Cognitive flexibility is affected by the age of onset and duration among patients with type 1 diabetes: a network analysis

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This study aimed to explore the relationships between specific cognitive functions—namely memory, attention, and cognitive flexibility—and clinical characteristics, including age of onset and diabetes duration, as well as glycemic factors, such as glycemic control and extreme glycemic events, in a cohort of 331 patients with type 1 diabetes. Cognitive performance was assessed using the Wechsler Memory Scale, Wisconsin Card Sorting Test, and the Sustained Attention to Response Task. Network analysis revealed that age of onset and diabetes duration were central nodes in the network, strongly influencing cognitive flexibility. Additionally, age of onset was associated with a history of severe hypoglycemia, while diabetes duration was linked to visual memory decline. Significant differences were observed between the network structures of the adult-onset and childhood-onset groups, with the childhood-onset group showing greater interconnectedness. These findings emphasize the critical impact of age of onset and disease duration on cognitive outcomes in type 1 diabetes and highlight the importance of personalized treatment strategies. Longitudinal studies are needed to further understand these relationships and guide targeted interventions to preserve cognitive function. Biological sciences/Psychology Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes mellitus type 1 diabetes cognitive function age of diabetes onset duration glycemic fluctuation Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Type 1 diabetes is a chronic metabolic disease caused by the destruction of pancreatic beta cells, leading to an absolute lack of insulin. Patients need to rely on long-term insulin therapy to maintain blood sugar stability 1 . With the continuous progress of medical technology and the growing concern for health, the health-related quality of Life of people with type 1 diabetes has significantly improved 2 . Meanwhile, the long-term complications have increasingly gained attention, with cognitive impairment being an important yet often overlooked complication of type 1 diabetes 3 . Type 1 diabetes patients may face the risk of impaired cognitive function, particularly in attention, memory, executive function and information processing speed 4 , 5 . Among these, cognitive flexibility, an essential aspect of executive function involving the ability to adapt thinking and behavior in response to environmental changes, has garnered increasing attention from researchers in recent years 6 . Previous studies have reported that type 1 diabetes patients may be at risk of impaired cognitive flexibility, as shown by more attempts to complete the classification, more errors, and difficulty in switching concepts in the Wisconsin Card Sorting Test compared to the healthy control group 7 . All these cognitive impairments can, in turn, significantly affect patients’ quality of life and disease management ability 8 . Palomo-Osuna et al. demonstrated that cognitive impairments could lead to reduced independence in daily life, increased risk of depression and anxiety, and negative impacts on social interactions and occupational performance 9 . Numerous studies have indicated a strong connection between the clinical characteristics of type 1 diabetes patients and cognitive impairment. Of these characteristics, the age of onset and disease duration were the most frequently investigated. While type 1 diabetes can develop at any age, its peak incidence is during early adolescence 10 . Several studies have shown that early-onset patients (≤ 6 years old) exhibit more pronounced impairments in attention, executive function, and information processing speed. Juvenile patients tended to experience greater impairments in learning and memory, while adult patients showed more significant declines in executive function and information processing speed 5 . Generally, adults with type 1 diabetes experienced relatively milder cognitive impairments. The disease duration reflected the ongoing impact of the disease and the accumulation of metabolic disturbance, making its association with cognitive function significant. As metabolic control worsens, individuals with high HbA1c levels and severe hypoglycemia episodes might face significant cognitive challenges 11 . Moreover, research suggested that blood glucose management behaviors in type 1 diabetes varied based on age of onset and current age 5 . Poor glycemic control, causing fluctuations like hypoglycemia and hyperglycemia, was linked to acute and chronic brain damage, affecting cognitive function. Empirical studies indicated that repeated diabetic ketoacidosis episodes in children with type 1 diabetes were independently associated with lower IQ and memory deficits 12 . Additionally, Jacobson et al. found that severe hypoglycemic episodes increase the risk of cognitive dysfunction, particularly in memory, psychomotor speed, and mental efficiency 11 . Similarly, chronic hyperglycemia was strongly associated with cognitive impairment, including mild cognitive impairment and dementia. Studies indicated that chronic hyperglycemia could cause memory impairment, issues with learning and retention, attention disorders, and neuropathy 13 . These findings underscored the importance of blood glucose control in maintaining cognitive health and suggested that better glycemic management could help slow cognitive decline, regardless of the age of onset. However, much existing literature on cognitive function in type 1 diabetes patients has focused on specific glycemic parameters and extreme events like age of onset in adulthood versus childhood, with inconsistent findings 8 , 11 . This might be due to the lack of a comprehensive examination of these complex relationships. Recently, network analysis has provided new perspectives for exploring multivariate complex relationships. Specifically, it offers a novel approach to exploring the dynamic interplay between cognitive function variables and type 1 diabetes-related clinical and glycemic characteristics 14 , identifying key variables that significantly influence cognitive outcomes. Integrating multiple cognitive functions and disease characteristics into a single network offers valuable insights into these complex interactions, revealing how cognitive function variables interact with clinical and glycemic characteristics in type 1 diabetes. Taken together, this study aimed to comprehensively explore the complex relationships between cognitive functions—specifically memory, attention, and cognitive flexibility—and various clinical and glycemic characteristics in type 1 diabetes patients, including age of onset, disease duration, glycemic metabolism parameters, and extreme glycemic events, using network analysis. To our knowledge, this has not yet been investigated. The study had two primary objectives. The first was to examine the detailed structure and connectivity characteristics of the network that links cognitive functions with clinical and glycemic characteristics in type 1 diabetes. Through this analysis, key clinical variables related to type 1 diabetes that significantly impact patients' cognitive functions were identified, as well as cognitive function indicators that are particularly sensitive to age of onset, disease duration, glycemic parameters and extreme glycemic events. The second objective involved constructing distinct networks for patients grouped by different ages of onset and life stages. These networks were analyzed to identify differences and unique characteristics specific to each group, providing a more comprehensive and dynamic understanding of the clinical features and treatment priorities for type 1 diabetes patients with different chronological disease indicators. RESEARCH DESIGN AND METHODS Participants Data were collected from November 2015 to March 2022. A total of 345 patients with type 1 diabetes mellitus were recruited from the Diabetes Clinic at the Second Xiangya Hospital in Changsha, Hunan, China, during regular visits. Fourteen patients declined participation. The final sample comprised 331 patients (160 males and 171 females), aged 6.5 to 52 years (M = 18.19, SD = 8.72). Diagnoses were confirmed by two endocrinologists using the Chinese Type 1 Diabetes Guidelines and the American Diabetes Association's criteria. Exclusion criteria included psychiatric or neurological disorders, hypertension, other chronic or acute diseases, premature birth (<36 weeks gestation), birth complications, or physical limitations affecting testing. Informed consent was obtained from all participants, and the study adhered to ethical standards. Measures Clinical and glycemic characteristics Researchers collected demographic and clinical data using a designed form and medical records. Variables included age, sex, educational age, age of type 1 diabetes onset, diabetes duration, blood glucose level, mean hemoglobin A1c (HbA1c) levels, fasting blood glucose (FBG) levels, fasting C-peptide levels, history of severe hypoglycemia (SH), and history of diabetic ketoacidosis (DKA). SH was defined as low blood glucose requiring assistance or glucagon injection due to seizure, loss of consciousness, or disorientation. DKA history was determined by medical records. Blood glucose levels were measured before cognitive testing, with snacks or insulin administered as needed to maintain levels between 4 and 17 mmol/L. The hyperglycemic exposure index was calculated using the patient's average HbA1c and disease duration, converted into z-scores. Cognitive functions Cognitive function was assessed using the Wechsler Intelligence Scale, Wechsler Memory Scale (WMS), Wisconsin Card Sorting Test (WCST), and Sustained Attention to Response Task (SART). Specifically, general intellectual ability was evaluated with the Wechsler Intelligence Scale for Children—Chinese Revision for participants under 16 years of age, and the Wechsler Adult Intelligence Scale—Chinese Revision for those 16 years or older. These assessments are highly correlated (r > 0.9). Intelligence Quotient was calculated from five subtests: Information, Digit Span, Similarities, Picture Completion, and Block Design. The Similarities subtest evaluated abstract thinking and visual reasoning, while the Block Design subtest assessed spatial visualization and problem-solving abilities. The WMS assessed logical and visual memory, with participants asked to recall stories and reproduce drawings both immediately and after a delay. The WCST, which measured executive function, involved card sorting tasks, with key outcome measures being the number of categories completed and the number of perseverative errors, both reflecting cognitive flexibilities. Finally, attention was evaluated using the SART, where participants responded to digits displayed on a screen. Further details of all these assessments are available in Table S1. Table 1 summarizes all the instruments and measurement variables derived from each instrument that were utilized as network nodes in the later analyses. Statistical Analysis The data were preprocessed and analyzed using SPSS 26.0 for descriptive statistics. Network analyses and comparisons were conducted with R, version 4.0.5. The network analysis approach followed the guidelines by Epskamp et al. 15 . A significance level of 0.05 was used. Missing values were analyzed and estimated using the Expectation-Maximization Algorithm (EM) in SPSS. Further details concerning missing values are shown in supplementary materials Figure S1. Network analysis was performed on data from all 331 participants to explore relationships among cognitive functions and clinical-glycemic characteristic. Adult patients were divided into childhood-onset and adult-onset groups based on age of onset, with separate networks constructed for comparison. Additionally, networks for minor patients were compared with those of childhood-onset adult patients to explore developmental differences. The standardized Gaussian graphical model network was estimated using the R-packages bootnet and qgraph . Nodewise errors, representing variance explained by connected nodes, were visualized using the mgm package. The graphical LASSO model with EBICglasso reduced the probability of false connections. A partial