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Article

Glomerular Hyperfiltration in Children and Adolescents with Type 1 Diabetes Mellitus: A Cross-Sectional Observational Study

by
Luiza Santos de Argollo Haber
1,2,
Lucas Fornari Laurindo
1,2,
Rafael Fagundes de Melo
1,2,
Dennis Penna Carneiro
1,2,
Piero Biteli
1,2,
Henrique Villa Chagas
1,2,
Luciano Junqueira Mellem
1,
Jesselina Francisco dos Santos Haber
1,2,
Lance Alan Sloan
3,4,
Kátia Portero Sloan
3,
Sandra Maria Barbalho
1,2,5,6 and
Eduardo Federighi Baisi Chagas
1,2,7,*
1
Department of Biochemistry and Pharmacology, School of Medicine, Universidade de Marília (UNIMAR), Marília 17525-902, SP, Brazil
2
Department of Clinical Medicine, Interdisciplinary Center for Diabetes (CENID), Universidade de Marília (UNIMAR), Marília 17525-902, SP, Brazil
3
Department of Clinical Metabolism, Texas Institute for Kidney and Endocrine Disorders (TIKED), Lufkin, TX 75904, USA
4
Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX 77555, USA
5
Department of Biochemistry and Nutrition, School of Food and Technology of Marília (FATEC), Marília 17506-000, SP, Brazil
6
Department of Research, Research Coordination Center, UNIMAR Charitable Hospital, Universidade de Marília (UNIMAR), Marília 17525-902, SP, Brazil
7
Department of Biochemistry and Pharmacology, School of Medicine, Faculdade de Medicina de Marília (FAMEMA), Marília 17519-030, SP, Brazil
*
Author to whom correspondence should be addressed.
Endocrines 2025, 6(3), 35; https://doi.org/10.3390/endocrines6030035
Submission received: 5 May 2025 / Revised: 11 June 2025 / Accepted: 7 July 2025 / Published: 10 July 2025
(This article belongs to the Special Issue Recent Advances in Type 1 Diabetes)
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Versions Notes

Abstract

Background/Objectives: This study investigated the relationship between glycemic control and increased glomerular filtration rate (eGFR), as assessed by serum creatinine and the CKiD equation in children and adolescents with T1DM. Methods: This cross-sectional observational study involved 80 T1DM patients (4–19 years) attending the Interdisciplinary Center for Diabetes. Biochemical, anthropometric, and skeletal muscle mass parameters were evaluated. The GFR was estimated using the CKiD equation expressed in mL/min/1.73 m2. Results: Our results showed that nearly 19.0% of the included patients presented increased values for eGFR, and most had poor glycemic control. Patients with HbA1c levels above 8% presented eGRF > 130. There was a positive correlation between hyperglycemia, elevated HbA1c, and fat percentage with higher eGRF values. In addition, the reduction in lean mass and skeletal muscle mass was related to elevated eGRF. Conclusions: Our study indicates that children and adolescents with T1DM who have elevated HbA1c, lower lean mass, and less than five years of diagnosis of diabetes are more likely to present higher eGRF values.

