Front. Endocrinol. Frontiers in Endocrinology Front. Endocrinol. 1664-2392 Frontiers Media S.A. 10.3389/fendo.2026.1793935 Original Research Phase angle as an early-warning indicator of glycaemic control in adults with type 1 diabetes mellitus: a cross-sectional study ZhangLun 1 † Conceptualization Writing – original draft ZhuJiani 1 † Writing – original draft Methodology Project administration YangFan 1 † Resources Formal analysis Writing – original draft CuiTongfen 2 † Writing – original draft Investigation Methodology XiXiaoyun 3 Writing – review & editing Resources Project administration YangLuying 4 Project administration Supervision Validation Writing – review & editing WangJi-Gan 5 * Software Writing – review & editing CaiYunying 6 * Visualization Conceptualization Software Writing – review & editing Department of Clinical Nutrition, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China Department of Internal Medicine, Buga Town Health Center, Zhaotong, Yunnan, China Department of Endocrine Metabolism, Changning County People’s Hospital, Baoshan, Yunnan, China Department of Clinical Nutrition, Baoshan Second People’s Hospital, Baoshan, Yunnan, China Department of Pediatrics, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China Department of Endocrine Metabolism, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming, Yunnan, China Correspondence: Ji-Gan Wang, wangjigan@163.com; Yunying Cai, caiyunying1202@163.com

†These authors have contributed equally to this work

 26 02 2026 2026 17 1793935 23 01 2026 11 02 2026 10 02 2026 Copyright © 2026 Zhang, Zhu, Yang, Cui, Xi, Yang, Wang and Cai. 2026 Zhang, Zhu, Yang, Cui, Xi, Yang, Wang and Cai https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Background

For patients with poor glycemic control of adult type 1 diabetes mellitus (T1DM), in addition to managing blood glucose levels, it is worth exploring further as an early warning indicator of blood glucose control.

 Methods

56 adults with T1DM, aged between 18 and 70 years, were included in the study. Data on body composition and laboratory indicators, including phase angle values and HbA1c levels, were collected. Statistical analysis was conducted to determine the correlation and the strength of association between the phase angle, and HbA1c levels.

 Results

Pearson’s correlation and linear regression models indicated a negative correlation between phase angle and HbA1c levels, even after controlling for age and weight in both males and females.

 Conclusion

The results indicate a significant negative relationship between Phase angle and HbA1c levels. Beyond being a simple body composition parameter, Phase angle can be used as a clinical indicator of improved blood glucose control.

 adult correlation glycated hemoglobin phase angle type 1 diabetes The author(s) declared that financial support was received for this work and/or its publication. This study is funded by National clinical key specialty cultivation project of endocrinology (2024NMKFKT-09). section-at-acceptance Clinical Diabetes Introduction

Diabetes is one of the fastest-growing global health challenges of the 21st century. Type 1 diabetes (T1DM) has become a significant global disease burden. According to the Global Burden of Disease study, in 2021, there were 19.6 million T1DM patients worldwide, with over 530,000 new cases. Among them, China had 1.44 million T1DM patients and over 32,000 new cases, ranking it third in the world (1).

The close relationship between progress and the development of microvascular, macrovascular, and neuropathic complications with glucose control has been extensively studied in patients with type 1 diabetes (2, 3). Data from the China T1DM Study indicates that patients with ketoacidosis still account for 41.1%, the rate of blood glucose control among hospitalized patients is only 12%, and the prevalence of chronic complications reaches 60.7% (4). Thus, exploring safer and more stable blood glucose management programs is worthwhile.