correlation network corrected correlations between variables for all other variables, visualized as nodes and edges. The Fruchterman Reingold algorithm detected node pairs with stronger interconnections and higher centrality indices. Centrality indices, including strength, closeness, and betweenness, indicated the most important nodes. Strength quantifies direct connections, closeness quantifies indirect connections, and betweenness quantifies the average path essentiality between nodes. Edge-weight accuracy was computed using bootnet with 1000 nonparametric bootstrap samples. Centrality stability was quantified using the correlation stability coefficient (CS-coefficient), ensuring 95% certainty with >0.7 correlation to original centrality. Bootstrapped difference tests identified non-zero differences between each edge weight and node strength. In the network constructed among 331 patients with type 1 diabetes, the role of individual items as bridges between communities was assessed. Nodes that connect different predefined domains are called bridge nodes, which act like a bridge to link the two communities in the network. Bridge strength, defined as the absolute sum of the edge weights connecting an item in one community to items in another community, and bridge expected influence, defined as the nonabsolute sum of all edges connecting an item to others in a different community, were calculated for each item. These metrics were computed separately for each cognitive function in relation to clinical and glycemic characteristics. The Network Comparison Test (NCT) package in R conducted the comparison using permutation tests with 1000 repetitions. Global network strength, defined as the weighted sum of absolute connection values, reflected node interconnectivity. Significant differences (p < 0.05) between groups were identified, with specific edge-weight differences examined using Bonferroni correction for multiple comparisons. RESULTS Descriptive Statistics and Item Inspection Detailed descriptive statistics for the entire sample and subgroups are provided in Table 2. The zero-order correlation matrixs are available in supplementary materials(Table S2-7). Cognitive Functions, Clinical and Glycemic Characteristics Network The network included 331 patients, as illustrated in Figure 1. It contained 51.2% nonzero edges, with edge weights ranging from -0.229 to 0.827. The edge weights matrix and an overview of the number of edges are in Table S8. Centrality indices are presented as standardized z scores in Figure 1. Items with high strength were highly connected, while those with high expected influence had more positive connections. Edge Weight and Centrality Accuracy Figure 1 shows the centrality indices of the network. The age of onset, diabetes duration, and visual memory delay test score had the highest centrality(near 1), with 8, 7, and 7 connected edges, respectively. This indicates that the age of onset was pivotal in the network. For edges, the age of onset was notably linked to completed categories, perseverative errors in the M-WCST and severe hypoglycemia history, with edge weights of 0.280, 0.229 and 0.204, respectively. The edge's and the strength's CS-coefficient were 0.749, indicating that the order of edge weight and node strength can be interpreted with caution. Bridges The bridge item between cognitive functions and clinical and glycemic characteristics was identified through bridge centrality. Three nodes (A_O, WCST_C and WCST_P) were identified with bridge strengths in the top . The bridge strength of each item is shown in Figure 1. Bridge strength was highly stable, with a CS coefficient of (CS[cor = 0.7] = 0.517). The edge connecting “WCST_P” had the strongest weight (0.507) with node “WCST_C” in the cognitive functions community, while the edge connecting “A_O” had the strongest weight (0.280) in the clinical and glycemic characteristics community. Network Comparisons Effect of Age of Onset: Childhood-Onset versus Adult-Onset Separate networks for childhood-onset (n=78) and adult-onset adult (n=72) patients to identify differences. Detailed networks and edge weights are shown in the Supplementary Materials. Figure 2 shows the centrality indices of the networks. In the childhood-onset group, diabetes duration and severe hypoglycemia history emerged as significant nodes, with diabetes duration linked to FCP levels and severe hypoglycemia linked to logical memory delay and WCST categories. Stability was confirmed with a CS-coefficient of edge at 0.44 and strength at 0.44 for the childhood-onset group, and 0.36 and 0.28 for the adult-onset group, which meant order of edge weight and node strength can be interpreted with caution. The Network Comparison Test revealed significant differences in network structure (M = 0.37, p = 0.02) and overall connectivity (S = 2.59, p = 0.00), with higher connectivity in the childhood-onset group (4.45 vs. 1.86, p < 0.05). Effect of Current Age in Childhood-Onset Type 1 Diabetes: Child versus Adult A network comparison was also conducted between child (n=181) and adult (n=78) groups among childhood-onset patients. Detailed networks and edge weights are shown in the Supplementary Materials. Figure 3 shows the centrality indices of the networks. The child group's network highlighted the age of onset and visual memory test scores as important nodes. The age of onset was linked to visual memory immediate test scores and the Wechsler Intelligence Scale similarity subtest. The hyperglycemia exposure index was linked to WCST perseverative errors. In the adult group, significant nodes were the similarity subtest score and diabetes duration, with the similarity subtest score linked to logical memory immediate test scores and DKA history. Stability was confirmed with a CS-coefficient of edge at 0.75 and strength at 0.67 for the child group, and 0.51 and 0.44 for the adult group. Thus, the order of edge weight and node strength can be interpreted with caution. The Network Comparison Test revealed significant differences in network structure (M=0.42, p=0.02) but no significant differences in global strength (S=1.14, p=0.63). This suggests substantial structural differences between child and adult groups, though global strength showed some similarities. DISCUSSIONS The present study was the first to examine the network structure of cognitive functions, clinical characteristics and glycemic characteristics in patients with type 1 diabetes, offering novel insights into their complex interplay. The present study constructed an overall network with 331 participants and found that the age of onset and diabetes duration were not only central nodes but also key bridge nodes. Notably, both variables were closely associated with cognitive flexibility, as evidenced by their strong connections to WCST Perseverative Errors and Categories Completed. Additionally, significant associations were also observed between the age of onset and severe hypoglycemia history, and between diabetes duration and visual memory performance. Comparative network analyses underscored the crucial roles of age of onset and disease duration, highlighting their impact on cognitive function, particularly cognitive flexibility. Cognitive Functions and Clinical-Glycemic Characteristics Network The present study revealed that the age of onset plays a critical role in shaping the relationship between cognitive functions and clinical-glycemic characteristics in patients with type 1 diabetes. Early-onset diabetes was closely associated with more pronounced cognitive impairments, particularly in areas of memory, learning, and executive function. These impairments were likely driven by disruptions in insulin signaling and brain development, particularly in the hippocampus and frontal lobes, which were highly sensitive to metabolic disturbances 11 . Additionally, a younger age of onset also typically resulted in a longer disease duration, increasing the likelihood of prolonged hyperglycemia 12 . Over time, chronic hyperglycemia led to the accumulation of glycation end products, oxidative stress, and microvascular damage, all of which contribute to brain insulin resistance and neurodegeneration 16,17 . These pathological changes progressively reduced gray and white matter volumes in critical brain regions responsible for cognitive functions 13,18 . MRI studies further supported this, showing long-term reductions in brain volume, particularly in the frontal and temporal lobes—regions essential for executive function and memory 5,19-21 . Consistent with these structural changes, current research has observed that the age of onset is significantly related to visual memory test performance, while the hyperglycemia exposure index was closely linked to performance the WCST, which assesses cognitive flexibility. Network analysis highlighted the age of onset as a central node directly influencing cognitive flexibility, demonstrated by its associations with the WCST Perseverative Errors and Categories Completed. Additionally, both the age of onset and cognitive flexibility served as key bridge nodes in the network, linking cognitive functions and clinical-glycemic characteristics. These findings underscored the cumulative impact of early-onset diabetes on cognitive flexibility, as well as the broader cognitive decline linked to prolonged disease duration. Structural changes have been observed in the striatum and thalamus, alongside impairments in the mesial temporal cortex. Despite these structural changes, cerebral blood flow alterations were not observed. These changes in the mentioned brain regions may lead to deficits in executive functions, particularly in tasks requiring memory and cognitive flexibility 22 . Furthermore, severe hypoglycemia was a key factor influencing processing speed and executive functions, especially in childhood-onset patients more vulnerable to glucose fluctuations. Importantly, these impairments in cognitive flexibility were highly relevant for diabetes management, as patients with higher cognitive flexibility tend to be more capable of problem-solving, adhering to treatment regimens, and maintaining stable glucose levels 23,24 . Taken together, these findings emphasized the importance of early and sustained interventions targeting glycemic control to mitigate long-term cognitive decline. While correlations between cognitive functions, clinical-glycemic characteristics were evident across the network, specific relationships varied depending on the patient's age of onset. Differences in network structures and connectivity patterns between patients diagnosed at different life stages suggested that these factors should be explored further in subsequent analyses. Effect of Age of Onset: Childhood-Onset versus Adult-Onset Comparing childhood-onset and adult-onset type 1 diabetes patients revealed important differences in network structure, global strength, and node connections, which are crucial for tailoring treatment strategies based on age of onset. In childhood-onset patients, the network showed stronger connections between nodes representing disease duration, chronic hyperglycemia, and cognitive functions, making cognitive impairment more prominent and complex to manage. Notably, the "SH" node connected with several cognitive function nodes in childhood-onset patients, a connection absent in the adult-onset network. This heightened connectivity indicated that cognitive functions and diabetes-related factors were more tightly linked in childhood-onset patients, contributing to greater impairments in executive function, attention, visual-spatial abilities, and processing speed 25 . Early onset, especially before age five, was associated with poorer school performance and motor skills 26 . In contrast, adult-onset patients experienced milder cognitive impairments, likely due to more stable brain energy metabolism and blood flow regulation during glucose fluctuations after key brain developmental stages are complete 17 . Severe hypoglycemia, a central node in the childhood-onset network, significantly impacted cognitive functions such as verbal memory and executive functions. Frequent hypoglycemic episodes in younger patients led to disruptions in brain metabolism, increasing free radicals and causing neuronal damage, particularly in the hippocampus and frontal lobes 13 . Over time, recurrent episodes of hypoglycemia could lead to chronic inflammation and