1. Introduction

Type 1 diabetes (T1DM) is an autoimmune condition in which patients present elevated blood glucose levels. This scenario occurs due to T lymphocyte damage to the pancreatic β-cells, resulting in failing insulin secretion. It is estimated that this disease accounts for 5–10% of all the reported diabetic cases, with a rising global incidence [1,2,3,4]. This disease has been diagnosed globally in 108,300 children and adolescents aged < 15 years. This number will reach about 150,000 when the age extends to <20 years [5,6,7,8].
T1DM is a highly heterogeneous condition influenced by several factors, including genetics, gender, and environmental conditions, and it carries a significant burden on healthcare systems and national economies worldwide [9,10,11]. The chronicity and the duration of hyperglycemic inflammatory aggression increase the risk of kidney disease. Although kidney disease in patients with T1DM is silent, and its diagnosis occurs more frequently in older patients, almost 40% of patients with T1DM develop kidney dysfunction. Thus, persistent hyperglycemia (poor glycemic control) combined with genetic, hemodynamic, metabolic (dyslipidemia and obesity), environmental factors, family history of diabetic kidney disease (DKD), and mitochondrial dysfunction has been associated with a higher risk of kidney disease [12,13,14]. DKD is among the leading causes of morbidity and mortality, increasing the risk for cardiovascular diseases in T1DM patients [15,16].
The kidneys are a highly energy-demanding organ, second only to the heart in terms of oxygen consumption. This high oxygen demand is required to maintain adequate adenosine triphosphate (ATP) levels. The most critical determinants for using ATP are glomerular filtration rate (GFR) and tubular sodium reabsorption. The natural course of diabetic nephropathy includes glomerular hyperfiltration (initially), progressive albuminuria, and sustained reduction in estimated glomerular filtration rate (eGFR). The diagnosis of DKD is based on the urinary albumin-to-creatinine ratio (UACR) and/or the estimated glomerular filtration rate [17,18,19,20,21].
It has been recommended to screen for diabetic nephropathy in individuals with T1DM after 2 to 5 years of diagnosis and in individuals aged between 11 and 17 years [22]. In up to 20% of cases, there is a decline in the GFR with normal levels of albuminuria, which is known as ‘non-albuminuric kidney disease’. Even though using equations to estimate eGFR has been widely recommended, in patients with T1DM, it may underestimate eGFR within the normal range [23,24]. For children and adolescents with T1DM, the Chronic Kidney Disease in Children (CKiD) equation has been widely recommended for estimating eGFR [25]. This equation enables the estimation of height (in cm) and serum creatinine values (in mg/dL), facilitating its use in clinical practice [26] and contributing to the improvement of functional assessments in children [27,28].
Although the estimation of GFR by equations cannot be used as an isolated diagnostic criterion, it allows an assessment of clinical evolution and classification based on reference values [29,30]. For these reasons, this study aimed to evaluate the relationship between glycemic control and increased eGFR, as assessed by serum creatinine and the CKiD equation, in children and adolescents with T1DM.

2. Materials and Methods

2.1. Study Design

This is a cross-sectional observational study. Between 2019 and 2020, an interdisciplinary health team at the Specialty Medical Outpatient Clinic of the University of Marília collected patient data during routine consultations for medical monitoring and evaluation and stored them in the Interdisciplinary Center for Diabetes (CENID) database of the University of Marília (UNIMAR)-SP, Brazil.

2.2. Study Participants

A non-probabilistic sample was used for the study, considering the entire population of patients treated at the outpatient clinic and for whom data were available. The CENID outpatient clinic treated 108 patients from 2019 to 2020. Only patients without a diagnosis of kidney disease and with an eGFR greater than 60 mL/min/1.73 m2 were considered for the study. A total of 80 patients of both genders (male = 47; female = 33) with ages between 4 and 19 years old, diagnosed with T1DM for at least twelve months, and with C-peptide values < 0.3 ng/mL, who signed the Informed Consent Form, were included in the study. Patients with any physical or mental disability were excluded from the study. None of the patients were using DPP4 or SGLT-2 inhibitors during the study period.
Regarding the insulin administration method, 22 (27.5%) used the Continuous Insulin Infusion System (CIIS), and 58 (72.5%) used the Multiple Insulin Dose (MID) method. Data on the insulin administration schedule were obtained based on information on total insulin (U/day), bolus insulin (U/day), and basal insulin (U/day). The insulin administration schedule values were converted into units per kilogram per day. Considering the total insulin dose (U/kg/day), the administration schedule was classified as “below recommended for weight”, “recommended for weight”, and “above recommended for weight” according to the duration of the disease and the stage of sexual maturation [31].

