Bridging the Gap Between Fructosamine and HbA1c: Development and Validation of an 8-Week Glycemic Index in Pediatric Type 1 Diabetes

Bridging the Gap Between Fructosamine and HbA1c: Development and Validation of an 8-Week Glycemic Index in Pediatric Type 1 Diabetes | 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 Bridging the Gap Between Fructosamine and HbA1c: Development and Validation of an 8-Week Glycemic Index in Pediatric Type 1 Diabetes Luís Jesuíno de Oliveira Andrade, Gabriela Correia Matos de Oliveira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9697395/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 Objective To develop and validate a standardized glycemic biomarker for intermediate-term glycemic control (~ 8 weeks), Intermediate Glycemic Index (IGI-8w), bridging the gap between fructosamine and HbA1c in the pediatric population with type 1 diabetes mellitus (T1DM). Methods In this methodological validation study, laboratory data from children and adolescents (aged 0–18 years) with T1DM patients were analyzed. HbA1c and fructosamine were normalized via min-max transformation using age-specific clinical reference intervals. Serial HbA1c, fructosamine, and fasting plasma glucose measurements over 8 weeks assessed temporal associations. Pearson/Spearman correlations, ROC curve analysis, one-way ANOVA, and Bland-Altman plots evaluated accuracy, agreement, and discrimination. Results Normalized HbA1c and fructosamine strongly correlated with each other and with glucose-derived glycemic control over 8 weeks. Both biomarkers' upper normality limits approximated 1.0. A composite index of normalized HbA1c and fructosamine improved DM control discrimination, with a superior ROC area under the curve versus individual markers, and results were consistent across different pediatric age groups (prepubertal, pubertal, and adolescents). Conclusions IGI-8w provided reliable intermediate-term glycemic assessment through integration of normalized HbA1c and fructosamine, demonstrating clinically meaningful discrimination and agreement with conventional monitoring, potentially supporting pediatric DM management where glucose monitoring technologies remain unavailable. Endocrinology & Metabolism Pediatrics Glycemic biomarkers Intermediate-term glycemic control Biomarker standardization Diabetes mellitus Figures Figure 1 Figure 2 INTRODUCTION Glycated hemoglobin (HbA1c) remains the reference biomarker for long-term glycemic control; however, its prolonged integration period limits the early assessment of therapeutic interventions. Conversely, short-term markers such as fructosamine and continuous glucose monitoring (CGM) capture recent glycemic exposure but may be affected by biological variability or limited accessibility. To date, there is no validated laboratory marker that accurately represents glycemic control over an intermediate time frame of approximately 8 weeks. 1 In the pediatric setting, diabetes mellitus (DM) represents a growing public health challenge. Type 1 DM (T1DM) is the most prevalent form in children and adolescents, while type 2 DM has increased in parallel with the global rise in childhood obesity. 2 Adequate glycemic control is of paramount importance in individuals with DM and is well established in the scientific literature. Epidemiological studies have demonstrated a continuous relationship between glycemic levels and the risk of complication progression, highlighting the benefits of improved glycemic control. 3 Optimization of glycemic control significantly reduces the risk of microvascular complications in DM and is also associated with macrovascular benefits demonstrated in long-term follow-up studies. 4 , 5 Inadequate glycemic control constitutes a major public health challenge, as it is associated with complications that substantially impair quality of life, reduce life expectancy, and markedly increase healthcare system costs. 6 In children and adolescents, poor glycemic control also adversely affects growth, neurocognitive development, and pubertal maturation. 7 Evidence indicates that only approximately half of individuals with DM worldwide achieve adequate glycemic control, with the prevalence of poor control ranging from 45% to 93% across different populations. 8 There is a significant methodological gap in glycemic assessment for intermediate periods between fructosamine and HbA1c. Fructosamine reflects glycemic control over the preceding two to three weeks, whereas HbA1c represents average glycemia over approximately 90 to 120 days. 9 In pediatric patients, this gap is particularly critical given the rapid metabolic changes associated with growth and pubertal development, which can affect glycation rates and biomarker reliability. 10 Studies have shown only moderate correlation between HbA1c and fructosamine, with frequent discordance between these markers in clinical practice, a phenomenon referred to as the "glycation gap". 11 Such discordances may be widely distributed and remain reproducible over time, and cannot be explained solely by differences in the turnover of the underlying proteins. 12 The absence of a biomarker covering an intermediate temporal window of approximately 8 weeks between fructosamine and HbA1c may compromise the timely evaluation of therapeutic changes, particularly in clinical contexts requiring more agile adjustments while maintaining greater stability than that provided by very short-term markers. 13 , 14 The present manuscript aims to develop and validate normalized glycemic markers capable of reflecting glycemic control over an intermediate period of approximately 8 weeks, using mathematical equations that enable the integration of HbA1c and fructosamine into comparable indices, with specific focus on their applicability in pediatric patients with DM. METHODS Study Design This constitutes a methodological and clinical validation study designed to develop and validate standardized laboratory biomarkers for the assessment of intermediate-term glycemic control spanning the temporal window between fructosamine and HbA1c, corresponding to approximately 8 weeks. The study was centered on the normalization of established glycemic markers and the development of a composite index to capture glycemic exposure over an intermediate timeframe situated between short-term metrics (fructosamine = 2 to 3 weeks) and HbA1c (12 weeks). Study Population The study cohort comprised laboratory results from children and adolescents (aged 0 to 18 years) with T1DM who were undergoing routine follow-up at the outpatient clinic of HIPERDIA Itabuna, Bahia, Brazil. Participants were required to demonstrate clinical stability and undergo concurrent assessment of HbA1c, fructosamine, and fasting plasma glucose. Individuals with conditions known to interfere with protein glycation or compromise assay reliability (e.g., severe hepatic disease, nephrotic syndrome, recent blood transfusion) were excluded from participation. Participants were stratified by developmental stage: prepubertal children (< 10 years), pubertal children (10–14 years), and adolescents (15–18 years), given that puberty significantly influences insulin resistance and glycemic variability. 15 Intermediate Glycemic Index A composite index designated as the Intermediate Glycemic Index (IGI-8w) was developed by integrating normalized values of HbA1c and fructosamine, differentially weighted to reflect their relative contributions to intermediate-term glycemic exposure: IGI-8w = (0.6 × HbA1cnormalized) + (0.4 × Fructosaminenormalized) The weighting coefficients attributed to HbA1c (0.6) and fructosamine (0.4) were preestablished on the basis of their respective biological integration periods and analytical characteristics. This weighting strategy was devised to approximate an intermediate temporal window of approximately 8 weeks, while simultaneously balancing the superior analytical stability of HbA1c against the heightened short-term responsiveness of fructosamine. Reference Methods HbA1c, expressed as a percentage, was quantified by high-performance liquid chromatography (HPLC) in accordance with International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) standards. Fructosamine was determined by colorimetric assay and expressed in µmol/L. Daily capillary glucose monitoring served as the clinical reference standard for glycemic control assessment, with primary metrics encompassing mean glucose concentration and time in target range (70–180 mg/dL, in accordance with the International Society for Pediatric and Adolescent Diabetes [ISPAD] 2022 guidelines for pediatric patients). Standardization (Normalization) of Glycemic Markers Normalization Concept Normalization was operationally defined as the transformation of raw laboratory values into a uniform dimensionless scale, thereby enabling comparison and mathematical integration of biomarkers expressed in disparate units and numerical ranges. This process is fundamental to preclude disproportionate weighting and preserve physiological interpretability in composite index construction. Min-Max Normalization Both HbA1c and fructosamine were normalized employing a min-max transformation predicated on pediatric clinically accepted reference intervals: Value normalized = \(\:\frac{\text{V}\text{a}\text{l}\text{u}\text{e}-\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{R}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\:\text{L}\text{i}\text{m}\text{i}\text{t}}{\text{U}\text{p}\text{p}\text{e}\text{r}\:\text{R}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\:\text{L}\text{i}\text{m}\text{i}\text{t}-\text{L}\text{o}\text{w}\text{e}\text{r}\:\text{R}\text{e}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\:\text{L}\text{i}\text{m}\text{i}\text{t}}\) For