further cognitive decline. MRI studies revealed these brain regions critical for memory and reasoning are particularly vulnerable to damage from severe hypoglycemia 12,18,27 . Additionally, metabolic differences between childhood-onset and adult-onset patients may further explained variations in cognitive outcomes. Childhood-onset patients exhibited a negative correlation between disease duration and fast C-peptide levels. Childhood-onset patients often had lower C-peptide levels at diagnosis, reflecting poorer initial metabolic control compared to adult-onset patients, who typically had higher C-peptide levels and more stable glucose regulation 8 . These metabolic differences, combined with more frequent glucose fluctuations in childhood-onset patients, contributed to greater brain structural changes and cognitive impairments 17 . Effective blood glucose management, especially reducing extreme glucose events, was crucial for preserving cognitive function in both childhood-onset and adult-onset patients, with early intervention being particularly important for those diagnosed in childhood. Effect of disease duration: current age (Child versus Adult) in Childhood-Onset Type 1 Diabetes Comparing childhood-onset patients who were still children with those who have reached adulthood revealed important differences in network structure and connectivity characteristics, even though overall network connectivity and edge strength remain similar. Cognitive function in childhood-onset patients continued to be influenced by blood glucose fluctuations into adulthood, with significant changes occurring as they age. Consequently, treatment and monitoring should be tailored to different ages. In childhood-onset patients, the age of onset served as a central node, strongly linked to outcomes on the immediate visual memory test and similarity test. In contrast, for adult-onset patients, disease duration became more influential, and diabetic ketoacidosis showed a stronger correlation with the similarity test. Long-term studies indicate that childhood-onset patients experience persistent structural brain damage, which continues to affect cognitive function into adulthood 28 . Functional MRI studies in children with type 1 diabetes have identified lasting damage in critical areas, including the prefrontal cortex and hippocampus, which are particularly vulnerable during childhood development 29,30 . While adults with childhood-onset diabetes may develop some resistance to blood glucose fluctuations, poor metabolic control can still result in cognitive difficulties 8 . The study also emphasizes the significant impact of repeated DKA episodes on the brain, particularly in regions crucial for memory, learning, and executive functions like the hippocampus, frontal lobes, and parietal lobes. MRI studies showed that DKA leads to reduced gray matter volume and white matter integrity in these regions, with childhood-onset patients experiencing more pronounced damage due to increased vulnerability during brain development. This damage contributes to deficits in cognitive functions such as memory and attention 7,12,31 . Additionally, mental health challenges such as anxiety and depression are more common among childhood-onset patients, highlighting the need for comprehensive care and psychological support throughout their lives 31,32 . Strengths and Limitations This study was the first to apply network analysis to explore the relationships between cognitive functions and clinical-glycemic characteristics in type 1 diabetes, emphasizing the critical role of age-related factors, such as the age of onset and current age. The findings underscored the need for tailored interventions and treatment strategies that account for variations in disease duration among patients. Personalized approaches considering diagnosis timing and disease progression are essential for mitigating cognitive decline and improving overall outcomes in type 1 diabetes patients. Despite its strengths, the study has several limitations. First, there are limitations associated with the sample and generalizability. The sample size was relatively small, especially after group division, and differences in group sizes might have limited the verification of some statistical associations. All participants were Chinese, which limits the generalizability of the findings to other populations. Second, the use of cross-sectional data prevents causal inferences about symptom-level relationships. Longitudinal analysis is needed to gain insight into temporal relationships and the sequence of item activation. Third, Network analysis doesn’t account for covariates or confounders, as the partial correlations between items only control for other items in the network. External factors not modeled, such as stressors, mood, emotion regulation abilities, and gender, might drive observed associations. A deeper exploration of these factors may provide further insight into how cognitive functions are influenced over time in type 1 diabetes patients. Declarations Conflict of Interest. No honorarium, grant, or other forms of payment was given to anyone to produce the manuscript. The authors have stated that they had no interests which might be perceived as posing a conflict or bias. Ethic Statement. This research has been approved by the Ethics Committee of Central South University Funding. This research was supported by the “Integrity disease-management programs for type 1 diabetes in China” which was supported by National Health and Family Planning Commission of the People's Republic of China (2013BA109B12), National Science and Technology Infrastructure Program during the 12th Five‐Year Plan Period (2015BAI12B13), Outstanding Youth Project for Scientific Research of Hunan Provincial Department of Education (19B115), National Social Science Foundation Program (BHA180158), and Hunan Provincial Natural Science Foundation Project (2023JJ30181). Author Contribution All authors have contributed significantly to the study. Ding Mojun designed the study, analyzed the data, and wrote and reviewed the manuscript. Yuan Dongling, Li Chuting edited the manuscript. He Jing, Wenjing Zou collected data, assisted in the data analysis. Zhu Xiongzhao and Li Chuting reviewed and edited the manuscript. Zhu Xiongzhao is the guarantor of this work and, as such, takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript. Acknowledgement We thank all the participants. We would like to express our heartfelt gratitude to the staff of the Department of Endocrinology and Metabolism affiliated with the Second Xiangya Hospital of Central South University. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. References ElSayed, N. A. et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes Care 46 , S19-S40, doi:10.2337/dc23-S002 (2023). 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes-2024. Diabetes Care 47 , doi:10.2337/dc24-S005 (2024). Zou, W. et al. 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Network analysis of depression, cognitive functions, and suicidal ideation in patients with diabetes: an epidemiological study in Iran. Acta Diabetol 61 , 609-622, doi:10.1007/s00592-024-02234-z (2024). Sen, Z. D. et al. Linking atypical depression and insulin resistance-related disorders via low-grade chronic inflammation: Integrating the phenotypic, molecular and neuroanatomical dimensions. Brain Behav Immun 93 , 335-352, doi:10.1016/j.bbi.2020.12.020 (2021). Tables Table 1-Overview of all the instruments and variables (nodes). Domains and instruments Nodes Cognitive function Cognitive flexibility Modified Wisconsin Card Sorting Test, M-WSCT Perseverative errors (WCST_P) Categories completed (WCST_C) Memory Wechsler Memory Scale, WMS Visual memory immediate test score (VM_1) Visual memory delay test score (VM_2) Logical memory immediate test score (LM_1) Logical memory delay test score (LM_2) Sustained attention Sustained Attention to Response Task, SART Intraindividual variability (VII) Omission error rate (O_R) Commission error rate (C_R) Mean reaction time (RT) Visual reasoning and abstract thinking abilities Wechsler Intelligence Scale— Chinese Revision,WAIS-RC The Similarity subtest of the WAIS-RC (IQ_S) Spatial perception and processing abilities Wechsler Intelligence Scale— Chinese Revision,WAIS-RC The Block Design subtest of the WAIS-RC (IQ_B) Clinical and Glycemic Characteristics Clinical chronological indicators Age of onset (A_O) Diabetes duration (D_D) Glycemic parameters and extreme events Diabetic ketoacidosis history (DKA) Severe hypoglycemia history (SHh) Fasting C-peptide levels (FCP) Hyperglycemic exposure index (HEI)* Note: Hyperglycemia exposure index score can reflect the impact of blood sugar control level and duration and is currently a widely used indicator to evaluate chronic hyperglycemia levels. In this study, all available HbA1c monitoring results and the patient's disease duration were collected from the medical records of each child with type 1 diabetes to calculate each child's hyperglycemic exposure index. The specific algorithm is: first, calculate the average HbA1c of each patient based on all HbA1c values of each patient, and then convert the two variables of the patient's average HbA1c and disease duration into standard scores (z-score); Second, the hyperglycemic exposure index for each patient was obtained by adding the patient's z-score over the course of the disease and the mean z-score for HbA1c. Table 2-Sample characteristics total type 1 diabetes patients (n=331) Adult patient (n=150) Child patient group (n=181) Childhood-Onset group (n=78) Adult-Onset group (n=72) Age, years (mean [SD], [IQR]) 18.19(8.72), (6.5-52) 21.61(4.61), (18-40) 28.87(7.41), (19-52) 12.06(3.32), (6.5-18) Female sex (n, %) 171(51.6) 44(56.4) 34(43.6) 89(49.2) Education age (mean [SD], [IQR]) 9.89(4.97), (1-20) 13.83(1.86), (9-19) 14.83(2.56), (9-20) 6.20(3.28), (1-14) Diabetes-related variables (mean [SD], [IQR]) Age of onset 14.54(8.16), (1-20) 13.60(7.88), (3-18) 26.92(6.77), (18-48) 9.98(3.80), (1-17) Diabetes duration 3.66(4.45), (0.08-28) 7.88(6.00), (0.50-28) 2.91(3.55), (0.08-17.17) 2.15(2.55), (0.25-13) DKA history 0.57(0.62), (0-5) 0.56(0.50), (0-1) 0.42(0.50), (0-1) 0.63(0.70), (0-5) SH history 0.49(0.84), (0-5) 0.37(0.49), (0-1) 0.20(0.40), (0-1) 0.66(1.03), (0-5) Mean HbA1c(%) 8.33(2.03), (3.92-15.60) 8.39(2.10), (3.92-15.3) 8.02(2.51), (4-15) 8.42(1.87), (5.20-15.60) Fasting C-peptide levels -0.77(1.07), (-0.77-3.63) -0.53(1.20), (-2.67-2.49) Hyperglycemic exposure index 0(1.32), (-2.66-5.69) 0.49(1.33), (-1.88-4.46) 0.09(0.92), (-0.77-4.00) 0(1.24), (-2.27-4. 