It has been demonstrated that numerous factors contribute to poor glycemic control, including older age, female sex, alcohol consumption, a higher body mass index (BMI), smoking, a longer duration of the disease, lower physical activity, failure to adhere to therapeutic recommendations, and numerous others (5). Hemoglobin A1c(HbA1c), a product of glucose binding with hemoglobin in red blood cells, reflects average blood sugar levels over the past two to three months. Its clinical significance includes evaluating blood sugar control, aiding in the diagnosis of diabetes, predicting risks of complications, guiding adjustments to treatment plans, and distinguishing between stress-induced hyperglycemia (6). Several factors influence HbA1c levels, including not only the average blood glucose level of patients but also the lifespan of red blood cells, age, race, and ethnicity. The HbA1c level can be impacted by the red blood cell (RBC) lifecycle. Since HbA1c is a result of the non-enzymatic chemical reaction between hemoglobin and blood glucose, alterations in hemoglobin can influence HbA1c levels. Research has indicated that RBC transfusions can lead to a decrease in HbA1c levels (7–9). The prevalence of dyslipidemia and diabetes mellitus among individuals over 30 years of age in Korea has continuously increased. A study found an association between high glycated hemoglobin levels and a diagnosis of dyslipidemia among Korean adults (10). Another analysis revealed the relationship between sedentary behavior, diastolic blood pressure, and HbA1c levels among a specific cohort of young female university students. In their study, sedentary hours and diastolic blood pressure were independently associated with HbA1c levels (11).

Bioelectrical impedance analysis (BIA) is a non-invasive method that assesses the electrical impedance properties of body tissues and has been widely applied in body composition evaluation. Among the parameters derived from BIA, Phase angle (PhA) is considered one of the most comprehensive indicators reflecting cellular health status. Phase angle is calculated from the phase shift between reactance and resistance, and its magnitude reflects the capacitive properties of cell membranes as well as the distribution of intracellular and extracellular water. From a biological perspective, Phase angle essentially represents an integrated manifestation of cell membrane integrity, cell quantity, and cellular functional status (12).

A higher Phase angle generally indicates intact cell membrane structure, a higher proportion of intracellular water, and favorable cellular metabolic activity. In contrast, a reduced Phase angle is commonly interpreted as a sign of impaired membrane permeability, decreased cellular function, or a relative increase in extracellular water. Therefore, Phase angle is not merely a body composition parameter but is increasingly regarded as a functional indicator reflecting cellular-level health. Previous studies have demonstrated that a lower Phase angle is closely associated with enhanced inflammatory responses, elevated oxidative stress, malnutrition, reduced muscle mass, and long-term metabolic disturbances, and it has been linked to adverse outcomes in various chronic diseases (13, 14).

Existing research has primarily focused on populations with type 2 diabetes mellitus (T2DM). Multiple studies have reported a significant negative association between Phase angle and glycated hemoglobin (HbA1c) levels, indicating that poorer glycaemic control is associated with lower phase angle values (15). In addition, Phase angle has been reported to be closely related to chronic diabetic complications such as neuropathy, nephropathy, and sarcopenia, suggesting that Phase angle reflects not only metabolic status but may also be associated with disease progression and tissue damage (16). These findings provide important evidence supporting the potential role of Phase angle as a biomarker in diabetes management.

However, compared with T2DM, evidence regarding Phase angle in type 1 diabetes mellitus (T1DM) remains markedly limited. T1DM is characterized by distinct pathophysiological features, primarily autoimmune-mediated destruction of pancreatic β-cells, earlier disease onset, and lifelong dependence on exogenous insulin therapy. Long-term glycaemic fluctuations, insulin dose adjustments, and sustained metabolic stress may exert cumulative effects on cellular structure and function. Therefore, investigating whether Phase angle can reflect glycaemic control in adults with T1DM and serve as a potential early-warning indicator is of significant clinical relevance.

 Materials and methods Trial design and patient enrollment

This cross-sectional study was conducted between September 2023 and May 2024. The subjects of the study were patients with T1DM in the Department of Endocrinology and Metabolism at the First People’s Hospital of Yunnan Province. The inclusion criteria were as follows: (1) Meeting the diagnostic criteria for T1DM as defined in the Expert Consensus on Adult T1DM Management (17); (2) Being fully conscious with normal communication skills and able to cooperate with study-related measurements and physical examinations; (3) An age range of 18–70 years; (4) No previous history of gastrointestinal diseases or digestive tract surgeries; (5) A disease duration of at least 1 year. The exclusion criteria included: (1) Inability to complete examinations; (2) Pregnant or breastfeeding women; (3) Long-term use of thyroid medications, steroids, or estrogen/progestogen drugs; (4) Implantation of cardiac pacemakers, cardioverter-defibrillators, or replacement joints.

 Procedures and measurements

Upon admission, comprehensive baseline data were systematically collected, including demographic characteristics (age, sex), anthropometric parameters (height, weight, waist circumference, hip circumference), and clinical information (diagnosis, disease duration).

