonstrates a systematic overestimation by HbA1c.\u003c/p\u003e\n\u003cp\u003eThis prevalence substantially exceeds prior reports in adults with Type 1 diabetes (30–40%), suggesting age-related or population-specific factors may influence HbA1c-GMI agreement.\u003c/p\u003e\n\u003cp\u003eThe magnitude of discordance was similar between diabetes types (Type 2: mean, 2.0% [SD, 1.7%] vs Type 1: mean, 1.4% [SD, 1.7%]; difference, 0.6% [95% CI, -0.2% to 1.4%]; p = 0.08), though the prevalence of discordance was uniformly high in both groups (Type 2: 25 of 29 [86.2%]; Type 1: 56 of 68 [82.4%]; p = 0.63).\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eAgreement Analysis\u003c/h2\u003e\n \u003cp\u003eBland-Altman analysis demonstrated a consistent positive bias, with a mean difference of + 1.55% and 95% limits of agreement ranging from − 1.77% to + 4.87% (see Fig. 2). This bias was stable across different mean glucose levels, showing no proportional bias (r = 0.09; p = 0.38). In cases where patients were discordant, HbA1c tended to overestimate glycemic burden in 78 out of 81 cases (96.3%), while only 3 patients (3.7%) experienced underestimation. The 6.64% width of the 95% limits of agreement (calculated as + 4.87% minus − 1.77%) highlights poor agreement between the measurement methods and underscores the importance of independent assessment rather than using them interchangeably.\u003c/p\u003e\n \u003cp\u003eScatter plot with x-axis showing mean of (HbA1c + GMI)/2 and y-axis showing difference (HbA1c-GMI). Horizontal lines indicate mean bias (+ 1.55%) and 95% limits of agreement (-1.77% to + 4.87%). Points colored by diabetes type (blue = Type 1, orange = Type 2). Demonstrates systematic positive bias with wide limits of agreement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003eCGM-Derived Glycaemic Metrics\u003c/h2\u003e\n \u003cp\u003eThe Mean Time-in-Range was 57.8% (SD, 16.7%), with only 26 of 97 patients (26.8%) reaching the guideline-recommended TIR ≥ 70%. Patients with Type 2 diabetes showed better glycaemic control than those with Type 1 across several CGM metrics: Time-in-Range (average 70.3% vs 52.5%; difference, 17.8% [95% CI, 10.4%-25.2%]; p \u0026lt; 0.001), Time-above-Range (average 19.6% vs 36.6%; difference, -17.0% [95% CI, -24.8% to -9.2%]; p \u0026lt; 0.001), and coefficient of variation (average 33.3% vs 41.9%; difference, -8.6% [95% CI, -12.5% to -4.7%]; p \u0026lt; 0.001). Time-below-Range was similar between groups (average 10.1% vs 10.9%; difference, -0.8% [95% CI, -5.5% to 3.9%]; p = 0.73).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eUnivariable Predictors of Discordance\u003c/h2\u003e\n \u003cp\u003eIn univariable analyses, none of the demographic, anthropometric, or clinical variables showed significant links with the level of discordance (all p \u0026gt; 0.10) (see Table 2). However, C-peptide levels indicated a trend toward an inverse relationship with discordance; patients with discordance had a mean C-peptide of 0.96 [SD, 1.49] ng/mL, compared to 1.36 [SD, 0.99] ng/mL in those without discordance (Cohen d, -0.31; P = 0.14). \u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable:2 Univariable and Multi variable Associations Between Clinical Variables and HbA1c-GMI Discordance Magnitude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDiscordant (Mean±SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNon-discordant (Mean±SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCohen's d\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCurrent Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.86±5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.62±6.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.58±0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.56±0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.41±8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.03±4.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration_DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.60±3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.62±5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC_peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96±1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36±0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.59±28.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.00±19.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.47±16.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.38±20.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.52±19.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.44±21.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTBR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.97±10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.19±8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.25±9.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.89±8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage_glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.42±2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.06±2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eβ Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-0.673, 1.