09) Cognitive functions (mean [SD], [IQR]) Wechsler Memory Scale Visual memory immediate test score 9.95(2.81), (1-20) 10.97(2.57), (5-20) 10.25(2.73), (3-14) 9.41(3.06), (1-17) Visual memory immediate test score 9.17(2.95), (0-20) 10.51(2.72), (4-20) 9.04(2.31), (3-14) 8.53(3.11), (0-16) Logical memory immediate test score 6.91(2.08), (0.75-18.50) 7.76(2.29), (1-16) 6.87(2.98), (1-18.5) 6.59(2.87), (1-15) Logical memory delay test score 5.89(2.71), (0-13.25) 6.65(2.56), (1-13) 5.47(2.73), (0-13) 5.77(2.78), (0-12.5) Modified Wisconsin Card Sorting Test Perseverative errors 48.11(13.36), (20-80) 47.57(8.36), (28-71) 45.31(8.57), (20-61) 50.02(15.44), (20-80) Categories completed 2.92(1.26), (0-5) 3.10(1.29), (0-5) 3.51(1.26), (0-5) 2.60(1.18), (0-5) Sustained Attention to Response Task Omission error rate 3.03(4.98), (0-21) 0.06(0.27), (0-20) Commission error rate 8.37(7.44), (0-24) 0.78(2.61), (0-20) Mean reaction time 377.28(95.39), (246.51-652.25) 388.79(89.32), (227-619) Intraindividual variability 0.29(0.11), (0.12-0.69) 0.25(0.09), (0-1) Chinese version of the Wechsler adult intelligence, WAIS-RC The Similarity subtest of the WAIS-RC 13.03(3.76), (5-24) 13.02(3.22), (4-23) The Block Design subtest of the WAIS-RC 16.03(10.45), (4-48) 12.08(4.06), (3-38) Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 19 Nov, 2024 Reviews received at journal 13 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor assigned by journal 28 Oct, 2024 Editor invited by journal 14 Oct, 2024 Submission checks completed at journal 13 Oct, 2024 First submitted to journal 12 Oct, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5251082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":385481263,"identity":"4343d202-31dd-415e-93ed-1cb087e9dea4","order_by":0,"name":"Ding Mojun","email":"","orcid":"","institution":"Medical Psychological Center The Second Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Mojun","suffix":""},{"id":385481264,"identity":"c1f32d88-b5a9-4e3f-adbd-02f31ff1bef5","order_by":1,"name":"Yuan Dongling","email":"","orcid":"","institution":"Medical Psychological Center The Second Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Dongling","suffix":""},{"id":385481265,"identity":"b28c5a53-c59c-4cc2-9ca8-a37d27edbfd5","order_by":2,"name":"He Jing","email":"","orcid":"","institution":"Department of Psychology, Hunan First Normal University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Jing","suffix":""},{"id":385481266,"identity":"674afcff-6386-4bde-8042-b40a6c21d02c","order_by":3,"name":"Zou Wenjing","email":"","orcid":"","institution":"Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zou","middleName":"","lastName":"Wenjing","suffix":""},{"id":385481267,"identity":"93d12334-de5e-4722-923a-bcf81023a0a3","order_by":4,"name":"Li Xia","email":"","orcid":"","institution":"National Clinical Research Center for Metabolic Diseases,Second Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Xia","suffix":""},{"id":385481268,"identity":"d31b2c1c-d944-4cad-b673-8f26c17257c8","order_by":5,"name":"Li Chuting","email":"","orcid":"","institution":"Medical Psychological Center The Second Xiangya Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Chuting","suffix":""},{"id":385481269,"identity":"6ad11c3b-905c-4ab4-b8ff-4e425a09050f","order_by":6,"name":"Zhu Xiongzhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDACZjB5AIh5GBg+QMQMiNfCOIMoLQxIWph5iNFicJz52MMvf+7I8/OfPSZtU1OX2MDevE2CoeYOTi2SzWzpxjI8zwxnNpxLk845djixgedYmQTDsWc4tfAz85hJS0gcZtxwsMdMOrfhQGKDRI6ZBGPDYZxa2MBaDA7bbzgMZFg2AB0m/wa/FpAtkh8SDiduOAbUwtjADLSFB78WoF/SpBkOHE6e2cNjbNlz7LBxG09asUXCMdxaDM4fPib5489h237+M4Y3ftTUyfazH95440MNbi0gAIsOqO9ARAJeDQwMjD8IKBgFo2AUjIIRDgBk7U7gdk3IJQAAAABJRU5ErkJggg==","orcid":"","institution":"Medical Psychological Center The Second Xiangya Hospital, Central South University","correspondingAuthor":true,"prefix":"","firstName":"Zhu","middleName":"","lastName":"Xiongzhao","suffix":""}],"badges":[],"createdAt":"2024-10-12 10:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5251082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5251082/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-99678-2","type":"published","date":"2025-07-07T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71725062,"identity":"a61dadfe-4323-4bb8-ae8d-8bb947948664","added_by":"auto","created_at":"2024-12-18 06:00:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260744,"visible":true,"origin":"","legend":"\u003cp\u003eThe estimated network with cognitive functions and clinical-glycemic characteristics is shown(a). Each circular node represents a clinical-glycemic characteristics or a score of cognitive testing. The edge (line) connecting nodes represents partial polychoric correlations, with thicker, more saturated edges denoting stronger connections, blue edges denoting positive relationships, and red edges denoting negative relationships. The strength and expected influence of each node is shown as a standardized z score(b). Nodes high in strength play critical role in shaping the relationships among other nodes, while node high in expected influence have more positive connections to other nodes. The bridge strength and bridge expected influence of each node is shown as a standardized z score(c).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5251082/v1/dc578d5ad59c470d3bf1c8cc.jpeg"},{"id":71726727,"identity":"9ca61c6d-324b-41f8-af31-6c3a03e419a3","added_by":"auto","created_at":"2024-12-18 06:16:03","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":288552,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated networks with cognitive functions and diabetes-related variables are shown. (a: Childhood-onset group; b: Adult-onset group). Strength and expected influence are shown as standardized z scores. (c: Blue-Childhood-onset group; Red-Adult-onset group). Each circular node represents a diabetes-related variable or a score of cognitive testing. The edge (line) connecting nodes represents partial polychoric correlations, with thicker, more saturated edges denoting stronger connections, blue edges denoting positive relationships, and red edges denoting negative relationships. Nodes high in strength play critical role in shaping the relationships among other nodes, while node high in expected influence have more positive connections to other nodes.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5251082/v1/e759c4a24246ce8bea844d50.jpeg"},{"id":71725063,"identity":"4ab84eb1-6250-4f56-a653-b98be2753835","added_by":"auto","created_at":"2024-12-18 06:00:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":300308,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated networks with cognitive functions and diabetes-related variables are shown. (a: Child group; b: Network b-Adult group). Strength and expected influence are shown as standardized z scores. (c: Blue-Child group; Red-Adult group). Each circular node represents a diabetes-related variable or a score of cognitive testing. The edge (line) connecting nodes represents partial polychoric correlations, with thicker, more saturated edges denoting stronger connections, blue edges denoting positive relationships, and red edges denoting negative relationships. Nodes high in strength play critical role in shaping the relationships among other nodes, while node high in expected influence have more positive connections to other node\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5251082/v1/0db01df4282d1015890e2977.jpeg"},{"id":86699443,"identity":"9a5f0646-121b-4126-9983-e30ba70030c9","added_by":"auto","created_at":"2025-07-14 16:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1924595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5251082/v1/8b3de760-b418-45fe-a377-1213b3258307.pdf"},{"id":71725501,"identity":"361e5b92-f67c-422e-9423-ff9f5f2d4933","added_by":"auto","created_at":"2024-12-18 06:08:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1072686,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5251082/v1/a93b88215f2ac8fc3fa04dad.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive flexibility is affected by the age of onset and duration among patients with type 1 diabetes: a network analysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eType 1 diabetes is a chronic metabolic disease caused by the destruction of pancreatic beta cells, leading to an absolute lack of insulin. Patients need to rely on long-term insulin therapy to maintain blood sugar stability\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. With the continuous progress of medical technology and the growing concern for health, the health-related quality of Life of people with type 1 diabetes has significantly improved\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the long-term complications have increasingly gained attention, with cognitive impairment being an important yet often overlooked complication of type 1 diabetes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eType 1 diabetes patients may face the risk of impaired cognitive function, particularly in attention, memory, executive function and information processing speed\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Among these, cognitive flexibility, an essential aspect of executive function involving the ability to adapt thinking and behavior in response to environmental changes, has garnered increasing attention from researchers in recent years\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Previous studies have reported that type 1 diabetes patients may be at risk of impaired cognitive flexibility, as shown by more attempts to complete the classification, more errors, and difficulty in switching concepts in the Wisconsin Card Sorting Test compared to the healthy control group\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. All these cognitive impairments can, in turn, significantly affect patients\u0026rsquo; quality of life and disease management ability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Palomo-Osuna et al. demonstrated that cognitive impairments could lead to reduced independence in daily life, increased risk of depression and anxiety, and negative impacts on social interactions and occupational performance\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous studies have indicated a strong connection between the clinical characteristics of type 1 diabetes patients and cognitive impairment. Of these characteristics, the age of onset and disease duration were the most frequently investigated. While type 1 diabetes can develop at any age, its peak incidence is during early adolescence\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Several studies have shown that early-onset patients (\u0026le;\u0026thinsp;6 years old) exhibit more pronounced impairments in attention, executive function, and information processing speed. Juvenile patients tended to experience greater impairments in learning and memory, while adult patients showed more significant declines in executive function and information processing speed\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Generally, adults with type 1 diabetes experienced relatively milder cognitive impairments. The disease duration reflected the ongoing impact of the disease and the accumulation of metabolic disturbance, making its association with cognitive function significant.\u003c/p\u003e \u003cp\u003eAs metabolic control worsens, individuals with high HbA1c levels and severe hypoglycemia episodes might face significant cognitive challenges\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Moreover, research suggested that blood glucose management behaviors in type 1 diabetes varied based on age of onset and current age\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Poor glycemic control, causing fluctuations like hypoglycemia and hyperglycemia, was linked to acute and chronic brain damage, affecting cognitive function. Empirical studies indicated that repeated diabetic ketoacidosis episodes in children with type 1 diabetes were independently associated with lower IQ and memory deficits\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Additionally, Jacobson et al. found that severe hypoglycemic episodes increase the risk of cognitive dysfunction, particularly in memory, psychomotor speed, and mental efficiency\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Similarly, chronic hyperglycemia was strongly associated with cognitive impairment, including mild cognitive impairment and dementia. Studies indicated that chronic hyperglycemia could cause memory impairment, issues with learning and retention, attention disorders, and neuropathy\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These findings underscored the importance of blood glucose control in maintaining cognitive health and suggested that better glycemic management could help slow cognitive decline, regardless of the age of onset. However, much existing literature on cognitive function in type 1 diabetes patients has focused on specific glycemic parameters and extreme events like age of onset in adulthood versus childhood, with inconsistent findings\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This might be due to the lack of a comprehensive examination of these complex relationships. Recently, network analysis has provided new perspectives for exploring multivariate complex relationships. Specifically, it offers a novel approach to exploring the dynamic interplay between cognitive function variables and type 1 diabetes-related clinical and glycemic characteristics\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, identifying key variables that significantly influence cognitive outcomes. Integrating multiple cognitive functions and disease characteristics into a single network offers valuable insights into these complex interactions, revealing how cognitive function variables interact with clinical and glycemic characteristics in type 1 diabetes.