 Body composition analysis and phase angle

We evaluated body composition and phase angle using a human body composition analyzer (Inbody S10), which employs the BIA method. Participants fasted for a minimum of 8 hours and emptied their bladders on the morning of the test. Measurement procedure: Clean the electrode contact surfaces of the instrument, measure the participant’s height and weight; barefoot, wipe the soles and palms of both hands with an alcohol swab, and ensure the feet and hands make maximum contact with the instrument’s silver electrode surfaces to introduce the electric current. After the measurement is completed, the phase angle value at a frequency of 50 kHz is obtained.

 Laboratory indicators include HbA1c levels

The analytical methods for the biochemical measurements were as follows: total cholesterol, HDL-c, triglycerides, and LDL-c were measured using the enzymatic colorimetric method; HbA1c was determined by high-performance liquid chromatography; total protein was assayed by the diacuprim method, albumin by the bromophenol green method, and prealbumin by the immunoturbidimetric method.

 Statistical analysis

All statistical analyses were performed using SPSS software (version 26.0; IBM Corp). Continuous variables with a normal distribution were presented as mean ± standard deviation ( x¯ ± s) and compared using an independent samples t-test. Non-normally distributed continuous variables were expressed as median with interquartile range [M (P25, P75)]. Categorical variables were reported as frequencies and percentages (n,%).

Pearson’s and Spearman’s correlation coefficients were utilized to assess the relationship between the dependent variable and independent variables. Linear regression models, both crude and adjusted, were employed to ascertain the strength of the association between the dependent variable and independent variables. Model fit was evaluated using the coefficient of determination (R²), with values closer to 1 indicating a better fit to the observed data.

 Results Basic characteristics of patients

56 adults with T1DM were included in this study. Table 1 summarizes the clinical data of the adults with T1DM. The ages of the men and women are 37.91 ± 15.13 years and 40.50 (21.00, 52.00) years, respectively, and their HbA1c levels are 10.05 ± 2.85% and 9.79 ± 1.94%, respectively. The phase angles are 6.00 (5.07, 6.80) for men and 5.16 ± 0.93 for women.

Characteristics of T1DM.

Variables Male(n=22) Female(n=34) P
Age (years) 37.91 ± 15.13 40.50(21.00,52.00) 0.840
Height (cm) 168.77 ± 6.56 158.00(155.75,162.00) <0.001
Weight (kg) 60.31 ± 10.29 51.85 ± 7.24 <0.001
BMI (kg/m2) 21.23 ± 3.40 20.40 ± 2.75 0.320
Total protein (g/L) 64.05 ± 8.73 69.11(63.60,74.15) 0.067
Albumin (g/L) 39.70 ± 6.39 41.66 ± 5.19 0.213
Prealbumin (mg/L) 194.11 ± 43.74 189.93 ± 47.42 0.741
HbA1c (%) 10.05 ± 2.85 9.79 ± 1.94 0.668
Total cholesterol (mmol/L) 4.34 ± 0.98 4.99 ± 1.07 0.027
Triglyceride (mmol/L) 0.96(0.69,1.62) 0.86(0.66,1.41) 0.586
HDL-C (mmol/L) 1.22 ± 0.32 1.67 ± 0.52 <0.001
LDL-C (mmol/L) 2.55 ± 0.75 2.76 ± 0.84 0.362
Waistline (cm) 82.11 ± 9.57 76.30 ± 8.69 0.023
Hip(cm) 92.22 ± 7.67 89.48 ± 6.70 0.164
Waist-hip ratio(%) 88.93 ± 6.01 85.34 ± 7.79 0.072
Phase angle (°) 6.00(5.07,6.80) 5.16 ± 0.93 0.027
Phase angle analysis results

Pearson’s or Spearman’s correlation coefficient analyses were used to measure the association between Phase angle and parameters, including HbA1c levels, as shown in Table 2. In males, a positive correlation was observed between the Phase angle and several indicators, including total protein, albumin, prealbumin, total cholesterol, and hip circumference, even after adjusting for age and weight. Conversely, the Phase angle and HbA1c levels showed a negative correlation, even after adjusting for age and weight, as indicated in Table 3. In females, a positive correlation was observed between the Phase angle and albumin, even after adjusting for age and weight. Similarly, the Phase angle and HbA1c levels exhibited a negative correlation, even after adjusting for age and weight.