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDuration_DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-0.099, 0.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eC_peptide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-0.679, -0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-0.006, 0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eType_of_Diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e(-0.227, 1.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eAmong CGM-derived metrics, patients with discordance paradoxically demonstrated higher Time-in-Range compared with concordant patients (mean, 58.5% [SD, 16.0%] vs 54.4% [SD, 20.1%]; Cohen d, 0.23; p = 0.40), though this difference did not reach statistical significance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eMultivariable Predictors of Discordance\u003c/h2\u003e\n \u003cp\u003eIn a multivariable linear regression that accounted for diabetes duration, total daily insulin dose, and diabetes type, C-peptide level was an independent predictor of discordance magnitude (adjusted β coefficient of -0.344 per ng/mL increase [95% CI, -0.679 to -0.008]; P = 0.045) (Table\u0026nbsp;3). A 1 ng/mL increase in C-peptide correlated with a 0.34% decrease in HbA1c-GMI discordance, indicating a meaningful reduction of HbA1c overestimation among patients with preserved residual beta cell function. No other variables showed independent associations (all P \u0026gt; 0.10).\u003c/p\u003e\n \u003cp\u003eThe model explained 13.0% of variance in discordance magnitude (R²=0.130; adjusted R²=0.050), indicating modest predictive value. While the overall F-test did not reach conventional significance (F = 1.64; P = 0.18), the individual C-peptide effect remained robust, reflecting biological plausibility despite limited overall variance explained by measured predictors.\u003c/p\u003e\n \u003cp\u003eMulticollinearity diagnostics showed acceptable variance inflation factors for all predictors (Duration_DM: VIF = 2.40; C-peptide: VIF = 2.09; TDD: VIF = 3.72; Type_of_Diabetes: VIF = 5.63), indicating minimal collinearity issues and valid coefficient estimates. The inverse association implies that a progressive loss of endogenous insulin secretion could contribute to the HbA1c-GMI discordance, possibly through mechanisms related to glycaemic variability.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eCorrelation Analyses\u003c/h2\u003e\n \u003cp\u003eC-peptide demonstrated strong correlations with CGM-derived glycaemic stability metrics (Time-in-Range: r = 0.549, p \u0026lt; 0.001; coefficient of variation: r=-0.575, p \u0026lt; 0.001). Notably, laboratory-measured HbA1c and CGM-derived GMI exhibited only a modest correlation (r = 0.393, p \u0026lt; 0.001), supporting the hypothesis of systematic measurement discordance rather than random error(Fig:3)\u003c/p\u003e\n \u003cp\u003eHbA1c-GMI discordance magnitude correlated most strongly with absolute HbA1c level (r = 0.839, p \u0026lt; 0.001), suggesting that patients with higher HbA1c experience proportionally greater overestimation of glycaemic burden. Discordance showed weak inverse correlation with C-peptide (r=-0.193, p = 0.06) and no significant correlation with age, BMI, diabetes duration, or treatment intensity (all |r|\u0026lt;0.15, p \u0026gt; 0.15).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eSubgroup and Sensitivity Analyses\u003c/h2\u003e\n \u003cp\u003eThe prevalence of discordance was similar between Type 1 (56 of 68 [82.4%]) and Type 2 diabetes (25 of 29 [86.2%; P = 0.63]). However, Type 2 patients exhibited a greater magnitude of discordance (mean, 2.0% versus 1.4%; difference, 0.6% [95% CI, -0.2% to 1.4%]; P = 0.08). No significant interaction between C-peptide and diabetes type was observed (P = 0.48), suggesting that the protective effect of residual beta cell function functions similarly across diabetes types.\u003c/p\u003e\n \u003cp\u003eResults remained consistent across three prespecified sensitivity analyses: (1) patients with ≥ 90% CGM adherence (n = 85; discordance 84.7%; C-peptide β=-0.35, P = 0.04); (2) excluding diabetes duration \u0026lt; 1 year (n = 91; discordance 83.5%; C-peptide β=-0.33, P = 0.046); and (3) using a stringent \u0026gt; 1.0% threshold (n = 61; discordance 62.9%; C-peptide β=-0.38, P = 0.04).\u003c/p\u003e\n \u003cp\u003eIn exploratory analyses, C-peptide stratification revealed dose-response relationships with both discordance magnitude (P for trend = 0.02) and Time-in-Range (P for trend \u0026lt; 0.001). Thirty-one patients (39.7% of those with HbA1c overestimation) demonstrated paradoxical patterns (HbA1c ≥ 8.0% despite Time-in-Range ≥ 60%), with significantly lower C-peptide levels (mean, 0.52 vs 1.24 ng/mL; P = 0.02).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cross-sectional study of 97 children and adolescents with diabetes, we found that clinically significant HbA1c-GMI discordance was present in 83.5% of patients, with HbA1c systematically overestimating glycaemic control by a mean of 1.55 percentage points. This prevalence substantially exceeds prior reports from adult cohorts, where discordance rates of 30\u0026ndash;50% have been documented.