\u003c/p\u003e \u003cp\u003eTaken together, this study aimed to comprehensively explore the complex relationships between cognitive functions\u0026mdash;specifically memory, attention, and cognitive flexibility\u0026mdash;and various clinical and glycemic characteristics in type 1 diabetes patients, including age of onset, disease duration, glycemic metabolism parameters, and extreme glycemic events, using network analysis. To our knowledge, this has not yet been investigated. The study had two primary objectives. The first was to examine the detailed structure and connectivity characteristics of the network that links cognitive functions with clinical and glycemic characteristics in type 1 diabetes. Through this analysis, key clinical variables related to type 1 diabetes that significantly impact patients' cognitive functions were identified, as well as cognitive function indicators that are particularly sensitive to age of onset, disease duration, glycemic parameters and extreme glycemic events. The second objective involved constructing distinct networks for patients grouped by different ages of onset and life stages. These networks were analyzed to identify differences and unique characteristics specific to each group, providing a more comprehensive and dynamic understanding of the clinical features and treatment priorities for type 1 diabetes patients with different chronological disease indicators.\u003c/p\u003e"},{"header":"RESEARCH DESIGN AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from November 2015 to March 2022. A total of 345 patients with type 1 diabetes mellitus were recruited from the Diabetes Clinic at the Second Xiangya Hospital in Changsha, Hunan, China, during regular visits. Fourteen patients declined participation. The final sample comprised 331 patients (160 males and 171 females), aged 6.5 to 52 years (M = 18.19, SD = 8.72). Diagnoses were confirmed by two endocrinologists using the Chinese Type 1 Diabetes Guidelines and the American Diabetes Association\u0026apos;s criteria. Exclusion criteria included psychiatric or neurological disorders, hypertension, other chronic or acute diseases, premature birth (\u0026lt;36 weeks gestation), birth complications, or physical limitations affecting testing. Informed consent was obtained from all participants, and the study adhered to ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical and glycemic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers collected demographic and clinical data using a designed form and medical records. Variables included age, sex, educational age, age of type 1 diabetes onset, diabetes duration, blood glucose level, mean hemoglobin A1c (HbA1c) levels, fasting blood glucose (FBG) levels, fasting C-peptide levels, history of severe hypoglycemia (SH), and history of diabetic ketoacidosis (DKA). SH was defined as low blood glucose requiring assistance or glucagon injection due to seizure, loss of consciousness, or disorientation. DKA history was determined by medical records. Blood glucose levels were measured before cognitive testing, with snacks or insulin administered as needed to maintain levels between 4 and 17 mmol/L. The hyperglycemic exposure index was calculated using the patient\u0026apos;s average HbA1c and disease duration, converted into z-scores.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive functions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognitive function was assessed using the Wechsler Intelligence Scale, Wechsler Memory Scale (WMS), Wisconsin Card Sorting Test (WCST), and Sustained Attention to Response Task (SART). Specifically, general intellectual ability was evaluated with the Wechsler Intelligence Scale for Children\u0026mdash;Chinese Revision for participants under 16 years of age, and the Wechsler Adult Intelligence Scale\u0026mdash;Chinese Revision for those 16 years or older. These assessments are highly correlated (r \u0026gt; 0.9). Intelligence Quotient was calculated from five subtests: Information, Digit Span, Similarities, Picture Completion, and Block Design. The Similarities subtest evaluated abstract thinking and visual reasoning, while the Block Design subtest assessed spatial visualization and problem-solving abilities. The WMS assessed logical and visual memory, with participants asked to recall stories and reproduce drawings both immediately and after a delay. The WCST, which measured executive function, involved card sorting tasks, with key outcome measures being the number of categories completed and the number of perseverative errors, both reflecting cognitive flexibilities. Finally, attention was evaluated using the SART, where participants responded to digits displayed on a screen. Further details of all these assessments are available in Table S1.\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes all the instruments and measurement variables derived from each instrument that were utilized as network nodes in the later analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were preprocessed and analyzed using SPSS 26.0 for descriptive statistics.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNetwork analyses and comparisons\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewere conducted with R, version 4.0.5. The network analysis approach followed the guidelines by Epskamp et al. \u003csup\u003e15\u003c/sup\u003e\u003cstrong\u003e.\u003c/strong\u003e A significance level of 0.05 was used.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMissing values were analyzed and estimated using the Expectation-Maximization Algorithm (EM) in SPSS. Further details concerning missing values are shown in supplementary materials Figure S1.\u003c/p\u003e\n\u003cp\u003eNetwork analysis was performed on data from all 331 participants to explore relationships among cognitive functions and clinical-glycemic characteristic. Adult patients were divided into childhood-onset and adult-onset groups based on age of onset, with separate networks constructed for comparison. Additionally, networks for minor patients were compared with those of childhood-onset adult patients to explore developmental differences.\u003c/p\u003e\n\u003cp\u003eThe standardized Gaussian graphical model network was estimated using the R-packages \u003cem\u003ebootnet\u003c/em\u003e and\u003cem\u003e\u0026nbsp;qgraph\u003c/em\u003e. Nodewise errors, representing variance explained by connected nodes, were visualized using the \u003cem\u003emgm\u003c/em\u003e package. The graphical\u003cem\u003e\u0026nbsp;LASSO\u003c/em\u003e model with \u003cem\u003eEBICglasso\u003c/em\u003e reduced the probability of false connections. A partial correlation network corrected correlations between variables for all other variables, visualized as nodes and edges.\u003c/p\u003e\n\u003cp\u003eThe Fruchterman Reingold algorithm detected node pairs with stronger interconnections and higher centrality indices. Centrality indices, including strength, closeness, and betweenness, indicated the most important nodes. Strength quantifies direct connections, closeness quantifies indirect connections, and betweenness quantifies the average path essentiality between nodes.\u003c/p\u003e\n\u003cp\u003eEdge-weight accuracy was computed using \u003cem\u003ebootnet\u0026nbsp;\u003c/em\u003ewith 1000 nonparametric bootstrap samples. Centrality stability was quantified using the correlation stability coefficient (CS-coefficient), ensuring 95% certainty with \u0026gt;0.7 correlation to original centrality. Bootstrapped difference tests identified non-zero differences between each edge weight and node strength.\u003c/p\u003e\n\u003cp\u003eIn the network constructed among 331 patients with type 1 diabetes, the role of individual items as bridges between communities was assessed. Nodes that connect different predefined domains are called bridge nodes, which act like a bridge to link the two communities in the network. Bridge strength, defined as the absolute sum of the edge weights connecting an item in one community to items in another community, and bridge expected influence, defined as the nonabsolute sum of all edges connecting an item to others in a different community, were calculated for each item. These metrics were computed separately for each cognitive function in relation to clinical and glycemic characteristics.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eNetwork Comparison Test\u003c/em\u003e (NCT) package in R conducted the comparison using permutation tests with 1000 repetitions. Global network strength, defined as the weighted sum of absolute connection values, reflected node interconnectivity. Significant differences (p \u0026lt; 0.05) between groups were identified, with specific edge-weight differences examined using Bonferroni correction for multiple comparisons.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDescriptive Statistics and Item Inspection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDetailed descriptive statistics for the entire sample and subgroups are provided in Table 2. The zero-order correlation matrixs are available in supplementary materials(Table S2-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive Functions, Clinical and Glycemic Characteristics Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe network included 331 patients, as illustrated in Figure 1. It contained 51.2% nonzero edges, with edge weights ranging from -0.229 to 0.827. The edge weights matrix and an overview of the number of edges are in Table S8. Centrality indices are presented as standardized z scores in Figure 1. Items with high strength were highly connected, while those with high expected influence had more positive connections.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEdge Weight and Centrality Accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the centrality indices of the network. The age of onset, diabetes duration, and visual memory delay test score had the highest centrality(near 1), with 8, 7, and 7 connected edges, respectively. This indicates that the age of onset was pivotal in the network. For edges, the age of onset was notably linked to completed categories, perseverative errors in the M-WCST and severe hypoglycemia history, with edge weights of 0.280, 0.229 and 0.204, respectively. The edge\u0026apos;s and the strength\u0026apos;s CS-coefficient were 0.749, indicating that the order of edge weight and node strength can be interpreted with caution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBridges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe bridge item between cognitive functions and clinical and glycemic characteristics was identified through bridge centrality. Three nodes (A_O, WCST_C and WCST_P) were identified with bridge strengths in the top . The bridge strength of each item is shown in Figure 1. Bridge strength was highly stable, with a CS coefficient of (CS[cor = 0.7] = 0.517). The edge connecting \u0026ldquo;WCST_P\u0026rdquo; had the strongest weight (0.507) with node \u0026ldquo;WCST_C\u0026rdquo; in the cognitive functions community, while the edge connecting \u0026ldquo;A_O\u0026rdquo; had the strongest weight (0.280) in the clinical and glycemic characteristics community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork Comparisons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of Age of Onset: Childhood-Onset versus Adult-Onset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeparate networks for childhood-onset (n=78) and adult-onset adult (n=72) patients to identify differences. Detailed networks and edge weights are shown in the Supplementary Materials. Figure 2 shows the centrality indices of the networks. In the childhood-onset group, diabetes duration and severe hypoglycemia history emerged as significant nodes, with diabetes duration linked to FCP levels and severe hypoglycemia linked to logical memory delay and WCST categories. Stability was confirmed with a CS-coefficient of edge at 0.44 and strength at 0.44 for the childhood-onset group, and 0.36 and 0.28 for the adult-onset group, which meant order of edge weight and node strength can be interpreted with caution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Network Comparison Test revealed significant differences in network structure (M = 0.37, p = 0.02) and overall connectivity (S = 2.59, p = 0.00), with higher connectivity in the childhood-onset group (4.45 vs. 1.86, p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of Current Age in Childhood-Onset Type 1 Diabetes: Child versus Adult\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA network comparison was also conducted between child (n=181) and adult (n=78) groups among childhood-onset patients. Detailed networks and edge weights are shown in the Supplementary Materials. Figure 3 shows the centrality indices of the networks. The child group\u0026apos;s network highlighted the age of onset and visual memory test scores as important nodes. The age of onset was linked to visual memory immediate test scores and the Wechsler Intelligence Scale similarity subtest. The hyperglycemia exposure index was linked to WCST perseverative errors. In the adult group, significant nodes were the similarity subtest score and diabetes duration, with the similarity subtest score linked to logical memory immediate test scores and DKA history. Stability was confirmed with a CS-coefficient of edge at 0.75 and strength at 0.67 for the child group, and 0.51 and 0.44 for the adult group. Thus, the order of edge weight and node strength can be interpreted with caution.