HbA1c, the reference interval was established as 4.0–5.7% for all pediatric age groups, and for fructosamine as 200–300 µmol/L, in accordance with established pediatric clinical guidelines and laboratory validation. Under this transformation, a normalized value ranging from 0.0 to 1.0 corresponds to the conventional normal range, whereas values exceeding 1.0 signify inadequate glycemic control. Definition of Normality and Clinical Interpretation The normalized values of HbA1c and fructosamine lack intrinsic absolute normal values; rather, normality is derived from the original laboratory reference intervals subsequent to mathematical transformation. Normality intervals were initially established as normalized values ranging from 0.0 to 1.0, obtained through mathematical transformation of validated laboratory reference intervals. These thresholds were subsequently assessed via internal consistency analyses and their concordance with glycemic metrics derived from CGM, as opposed to empirical calibration against an external normoglycemic control population. Clinical interpretation of the IGI-8w adhered to predefined categorical thresholds: 1.5 (high glycemic risk), with the acknowledgment that target HbA1c for pediatric patients is < 7.0% as per ISPAD 2022 guidelines, which may influence threshold calibration in future pediatric-specific studies. Statistical Analysis Continuous variables were summarized as mean ± standard deviation or median (interquartile range), and categorical variables were expressed as frequencies and percentages. Intergroup comparisons were conducted employing one-way analysis of variance (ANOVA) or appropriate non-parametric equivalents, with post hoc correction applied as indicated. Subgroup analyses by age group (prepubertal, pubertal, adolescent) were performed. Receiver operating characteristic (ROC) curve analysis was utilized to assess the discriminative performance of standardized HbA1c, standardized fructosamine, and the composite index with respect to predefined glycemic control categories established on the basis of laboratory criteria. Areas under the curve were compared utilizing DeLong's method. Statistical calculations were performed using R software, and figures and charts were generated using public domain software. Statistical significance was established at a two-tailed p-value threshold of < 0.05. Ethical Considerations This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and international guidelines for research involving human data. Approval by a Research Ethics Committee was not required under Brazilian regulations (CEP/CONEP system), as the study exclusively involved secondary analysis of routine laboratory results that were fully anonymized prior to data access. No direct participant contact occurred, and no personally identifiable information was available to the investigators, thereby ensuring confidentiality and minimal ethical risk. The anonymization process ensured the impossibility of identifying minors participating in the study. RESULTS Study Population Characteristics A total of 50 pediatric patients with established type 1 diabetes mellitus (T1DM) were included in the present methodological validation study. The mean age of the cohort was 11.4 ± 3.8 years, with a slight predominance of female participants (54.0%). Median disease duration was 4.2 years (interquartile range [IQR]: 2.0–7.5 years), reflecting a clinically heterogeneous pediatric population encompassing different stages of metabolic adaptation and glycemic management. Baseline glycemic markers demonstrated substantial inter-individual variability. Mean HbA1c was 8.4 ± 2.3%, ranging from 5.2% to 12.8%, while fructosamine concentrations ranged from 185 to 485 µmol/L, with a mean value of 312 ± 78 µmol/L. Mean capillary glucose concentration during the observation period was 168 ± 52 mg/dL (range: 98–298 mg/dL). Coefficients of variation were elevated for both HbA1c (27.4%) and fructosamine (25.0%), supporting the presence of marked heterogeneity in glycemic control status across the study population. Baseline demographic and laboratory characteristics are summarized in Table 1. Table 1 . Baseline Demographic and Clinical Characteristics of the Study Population Characteristic Value / Statistics Unit / Range Demographic Parameters Total sample size (N) 50 Participants Age, mean ± SD 11.4 ± 3.8 Years Sex distribution (female) 54.0% % of total Diabetes mellitus duration, median (IQR) 4.2 (2.0–7.5) Years Baseline Glycemic Control Markers HbA1c, mean ± SD 8.4 ± 2.3 % HbA1c, range 5.2–12.8 % Fructosamine, mean ± SD 312 ± 78 µmol/L Fructosamine, range 185–485 µmol/L Mean capillary glucose, mean ± SD 168 ± 52 mg/dL Mean capillary glucose, range 98–298 mg/dL Population Heterogeneity Assessment Glycemic control classification Heterogeneous across study population HbA1c coefficient of variation (CV) 27.4% % (SD/mean × 100) Fructosamine coefficient of variation (CV) 25.0% % (SD/mean × 100) Abbreviations: HbA1c, glycated hemoglobin; IQR, interquartile range; SD, standard deviation; CV, coefficient of variation; T1DM, type 1 diabetes mellitus; µmol/L, micromoles per liter; mg/dL, milligrams per deciliter. Note: CV was calculated as (SD/mean) × 100 to quantify inter-individual variability. Substantial heterogeneity in baseline glycemic control was observed across all three biomarker indices. Normalization Performance of Glycemic Biomarkers Application of the min-max normalization model successfully transformed HbA1c and fructosamine into dimensionless standardized indices with comparable mathematical scales. Following normalization, values corresponding to clinically accepted pediatric reference intervals consistently ranged between 0.0 and 1.0, while progressively elevated normalized values were associated with worsening glycemic control. Normalized HbA1c and normalized fructosamine exhibited a strong positive correlation (r = 0.82, p < 0.001), indicating substantial concordance between long-term and short-term glycemic exposure after mathematical standardization. Both normalized markers also demonstrated significant associations with mean capillary glucose levels over the 8-week observation period. Normalized HbA1c correlated strongly with mean glucose concentration (r = 0.86, p < 0.001), whereas normalized fructosamine showed a slightly stronger association with recent glycemic fluctuations (r = 0.88, p < 0.001). Importantly, the upper boundary of the normalized physiological interval approximated 1.0 for both biomarkers across all pediatric age groups, supporting the internal consistency and interpretability of the proposed normalization strategy. Scatterplot correlations between normalized biomarkers and glucose-derived glycemic metrics are illustrated in Figure 1. Figure 1. Correlation analyses between normalized biomarkers and glucose-derived glycemic metrics Performance of the Intermediate Glycemic Index (IGI-8w) The IGI-8w, generated through weighted integration of normalized HbA1c and normalized fructosamine values, demonstrated superior discrimination of glycemic control categories when compared with isolated biomarkers. The composite index showed excellent correlation with mean capillary glucose concentration (r = 0.91, p < 0.001), exceeding the performance observed for either normalized HbA1c or normalized fructosamine individually. Similarly, IGI-8w demonstrated stronger inverse association with time in target range (70–180 mg/dL) than either isolated marker, suggesting enhanced capacity to reflect clinically meaningful glycemic exposure. Receiver operating characteristic (ROC) curve analysis demonstrated that IGI-8w yielded the highest area under the curve (AUC) for identification of inadequate glycemic control. The composite index achieved an AUC of 0.93 (95% confidence interval [CI]: 0.84–0.99), compared with 0.87 (95% CI: 0.75–0.96) for normalized HbA1c and 0.84 (95% CI: 0.71–0.94) for normalized fructosamine alone. Comparative analysis using DeLong’s method demonstrated statistically superior discriminatory performance for IGI-8w relative to isolated fructosamine measurements (p = 0.032). ROC curve analyses comparing the discriminatory performance of the three biomarkers are presented in Figure 2. Figure 2 . Receiver operating characteristic ROC curve comparing normalized biomarkers and the IGI-8w Agreement Analysis Between IGI-8w and Conventional Glycemic Markers The agreement between normalized biomarkers and clinically established glycemic targets was evaluated through Bland-Altman analysis. The IGI-8 index demonstrated the best agreement, evidenced by a mean bias of −0.05 and narrow 95% limits of agreement (−0.52 to 0.42). No systematic patterns of discrepancy were observed across the spectrum of glycemic control. Limits of agreement were clinically acceptable and remained stable across low, moderate, and high glycemic exposure categories. Importantly, no age-related systematic deviation was observed, suggesting robust applicability of the composite index across different pediatric developmental stages. The agreement analysis between IGI-8w and conventional glycemic monitoring methods is depicted in Figure 3. Figure 3 . Bland-Altman plots demonstrating agreement between IGI-8w and conventional glycemic monitoring metrics Subgroup Analysis According to Developmental Stage Subgroup analyses stratified by developmental stage demonstrated consistent performance of the normalization strategy and composite index across prepubertal children (<10 years), pubertal children (10–14 years), and adolescents (15–18 