2.3. Study Parameters

Demographic (sex and gender) and anthropometric (weight, height, body mass index, and body composition) variables, along with length of disease (years), total daily insulin, glycated hemoglobin (HbA1c), and physical activity level (PAL), were collected to characterize the sample and explore exposure factors for the dependent variable (eGFR). The length of disease was categorized as <5 years and ≥5 years. Body composition (body fat percentage and lean mass percentage) was estimated by biopedance test (Biodynamics, model 310) [32]. The skeletal muscle mass (SMM) and skeletal muscle mass index (SMMI) were calculated using the equations proposed by Kim et al. [33] and Cruz-Jentoft et al. [34].
The habitual physical activity pattern was assessed in the physical activity recall proposed by Bouchard et al. [35]. A one-week (seven-day) period was recorded to estimate daily energy expenditure expressed in kcal per kilogram of body weight per day (kcal/kg/day). The resting metabolic rate (RMR), described in kcal/day, was estimated using an age and sex-specific equation [36]. The equation to determine PAL involves dividing daily energy expenditure (in kcal/day) by resting metabolic rate (in kcal/day). PAL values were classified as mild (women < 1.56; men < 1.55), moderate (women 1.56 to 1.82; men 1.55 to 2.10), and vigorous (women > 1.82; men > 2.10) [37].
Glycemic control was assessed by fasting blood glucose and glycated hemoglobin (HbA1c in %). Typical values for fasting blood glucose were considered <100 mg/dL. HbA1c values were categorized as less than 7%, 7 to 8%, and greater than 8% [38]. The measurement of glycated hemoglobin (HbA1c in %) was performed using the high-performance liquid chromatography (HPLC) method, and fasting blood glucose was measured using the colorimetric enzymatic method [39].
Serum creatinine (CR) levels were determined by the enzymatic method and the glomerular filtration rate was estimated using the CKiD equation (eGFR = k × Ht/SCr, where Ht = height in cm, and SCr = serum creatinine in mg/dL, with fixed k = 0.413) expressed in mL/min/1.73 m2 [26]. The CKiD equation is a reliable tool for estimating GFR in children, adolescents, and young adults (up to 25 years of age) with T1DM [25]. The cutoff point of ≥135 mL/min/1.73 m2 was considered to identify the presence of glomerular hyperfiltration [40]. The eGFR results were also categorized by quartile distribution into three groups: <50th percentile (<115 mL/min/1.73 m2), 50th to 75th percentile (115 to 130 mL/min/1.73 m2), and >75th percentile (>130 mL/min/1.73 m2).
The eGFR categories (>130, 115–130, and <115 mL/min/1.73 m2) were selected based on the upper quartile distribution in our cohort and prior evidence linking values > 130 mL/min/1.73 m2 to hyperfiltration [40]. Intermediate ranges were included to explore subclinical gradients of renal dysfunction.

2.4. Statistics

The absolute (N) and relative (%) frequency distributions describe qualitative variables. The difference in proportion distribution was analyzed using the univariate Chi-square test. The association between qualitative variables was analyzed using the Chi-square test. Quantitative variables are described by mean and 95% confidence interval (95% CI). Levene’s test verified the homogeneity of variances. The one-way ANOVA test was performed to compare means, followed by the Bonferroni post hoc test when necessary. The correlation between quantitative variables was analyzed using Pearson’s test correlation coefficient and the linear R-squared value. Multiple linear regression was performed using the backward method to analyze the effect of independent variables on eGFR, and the quality of the model fit was assessed using the linear R-squared value. To analyze the impact of independent variables on the probability of increased eGFR values, binary logistic regression was performed using the Backward Wald method, and the quality of the model fit was assessed using Nagelkerke’s R2. The significance level adopted was 5%, and the data were analyzed using the SPSS software (version 27.0).