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Residual beta cell function, measured by C-peptide, emerged as the sole independent predictor of discordance magnitude, with each 1 ng/mL increase associated with 0.34% reduction in HbA1c overestimation. Bland-Altman analysis confirmed systematic positive bias with wide limits of agreement, indicating that HbA1c and GMI cannot be considered interchangeable measures in paediatric diabetes. These findings have important implications for clinical decision-making and guideline development(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur observed prevalence of discordance is significantly higher than what has been reported in adult populations. Perlman and colleagues found HbA1c-GMI differences over 0.5% in only 50% of mostly adult patients with type 1 diabetes, while Fellinger and colleagues reported an average absolute discordance of 0.6% in adults using intermittent scanning CGM. The elevated discordance rate in our paediatric group might result from age-related variations in erythrocyte turnover, haemoglobin glycation rates, or metabolic factors unique to children and adolescents. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eConsistent with previous studies, we found a systematic overestimation of HbA1c, with a bias toward higher HbA1c values in 96.3% of discordant cases. This trend is especially notable in patients with chronic kidney disease. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Notably, the average HbA1c in our group (8.5%) generally warrants treatment intensification per current guidelines (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), but the average GMI of 6.9% suggests that actual glycaemic control may be significantly better than HbA1c alone indicates(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultiple mechanisms might account for the consistent disagreement observed. First, differences in erythrocyte lifespan are a key biological factor contributing to HbA1c variability, regardless of glycaemic exposure. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) Second, the negative correlation between C-peptide levels and the degree of discordance indicates that residual beta cell function influences the relationship between mean glucose and haemoglobin glycation. Patients maintaining endogenous insulin production tend to show lower glycaemic variability and spend more time in the target range, both of which are strongly linked to C-peptide levels in our data.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) Reduced fluctuations in blood glucose may lessen the impact of post-meal spikes on HbA1c, while the GMI calculation treats all glucose readings equally.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Our findings support this, as patients with very low beta cell activity (C-peptide\u0026thinsp;\u0026lt;\u0026thinsp;0.2 ng/mL) showed 2.4 times higher discordance compared to those with preserved function (C-peptide\u0026thinsp;\u0026gt;\u0026thinsp;1.0 ng/mL).\u003c/p\u003e \u003cp\u003eThird, the GMI equation, primarily calibrated for adult type 1 diabetes populations, may not fully capture physiological differences in paediatric or type 2 diabetes populations.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Our observation of a trend toward more discordance in type 2 diabetes (2.0% versus 1.4%, P\u0026thinsp;=\u0026thinsp;0.08) supports reports that identify type 2 diabetes as a predictor of HbA1c-GMI deviation. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe clinical importance is high. Notably, 39.7% of patients showed paradoxical discordance patterns\u0026mdash;having HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;8.0% despite good CGM control (time-in-range\u0026thinsp;\u0026ge;\u0026thinsp;60%). These patients risk unnecessary treatment escalation. Increasing therapy based only on high HbA1c can raise hypoglycaemia risk and treatment complexity without significant glycaemic benefit (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Conversely, clinicians who assume that HbA1c equals GMI may miss opportunities for timely intervention in the smaller group in which HbA1c underestimates the glycaemic burden.(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCurrent paediatric guidelines emphasise HbA1c targets of \u0026lt;\u0026thinsp;7.0-7.5% as primary therapeutic goals. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) Our data suggest these HbA1c-based targets require reconsideration in the CGM era. The weak correlation between HbA1c and GMI (r\u0026thinsp;=\u0026thinsp;0.393) indicates these metrics capture different aspects of glycaemic control, with GMI potentially offering a more accurate reflection of continuous glucose exposure. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn practice, our results support the regular assessment of both HbA1c and CGM metrics. When significant disagreement occurs, it's essential to investigate potential causes of HbA1c inaccuracy, such as haemoglobinopathies, anaemia, or haemolysis. In cases of discordance, CGM data should be considered the main reference. Measuring C-peptide can help identify patients most at risk for discrepancies, although the cost-effectiveness of routine testing still needs evaluation.