Pearson’s and partial correlation between phase angle and metabolic parameters (male).

VariablesPhase angle (°) Correlation coefficient Partial correlation coefficient
r p r p
Total protein (g/L) 0.509 0.016 0.611 0.004
Albumin (g/L) 0.613 0.002 0.641 0.002
Prealbumin (mg/L) 0.462 0.030 0.609 0.004
HbA1c (%) -0.488 0.021 -0.552 0.012
Total cholesterol mmol/L) 0.500 0.018 0.703 <0.001
Triglyceride (mmol/L) 0.211 0.346 0.222 0.347
HDL-C (mmol/L) 0.368 0.092 0.608 0.004
LDL-C (mmol/L) 0.469 0.028 0.592 0.006
Waistline (cm) 0.510 0.015 0.177 0.456
Hip circumference(cm) 0.624 0.002 0.485 0.030
Waist-hip ratio(%) 0.117 0.603 -0.212 0.369

Partial correlation in control of age and weight.

Phase angle: Outcome variable.

HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin.

Pearson’s and partial correlation between phase angle and metabolic parameters (female).

VariablesPhase angle (°) Correlation coefficient Partial correlation coefficient
r p r p
Total protein (g/L) 0.311 0.073 0.387 0.029
Albumin (g/L) 0.459 0.006 0.539 0.001
Prealbumin (mg/L) 0.160 0.365 0.251 0.166
HbA1c (%) -0.374 0.029 -0.381 0.032
Total cholesterol mmol/L) 0.105 0.555 0.107 0.559
Triglyceride (mmol/L) -0.144 0.416 -0.163 0.372
HDL-C (mmol/L) 0.246 0.160 0.259 0.152
LDL-C (mmol/L) -0.029 0.873 -0.025 0.891
Waistline (cm) 0.008 0.962 -0.086 0.639
Hip circumference(cm) 0.167 0.344 0.150 0.413
Waist-hip ratio(%) -0.126 0.476 -0.188 0.304

Partial correlation in control of age and weight.

Phase angle: Outcome variable.

HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin.

Simple linear regression and hierarchical regression analysis were used to measure the strength of the association between Phase angle and parameters, including HbA1c levels.

Table 4 illustrates that in males, simple linear regression analyses revealed a positive association between Phase angle and several indicators, including total protein, albumin, prealbumin, total cholesterol, LDL-C, waist circumference, and hip circumference. After adjusting for age and weight using hierarchical regression analysis, several indicators maintained a significant positive association with Phase angle. Additionally, Phase angle and HbA1c levels initially showed a negative correlation; however, after adjusting for age and weight, the negative correlation persisted.

Simple and adjusted linear regression (male).

VariablesPhase angle (°) Simple linear regression Adjusted linear regression
ß r2 p ß r2 p
Total protein (g/L) 0.070 0.259 0.016 0.073 0.523 0.004
Albumin (g/L) 0.115 0.375 0.002 0.016 0.052 0.002
Prealbumin (mg/L) 0.013 0.174 0.030 0.017 0.442 0.004
HbA1c (%) -0.204 0.238 0.021 -0.482 0.384 0.012
Total cholesterol mmol/L) 0.605 0.250 0.018 0.769 0.551 <0.001
Triglyceride (mmol/L) 0.364 0.045 0.346 0.335 0.157 0.347
HDL-C (mmol/L) 1.336 0.135 0.092 2.007 0.441 0.004
LDL-C (mmol/L) 0.738 0.220 0.028 0.823 0.424 0.006
Waistline (cm) 0.064 0.260 0.015 0.052 0.141 0.456
Hip circumference(cm) 0.097 0.389 0.002 0.445 0.312 0.030
Waist-hip ratio(%) 2.334 0.014 0.603 -4.641 0.153 0.369

Adjusted with age and weight.

Phase angle: Outcome variable.

HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin.

Table 5 indicates that in females, simple linear regression analyses revealed a positive association between Phase angle and albumin. Upon adjusting for age and weight using hierarchical regression analysis, the association between Phase angle and albumin remained significant and positive. Furthermore, Phase angle and HbA1c levels exhibited a negative correlation, which continued to be observed after adjusting for age and weight.

Simple and adjusted linear regression (female).