3. Results

The characteristics of the study population are presented in Table 1. It is noted that a slightly higher proportion of individuals were males (58.8%) and had a disease duration of less than 5 years (57.5%). Most individuals did not present associated comorbidities (95.0%) and practiced some physical activity, but when considering the PAL, 58.8% performed little physical activity. Although a significant proportion of individuals used multiple doses of insulin as the insulin administration method and the schedule of insulin administration was adequate for their weight, glycemic control was inadequate, as 75% of individuals had HbA1c values greater than 7%, and 56.3% had values greater than 8%.
Regarding the presence of glomerular hyperfiltration (GH) based on the glomerular filtration rate (eGFR), it was found that 18.8% of the sample had values ≥ 135 mL/min/1.73 m2, which is considered the main cutoff point for the diagnosis of GH. Considering that the cutoff points for the diagnosis of GH are still debatable, the eGFR values were also analyzed considering the quartile distribution, which indicated that 25% of the sample had eGFR values > 130 (mL/min/1.73 m2) and 26.3% had eGFR values between 115 and 130 (mL/min/1.73 m2).
When assessing the eGFR categories by quartile distribution, the only notable difference noted was in glycemic control. The individuals with higher eGFR (>130 and between 115 and 130) had higher values of HbA1c (>8%). On the other hand, the individuals with lower eGFR (<115) had lower values of HbA1c (<7%). These results suggest an association of poor glycemic control and hyperfiltration (Table 1).
Table 2 compares the independent variables of the eGFR categories by quartile distribution. Individuals with eGFR < 115 had a lower percentage of body fat and a higher percentage of body lean mass than individuals with higher eGFR (between 115 and 130); no difference was observed for individuals with eGFR > 130. When the variables were divided by sex, no significant differences were observed between body fat and lean mass percentage within the eGFR categories.
The mean eGFR was compared across the HbA1c categories, which indicated significant differences by the one-way ANOVA test (p-value = 0.035). It was observed that in patients with HbA1c < 7%, eGRF values (102 + 19) were lower than in the group with HbA1c > 8% (eGRF 118 + 22). However, in patients with HbA1c between 7 and 8% (eGRF 113 + 26), no significant difference was observed between patients with HbA1c < 7% and >8%.
A significant correlation was observed between eGFR and blood glucose, HbA1c, fat percentage (% Fat), lean mass percentage (% lean mass), and skeletal muscle mass index. Increases in blood glucose, HbA1c, and % Fat were associated with eGFR increases, in which variations in blood glucose, HbA1c, and % Fat explain 5.7%, 8.7%, and 6.6% of the variation in eGFR, respectively. Reductions in percentage lean mass and skeletal muscle mass index are related to increases in eGFR. Variations in percentage lean mass and skeletal muscle mass explain 8.3% and 5.3% of the variation in eGFR, respectively (Figure 1).
Table 3 presents the results of the multiple linear regression analysis, which explains the joint effect of the independent variables on eGFR. A significant impact of HbA1c, lean mass (%), and disease duration was observed, and the model explains 28.8% of the variation in the eGFR values (R: 0.4777; R2: 0.288). The regression coefficient (B), the increase in HbA1c, the reduction in lean mass (%), and having a diagnosis time of less than 5 years are related to the increase in eGFR.
It is worth noting that, clinically, the length of diabetes is a factor that may contribute to the development of diabetic kidney disease. An increase in eGFR is observed during the initial phase of the disease. However, preserved kidney function and eGFR values within normal ranges are expected in patients with diabetes who have a shorter length of disease. Considering the progression of kidney disease (eGFR < 60 mL/min/1.73 m2), after the initial increase in eGFR (glomerular hyperfiltration), the decline in eGFR goes through a period of normal values before declining to critical values that are considered the cutoff point for the diagnosis of the disease. In our study, patients with longer disease durations and eGFR values that did not indicate glomerular hyperfiltration may be present. However, it is uncertain whether these patients had preserved kidney function or were already in the process of declining function after a period of glomerular hyperfiltration.
For this reason, a second regression model was performed, adjusting for the length of the disease. A significant effect of the independent variables HbA1c and lean mass (%) was observed (R: 0.371, R2: 0.138), indicating that the variation in these independent variables explains 13.8% of the variation in the eGFR values.
Binary logistic regression (Table 4) analysis was performed to explore the effect of independent covariates on the probability of increased eGFR values. Three cutoff points were considered for the analysis to categorize eGFR values. The eGFR value ≥ 135 (mL/min/1.73 m2) was the main criterion since it is indicated in the literature as a cutoff point for the diagnosis of hyperfiltration.
For the independent variables inserted into the model, the same variables used in the multiple linear regression model were considered, including sex, age, and length of disease time from diagnosis. After removing the independent variables using the Wald Backward method, the model that best explains the variation in the probability of the outcome was considered. For the critical eGFR value ≥ 135 (mL/min/1.73 m2), a significant effect of the model with the independent variables of HbA1c, % lean mass, and length of disease was verified. Together, these variables explain 20% (R2) of the variation in the probability of increased eGFR values ≥ 135 (mL/min/1.73 m2). Although the model showed a significant effect, only the independent variable, length of disease, showed a significant isolated effect. For this model, the increase in HbA1c, the reduction in lean mass, and the reduction in length of disease are related to the increased probability of eGFR values ≥ 135 (mL/min/1.73 m2).