\u003c/p\u003e \u003cp\u003eStrengths include blinded, high-adherence CGM, robust methods, and inclusion of both diabetes types across a wide paediatric age range, with sensitivity analyses supporting main findings. Limitations are the cross-sectional design, preventing evaluation of discordance over time, and a CGM period shorter than the HbA1c timeframe, though prior evidence supports our duration. C-peptide was measured in only half the cohort, but results were consistent in sensitivity analyses. Hemoglobinopathies and erythrocyte turnover markers, which may influence HbA1c, were not assessed. Finally, generalizability is limited by the single-centre design.\u003c/p\u003e \u003cp\u003eFuture directions involve investigating whether discordance predicts complications, whether CGM-based treatment intensification enhances outcomes, and assessing the value of tailored GMI equations or the cost-effectiveness of broader CGM use.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this paediatric cohort, HbA1c often overestimates glycaemic control compared to CGM, particularly with lower beta cell function, which questions the equivalence of these measures. Paediatric diabetes management should progressively focus more on CGM-based metrics, and guidelines should be revised to address these discrepancies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThis study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. Ethics approval was granted by the Diabetic Association of Bangladesh Ethics Review Committee (protocol BADAS-ERC/EC/24/16) on 5 March 2024. Written informed consent was obtained from parents or legal guardians of all participants, and assent was secured from children and adolescents in accordance with local regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study are not publicly available due to institutional policy but are available from the corresponding author on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work did not receive any specific funding dedicated to the conduct, analysis, or publication of this study. All authors were employed within the HumAn-1 clinical trial, which is supported by a grant from The Leona M. and Harry B. Helmsley Charitable Trust to Jing Luo at the University of Pittsburgh. As part of this trial, A.H. Flabe was granted permission to use additional continuous glucose monitoring (CGM) sensors for this independent study; the CGM devices used were therefore provided through the HumAn-1 trial. No other funds were provided for this secondary analysis, and the funder had no role in the study design, data collection specific to this analysis, data analysis, interpretation of results, decision to submit the manuscript, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eAHF conceived and designed the study, performed the statistical analyses, interpreted the data, and drafted the manuscript. BZ supervised the project, provided clinical oversight, and validated the analyses. NKP and SA collected and curated the clinical and CGM data. JL provided methodological input, resources, and overall guidance on study design and analysis. KH and AF provided administrative and logistical support for study implementation. All authors critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003eWe acknowledge the University of Pittsburgh for approving the use of additional continuous glucose monitoring (CGM) devices and resources from the HumAn-1 trial (https://doi.org/10.1136/bmjopen-2024-092432) for this study. We also thank Life for a Child for supporting patient care and providing insulin assistance. The author is grateful to the Foreign, Commonwealth \u0026amp; Development Office (FCDO) of the UK Government for awarding the Chevening Scholarship, which enriched her research knowledge and skills in the United Kingdom.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAHF is a physician and clinical researcher who served as the clinical trial coordinator for the HumAn-1 trial at the BADAS Paediatric Diabetes Care \u0026amp; Research Centre (PDRC), Dhaka, Bangladesh, with a focus on continuous glucose monitoring and the management of type 1 diabetes in youth. In parallel with this role, AHF developed this independent study under formal institutional approval, reflecting her interest in improving the interpretation of CGM metrics and HbA1c in paediatric diabetes care. During the later stages of this work, AHF commenced an MSc in Advanced Paediatrics and Child Health at University College London as a recipient of the Chevening Scholarship, continuing to work on this manuscript alongside her studies as a demonstration of her long-term commitment to children and young people living with diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eGenerative AI disclosure:\u0026nbsp;\u003c/strong\u003eAI tools helped with language editing, abstract formatting, and manuscript organization. The authors remain fully responsible for study design, data collection, statistical analysis, interpretation of results, and all scientific and clinical conclusions. AI tools were not used for data analysis, interpreting findings, or clinical decision-making.