VariablesPhase angle (°) Simple linear regression Adjusted linear regression
ß r2 p ß r2 p
Total protein (g/L) 0.023 0.097 0.073 0.031 0.162 0.004
Albumin (g/L) 0.082 0.211 0.006 0.104 0.301 0.002
Prealbumin (mg/L) 0.003 0.026 0.365 0.006 0.77 0.004
HbA1c (%) -0.179 0.140 0.029 -0.187 0.158 0.012
Total cholesterol (mmol/L) 0.091 0.011 0.555 0.092 0.026 <0.001
Triglyceride (mmol/L) -0.175 0.021 0.416 -0.020 0.041 0.347
HDL-C (mmol/L) 0.434 0.061 0.160 0.464 0.081 0.004
LDL-C (mmol/L) -0.031 0.001 0.873 -0.028 0.015 0.006
Waistline (cm) 0.001 0.001 0.962 -0.012 0.022 0.456
Hip circumference(cm) 0.023 0.028 0.344 0.029 0.037 0.030
Waist-hip ratio(%) -1.510 0.016 0.476 -2.413 0.049 0.369

Adjusted with age and weight.

Phase angle: Outcome variable.

HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, glycated hemoglobin.

 Discussion

In this study, we aimed to explore the relationship between Phase angle and HbA1c levels, in adult T1DM. The findings of this study revealed a negative correlation between Phase angle and HbA1c in males, even after adjusting for age and weight. At the same time, this study also revealed a negative correlation between Phase angle and HbA1c in females. This implies that higher HbA1c levels are associated with lower Phase angle in adult T1DM. This relationship suggests that Phase angle can be used as a clinical observational indicator of improved blood glucose control.

As a core parameter of bioelectrical impedance analysis (BIA), Phase angle (PhA) is theoretically associated with the capacitive properties of cell membranes, the distribution of intracellular and extracellular water, and body cell mass, thereby reflecting cell membrane integrity and overall cellular function. From a clinical perspective, PhA tends to decrease in conditions characterized by reduced muscle mass or cell mass, increased inflammatory or metabolic stress, and abnormal fluid distribution (such as an increased extracellular water proportion) (13). Therefore, PhA can be regarded as a composite indicator of overall health status. Phase angle exhibits a decline in value under certain ill health conditions, including cancer, and viral infections et al (18). These diseases are associated with damage to the body’s cells, causing a decrease in the capacity of cells to capture electrical energy. Its clinical significance has been increasingly recognized, particularly in elderly, bedridden, and edematous patients, as well as in those experiencing declines in cognitive and physical function (19–21). In our study, a positive correlation was observed between the phase angle and several indicators in males, including total protein, albumin, prealbumin, total cholesterol, and hip circumference. A positive correlation was observed between the Phase angle and albumin in females. A population-based study conducted on adults showed that an increase in WC is likely to decrease the Phase angle value (22). But in our study, only in males was there a positive correlation between hip circumference and Phase angle. A study to evaluate the relationship between Phase angle and nutritional status and the prognostic significance of Phase angle in critically ill cancer patients showed that a positive correlation was ascertained between the phase angle and albumin (23). In a study to determine the association between Phase angle and nutritional status in community-dwelling patients with type 2 diabetes, concluding observations that Phase angle was positively associated with albumin, a nutritional assessment index (24).

In this study, Phase angle values were higher in males than in females. This finding is biologically plausible and may be related to sex-specific differences in body composition and cellular characteristics, which is consistent with previous reports (25). One possible explanation is that males generally have higher fat-free mass and skeletal muscle mass and a lower relative fat mass. Because phase angle is considered to reflect cell membrane integrity, cellular health, and the distribution of intracellular and extracellular water, greater muscle mass and higher intracellular water content in males may contribute to their higher phase angle values. In addition, differences in sex hormones, particularly their effects on muscle metabolism and body water distribution, may also play a role.