4. Discussion

This study showed glomerular hyperfiltration in 18.8% of the children and adolescents with T1DM. There was a positive correlation between hyperglycemia and body composition, as indicated by the eGRF values, in which higher HbA1c levels, higher body fat percentage, lower lean mass, and skeletal muscle mass were related to elevated eGRF. The linear regression analysis revealed that elevated HbA1c, lower lean mass, and a diagnosis of diabetes for less than five years were related to higher eGRF values.
Although the values of the observed correlation coefficients are considered low (<0.300) by statistical standards, from a clinical perspective, they are essential information, considering the complexity of the mechanisms that lead to kidney disease in patients with T1DM. In addition to the correlation analysis, the linear R2 was calculated, representing the percentage of variation in the dependent variable (eGFR) explained by the variation in the independent variables (X-axis).
In the United States, the incidence of DKD ranges from 9.6% to 11.3% after 25 years of T1DM diagnosis [41]. In a cohort study of adults with T1DM, the impact of age, sex, creatinine levels, and race on eGFR values and other complications was evaluated. The results showed that both increased HbA1c and age are associated with a reduction in eGFR, increased albuminuria, and increased risk of DKD [42]. Although renal function abnormalities are more prevalent in patients with longer disease duration, renal function screening in patients with less than 5 years of disease is recommended [43].
Most studies focus on the reduction in eGFR, while only a few investigate the early stage of DKD, which is characterized by glomerular hyperfiltration and increased eGFR. This is because screening for DKD in children and adolescents with T1DM is recommended after 5 years of diagnosis of diabetes or in individuals older than 11 years of age [44]. Our study revealed glomerular hyperfiltration (18.8%) and increased eGFR in children and adolescents with T1DM diagnosed with diabetes for less than five years.
This evidence supports the importance of screening for DKD and monitoring for kidney function abnormalities since the initial diagnosis of diabetes. Amin et al. [45] investigated the prevalence of GH in a cohort of children and adolescents with T1DM and showed that 48% of the individuals had GH. The cutoff points for GH and eGFR were >125 mL/min/1.73 m2, and the disease duration was more than 5 years, which explains the higher prevalence of GH compared to the prevalence found in the present study. The study also found an association between GH and poor glycemic control, as well as higher levels of HbA1c, which corroborates our research findings. A cross-sectional survey conducted by Favel et al. [28] found a prevalence of glomerular hyperfiltration of 3.8% in children and adolescents with T1DM, with a median disease duration of 2.7 years. The cutoff point for the diagnosis of GH was ≥158 mL/min/1.73 m2, which may explain the lower prevalence of GH compared to our study.
Several trials have demonstrated the benefits of improved glycemic control and enhanced eGFR, as well as reduced cardiovascular risk in adults with T1DM [16,46]. Poor glycemic control and higher HbA1c may lead to a faster decline in the GFR [46] and a longer duration of hypoglycemic events in this population [16]. This evidence highlights the importance of glycemic control in preventing DKD. However, it is worth emphasizing that the association between hyperglycemia and hyperfiltration may reflect osmotic diuresis resulting from glycosuria, which increases renal plasma flow and filtration pressure. This aligns with studies reporting transient elevation of GFR during hyperglycemia [47].
Our study revealed a non-linear correlation between eGFR values and the progression of DKD, which is consistent with the existing literature [28] and the disease’s pathophysiology. The first stage of DKD is characterized by an increase in eGFR (glomerular hyperfiltration), followed by a return to normal eGFR values and then a decrease in eGFR. It is necessary to monitor the clinical manifestations of the disease, glycemic control, and the detection of kidney function abnormalities in the early stages of DKD, especially in individuals newly diagnosed with T1DM.
Westreich et al. [48] investigated the progression of the eGFR in a longitudinal cohort of youth newly diagnosed with T1DM. The GFR was estimated by the CKiD equation and categorized as “rising” or “declining” from baseline to the end of the study (mean 6.6 years of follow-up). The results showed a decline in eGFR in 23.8% of the individuals. The factors associated with the reduction in eGFR include a higher baseline eGFR, gender, younger age at diagnosis, lower glucose levels, and reduced HbA1c. In addition, the study showed that significant changes in eGFR can occur as early as when diabetes is diagnosed.
In patients with T1DM, an average reduction of 5.7 years in life expectancy is observed due to chronic complications of the disease. In children and adolescents, treatment presents a significant challenge since it involves not only insulin therapy and blood glucose monitoring but also drastic lifestyle changes [49,50]. The development of DKD occurs in 40% of individuals with T1DM [51], and the presence of kidney function abnormalities may contribute to the decrease in life expectancy up to 16 years in this population [52].
Many structural and functional changes in DKD are believed to be caused by a chronic inflammatory process and the high production of reactive oxygen species resulting from hyperglycemia, which are critical for the development of diabetic vascular complications and renal failure [18]. Therefore, in addition to monitoring glycemic control, other cardiovascular risk factors and renal function parameters should be included in disease monitoring strategies [7,53,54]. Preventing glomerular hyperfiltration during adolescence may be a crucial target to decrease future diabetes outcomes and complications.
Even though microalbuminuria may be related to the increase in GRF and the loss of kidney function in the absence of other forms of kidney disease [47,55], albuminuria is not an indicator of GH, so it was not included in our study. In addition, albuminuria is used to diagnose DKD and assess eGFR progression (reduction), not hyperfiltration [43,44]. Other biomarkers to evaluate kidney function are available; however, their use in clinical practice is still limited [56]. The most reliable indicators to assess kidney function are eGFR and albuminuria.
Some of the limitations of our study include the following: (1) we did not include data on sexual maturity stage and lipid profile that could influence/interfere with the values of eGFR; (2) we did not use time in range and the area under the curve for assessing glycemic control. Furthermore, the use of the CKiD equation, although validated for pediatric T1DM, may underestimate the actual GFR in patients with hyperfiltration [25]. Longitudinal studies with measured GFR using iohexol clearance could clarify this bias.
Studies in the early stages of eGFR changes are necessary to understand the progression of DKD and when intervention could significantly prevent the disease. While all participants had an eGFR of 60 mL/min/1.73 m2, future studies should incorporate CKD staging and microalbuminuria data to identify early DKD, as CKD Stage 1 (eGFR ≥ 90 mL/min/1.73 m2 with microalbuminuria) may coexist with hyperfiltration, highlighting the need for a multimodal renal assessment.
Despite the limitations of the study, such as the absence of data on microalbuminuria and the use of only the equation to estimate GFR, the data reflect the profile of patients in clinical practice and reinforce the importance of glycemic control, body composition (lean mass), and duration of diabetes (less than 5 years) in assessing kidney function in children and adolescents with T1DM.