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHypoglycemia in the Diabetes Control and Complications Trial. 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Residual C-peptide secretion is associated with better CGM-metrics in adults with short-lasting type 1 diabetes. Diabetes Res Clin Pract. 2025;221:112006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrahalad P, Yang J, Scheinker D, Desai M, Hood K, Maahs DM. Hemoglobin A1c Trajectory in Pediatric Patients with Newly Diagnosed Type 1 Diabetes. Diabetes Technol Ther. 2019;21(8):456\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn T, Xu Y, Bergenstal RM, Ogawa W, Ramzi Ajjan. Personalized Glycated Hemoglobin in Diabetes Management: Closing the Gap with Glucose Management Indicator. Diabetes Technol Ther. 2023;25(S3):S\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bock M, Codner E, Craig ME, Huynh T, Maahs DM, Mahmud FH, et al. ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes. Pediatr Diabetes. 2022;23(8):1270\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Wang D, Rooney MR, Allison MA, Coresh J, Nisha Aurora R et al. Performance of the Glucose Management Indicator (GMI) in Type 2 Diabetes. 2023 Feb 4 [cited 2025 Jul 9];69(4):422\u0026ndash;8. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://academic.oup.com/clinchem/article-abstract/69/4/422/7026054\u003c/span\u003e\u003cspan address=\"https://academic.oup.com/clinchem/article-abstract/69/4/422/7026054\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8356318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8356318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eHbA1c guides paediatric diabetes treatment, but biological factors like erythrocyte lifespan cause differences between HbA1c and CGM measures, risking overtreatment, undertreatment, and misclassification. This study quantifies HbA1c\u0026ndash;GMI discordance in children, exploring clinical and biological predictors, including residual beta cell function via C-peptide.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eIn this prospective cross-sectional study at a paediatric diabetes centre in Dhaka, Bangladesh, 120 youths aged 5\u0026ndash;25 years with type 1 or type 2 diabetes (duration\u0026thinsp;\u0026ge;\u0026thinsp;6 months) were enrolled between March 2024 and November 2025. Participants underwent 7\u0026ndash;14 days of blinded CGM with the Abbott FreeStyle Libre Pro iQ system, and concurrent laboratory HbA1c; fasting C-peptide was measured in a subset, and demographic, clinical, and treatment data were abstracted from medical records. The primary outcome was HbA1c\u0026ndash;GMI discordance, defined \u0026ldquo;a priori\u0026rdquo; as an absolute difference greater than 0.5 percentage points; agreement was assessed with Bland\u0026ndash;Altman analysis, and multivariable linear regression examined associations between discordance magnitude and diabetes duration, C-peptide, total daily insulin dose, and diabetes type.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOut of 120 enrolled patients, 97 with concurrent HbA1c and sufficient CGM data were included in the analysis (mean age 14.8 years; 57.7% female; 70.1% with type 1 diabetes). HbA1c and GMI showed only a modest correlation, and a significant proportion of participants met the predefined discordance threshold. Bland\u0026ndash;Altman plots revealed a systematic positive bias, with HbA1c generally exceeding GMI. The mean cohort HbA1c was notably higher than the mean GMI, suggesting that many youths would be classified as poorly controlled and considered for intensification based on HbA1c alone, despite CGM indicating closer-to-target glycaemia; lower C-peptide levels were independently associated with greater discordance magnitude after adjusting for clinical covariates.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eIn this paediatric diabetes cohort, HbA1c often overestimated glycaemic burden compared to CGM-derived GMI, especially among young people with reduced residual beta cell function. This challenges the assumption that these metrics are interchangeable. Using CGM measures alongside HbA1c and considering discordance patterns\u0026mdash;possibly guided by C-peptide\u0026mdash;can prevent unnecessary treatment increases and promote more personalised, physiology-informed management.\u003c/p\u003e","manuscriptTitle":"Systematic Discordance Between HbA1c and Continuous Glucose Monitoring-Derived Glycaemic Metrics in Paediatric Diabetes: Implications for Treatment Decisions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 01:13:23","doi":"10.21203/rs.3.rs-8356318/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa518844-a82e-439c-b555-0050aac53aa7","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-25T05:40:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 01:13:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8356318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8356318","identity":"rs-8356318","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}