years). Although pubertal participants exhibited higher mean glycemic variability and greater dispersion of fructosamine concentrations, the IGI-8w maintained stable discriminatory performance across all subgroups. No statistically significant difference was identified in the correlation strength between IGI-8w and mean glucose levels among the three developmental stages (p for interaction = 0.41). Adolescents presented numerically higher IGI-8w values compared with prepubertal children, consistent with the known increase in insulin resistance during puberty; however, normalization preserved the interpretability of biomarker values within comparable mathematical scales. Clinical Classification Performance of the IGI-8w Application of predefined IGI-8w thresholds enabled clinically meaningful categorization of glycemic control. Participants classified as having excellent glycemic control (IGI-8w 1.0). One-way ANOVA demonstrated significant differences in mean glucose concentrations across IGI-8w glycemic risk categories (p < 0.001). Post hoc analyses confirmed progressive deterioration of glycemic metrics with increasing IGI-8w classification levels. Distribution of participants according to IGI-8w glycemic control categories is presented in Figure 4. Figure 4 . Distribution of pediatric participants according to IGI-8w glycemic control categories and corresponding mean glucose levels Summary of Main Results The present results demonstrate that mathematical normalization of HbA1c and fructosamine enables reliable integration of these biomarkers into a unified intermediate-term glycemic metric. The proposed IGI-8w exhibited robust analytical performance, superior discrimination of inadequate glycemic control relative to isolated biomarkers, satisfactory agreement with conventional glycemic monitoring strategies, and stable applicability across pediatric developmental stages. DISCUSSION The present study achieved its primary objective by developing and validating a standardized intermediate-term glycemic biomarker capable of integrating the complementary temporal properties of HbA1c and fructosamine into a unified index applicable to pediatric patients with T1DM. The successful normalization of HbA1c and fructosamine into comparable dimensionless scales, combined with their integration into a composite index, demonstrates feasibility for clinical implementation. In this context, our results encourage an objective evaluation of glycemic monitoring, emphasizing the clinical relevance, methodological significance, and potential integration of normalized indices to better reflect dynamic metabolic exposure. One of the most relevant results of our study was the ability of the normalization strategy to transform HbA1c and fructosamine into dimensionless and mathematically comparable indices while preserving physiological interpretability. The observation that the upper boundary of the normalized physiological interval consistently approximated 1.0 across pediatric age groups suggests internal coherence of the transformation model and reinforces the feasibility of integrating biomarkers expressed in different analytical units. This mathematical harmonization addresses an important methodological limitation in DM monitoring, namely the difficulty of directly comparing biomarkers with distinct biological integration periods and laboratory scales. Previous studies have demonstrated that fructosamine and HbA1c often exhibit only moderate concordance because they reflect different aspects of glycemic exposure and are influenced by distinct biological processes. 16 The strong correlation observed between normalized HbA1c and normalized fructosamine supports the biological plausibility of the proposed approach. HbA1c primarily reflects cumulative glycemic exposure over approximately 8–12 weeks, whereas fructosamine captures shorter-term glycemic changes occurring within the preceding 2–3 weeks. 17 The integration of both biomarkers into a composite index therefore theoretically permits simultaneous representation of both sustained and recent glycemic exposure. This concept is particularly relevant in pediatric diabetes care, where glycemic variability is frequently amplified by growth, pubertal hormonal fluctuations, irregular dietary habits, and inconsistent adherence to insulin therapy. The stronger association of fructosamine with recent glycemic fluctuations observed in the present study further supports prior evidence indicating that short-term glycemic markers may respond more rapidly to therapeutic modifications than HbA1c alone. 18 Another important observation was the superior discriminative performance of IGI-8w relative to isolated biomarkers. The composite index yielded the highest area under the ROC curve for identification of inadequate glycemic control, exceeding the performance of normalized HbA1c and normalized fructosamine individually. These findings suggest that combining biomarkers with distinct temporal characteristics may improve the detection of clinically meaningful dysglycemia. Previous investigations evaluating alternative glycemic markers have similarly suggested that multimarker approaches may offer advantages over isolated laboratory parameters, particularly in situations where HbA1c interpretation is limited or where recent glycemic exposure is clinically relevant. 19 The present results extend this concept by proposing a mathematically standardized composite index specifically designed to bridge the temporal gap between short- and long-term glycemic assessment. The excellent correlation between IGI-8w and mean capillary glucose concentration also deserves particular consideration. Although HbA1c remains the reference biomarker for long-term diabetes monitoring, several studies have demonstrated that HbA1c alone does not fully capture glycemic variability or acute glucose excursions. 20,21 CGM has partially addressed this limitation by providing detailed temporal glucose profiles and time-in-range metrics; however, widespread access to CGM remains restricted in many low- and middle-income settings due to economic and logistical barriers. In this context, the proposed IGI-8w may represent a pragmatic and accessible laboratory-based alternative capable of approximating intermediate glycemic exposure using routinely available assays. This potential applicability is especially relevant in pediatric populations receiving care in public healthcare systems or resource-constrained environments. The Bland–Altman analyses demonstrated satisfactory agreement between IGI-8w and conventional glycemic monitoring metrics, with minimal mean bias and narrow limits of agreement. Importantly, no systematic deviation was identified across different levels of glycemic exposure or developmental stages. These findings suggest that the composite index maintains analytical stability despite the physiological metabolic changes characteristic of childhood and adolescence. Puberty is known to induce transient insulin resistance mediated by growth hormone and sex steroids, often resulting in increased glycemic variability and deterioration of glycemic control in adolescents with T1DM. 22,23 Nevertheless, IGI-8w preserved consistent interpretability across developmental subgroups, supporting the robustness of the normalization strategy in heterogeneous pediatric populations. The clinical classification performance of IGI-8w also demonstrated potential practical utility. Participants classified within higher IGI-8w categories exhibited progressively worse glycemic metrics, including higher mean glucose concentrations and lower time in target range. The progressive deterioration observed across risk categories reinforces the clinical interpretability of the proposed thresholds and suggests that the index may facilitate stratification of pediatric patients according to glycemic risk. Such stratification may support earlier therapeutic adjustments and more individualized monitoring strategies. From a clinical perspective, an intermediate-term biomarker capable of identifying worsening glycemic control before substantial changes in HbA1c occur could improve therapeutic responsiveness and potentially reduce long-term complications associated with prolonged hyperglycemia. The present study should be interpreted in light of four main limitations. First, the sample size was relatively small, which may limit the generalizability of the findings and reduce statistical power for subgroup analyses. Second, the study was conducted in a single regional pediatric diabetes population, potentially introducing selection bias and limiting external validity. Third, although capillary glucose monitoring was used as the clinical reference standard, CGM-derived metrics were not universally available for all participants. Consequently, direct comparison between IGI-8w and standardized CGM-derived glycemic variability indices remains limited. In addition, the weighting coefficients adopted for construction of the composite index were theoretically predefined based on biological assumptions rather than derived through machine-learning optimization or external validation cohorts. Future investigations should focus on external validation of the IGI-8w in larger and more diverse pediatric cohorts, including individuals with type 2 DM and other dysglycemic conditions. Prospective longitudinal studies are also needed to determine whether IGI-8w better predicts diabetes-related complications, glycemic variability, or therapeutic responsiveness compared with isolated biomarkers. Additional research integrating CGM metrics, artificial intelligence-based weighting strategies, and dynamic modeling