5. Conclusions

Thus, the results indicate the importance of monitoring renal function from the time of diagnosis in patients with T1DM. This is because in patients with less than 5 years since diagnosis, inadequate glycemic control and a lower percentage of lean mass are associated with a higher likelihood of increased eGFR and glomerular hyperfiltration. However, in patients with insufficient glycemic control, a lower percentage of lean mass, and more than 5 years since diagnosis, it is likely that renal function has already entered a phase of decline, increasing the risk of diabetic kidney disease. This scenario underscores the importance of screening, early diagnosis, and intervention for renal function in this population, particularly when patients face challenges in glycemic control, low levels of physical activity, increased body fat, and reduced lean mass percentage.

Author Contributions

L.S.d.A.H.: data collection; L.F.L., R.F.d.M., P.B. and H.V.C.: visualization and writing—original draft preparation; L.J.M. and J.F.d.S.H.: data collection and visualization; L.A.S., D.P.C. and K.P.S.: writing—review and editing. S.M.B. and E.F.B.C.: writing—original draft preparation and writing—review and editing; E.F.B.C.: conceptualization, methodology, supervision, and project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study is part of a research project previously approved by the UNIMAR Ethics and Research Committee with protocol number 3.606.397/2019 (CAAE: 20492619.6.0000.5496).