\u003c/p\u003e\n\u003cp\u003eThe Network Comparison Test revealed significant differences in network structure (M=0.42, p=0.02) but no significant differences in global strength (S=1.14, p=0.63). This suggests substantial structural differences between child and adult groups, though global strength showed some similarities.\u003c/p\u003e"},{"header":"DISCUSSIONS","content":"\u003cp\u003eThe present study was the first to examine the network structure of cognitive functions, clinical characteristics and glycemic characteristics in patients with type 1 diabetes, offering novel insights into their complex interplay.\u0026nbsp;The present study constructed an overall network with 331 participants and found that the age of onset and diabetes duration were not only central nodes but also key bridge nodes.\u0026nbsp;Notably, both variables were closely associated with cognitive flexibility, as evidenced by their strong connections to WCST Perseverative Errors and Categories Completed. Additionally, significant associations were also observed between the age of onset and severe hypoglycemia history, and between diabetes duration and visual memory performance. Comparative network analyses underscored the crucial roles of age of onset and disease duration, highlighting their impact on cognitive function, particularly cognitive flexibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive Functions and Clinical-Glycemic Characteristics Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study revealed that the age of onset plays a critical role in shaping the relationship between cognitive functions and clinical-glycemic characteristics in patients with type 1 diabetes. Early-onset diabetes was closely associated with more pronounced cognitive impairments, particularly in areas of memory, learning, and executive function. These impairments were likely driven by disruptions in insulin signaling and brain development, particularly in the hippocampus and frontal lobes, which were highly sensitive to metabolic disturbances\u003csup\u003e11\u003c/sup\u003e. Additionally, a younger age of onset also typically resulted in a longer disease duration, increasing the likelihood of prolonged hyperglycemia\u003csup\u003e12\u003c/sup\u003e. Over time, chronic hyperglycemia led to the accumulation of glycation end products, oxidative stress, and microvascular damage, all of which contribute to brain insulin resistance and neurodegeneration\u003csup\u003e16,17\u003c/sup\u003e. These pathological changes progressively reduced gray and white matter volumes in critical brain regions responsible for cognitive functions\u003csup\u003e13,18\u003c/sup\u003e. MRI studies further supported this, showing long-term reductions in brain volume, particularly in the frontal and temporal lobes\u0026mdash;regions essential for executive function and memory\u003csup\u003e5,19-21\u003c/sup\u003e. Consistent with these structural changes, current research has observed that the age of onset is significantly related to visual memory test performance, while the hyperglycemia exposure index was closely linked to performance the WCST, which assesses cognitive flexibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNetwork analysis highlighted the age of onset as a central node directly influencing cognitive flexibility, demonstrated by its associations with the WCST Perseverative Errors and Categories Completed. Additionally, both the age of onset and cognitive flexibility served as key bridge nodes in the network, linking cognitive functions and clinical-glycemic characteristics. These findings underscored the cumulative impact of early-onset diabetes on cognitive flexibility, as well as the broader cognitive decline linked to prolonged disease duration. Structural changes have been observed in the striatum and thalamus, alongside impairments in the mesial temporal cortex. Despite these structural changes, cerebral blood flow alterations were not observed. These changes in the mentioned brain regions may lead to deficits in executive functions, particularly in tasks requiring memory and cognitive flexibility\u003csup\u003e22\u003c/sup\u003e. Furthermore, severe hypoglycemia was a key factor influencing processing speed and executive functions, especially in childhood-onset patients more vulnerable to glucose fluctuations. Importantly, these impairments in cognitive flexibility were highly relevant for diabetes management, as patients with higher cognitive flexibility tend to be more capable of problem-solving, adhering to treatment regimens, and maintaining stable glucose levels\u003csup\u003e23,24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaken together, these findings emphasized the importance of early and sustained interventions targeting glycemic control to mitigate long-term cognitive decline. While correlations between cognitive functions, clinical-glycemic characteristics were evident across the network, specific relationships varied depending on the patient\u0026apos;s age of onset. Differences in network structures and connectivity patterns between patients diagnosed at different life stages suggested that these factors should be explored further in subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of Age of Onset: Childhood-Onset versus Adult-Onset\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparing childhood-onset and adult-onset type 1 diabetes patients revealed important differences in network structure, global strength, and node connections, which are crucial for tailoring treatment strategies based on age of onset. In childhood-onset patients, the network showed stronger connections between nodes representing disease duration, chronic hyperglycemia, and cognitive functions, making cognitive impairment more prominent and complex to manage. Notably, the \u0026quot;SH\u0026quot; node connected with several cognitive function nodes in childhood-onset patients, a connection absent in the adult-onset network. This heightened connectivity indicated that cognitive functions and diabetes-related factors were more tightly linked in childhood-onset patients, contributing to greater impairments in executive function, attention, visual-spatial abilities, and processing speed\u003csup\u003e25\u003c/sup\u003e. Early onset, especially before age five, was associated with poorer school performance and motor skills\u003csup\u003e26\u003c/sup\u003e. In contrast, adult-onset patients experienced milder cognitive impairments, likely due to more stable brain energy metabolism and blood flow regulation during glucose fluctuations after key brain developmental stages are complete\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSevere hypoglycemia, a central node in the childhood-onset network, significantly impacted cognitive functions such as verbal memory and executive functions.\u0026nbsp;Frequent hypoglycemic episodes in younger patients led to disruptions in brain metabolism, increasing free radicals and causing neuronal damage, particularly in the hippocampus and frontal lobes\u003csup\u003e13\u003c/sup\u003e. Over time, recurrent episodes of hypoglycemia could lead to chronic inflammation and further cognitive decline. MRI studies revealed these brain regions critical for memory and reasoning are particularly vulnerable to damage from severe hypoglycemia\u003csup\u003e12,18,27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, metabolic differences between childhood-onset and adult-onset patients may further explained variations in cognitive outcomes. Childhood-onset patients exhibited a negative correlation between disease duration and fast C-peptide levels. Childhood-onset patients often had lower C-peptide levels at diagnosis, reflecting poorer initial metabolic control compared to adult-onset patients, who typically had higher C-peptide levels and more stable glucose regulation\u003csup\u003e8\u003c/sup\u003e. These metabolic differences, combined with more frequent glucose fluctuations in childhood-onset patients, contributed to greater brain structural changes and cognitive impairments\u003csup\u003e17\u003c/sup\u003e. Effective blood glucose management, especially reducing extreme glucose events, was crucial for preserving cognitive function in both childhood-onset and adult-onset patients, with early intervention being particularly important for those diagnosed in childhood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffect of disease duration: current age (Child versus Adult) in Childhood-Onset Type 1 Diabetes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparing childhood-onset patients who were still children with those who have reached adulthood revealed important differences in network structure and connectivity characteristics, even though overall network connectivity and edge strength remain similar. Cognitive function in childhood-onset patients continued to be influenced by blood glucose fluctuations into adulthood, with significant changes occurring as they age. Consequently, treatment and monitoring should be tailored to different ages. In childhood-onset patients, the age of onset served as a central node, strongly linked to outcomes on the immediate visual memory test and similarity test. In contrast, for adult-onset patients, disease duration became more influential, and diabetic ketoacidosis showed a stronger correlation with the similarity test. Long-term studies indicate that childhood-onset patients experience persistent structural brain damage, which continues to affect cognitive function into adulthood\u003csup\u003e28\u003c/sup\u003e. Functional MRI studies in children with type 1 diabetes have identified lasting damage in critical areas, including the prefrontal cortex and hippocampus, which are particularly vulnerable during childhood development\u003csup\u003e29,30\u003c/sup\u003e. While adults with childhood-onset diabetes may develop some resistance to blood glucose fluctuations, poor metabolic control can still result in cognitive difficulties\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe study also emphasizes the significant impact of repeated DKA episodes on the brain, particularly in regions crucial for memory, learning, and executive functions like the hippocampus, frontal lobes, and parietal lobes. MRI studies showed that DKA leads to reduced gray matter volume and white matter integrity in these regions, with childhood-onset patients experiencing more pronounced damage due to increased vulnerability during brain development. This damage contributes to deficits in cognitive functions such as memory and attention\u003csup\u003e7,12,31\u003c/sup\u003e. Additionally, mental health challenges such as anxiety and depression are more common among childhood-onset patients, highlighting the need for comprehensive care and psychological support throughout their lives\u003csup\u003e31,32\u003c/sup\u003e.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was the first to apply network analysis to explore the relationships between cognitive functions and clinical-glycemic characteristics in type 1 diabetes, emphasizing the critical role of age-related factors, such as the age of onset and current age. The findings underscored the need for tailored interventions and treatment strategies that account for variations in disease duration among patients. Personalized approaches considering diagnosis timing and disease progression are essential for mitigating cognitive decline and improving overall outcomes in type 1 diabetes patients.