approaches may further improve the precision and clinical applicability of intermediate-term glycemic indices. Moreover, evaluation of the proposed normalization methodology in adults, pregnant individuals, and patients with hemoglobinopathies or altered erythrocyte turnover may broaden its translational relevance. CONCLUSION The present study introduces a novel standardized approach for intermediate-term glycemic assessment in pediatric DM. By mathematically integrating normalized HbA1c and fructosamine into a unified composite biomarker, IGI-8w demonstrated strong analytical performance, clinically meaningful discrimination of glycemic control categories, and important agreement with conventional monitoring methods. These findings suggest that standardized multimarker strategies may represent a promising avenue for improving glycemic assessment in pediatric diabetes care, particularly in settings where access to advanced glucose monitoring technologies remains limited. Declarations Ethical Considerations This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and international guidelines for research involving human data. Approval by a Research Ethics Committee was not required under Brazilian regulations (CEP/CONEP system), in accordance with Resolution CNS 510/2016 (Article 1, sole paragraph, item VII), which exempts retrospective studies involving exclusively secondary analysis of anonymized routine data from mandatory ethics committee review, as the study exclusively involved secondary analysis of routine laboratory results that were fully anonymized prior to data access. No direct participant contact occurred, and no personally identifiable information was available to the investigators, thereby ensuring confidentiality and minimal ethical risk. The anonymization process ensured the impossibility of identifying minors participating in the study. Declaration of competing interest: The authors have no conflict of interest to declare. References Lundholm MD, Emanuele MA, Ashraf A, Nadeem S (2020) Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring. 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Is it important? How to measure it? J Diabetes Sci Technol 2(6):1094–1100 Kohnert KD, Vogt L, Augstein P, Heinke P, Zander E, Peterson K et al (2009) Relationships between glucose variability and conventional measures of glycemic control in continuously monitored patients with type 2 diabetes. Horm Metab Res 41(2):137–141 Bombaci B, Torre A, Longo A, Pecoraro M, Papa M, Sorrenti L, La Rocca M, Lombardo F, Salzano G (2024) Psychological and Clinical Challenges in the Management of Type 1 Diabetes during Adolescence: A Narrative Review. Child (Basel) 11(9):1085 Roma-Wilson MA, Buzzetti R, Zampetti S (2025) Bridging Pubertal Changes and Endotype Based Therapy in Type 1 Diabetes. Diabetes Metab Res Rev 41(3):e70038 Additional Declarations The authors declare no competing interests. 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UNAERP, Ribeirão Preto, São Paulo, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"Correia Matos","lastName":"de Oliveira","suffix":""},{"id":639354716,"identity":"ad9dd02c-fbcb-4706-b00f-311b49c5684e","order_by":2,"name":"Alcina Maria Vinhaes Bittencourt","email":"","orcid":"https://orcid.org/0000-0003-0506-9210","institution":"School of Medicine, Federal University of Bahia, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Alcina","middleName":"Maria Vinhaes","lastName":"Bittencourt","suffix":""},{"id":639354717,"identity":"cdb3741a-dfa6-42b4-98ed-41a41acaca09","order_by":3,"name":"Osmário Jorge de Mattos Salles","email":"","orcid":"https://orcid.org/0009-0002-1859-0478","institution":"Bahiana School of Medicine and Public Health, Salvador, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Osmário","middleName":"Jorge de Mattos","lastName":"Salles","suffix":""},{"id":639354718,"identity":"818539b6-7b76-4b3d-bfc9-2200d4218f04","order_by":4,"name":"Luís Matos de Oliveira","email":"","orcid":"https://orcid.org/0000-0003-4854-6910","institution":"Department of Health, Santa Cruz State University, Ilhéus, Bahia, Brazil.","correspondingAuthor":false,"prefix":"","firstName":"Luís","middleName":"Matos","lastName":"de Oliveira","suffix":""}],"badges":[],"createdAt":"2026-05-13 02:10:34","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9697395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9697395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109338082,"identity":"ea14dbd9-9f99-4c60-8b6b-97d4934c2997","added_by":"auto","created_at":"2026-05-15 17:49:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":425033,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analyses between normalized biomarkers and glucose-derived glycemic metrics\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9697395/v1/dded16bb1e2c4ee54012185d.png"},{"id":109405503,"identity":"a92e5e9f-68f2-4511-a53a-36c2d4eb26a3","added_by":"auto","created_at":"2026-05-17 13:18:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":162349,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic ROC curve comparing normalized biomarkers and the IGI-8w\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9697395/v1/a7b01caf9891973069871e5d.png"},{"id":109405798,"identity":"28f1110c-4e6b-4c0a-b4df-8865d274fc4a","added_by":"auto","created_at":"2026-05-17 13:20:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":795360,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9697395/v1/c4ab0943-4838-4a50-9fbf-4fe86b83c5ab.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eBridging the Gap Between Fructosamine and HbA1c: Development and Validation of an 8-Week Glycemic Index in Pediatric Type 1 Diabetes\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlycated hemoglobin (HbA1c) remains the reference biomarker for long-term glycemic control; however, its prolonged integration period limits the early assessment of therapeutic interventions. Conversely, short-term markers such as fructosamine and continuous glucose monitoring (CGM) capture recent glycemic exposure but may be affected by biological variability or limited accessibility. To date, there is no validated laboratory marker that accurately represents glycemic control over an intermediate time frame of approximately 8 weeks.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the pediatric setting, diabetes mellitus (DM) represents a growing public health challenge. Type 1 DM (T1DM) is the most prevalent form in children and adolescents, while type 2 DM has increased in parallel with the global rise in childhood obesity.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Adequate glycemic control is of paramount importance in individuals with DM and is well established in the scientific literature. Epidemiological studies have demonstrated a continuous relationship between glycemic levels and the risk of complication progression, highlighting the benefits of improved glycemic control.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Optimization of glycemic control significantly reduces the risk of microvascular complications in DM and is also associated with macrovascular benefits demonstrated in long-term follow-up studies.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eInadequate glycemic control constitutes a major public health challenge, as it is associated with complications that substantially impair quality of life, reduce life expectancy, and markedly increase healthcare system costs.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In children and adolescents, poor glycemic control also adversely affects growth, neurocognitive development, and pubertal maturation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Evidence indicates that only approximately half of individuals with DM worldwide achieve adequate glycemic control, with the prevalence of poor control ranging from 45% to 93% across different populations.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere is a significant methodological gap in glycemic assessment for intermediate periods between fructosamine and HbA1c. Fructosamine reflects glycemic control over the preceding two to three weeks, whereas HbA1c represents average glycemia over approximately 90 to 120 days.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e In pediatric patients, this gap is particularly critical given the rapid metabolic changes associated with growth and pubertal development, which can affect glycation rates and biomarker reliability.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Studies have shown only moderate correlation between HbA1c and fructosamine, with frequent discordance between these markers in clinical practice, a phenomenon referred to as the \"glycation gap\".\u003csup\u003e11\u003c/sup\u003e Such discordances may be widely distributed and remain reproducible over time, and cannot be explained solely by differences in the turnover of the underlying proteins.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e The absence of a biomarker covering an intermediate temporal window of approximately 8 weeks between fructosamine and HbA1c may compromise the timely evaluation of therapeutic changes, particularly in clinical contexts requiring more agile adjustments while maintaining greater stability than that provided by very short-term markers.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe present manuscript aims to develop and validate normalized glycemic markers capable of reflecting glycemic control over an intermediate period of approximately 8 weeks, using mathematical equations that enable the integration of HbA1c and fructosamine into comparable indices, with specific focus on their applicability in pediatric patients with DM.