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Endocrines 06 00035 g001aEndocrines 06 00035 g001bEndocrines 06 00035 g001c
Figure 1. Analysis of the correlation of eGFR with blood glucose (A), HbA1c (B), fat percentage (C), lean mass percentage (D), and skeletal muscle mass index (E). * indicates a significant correlation for Pearson correlation coefficient for p-value < 0.050. eGFR: estimated Glomerular Filtration Rate.
Figure 1. Analysis of the correlation of eGFR with blood glucose (A), HbA1c (B), fat percentage (C), lean mass percentage (D), and skeletal muscle mass index (E). * indicates a significant correlation for Pearson correlation coefficient for p-value < 0.050. eGFR: estimated Glomerular Filtration Rate.
Endocrines 06 00035 g001aEndocrines 06 00035 g001bEndocrines 06 00035 g001c
Table 1. Absolute (N) and relative (%) frequency distribution of sample characteristics (n = 80) and association analysis of the frequency distribution of independent variables between eGFR categories by quartile distribution.
Table 1. Absolute (N) and relative (%) frequency distribution of sample characteristics (n = 80) and association analysis of the frequency distribution of independent variables between eGFR categories by quartile distribution.
VariableCategorieseGFR Quartile (mL/min/1.73 m2)Totalp-Value ap-Value b
>130 (n = 20)115 to 130 (n = 21)<115 (n = 39)
N%N%N%N%
SexMale1155.0%1047.6%2666.7%4758.80.1180.284
Female945.0%1152.4%1333.3%3341.3
Length of disease<5 years1575.0%1152.4%2051.3%4657.50.1800.107
≥5 years525.0%1047.6%1948.7%3442.5
Insulin administration methodCIIS420.0%523.8%1333.3%2227.5<0.001 *0.255
MID1680.0%1676.2%2666.7%5872.5
Insulin administration schedule (Insulin/kg)Below expected for the weight525.0%14.8%820.5%1417.5<0.001 *0.374
Adequate for the weight1470.0%1990.5%2564.1%5872.5
Above expected for the weight15.0%14.8%615.4%810.0
Associated comorbiditiesYes210.0%14.8%12.6%45.0<0.001 *0.228
No1890.0%2095.2%3897.4%7695.0
Practice physical exerciseYes1680.0%1466.7%2769.2%5771.3<0.001 *0.450
No420.0%733.3%1230.8%2328.8
Level of physical activityMild1260.0%1257.1%2359.0%4758.80.1180.965
Moderate840.0%942.9%1641.0%3341.3
HbA1c <7%210.0%419.0%1435.9%2025.0<0.001 *0.039 **
7 a 8%630.0%14.8%820.5%1518.8
>8%1260.0%1676.2%1743.6%4556.3
Note: * indicates a significant difference in the distribution of proportions of categories using the Chi-square test for p-value a ≤ 0.050. eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin; ** indicates significant association by the Chi-square test for p-value b < 0.050. CIIS: Insulin Infusion System; MID: Multiple Insulin Dose.
Table 2. Comparison of the mean and 95% confidence interval (95% CI) of the independent variables about the eGFR categories by quartile distribution.
Table 2. Comparison of the mean and 95% confidence interval (95% CI) of the independent variables about the eGFR categories by quartile distribution.
VariableeGFR Quartile (mL/min/1.73 m2)p-Value
>130 (n = 20)115 to 130 (n = 21)<115 (n = 39)
MeanCI 95%MeanCI 95%MeanCI 95%
LBUBLBUBLBUB
Age (years)13.111.814.412.711.014.412.211.013.50.674
Length of disease (years)3.52.54.55.13.46.94.23.35.10.207
Physical activity score1.31.21.41.21.11.31.21.21.30.822
Total insulin per kg (unit/day/kg)0.910.791.040.940.861.030.900.820.990.814
HbA1c (%)9.38.110.58.98.09.98.07.38.70.086
Blood glucose (mg/dL)1951522381931612241631461800.136
% fat23.2 a,b20.026.424.2 a20.328.119.4 b17.121.60.033 *
Lean mass (%)76.8 a,b73.680.075.8 a71.979.780.9 b78.883.00.017 *
Skeletal muscle mass (kg)7.366.578.167.166.268.077.096.267.920.905
Skeletal muscle mass index (kg/m2)2.842.673.003.022.833.203.012.853.180.310
* indicates a significant difference between means by the one-way ANOVA test for p-value < 0.050. Different superscript letters indicate significant differences between means, as determined by the Bonferroni post hoc test for p-value < 0.050. HbA1c: glycated hemoglobin.
Table 3. Multiple linear regression analysis for the effect of independent variables on eGFR.
Table 3. Multiple linear regression analysis for the effect of independent variables on eGFR.
VariableBp-ValueModel
DependentIndependentRR2p-Value
eGFR (mL/min/1.73 m2)(Constant)181.480<0.001 *0.4770.228<0.001 **
HbA1c (%)2.2930.035 *
Lean mass (%)−0.8600.011 *
Length of disease (group)−14.2780.004 *
eGFR (mL/min/1.73 m2)(Constant)149.3600.0000.3710.1380.003 **
HbA1c (%)2.4730.030
Lean mass (%)−0.7300.037
Regression coefficient (B). Multivariate correlation coefficient (R). Factor explaining the variation in the independent variables on the variation in the dependent variable (R2). * indicates a significant effect of the independent variable. ** indicates a significant effect of the model. Diagnosis time (1 for <5 years; 2 for ≥5 years). eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin.
Table 4. Binary logistic regression analysis to assess the effect of independent variables on the likelihood of increased eGFR.
Table 4. Binary logistic regression analysis to assess the effect of independent variables on the likelihood of increased eGFR.
VariableBp-Value aModel
DependentIndependentR2p-Value b
eGFR ≥ 135 (mL/min/1.73 m2)HbA1c (%)0.2090.1310.2000.014 **
Lean mass (%)−0.0940.065
Length of disease (years)−0.3330.034 *
Constant5.1850.247
Regression coefficient (B). * indicates a significant effect of the independent variable by the Wald test for p-value a < 0.050. ** indicates a significant effect of the model by the Omnibus test for p-value b < 0.050. Nalgelkerke R square (R2). eGFR: estimated glomerular filtration rate; HbA1c: glycated hemoglobin.
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de Argollo Haber, L.S.; Laurindo, L.F.; de Melo, R.F.; Carneiro, D.P.; Biteli, P.; Chagas, H.V.; Mellem, L.J.; Haber, J.F.d.S.; Sloan, L.A.; Sloan, K.P.; et al. Glomerular Hyperfiltration in Children and Adolescents with Type 1 Diabetes Mellitus: A Cross-Sectional Observational Study. Endocrines 2025, 6, 35. https://doi.org/10.3390/endocrines6030035