\u003c/p\u003e\n\u003cp\u003eDespite its strengths, the study has several limitations. First, there are limitations associated with the sample and generalizability. The sample size was relatively small, especially after group division, and differences in group sizes might have limited the verification of some statistical associations. All participants were Chinese, which limits the generalizability of the findings to other populations. Second, the use of cross-sectional data prevents causal inferences about symptom-level relationships. Longitudinal analysis is needed to gain insight into temporal relationships and the sequence of item activation. Third, Network analysis doesn\u0026rsquo;t account for covariates or confounders, as the partial correlations between items only control for other items in the network. External factors not modeled, such as stressors, mood, emotion regulation abilities, and gender, might drive observed associations. A deeper exploration of these factors may provide further insight into how cognitive functions are influenced over time in type 1 diabetes patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest.\u003c/h2\u003e \u003cp\u003eNo honorarium, grant, or other forms of payment was given to anyone to produce the manuscript. The authors have stated that they had no interests which might be perceived as posing a conflict or bias.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthic Statement.\u003c/h2\u003e \u003cp\u003e This research has been approved by the Ethics Committee of Central South University\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding.\u003c/h2\u003e \u003cp\u003eThis research was supported by the \u0026ldquo;Integrity disease-management programs for type 1 diabetes in China\u0026rdquo; which was supported by National Health and Family Planning Commission of the People's Republic of China (2013BA109B12), National Science and Technology Infrastructure Program during the 12th Five‐Year Plan Period (2015BAI12B13), Outstanding Youth Project for Scientific Research of Hunan Provincial Department of Education (19B115), National Social Science Foundation Program (BHA180158), and Hunan Provincial Natural Science Foundation Project (2023JJ30181).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have contributed significantly to the study. Ding Mojun designed the study, analyzed the data, and wrote and reviewed the manuscript. Yuan Dongling, Li Chuting edited the manuscript. He Jing, Wenjing Zou collected data, assisted in the data analysis. Zhu Xiongzhao and Li Chuting reviewed and edited the manuscript. Zhu Xiongzhao is the guarantor of this work and, as such, takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the participants. We would like to express our heartfelt gratitude to the staff of the Department of Endocrinology and Metabolism affiliated with the Second Xiangya Hospital of Central South University.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eElSayed, N. A.\u003cem\u003e et al.\u003c/em\u003e 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, S19-S40, doi:10.2337/dc23-S002 (2023).\u003c/li\u003e\n\u003cli\u003e5. 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Estimating psychological networks and their accuracy: A tutorial paper. \u003cem\u003eBehav Res Methods\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 195-212, doi:10.3758/s13428-017-0862-1 (2018).\u003c/li\u003e\n\u003cli\u003eJin, C.-Y., Yu, S.-W., Yin, J.-T., Yuan, X.-Y. \u0026amp; Wang, X.-G. Corresponding risk factors between cognitive impairment and type 1 diabetes mellitus: A narrative review. \u003cem\u003eHeliyon\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e10073, doi:10.1016/j.heliyon.2022.e10073 (2022).\u003c/li\u003e\n\u003cli\u003evan Duinkerken, E. \u0026amp; Ryan, C. M. Diabetes mellitus in the young and the old: Effects on cognitive functioning across the life span. \u003cem\u003eNeurobiol Dis\u003c/em\u003e \u003cstrong\u003e134\u003c/strong\u003e, 104608, doi:10.1016/j.nbd.2019.104608 (2020).\u003c/li\u003e\n\u003cli\u003eUrakami, T. Severe Hypoglycemia: Is It Still a Threat for Children and Adolescents With Type 1 Diabetes? \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 609, doi:10.3389/fendo.2020.00609 (2020).\u003c/li\u003e\n\u003cli\u003eLi, J. R.\u003cem\u003e et al.\u003c/em\u003e Relationship between Illness Perception and Depressive Symptoms among Type 2 Diabetes Mellitus Patients in China: A Mediating Role of Coping Style. \u003cem\u003eJ Diabetes Res\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, 6, doi:10.1155/2020/3142495 (2020).\u003c/li\u003e\n\u003cli\u003eFoland-Ross, L. C.\u003cem\u003e et al.\u003c/em\u003e Brain Function Differences in Children With Type 1 Diabetes: A Functional MRI Study of Working Memory. \u003cem\u003eDiabetes\u003c/em\u003e \u003cstrong\u003e69\u003c/strong\u003e, 1770-1778, doi:10.2337/db20-0123 (2020).\u003c/li\u003e\n\u003cli\u003eLiu, J.\u003cem\u003e et al.\u003c/em\u003e Altered Gray Matter Volume in Patients With Type 1 Diabetes Mellitus. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 45, doi:10.3389/fendo.2020.00045 (2020).\u003c/li\u003e\n\u003cli\u003eBednař\u0026iacute;k, P.\u003cem\u003e et al.\u003c/em\u003e Hippocampal Neurochemical Profile and Glucose Transport Kinetics in Patients With Type 1 Diabetes. \u003cem\u003eJ Clin Endocrinol Metab\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, 479-491, doi:10.1210/clinem/dgz062 (2020).\u003c/li\u003e\n\u003cli\u003eSalah, N. Y.\u003cem\u003e et al.\u003c/em\u003e Metabolism and memory: \u0026alpha;-synuclein level in children with obesity and children with type 1 diabetes; relation to glucotoxicity, lipotoxicity and executive functions. \u003cem\u003eInt J Obes (Lond)\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 2040-2049, doi:10.1038/s41366-022-01222-z (2022).\u003c/li\u003e\n\u003cli\u003eHawks, Z. W.\u003cem\u003e et al.\u003c/em\u003e Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes. \u003cem\u003eNPJ Digit Med\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 59, doi:10.1038/s41746-024-01036-5 (2024).\u003c/li\u003e\n\u003cli\u003eMauras, N.\u003cem\u003e et al.\u003c/em\u003e Impact of Type 1 Diabetes in the Developing Brain in Children: A Longitudinal Study. \u003cem\u003eDiabetes Care\u003c/em\u003e \u003cstrong\u003e44\u003c/strong\u003e, 983-992, doi:10.2337/dc20-2125 (2021).\u003c/li\u003e\n\u003cli\u003eGoethals, E. R.\u003cem\u003e et al.\u003c/em\u003e Executive function mediates the link between externalizing behavior and HbA1c in children and adolescents with type 1 diabetes: A cross-national investigation. \u003cem\u003ePediatr Diabetes\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 503-510, doi:10.1111/pedi.13172 (2021).\u003c/li\u003e\n\u003cli\u003eNevo-Shenker, M. \u0026amp; Shalitin, S. The Impact of Hypo- and Hyperglycemia on Cognition and Brain Development in Young Children with Type 1 Diabetes. \u003cem\u003eHorm Res Paediatr\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 115-123, doi:10.1159/000517352 (2021).\u003c/li\u003e\n\u003cli\u003eSchoenle, E. J., Schoenle, D., Molinari, L. \u0026amp; Largo, R. H. Impaired intellectual development in children with Type I diabetes: association with HbA(1c), age at diagnosis and sex. \u003cem\u003eDiabetologia\u003c/em\u003e \u003cstrong\u003e45\u003c/strong\u003e, 108-114 (2002).\u003c/li\u003e\n\u003cli\u003eParikh, L.\u003cem\u003e et al.\u003c/em\u003e Differential Resting State Connectivity Responses to Glycemic State in Type 1 Diabetes. \u003cem\u003eJ Clin Endocrinol Metab\u003c/em\u003e \u003cstrong\u003e105\u003c/strong\u003e, doi:10.1210/clinem/dgz004 (2020).\u003c/li\u003e\n\u003cli\u003eFoland-Ross, L. C.\u003cem\u003e et al.\u003c/em\u003e Executive task-based brain function in children with type 1 diabetes: An observational study. \u003cem\u003ePLoS Med\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, e1002979, doi:10.1371/journal.pmed.1002979 (2019).\u003c/li\u003e\n\u003cli\u003eDaneshmand, M., Kashefizadeh, M., Soleimani, M., Mirzaei, S. \u0026amp; Tayim, N. Network analysis of depression, cognitive functions, and suicidal ideation in patients with diabetes: an epidemiological study in Iran. \u003cem\u003eActa Diabetol\u003c/em\u003e \u003cstrong\u003e61\u003c/strong\u003e, 609-622, doi:10.1007/s00592-024-02234-z (2024).\u003c/li\u003e\n\u003cli\u003eSen, Z. D.\u003cem\u003e et al.\u003c/em\u003e Linking atypical depression and insulin resistance-related disorders via low-grade chronic inflammation: Integrating the phenotypic, molecular and neuroanatomical dimensions. \u003cem\u003eBrain Behav Immun\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 335-352, doi:10.1016/j.bbi.2020.12.020 (2021).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 616px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1-Overview of all the instruments and variables (nodes).\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomains and instruments\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive function\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive flexibility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 301px;\"\u003e\n \u003cp\u003eModified Wisconsin Card Sorting Test, M-WSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003ePerseverative errors (WCST_P)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eCategories completed (WCST_C)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMemory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eWechsler Memory Scale, WMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eVisual memory immediate test score (VM_1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eVisual memory delay test score (VM_2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eLogical memory immediate test score (LM_1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eLogical memory delay test score (LM_2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSustained attention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003eSustained Attention to Response Task, SART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eIntraindividual variability (VII)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eOmission error rate (O_R)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eCommission error rate (C_R)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eMean reaction time (RT)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisual reasoning and abstract thinking abilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eWechsler Intelligence Scale\u0026mdash; Chinese Revision,WAIS-RC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eThe Similarity subtest of the WAIS-RC (IQ_S)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial perception and processing abilities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003eWechsler Intelligence Scale\u0026mdash; Chinese Revision,WAIS-RC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eThe Block Design subtest of the WAIS-RC \u0026nbsp;(IQ_B)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical and Glycemic Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical chronological \u0026nbsp;indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eAge of onset (A_O)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 301px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eDiabetes duration (D_D)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 301px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Glycemic parameters and extreme events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eDiabetic ketoacidosis history (DKA)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eSevere hypoglycemia history (SHh)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eFasting C-peptide levels (FCP)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 315px;\"\u003e\n \u003cp\u003eHyperglycemic exposure index (HEI)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 616px;\"\u003e\n \u003cp\u003eNote: Hyperglycemia exposure index score can reflect the impact of blood sugar control level and duration and is currently a widely used indicator to evaluate chronic hyperglycemia levels. In this study, all available HbA1c monitoring results and the patient\u0026apos;s disease duration were collected from the medical records of each child with type 1 diabetes to calculate each child\u0026apos;s hyperglycemic exposure index. The specific algorithm is: first, calculate the average HbA1c of each patient based on all HbA1c values of each patient, and then convert the two variables of the patient\u0026apos;s average HbA1c and disease duration into standard scores (z-score); Second, the hyperglycemic exposure index for each patient was obtained by adding the patient\u0026apos;s z-score over the course of the disease and the mean z-score for HbA1c.