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis constitutes a methodological and clinical validation study designed to develop and validate standardized laboratory biomarkers for the assessment of intermediate-term glycemic control spanning the temporal window between fructosamine and HbA1c, corresponding to approximately 8 weeks. The study was centered on the normalization of established glycemic markers and the development of a composite index to capture glycemic exposure over an intermediate timeframe situated between short-term metrics (fructosamine\u0026thinsp;=\u0026thinsp;2 to 3 weeks) and HbA1c (12 weeks).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study cohort comprised laboratory results from children and adolescents (aged 0 to 18 years) with T1DM who were undergoing routine follow-up at the outpatient clinic of HIPERDIA Itabuna, Bahia, Brazil. Participants were required to demonstrate clinical stability and undergo concurrent assessment of HbA1c, fructosamine, and fasting plasma glucose. Individuals with conditions known to interfere with protein glycation or compromise assay reliability (e.g., severe hepatic disease, nephrotic syndrome, recent blood transfusion) were excluded from participation.\u003c/p\u003e \u003cp\u003eParticipants were stratified by developmental stage: prepubertal children (\u0026lt;\u0026thinsp;10 years), pubertal children (10\u0026ndash;14 years), and adolescents (15\u0026ndash;18 years), given that puberty significantly influences insulin resistance and glycemic variability.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eIntermediate Glycemic Index\u003c/h3\u003e\n\u003cp\u003eA composite index designated as the Intermediate Glycemic Index (IGI-8w) was developed by integrating normalized values of HbA1c and fructosamine, differentially weighted to reflect their relative contributions to intermediate-term glycemic exposure:\u003c/p\u003e\n\u003ch3\u003eIGI-8w = (0.6 × HbA1cnormalized) + (0.4 × Fructosaminenormalized)\u003c/h3\u003e\n\u003cp\u003eThe weighting coefficients attributed to HbA1c (0.6) and fructosamine (0.4) were preestablished on the basis of their respective biological integration periods and analytical characteristics. This weighting strategy was devised to approximate an intermediate temporal window of approximately 8 weeks, while simultaneously balancing the superior analytical stability of HbA1c against the heightened short-term responsiveness of fructosamine.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference Methods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHbA1c, expressed as a percentage, was quantified by high-performance liquid chromatography (HPLC) in accordance with International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) standards. Fructosamine was determined by colorimetric assay and expressed in \u0026micro;mol/L. Daily capillary glucose monitoring served as the clinical reference standard for glycemic control assessment, with primary metrics encompassing mean glucose concentration and time in target range (70\u0026ndash;180 mg/dL, in accordance with the International Society for Pediatric and Adolescent Diabetes [ISPAD] 2022 guidelines for pediatric patients).\u003c/p\u003e\n\u003ch3\u003eStandardization (Normalization) of Glycemic Markers\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNormalization Concept\u003c/h2\u003e \u003cp\u003eNormalization was operationally defined as the transformation of raw laboratory values into a uniform dimensionless scale, thereby enabling comparison and mathematical integration of biomarkers expressed in disparate units and numerical ranges. This process is fundamental to preclude disproportionate weighting and preserve physiological interpretability in composite index construction.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMin-Max Normalization\u003c/h3\u003e\n\u003cp\u003eBoth HbA1c and fructosamine were normalized employing a min-max transformation predicated on pediatric clinically accepted reference intervals:\u003c/p\u003e \u003cp\u003eValue\u003csub\u003enormalized\u003c/sub\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{V}\\text{a}\\text{l}\\text{u}\\text{e}-\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{R}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}}{\\text{U}\\text{p}\\text{p}\\text{e}\\text{r}\\:\\text{R}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}-\\text{L}\\text{o}\\text{w}\\text{e}\\text{r}\\:\\text{R}\\text{e}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\:\\text{L}\\text{i}\\text{m}\\text{i}\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003e For HbA1c, the reference interval was established as 4.0\u0026ndash;5.7% for all pediatric age groups, and for fructosamine as 200\u0026ndash;300 \u0026micro;mol/L, in accordance with established pediatric clinical guidelines and laboratory validation. Under this transformation, a normalized value ranging from 0.0 to 1.0 corresponds to the conventional normal range, whereas values exceeding 1.0 signify inadequate glycemic control.\u003c/p\u003e\n\u003ch3\u003eDefinition of Normality and Clinical Interpretation\u003c/h3\u003e\n\u003cp\u003eThe normalized values of HbA1c and fructosamine lack intrinsic absolute normal values; rather, normality is derived from the original laboratory reference intervals subsequent to mathematical transformation. Normality intervals were initially established as normalized values ranging from 0.0 to 1.0, obtained through mathematical transformation of validated laboratory reference intervals. These thresholds were subsequently assessed via internal consistency analyses and their concordance with glycemic metrics derived from CGM, as opposed to empirical calibration against an external normoglycemic control population. Clinical interpretation of the IGI-8w adhered to predefined categorical thresholds: \u0026lt;0.5 (excellent control), 0.5\u0026ndash;1.0 (adequate control), 1.0\u0026ndash;1.5 (inadequate control), and \u0026gt;\u0026thinsp;1.5 (high glycemic risk), with the acknowledgment that target HbA1c for pediatric patients is \u0026lt;\u0026thinsp;7.0% as per ISPAD 2022 guidelines, which may influence threshold calibration in future pediatric-specific studies.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), and categorical variables were expressed as frequencies and percentages. Intergroup comparisons were conducted employing one-way analysis of variance (ANOVA) or appropriate non-parametric equivalents, with post hoc correction applied as indicated. Subgroup analyses by age group (prepubertal, pubertal, adolescent) were performed. Receiver operating characteristic (ROC) curve analysis was utilized to assess the discriminative performance of standardized HbA1c, standardized fructosamine, and the composite index with respect to predefined glycemic control categories established on the basis of laboratory criteria. Areas under the curve were compared utilizing DeLong's method. Statistical calculations were performed using R software, and figures and charts were generated using public domain software. Statistical significance was established at a two-tailed p-value threshold of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and international guidelines for research involving human data. Approval by a Research Ethics Committee was not required under Brazilian regulations (CEP/CONEP system), as the study exclusively involved secondary analysis of routine laboratory results that were fully anonymized prior to data access. No direct participant contact occurred, and no personally identifiable information was available to the investigators, thereby ensuring confidentiality and minimal ethical risk. The anonymization process ensured the impossibility of identifying minors participating in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eStudy Population Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 50 pediatric patients with established type 1 diabetes mellitus (T1DM) were included in the present methodological validation study. The mean age of the cohort was 11.4 \u0026plusmn; 3.8 years, with a slight predominance of female participants (54.0%). Median disease duration was 4.2 years (interquartile range [IQR]: 2.0\u0026ndash;7.5 years), reflecting a clinically heterogeneous pediatric population encompassing different stages of metabolic adaptation and glycemic management.\u003c/p\u003e\n\u003cp\u003eBaseline glycemic markers demonstrated substantial inter-individual variability. Mean HbA1c was 8.4 \u0026plusmn; 2.3%, ranging from 5.2% to 12.8%, while fructosamine concentrations ranged from 185 to 485 \u0026micro;mol/L, with a mean value of 312 \u0026plusmn; 78 \u0026micro;mol/L. Mean capillary glucose concentration during the observation period was 168 \u0026plusmn; 52 mg/dL (range: 98\u0026ndash;298 mg/dL). Coefficients of variation were elevated for both HbA1c (27.4%) and fructosamine (25.0%), supporting the presence of marked heterogeneity in glycemic control status across the study population.\u003c/p\u003e\n\u003cp\u003eBaseline demographic and laboratory characteristics are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Baseline Demographic and Clinical Characteristics of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue / Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit / Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eTotal sample size (N)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eParticipants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eAge, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e11.4 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eSex distribution (female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e54.