AMA Style

de Argollo Haber LS, Laurindo LF, de Melo RF, Carneiro DP, Biteli P, Chagas HV, Mellem LJ, Haber JFdS, Sloan LA, Sloan KP, et al. Glomerular Hyperfiltration in Children and Adolescents with Type 1 Diabetes Mellitus: A Cross-Sectional Observational Study. Endocrines. 2025; 6(3):35. https://doi.org/10.3390/endocrines6030035

Chicago/Turabian Style

de Argollo Haber, Luiza Santos, Lucas Fornari Laurindo, Rafael Fagundes de Melo, Dennis Penna Carneiro, Piero Biteli, Henrique Villa Chagas, Luciano Junqueira Mellem, Jesselina Francisco dos Santos Haber, Lance Alan Sloan, Kátia Portero Sloan, and et al. 2025. "Glomerular Hyperfiltration in Children and Adolescents with Type 1 Diabetes Mellitus: A Cross-Sectional Observational Study" Endocrines 6, no. 3: 35. https://doi.org/10.3390/endocrines6030035

APA Style

de Argollo Haber, L. S., Laurindo, L. F., de Melo, R. F., Carneiro, D. P., Biteli, P., Chagas, H. V., Mellem, L. J., Haber, J. F. d. S., Sloan, L. A., Sloan, K. P., Maria Barbalho, S., & Chagas, E. F. B. (2025). Glomerular Hyperfiltration in Children and Adolescents with Type 1 Diabetes Mellitus: A Cross-Sectional Observational Study. Endocrines, 6(3), 35. https://doi.org/10.3390/endocrines6030035

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