The main findings of this study are consistent with previous results reported in populations with type 2 diabetes mellitus (T2DM), in which several studies have demonstrated a negative association between Phase angle and HbA1c (26, 27). The present study extends this observation to individuals with type 1 diabetes mellitus (T1DM). T1DM and T2DM differ fundamentally in their pathophysiological mechanisms: T1DM is characterized by autoimmune destruction of pancreatic β-cells, requiring lifelong dependence on exogenous insulin and being associated with greater glycemic variability and a higher risk of hypoglycemia. In T1DM, changes in PhA may therefore be more strongly related to glycemic fluctuations, protein catabolism, alterations in lean body mass, and the burden of diabetes-related complications. Despite these differences, our findings indicate that the pattern of association between PhA and glycemic control is similar in both types of diabetes, suggesting that PhA may reflect a shared pathological basis of diabetic metabolic dysregulation—namely, the detrimental effects of chronic hyperglycemia on cellular structure and function (28). Moreover, this association has also been observed in pediatric populations (29) and in other special groups, such as young Japanese sumo wrestlers (30), further supporting the broad applicability of PhA as an important biomarker for assessing metabolic health across diverse populations.

This study has several limitations. First, although the main analyses were based on prespecified hypotheses, Bonferroni correction for multiple comparisons was not applied, which may increase the risk of type I error. Therefore, the results should be interpreted with caution, and future studies with larger sample sizes or independent populations are needed to further validate our findings. Second, due to the cross-sectional study design, although an association between phase angle and glycated hemoglobin levels was observed, a causal relationship cannot be established. Third, several key factors that may influence glycemic control and phase angle, such as diabetes duration, daily insulin dose, and educational level, were not included in the analysis. In addition, lifestyle-related factors, including physical activity status, exercise intensity, and nutritional intake, were not assessed, and these unmeasured variables may have introduced potential residual confounding.

Future studies should adopt longitudinal designs to more comprehensively elucidate the factors influencing phase angle and glycated hemoglobin levels, and to determine whether temporal changes in phase angle can predict subsequent deterioration in glycemic control. Moreover, it would be valuable to explore whether interventions such as nutritional modification and structured physical exercise can simultaneously improve glycemic variability and increase phase angle, thereby providing further evidence to support comprehensive management strategies for patients with type 1 diabetes.

 Conclusion

This study has, for the first time, demonstrated a negative correlation between Phase angle and HbA1C levels in patients with T1DM. Beyond being a simple body composition parameter, Phase angle serves as a biomarker reflecting cellular health, nutritional status, and metabolic stability. Incorporating Phase angle into comprehensive evaluation systems for patients with type 1 diabetes could provide clinicians with a novel perspective, enabling more precise and personalized glucose management and ultimately improving long-term patient outcomes.

 Data availability statement

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

 Ethics statement

The studies involving humans were approved by The First People’s Hospital of Yunnan Province (KHLL2025-KY337). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

 Author contributions

LZ: Conceptualization, Writing – original draft. JZ: Writing – original draft, Methodology, Project administration. YF: Resources, Formal analysis, Writing – original draft. TC: Writing – original draft, Investigation, Methodology. XX: Writing – review & editing, Resources, Project administration. LY: Project administration, Supervision, Validation, Writing – review & editing. J-GW: Software, Writing – review & editing. YC: Visualization, Conceptualization, Software, Writing – review & editing.

 Acknowledgments

We thank the following investigators for their support, assistance, and cooperation: Yingluan Feng (Baoshan College of Chinese Medicine), XX (Changning County People’s Hospital), Xue Yang (Lanping County People’s Hospital, Nujiang Prefecture). Finally, we thank all participants.

 Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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 References Deng W Zhao L Chen C Ren Z Jing Y Qiu J . National burden and risk factors of diabetes mellitus in China from 1990 to 2021: results from the global burden of disease study 2021. J Diabetes. (2024) 16:e70012. doi: 10.1111/1753-0407.70012, PMID: 39373380 Vinker-Shuster M Eldor R Green I Golan-Cohen A Manor I Merzon E . Glycemic control and diabetes related complications in adults with type 1 diabetes mellitus and adhd. J Atten Disord. (2022) 26:1235–44. doi: 10.1177/10870547211068039, PMID: 34933573 Eshetu K Regassa LD Dehresa M Genete D . Chronic microvascular complication of type 1 diabetes mellitus and its predictors among children with type 1 diabetes mellitus in Ethiopia; A single center experience: ambi directional cohort study. Pediatr health Med Ther. (2024) 15:201–12. doi: 10.2147/PHMT.S456541, PMID: 38808177 Deng C Xie Y Li J Jiang H Niu X Yan D . Care, control and complications of hospitalised patients with type 1 diabetes in China: a nationwide-based registry study. Diabetes/metabolism Res Rev. (2024) 40:e3796. doi: 10.1002/dmrr.3796, PMID: 38529788 Cheneke W Suleman S Yemane T Abebe G . Assessment of glycemic control using glycated hemoglobin among diabetic patients in jimma university specialized hospital, Ethiopia. BMC Res Notes. (2016) 9:96. doi: 10.1186/s13104-016-1921-x, PMID: 26879899 Jakubiak GK Chwalba A Basek A Cieślar G Pawlas N . Glycated hemoglobin and cardiovascular disease in patients without diabetes. J Clin Med. (2024) 14:53. doi: 10.3390/jcm14010053, PMID: 39797136 Sim SY Park SJ Yoo JW Kim S Lee JW Chung N . Glycated albumin may have a complementary role to glycated hemoglobin in glucose monitoring in childhood acute leukemia. Ann Pediatr Endocrinol Metab. (2024) 29:266–75. doi: 10.6065/apem.2346100.050, PMID: 39231488 Chan CL Pyle L Kelsey M Newnes L Zeitler PS Nadeau KJ . Screening for type 2 diabetes and prediabetes in obese youth: evaluating alternate markers of glycemia - 1,5-anhydroglucitol, fructosamine, and glycated albumin. Pediatr Diabetes. (2016) 17:206–11. doi: 10.1111/pedi.12258, PMID: 25652226 Spencer DH Grossman BJ Scott MG . Red cell transfusion decreases hemoglobin a1c in patients with diabetes. Clin Chem. (2011) 57:344–6. doi: 10.1373/clinchem.2010.157321, PMID: 21059826 Kang P Kim KY Shin HY . Association between dyslipidemia and glycated hemoglobin in a population-based study. Metabolites. (2024) 14:92. doi: 10.3390/metabo14020092, PMID: 38392984 Anver Pasha SF Sadathulla Syed S Mohamed Saber Mohamed Mohamed Sharafeldin A Nazari Fard EH Srinivasamurthy SK Goud M . Interrelation among anthropometric indices, body composition, physical fitness, and glycated hemoglobin in a cohort of young female university students: a cross-sectional study. Cureus. (2025) 17:e80324. doi: 10.7759/cureus.80324, PMID: 40206940 Pedroso MS Moraes MS Martins PC Silva DAS . Body composition and phase angle in rugby union athletes: a comparison between forward and back positions. J Bodyw Mov Ther. (2024) 40:1060–5. doi: 10.1016/j.jbmt.2024.07.013, PMID: 39593412 De Benedetto F Marinari S De Blasio F . Phase angle in assessment and monitoring treatment of individuals with respiratory disease. Rev Endocr Metab Disord. (2023) 24:491–502. doi: 10.1007/s11154-023-09786-5, PMID: 36694055 Román E Poca M Amorós-Figueras G Rosell-Ferrer J Gely C Nieto JC . Phase angle by electrical bioimpedance is a predictive factor of hospitalisation, falls and mortality in patients with cirrhosis. Sci Rep. (2021) 11:20415. doi: 10.1038/s41598-021-99199-8, PMID: 34650096 Hu Y Jin H . Association between phase angle, body mass index and insulin resistance in patients with type 2 diabetes mellitus: a cross-sectional study. PeerJ. (2025) 13:e18815. doi: 10.7717/peerj.18815, PMID: 39830963 Zhang J Xu Z Fu Y Chen L . Association between phase angle and diabetic peripheral neuropathy in type 2 diabetes patients. Endocrine. (2024) 85:196–205. doi: 10.1007/s12020-024-03689-4, PMID: 38367144 Holt RIG Devries JH Hess-Fischl A Hirsch