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e% of total\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eDiabetes mellitus duration, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e4.2 (2.0\u0026ndash;7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Glycemic Control Markers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eHbA1c, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e8.4 \u0026plusmn; 2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eHbA1c, range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e5.2\u0026ndash;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eFructosamine, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e312 \u0026plusmn; 78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eFructosamine, range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e185\u0026ndash;485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eMean capillary glucose, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e168 \u0026plusmn; 52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003emg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eMean capillary glucose, range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e98\u0026ndash;298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003emg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation Heterogeneity Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eGlycemic control classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003eHeterogeneous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003eacross study population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eHbA1c coefficient of variation (CV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e27.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e% (SD/mean \u0026times; 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 46.9551%;\"\u003e\n \u003cp\u003eFructosamine coefficient of variation (CV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.1987%;\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 28.8462%;\"\u003e\n \u003cp\u003e% (SD/mean \u0026times; 100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: HbA1c, glycated hemoglobin; IQR, interquartile range; SD, standard deviation; CV, coefficient of variation; T1DM, type 1 diabetes mellitus; \u0026micro;mol/L, micromoles per liter; mg/dL, milligrams per deciliter.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: CV was calculated as (SD/mean) \u0026times; 100 to quantify inter-individual variability. Substantial heterogeneity in baseline glycemic control was observed across all three biomarker indices.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNormalization Performance of Glycemic Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplication of the min-max normalization model successfully transformed HbA1c and fructosamine into dimensionless standardized indices with comparable mathematical scales. Following normalization, values corresponding to clinically accepted pediatric reference intervals consistently ranged between 0.0 and 1.0, while progressively elevated normalized values were associated with worsening glycemic control.\u003c/p\u003e\n\u003cp\u003eNormalized HbA1c and normalized fructosamine exhibited a strong positive correlation (r = 0.82, p \u0026lt; 0.001), indicating substantial concordance between long-term and short-term glycemic exposure after mathematical standardization. Both normalized markers also demonstrated significant associations with mean capillary glucose levels over the 8-week observation period. Normalized HbA1c correlated strongly with mean glucose concentration (r = 0.86, p \u0026lt; 0.001), whereas normalized fructosamine showed a slightly stronger association with recent glycemic fluctuations (r = 0.88, p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eImportantly, the upper boundary of the normalized physiological interval approximated 1.0 for both biomarkers across all pediatric age groups, supporting the internal consistency and interpretability of the proposed normalization strategy.\u003c/p\u003e\n\u003cp\u003eScatterplot correlations between normalized biomarkers and glucose-derived glycemic metrics are illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e Correlation analyses between normalized biomarkers and glucose-derived glycemic metrics\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerformance of the Intermediate Glycemic Index (IGI-8w)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe IGI-8w, generated through weighted integration of normalized HbA1c and normalized fructosamine values, demonstrated superior discrimination of glycemic control categories when compared with isolated biomarkers.\u003c/p\u003e\n\u003cp\u003eThe composite index showed excellent correlation with mean capillary glucose concentration (r = 0.91, p \u0026lt; 0.001), exceeding the performance observed for either normalized HbA1c or normalized fructosamine individually. Similarly, IGI-8w demonstrated stronger inverse association with time in target range (70\u0026ndash;180 mg/dL) than either isolated marker, suggesting enhanced capacity to reflect clinically meaningful glycemic exposure.\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis demonstrated that IGI-8w yielded the highest area under the curve (AUC) for identification of inadequate glycemic control. The composite index achieved an AUC of 0.93 (95% confidence interval [CI]: 0.84\u0026ndash;0.99), compared with 0.87 (95% CI: 0.75\u0026ndash;0.96) for normalized HbA1c and 0.84 (95% CI: 0.71\u0026ndash;0.94) for normalized fructosamine alone. Comparative analysis using DeLong\u0026rsquo;s method demonstrated statistically superior discriminatory performance for IGI-8w relative to isolated fructosamine measurements (p = 0.032).\u003c/p\u003e\n\u003cp\u003eROC curve analyses comparing the discriminatory performance of the three biomarkers are presented in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2\u003c/strong\u003e. Receiver operating characteristic ROC curve comparing normalized biomarkers and the IGI-8w\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgreement Analysis Between IGI-8w and Conventional Glycemic Markers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe agreement between normalized biomarkers and clinically established glycemic targets was evaluated through Bland-Altman analysis. The IGI-8 index demonstrated the best agreement, evidenced by a mean bias of \u0026minus;0.05 and narrow 95% limits of agreement (\u0026minus;0.52 to 0.42). No systematic patterns of discrepancy were observed across the spectrum of glycemic control.\u003c/p\u003e\n\u003cp\u003eLimits of agreement were clinically acceptable and remained stable across low, moderate, and high glycemic exposure categories. Importantly, no age-related systematic deviation was observed, suggesting robust applicability of the composite index across different pediatric developmental stages.\u003c/p\u003e\n\u003cp\u003eThe agreement analysis between IGI-8w and conventional glycemic monitoring methods is depicted in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e. Bland-Altman plots demonstrating agreement between IGI-8w and conventional glycemic monitoring metrics\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis According to Developmental Stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses stratified by developmental stage demonstrated consistent performance of the normalization strategy and composite index across prepubertal children (\u0026lt;10 years), pubertal children (10\u0026ndash;14 years), and adolescents (15\u0026ndash;18 years).\u003c/p\u003e\n\u003cp\u003eAlthough pubertal participants exhibited higher mean glycemic variability and greater dispersion of fructosamine concentrations, the IGI-8w maintained stable discriminatory performance across all subgroups. No statistically significant difference was identified in the correlation strength between IGI-8w and mean glucose levels among the three developmental stages (p for interaction = 0.41).\u003c/p\u003e\n\u003cp\u003eAdolescents presented numerically higher IGI-8w values compared with prepubertal children, consistent with the known increase in insulin resistance during puberty; however, normalization preserved the interpretability of biomarker values within comparable mathematical scales.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Classification Performance of the IGI-8w\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplication of predefined IGI-8w thresholds enabled clinically meaningful categorization of glycemic control. Participants classified as having excellent glycemic control (IGI-8w \u0026lt;0.5) demonstrated significantly lower mean glucose concentrations and higher time in target range compared with those categorized as having inadequate control (IGI-8w \u0026gt;1.0).\u003c/p\u003e\n\u003cp\u003eOne-way ANOVA demonstrated significant differences in mean glucose concentrations across IGI-8w glycemic risk categories (p \u0026lt; 0.001). Post hoc analyses confirmed progressive deterioration of glycemic metrics with increasing IGI-8w classification levels.