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"943\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 943px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2-Sample characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 263px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 175px;\"\u003e\n \u003cp\u003etotal type 1 diabetes patients\u003c/p\u003e\n \u003cp\u003e(n=331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 355px;\"\u003e\n \u003cp\u003eAdult patient\u003c/p\u003e\n \u003cp\u003e(n=150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eChild patient group\u003c/p\u003e\n \u003cp\u003e(n=181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003eChildhood-Onset group\u003c/p\u003e\n \u003cp\u003e(n=78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eAdult-Onset group\u003c/p\u003e\n \u003cp\u003e(n=72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eAge, years (mean [SD], [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e18.19(8.72), (6.5-52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e21.61(4.61), (18-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e28.87(7.41), (19-52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e12.06(3.32), (6.5-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eFemale sex (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e171(51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e44(56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e34(43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e89(49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eEducation age (mean [SD], [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e9.89(4.97), (1-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e13.83(1.86), (9-19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e14.83(2.56), (9-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6.20(3.28), (1-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes-related variables\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean [SD], [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;Age of onset\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e14.54(8.16), (1-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e13.60(7.88), (3-18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e26.92(6.77), (18-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e9.98(3.80), (1-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;Diabetes duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e3.66(4.45), (0.08-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e7.88(6.00), (0.50-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e2.91(3.55), (0.08-17.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.15(2.55), (0.25-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;DKA history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e0.57(0.62), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e0.56(0.50), (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.42(0.50), (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.63(0.70), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;SH history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e0.49(0.84), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e0.37(0.49), (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.20(0.40), (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0.66(1.03), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;Mean HbA1c(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e8.33(2.03), (3.92-15.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e8.39(2.10), (3.92-15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e8.02(2.51), (4-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e8.42(1.87), (5.20-15.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;Fasting C-peptide levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.77(1.07), (-0.77-3.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;-0.53(1.20), (-2.67-2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hyperglycemic exposure index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e0(1.32), (-2.66-5.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e0.49(1.33), (-1.88-4.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.09(0.92), (-0.77-4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e0(1.24), (-2.27-4. 09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive functions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(mean [SD], [IQR])\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eWechsler Memory Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eVisual memory immediate test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e9.95(2.81), (1-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e10.97(2.57), (5-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e10.25(2.73), (3-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e9.41(3.06), (1-17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eVisual memory immediate test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e9.17(2.95), (0-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e10.51(2.72), (4-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e9.04(2.31), (3-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e8.53(3.11), (0-16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eLogical memory immediate test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e6.91(2.08), (0.75-18.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e7.76(2.29), (1-16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e6.87(2.98), (1-18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e6.59(2.87), (1-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eLogical memory delay test score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e5.89(2.71), (0-13.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e6.65(2.56), (1-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e5.47(2.73), (0-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e5.77(2.78), (0-12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eModified Wisconsin Card Sorting Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003ePerseverative errors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e48.11(13.36), (20-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e47.57(8.36), (28-71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e45.31(8.57), (20-61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e50.02(15.44), (20-80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eCategories completed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e2.92(1.26), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e3.10(1.29), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e3.51(1.26), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e2.60(1.18), (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eSustained Attention to Response Task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eOmission error rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e3.03(4.98), (0-21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.06(0.27), (0-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eCommission error rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e8.37(7.44), (0-24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.78(2.61), (0-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eMean reaction time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e377.28(95.39), (246.51-652.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e388.79(89.32), (227-619)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eIntraindividual variability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e0.29(0.11), (0.12-0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e0.25(0.09), (0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eChinese version of the Wechsler adult intelligence, WAIS-RC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eThe Similarity subtest of the WAIS-RC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e13.03(3.76), (5-24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e13.02(3.22), (4-23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 263px;\"\u003e\n \u003cp\u003eThe Block Design subtest of the WAIS-RC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 175px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 196px;\"\u003e\n \u003cp\u003e16.03(10.45), (4-48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e12.08(4.06), (3-38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"type 1 diabetes, cognitive function, age of diabetes onset, duration, glycemic fluctuation","lastPublishedDoi":"10.21203/rs.3.rs-5251082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5251082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive impairment is a recognized risk in patients with type 1 diabetes, yet its contributing factors and overall impact remain insufficiently understood. This study aimed to explore the relationships between specific cognitive functions—namely memory, attention, and cognitive flexibility—and clinical characteristics, including age of onset and diabetes duration, as well as glycemic factors, such as glycemic control and extreme glycemic events, in a cohort of 331 patients with type 1 diabetes. Cognitive performance was assessed using the Wechsler Memory Scale, Wisconsin Card Sorting Test, and the Sustained Attention to Response Task. Network analysis revealed that age of onset and diabetes duration were central nodes in the network, strongly influencing cognitive flexibility. Additionally, age of onset was associated with a history of severe hypoglycemia, while diabetes duration was linked to visual memory decline. Significant differences were observed between the network structures of the adult-onset and childhood-onset groups, with the childhood-onset group showing greater interconnectedness. These findings emphasize the critical impact of age of onset and disease duration on cognitive outcomes in type 1 diabetes and highlight the importance of personalized treatment strategies. Longitudinal studies are needed to further understand these relationships and guide targeted interventions to preserve cognitive function.\u003c/p\u003e","manuscriptTitle":"Cognitive flexibility is affected by the age of onset and duration among patients with type 1 diabetes: a network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 05:59:58","doi":"10.21203/rs.3.rs-5251082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T06:46:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-19T21:43:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T16:38:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202082971043624253067415177085180316491","date":"2024-11-04T20:57:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75532442059098272027884670810106298926","date":"2024-11-04T13:18:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-04T05:29:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-28T14:08:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-14T18:12:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-14T03:25:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-12T10:47:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a714f49-e528-480c-9ea7-dff8dc238db1","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41065446,"name":"Biological sciences/Psychology"},{"id":41065447,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Diabetes/Type 1 diabetes mellitus"}],"tags":[],"updatedAt":"2025-07-14T16:04:19+00:00","versionOfRecord":{"articleIdentity":"rs-5251082","link":"https://doi.org/10.1038/s41598-025-99678-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-07 15:57:58","publishedOnDateReadable":"July 7th, 2025"},"versionCreatedAt":"2024-12-18 05:59:58","video":"","vorDoi":"10.1038/s41598-025-99678-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-99678-2","workflowStages":[]},"version":"v1","identity":"rs-5251082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5251082","identity":"rs-5251082","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}