IB Kirkman MS Klupa T . The management of type 1 diabetes in adults. A consensus report by the american diabetes association (ADA) and the european association for the study of diabetes (EASD). Diabetes Care. (2021) 44:2589–625. doi: 10.2337/dci21-0043, PMID: 34593612 Lukaski HC Talluri A . Phase angle as an index of physiological status: validating bioelectrical assessments of hydration and cell mass in health and disease. Rev Endocr Metab Disord. (2023) 24:371–9. doi: 10.1007/s11154-022-09764-3, PMID: 36336754 Bellido D García-García C Talluri A Lukaski HC García-Almeida JM . Future lines of research on phase angle: strengths and limitations. Rev Endocr Metab Disord. (2023) 24:563–83. doi: 10.1007/s11154-023-09803-7, PMID: 37043140 Marra M Di Vincenzo O Cioffi I Sammarco R Morlino D Scalfi L . Resting energy expenditure in elite athletes: development of new predictive equations based on anthropometric variables and bioelectrical impedance analysis derived phase angle. J Int Soc Sports Nutr. (2021) 18:68. doi: 10.1186/s12970-021-00465-x, PMID: 34702296 Son JW Han B Bennett JP Heymsfield S Lim S . Development and clinical application of bioelectrical impedance analysis method for body composition assessment. Obes Rev. (2025) 26:e13844. doi: 10.1111/obr.13844, PMID: 39350475 Longo GZ Silva DAS Gabiatti MP Martins PC Hansen F . Phase angle association with metabolic profile in adults: a population-based study. Nutr (Burbank Los Angeles County Calif.). (2021) 90:111233. doi: 10.1016/j.nut.2021.111233, PMID: 33934055 Do Amaral Paes TC de Oliveira KCC de Carvalho Padilha P Peres WAF . Phase angle assessment in critically ill cancer patients: relationship with the nutritional status, prognostic factors and death. J Crit Care. (2018) 44:430–5. doi: 10.1016/j.jcrc.2018.01.006, PMID: 29353120 Mori Y Tachi T Koizumi H Matsumoto K Sakurai H . Association of bio-impedance phase angle and a nutritional indicator in community-dwelling patients with type 2 diabetes mellitus. J Diabetes Metab Disord. (2023) 23:1839–44. doi: 10.1007/s40200-023-01196-3, PMID: 39610536 Barbosa-Silva MCG Barros AJD Wang J Heymsfield SB Pierson RNJ . Bioelectrical impedance analysis: population reference values for phase angle by age and sex. Am J Clin Nutr. (2005) 82:49–52. doi: 10.1093/ajcn.82.1.49, PMID: 16002799 Yücel HE Ulcay T Gorgulu O Tur K Kırındı MH Çömlekçi E . The relationship between phase angle obtained from the maximum reactance and fasting glucose, hemoglobin A1c in type 2 diabetes mellitus. Med Records. (2023) 5:549–53. doi: 10.37990/medr.1292694 Ito H Miura S Mori T Miura S I C Matsumoto S . Phase angle evaluated by a bioimpedance analysis is closely related to diabetic nephropathy and peripheral neuropathy in patients with type 2 diabetes. JMA J. (2025) 8:925–35. doi: 10.31662/jmaj.2025-0071, PMID: 40786473 Giri B Dey S Das T Sarkar M Banerjee J Dash SK . Chronic hyperglycemia mediated physiological alteration and metabolic distortion leads to organ dysfunction, infection, cancer progression and other pathophysiological consequences: an update on glucose toxicity. Biomedicine pharmacotherapy = Biomedecine pharmacotherapie. (2018) 107:306–28. doi: 10.1016/j.biopha.2018.07.157, PMID: 30098549 Giardinelli S Lambertini AG Lumaca A Boschiero D Cavallin F Zucchini S . Bioelectrical impedance analysis of body composition in children and adolescents with type 1 diabetes: a prospective case-control study. Eur J Pediatr. (2025) 184:560. doi: 10.1007/s00431-025-06401-4, PMID: 40825909 Khatun S Ogawa M Uchizawa A Hoshi D Tamai S Momma R . Glycated hemoglobin (hba1c) is independently associated with the bioelectrical impedance phase angle in junior sumo wrestlers: a pilot study. Physiol Rep. (2024) 12:e16045. doi: 10.14814/phy2.16045, PMID: 38740565

Edited by: David Wagner, University of Colorado, United States

Reviewed by: Hasan Esat Yücel, Ahi Evran University, Türkiye

Ahmet Uzun, Ondokuz Mayıs University, Türkiye

T1DM, Type 1 diabetes; HbA1c, Hemoglobin A1c; HDL-c, High-Density Lipoprotein Cholesterol; BIA, Bioelectrical Impedance Analysis; BMI, Body Mass Index.