\u003c/p\u003e\n\u003cp\u003eDistribution of participants according to IGI-8w glycemic control categories is presented in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e. Distribution of pediatric participants according to IGI-8w glycemic control categories and corresponding mean glucose levels\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSummary of Main Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present results demonstrate that mathematical normalization of HbA1c and fructosamine enables reliable integration of these biomarkers into a unified intermediate-term glycemic metric. The proposed IGI-8w exhibited robust analytical performance, superior discrimination of inadequate glycemic control relative to isolated biomarkers, satisfactory agreement with conventional glycemic monitoring strategies, and stable applicability across pediatric developmental stages.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe present study achieved its primary objective by developing and validating a standardized intermediate-term glycemic biomarker capable of integrating the complementary temporal properties of HbA1c and fructosamine into a unified index applicable to pediatric patients with T1DM. The successful normalization of HbA1c and fructosamine into comparable dimensionless scales, combined with their integration into a composite index, demonstrates feasibility for clinical implementation. In this context, our results encourage an objective evaluation of glycemic monitoring, emphasizing the clinical relevance, methodological significance, and potential integration of normalized indices to better reflect dynamic metabolic exposure.\u003c/p\u003e\n\u003cp\u003eOne of the most relevant results of our study was the ability of the normalization strategy to transform HbA1c and fructosamine into dimensionless and mathematically comparable indices while preserving physiological interpretability. The observation that the upper boundary of the normalized physiological interval consistently approximated 1.0 across pediatric age groups suggests internal coherence of the transformation model and reinforces the feasibility of integrating biomarkers expressed in different analytical units. This mathematical harmonization addresses an important methodological limitation in DM monitoring, namely the difficulty of directly comparing biomarkers with distinct biological integration periods and laboratory scales. Previous studies have demonstrated that fructosamine and HbA1c often exhibit only moderate concordance because they reflect different aspects of glycemic exposure and are influenced by distinct biological processes.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe strong correlation observed between normalized HbA1c and normalized fructosamine supports the biological plausibility of the proposed approach. HbA1c primarily reflects cumulative glycemic exposure over approximately 8–12 weeks, whereas fructosamine captures shorter-term glycemic changes occurring within the preceding 2–3 weeks.\u003csup\u003e17\u003c/sup\u003e The integration of both biomarkers into a composite index therefore theoretically permits simultaneous representation of both sustained and recent glycemic exposure. This concept is particularly relevant in pediatric diabetes care, where glycemic variability is frequently amplified by growth, pubertal hormonal fluctuations, irregular dietary habits, and inconsistent adherence to insulin therapy. The stronger association of fructosamine with recent glycemic fluctuations observed in the present study further supports prior evidence indicating that short-term glycemic markers may respond more rapidly to therapeutic modifications than HbA1c alone.\u003csup\u003e18\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Another important observation was the superior discriminative performance of IGI-8w relative to isolated biomarkers. The composite index yielded the highest area under the ROC curve for identification of inadequate glycemic control, exceeding the performance of normalized HbA1c and normalized fructosamine individually. These findings suggest that combining biomarkers with distinct temporal characteristics may improve the detection of clinically meaningful dysglycemia. Previous investigations evaluating alternative glycemic markers have similarly suggested that multimarker approaches may offer advantages over isolated laboratory parameters, particularly in situations where HbA1c interpretation is limited or where recent glycemic exposure is clinically relevant.\u003csup\u003e19\u003c/sup\u003e The present results extend this concept by proposing a mathematically standardized composite index specifically designed to bridge the temporal gap between short- and long-term glycemic assessment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The excellent correlation between IGI-8w and mean capillary glucose concentration also deserves particular consideration. Although HbA1c remains the reference biomarker for long-term diabetes monitoring, several studies have demonstrated that HbA1c alone does not fully capture glycemic variability or acute glucose excursions.\u003csup\u003e20,21\u003c/sup\u003e CGM has partially addressed this limitation by providing detailed temporal glucose profiles and time-in-range metrics; however, widespread access to CGM remains restricted in many low- and middle-income settings due to economic and logistical barriers. In this context, the proposed IGI-8w may represent a pragmatic and accessible laboratory-based alternative capable of approximating intermediate glycemic exposure using routinely available assays. This potential applicability is especially relevant in pediatric populations receiving care in public healthcare systems or resource-constrained environments.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Bland–Altman analyses demonstrated satisfactory agreement between IGI-8w and conventional glycemic monitoring metrics, with minimal mean bias and narrow limits of agreement. Importantly, no systematic deviation was identified across different levels of glycemic exposure or developmental stages. These findings suggest that the composite index maintains analytical stability despite the physiological metabolic changes characteristic of childhood and adolescence. Puberty is known to induce transient insulin resistance mediated by growth hormone and sex steroids, often resulting in increased glycemic variability and deterioration of glycemic control in adolescents with T1DM.\u003csup\u003e22,23\u003c/sup\u003e Nevertheless, IGI-8w preserved consistent interpretability across developmental subgroups, supporting the robustness of the normalization strategy in heterogeneous pediatric populations.\u003c/p\u003e\n\u003cp\u003eThe clinical classification performance of IGI-8w also demonstrated potential practical utility. Participants classified within higher IGI-8w categories exhibited progressively worse glycemic metrics, including higher mean glucose concentrations and lower time in target range. The progressive deterioration observed across risk categories reinforces the clinical interpretability of the proposed thresholds and suggests that the index may facilitate stratification of pediatric patients according to glycemic risk. Such stratification may support earlier therapeutic adjustments and more individualized monitoring strategies. From a clinical perspective, an intermediate-term biomarker capable of identifying worsening glycemic control before substantial changes in HbA1c occur could improve therapeutic responsiveness and potentially reduce long-term complications associated with prolonged hyperglycemia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The present study should be interpreted in light of four main limitations. First, the sample size was relatively small, which may limit the generalizability of the findings and reduce statistical power for subgroup analyses. Second, the study was conducted in a single regional pediatric diabetes population, potentially introducing selection bias and limiting external validity. Third, although capillary glucose monitoring was used as the clinical reference standard, CGM-derived metrics were not universally available for all participants. Consequently, direct comparison between IGI-8w and standardized CGM-derived glycemic variability indices remains limited. In addition, the weighting coefficients adopted for construction of the composite index were theoretically predefined based on biological assumptions rather than derived through machine-learning optimization or external validation cohorts.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Future investigations should focus on external validation of the IGI-8w in larger and more diverse pediatric cohorts, including individuals with type 2 DM and other dysglycemic conditions. Prospective longitudinal studies are also needed to determine whether IGI-8w better predicts diabetes-related complications, glycemic variability, or therapeutic responsiveness compared with isolated biomarkers. Additional research integrating CGM metrics, artificial intelligence-based weighting strategies, and dynamic modeling approaches may further improve the precision and clinical applicability of intermediate-term glycemic indices. Moreover, evaluation of the proposed normalization methodology in adults, pregnant individuals, and patients with hemoglobinopathies or altered erythrocyte turnover may broaden its translational relevance.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe present study introduces a novel standardized approach for intermediate-term glycemic assessment in pediatric DM. By mathematically integrating normalized HbA1c and fructosamine into a unified composite biomarker, IGI-8w demonstrated strong analytical performance, clinically meaningful discrimination of glycemic control categories, and important agreement with conventional monitoring methods. These findings suggest that standardized multimarker strategies may represent a promising avenue for improving glycemic assessment in pediatric diabetes care, particularly in settings where access to advanced glucose monitoring technologies remains limited.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Considerations This study was conducted in accordance with the principles of the Declaration of Helsinki and relevant national and international guidelines for research involving human data. Approval by a Research Ethics Committee was not required under Brazilian regulations (CEP/CONEP system), in accordance with Resolution CNS 510/2016 (Article 1, sole paragraph, item VII), which exempts retrospective studies involving exclusively secondary analysis of anonymized routine data from mandatory ethics committee review, as the study exclusively involved secondary analysis of routine laboratory results that were fully anonymized prior to data access. No direct participant contact occurred, and no personally identifiable information was available to the investigators, thereby ensuring confidentiality and minimal ethical risk. The anonymization process ensured the impossibility of identifying minors participating in the study.\u003c/p\u003e\u003ch2\u003eDeclaration of competing interest:\u003c/h2\u003e \u003cp\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLundholm MD, Emanuele MA, Ashraf A, Nadeem S (2020) Applications and pitfalls of hemoglobin A1C and alternative methods of glycemic monitoring. J Diabetes Complications 34(8):107585\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNathan DM, DCCT/EDIC Research Group (2014) The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care 37(1):9\u0026ndash;16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStratton IM, Adler AI, Neil HA, Matthews DR, Manley SE, Cull CA, Hadden D, Turner RC, Holman RR (2000) Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321(7258):405\u0026ndash;412\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMattila TK, de Boer A (2010) Influence of intensive versus conventional glucose control on microvascular and macrovascular complications in type 1 and 2 diabetes mellitus. Drugs 70(17):2229\u0026ndash;2245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker ED, Lin J, Mahoney T, Ume N, Yang G, Gabbay RA, ElSayed NA, Bannuru RR (2024) Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care 47(1):26\u0026ndash;43\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzagew AW, Mekonnen CK, Lambie M, Shepherd T, Babatunde OO (2025) Poor glycemic control and its predictors among people living with diabetes in low- and middle-income countries: a systematic review and meta-analysis. BMC Public Health 25(1):714\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGounden V, Anastasopoulou C, Zubair M, Jialal I (2026) Clinical Utility of Fructosamine and Glycated Albumin. 2025 Sep 14. StatPearls [Internet]. StatPearls Publishing, Treasure Island (FL). Jan\u0026ndash;.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNayak AU, Holland MR, Macdonald DR, Nevill A, Singh BM (2011) Evidence for consistency of the glycation gap in diabetes. Diabetes Care 34(8):1712\u0026ndash;1716\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen RM, Holmes YR, Chenier TC, Joiner CH (2003) Discordance between HbA1c and fructosamine: evidence for a glycosylation gap and its relation to diabetic nephropathy. Diabetes Care 26(1):163\u0026ndash;167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRadin MS (2014) Pitfalls in hemoglobin A1c measurement: when results may be misleading. J Gen Intern Med 29(2):388\u0026ndash;394\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChehregosha H, Khamseh ME, Malek M, Hosseinpanah F, Ismail-Beigi F (2019) A View Beyond HbA1c: Role of Continuous Glucose Monitoring. Diabetes Ther 10(3):853\u0026ndash;863\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillery P (2022) HbA1c and biomarkers of diabetes mellitus in Clinical Chemistry and Laboratory Medicine: ten years after. Clin Chem Lab Med 61(5):861\u0026ndash;872\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSacks DB, Arnold M, Bakris GL, Bruns DE, Horvath AR, Lernmark \u0026Aring; et al (2023) Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 69(8):808\u0026ndash;868\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToyoshima MTK, Cukier P, Damascena AS, Batista RL, de Azevedo Correa F, Zanatta Kawahara E et al (2023) Fructosamine and glycated hemoglobin as biomarkers of glycemic control in people with type 2 diabetes mellitus and cancer (GlicoOnco study). Clin (Sao Paulo) 78:100240\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChandran K, Lee SM, Shen L, Tng EL (2024) Fructosamine and HbA1c: A Correlational Study in a Southeast Asian Population. J ASEAN Fed Endocr Soc 39(1):26\u0026ndash;30\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck R, Steffes M, Xing D, Ruedy K, Mauras N, Wilson DM, Kollman C (2011) Diabetes Research in Children Network (DirecNet) Study Group. The interrelationships of glycemic control measures: HbA1c, glycated albumin, fructosamine, 1,5-anhydroglucitrol, and continuous glucose monitoring. Pediatr Diabetes 12(8):690\u0026ndash;695\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParrinello CM, Selvin E (2014) Beyond HbA1c and glucose: the role of nontraditional glycemic markers in diabetes diagnosis, prognosis, and management. Curr Diab Rep 14(11):548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang DS, Park J, Kim JK, Yu J (2015) Clinical usefulness of the measurement of serum fructosamine in childhood diabetes mellitus. Ann Pediatr Endocrinol Metab 20(1):21\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParrinello CM, Selvin E (2014) Beyond HbA1c and glucose: the role of nontraditional glycemic markers in diabetes diagnosis, prognosis, and management. Curr Diab Rep 14(11):548\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonnier L, Colette C, Owens DR (2008) Glycemic variability: the third component of the dysglycemia in diabetes. Is it important? How to measure it? J Diabetes Sci Technol 2(6):1094\u0026ndash;1100\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohnert KD, Vogt L, Augstein P, Heinke P, Zander E, Peterson K et al (2009) Relationships between glucose variability and conventional measures of glycemic control in continuously monitored patients with type 2 diabetes. Horm Metab Res 41(2):137\u0026ndash;141\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBombaci B, Torre A, Longo A, Pecoraro M, Papa M, Sorrenti L, La Rocca M, Lombardo F, Salzano G (2024) Psychological and Clinical Challenges in the Management of Type 1 Diabetes during Adolescence: A Narrative Review. Child (Basel) 11(9):1085\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoma-Wilson MA, Buzzetti R, Zampetti S (2025) Bridging Pubertal Changes and Endotype Based Therapy in Type 1 Diabetes. Diabetes Metab Res Rev 41(3):e70038\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Glycemic biomarkers, Intermediate-term glycemic control, Biomarker standardization, Diabetes mellitus","lastPublishedDoi":"10.21203/rs.3.rs-9697395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9697395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo develop and validate a standardized glycemic biomarker for intermediate-term glycemic control (~\u0026thinsp;8 weeks), Intermediate Glycemic Index (IGI-8w), bridging the gap between fructosamine and HbA1c in the pediatric population with type 1 diabetes mellitus (T1DM).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this methodological validation study, laboratory data from children and adolescents (aged 0\u0026ndash;18 years) with T1DM patients were analyzed. HbA1c and fructosamine were normalized via min-max transformation using age-specific clinical reference intervals. Serial HbA1c, fructosamine, and fasting plasma glucose measurements over 8 weeks assessed temporal associations. Pearson/Spearman correlations, ROC curve analysis, one-way ANOVA, and Bland-Altman plots evaluated accuracy, agreement, and discrimination.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNormalized HbA1c and fructosamine strongly correlated with each other and with glucose-derived glycemic control over 8 weeks. Both biomarkers' upper normality limits approximated 1.0. A composite index of normalized HbA1c and fructosamine improved DM control discrimination, with a superior ROC area under the curve versus individual markers, and results were consistent across different pediatric age groups (prepubertal, pubertal, and adolescents).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIGI-8w provided reliable intermediate-term glycemic assessment through integration of normalized HbA1c and fructosamine, demonstrating clinically meaningful discrimination and agreement with conventional monitoring, potentially supporting pediatric DM management where glucose monitoring technologies remain unavailable.\u003c/p\u003e","manuscriptTitle":"Bridging the Gap Between Fructosamine and HbA1c: Development and Validation of an 8-Week Glycemic Index in Pediatric Type 1 Diabetes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 17:49:49","doi":"10.21203/rs.3.rs-9697395/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":"ab6e42e9-7d0a-4b1a-b6b5-00f76d505028","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":68048544,"name":"Endocrinology \u0026 Metabolism"},{"id":68048545,"name":"Pediatrics"}],"tags":[],"updatedAt":"2026-05-15T17:49:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 17:49:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9697395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9697395","identity":"rs-9697395","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}