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NCT Number: NCT07021326

Phase Angle and TyG Index as Markers of Glycaemic Control, Adiposity and Cardiovascular Risk in T1D Adolescents

This study aimed to identify new clinical biomarkers that may improve the follow-up and health outcomes of adolescents with type 1 diabetes. The current clinical practice includes a standard set of measurements for monitoring glycemic control and general health. However, other parameters such as phase angle (obtained through a fast, simple, and painless body composition analysis) might also provide valuable insight into the metabolic status of these patients.

This is a cross-sectional observational study that involved a one-time data collection process. Participants underwent a single body composition measurement (10 seconds, using a bioimpedance analyzer). No interventions or follow-up visits were required.

Additional data was extracted from medical records, including clinical information (e.g., age of diabetes onset, HbA1c, anthropometrics) and results from recent blood tests.

The goal is to determine whether indicators such as the phase angle and triglyceride-glucose (TyG) index could serve as non-invasive tools to assess glycemic control, glycaemic control and cardiovascular risk in adolescents with type 1 diabetes.

The study was conducted at two hospitals from Alicante (Spain) and included patients with a confirmed diagnosis of type 1 diabetes who met the inclusion criteria. Participation was entirely voluntary with informed consent obtained from legal guardians. All collected data were anonymized to prevent reidentification and unauthorized access.

This research seeks to expand scientific knowledge on diabetes management and support the development of more precise, individualized monitoring tools for young people living with type 1 diabetes.

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Key information

Conditions

Type 1 Diabetes Mellitus Autoimmune Diseases Diabetes Mellitus Diabetes Mellitus, Type 1 Endocrine System Diseases Glucose Metabolism Disorders Immune System Diseases Metabolic Diseases Nutritional and Metabolic Diseases

Age range

10 year–19 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Hospital General Universitario de Elche

Elche, Alicante, 03203, Spain

About this study

This cross-sectional study was conducted in the General Hospital of Elche and the San Juan Hospital (Alicante, Spain).

The assessment of glycemic control was conducted through the analysis of variables derived from continuous glucose monitoring (CGM), specifically focusing on metrics such as time in range (TIR) and the coefficient of glucose variability (CV), which provide detailed insights into glucose fluctuations and stability over time. Additionally, glycated hemoglobin (HbA1c) was taken into account and measured directly during the medical appointment using the Alere Afinion AS100 Analyzer (Abbott, Illinois, United States) device. Body composition was evaluated using a bioelectrical impedance device (Biody-Xpert) and cardiovascular risk was estimated by calculating established indices based on biochemical markers, including the triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio, as well as anthropometric and body composition-derived indices, such as the fat mass index (FMI), defined as fat mass normalized to height squared (kg/m²).

These multidimensional approaches allowed for a comprehensive evaluation of metabolic control, body composition and cardiovascular risk factors in adolescents with type 1 diabetes, facilitating the exploration of phase angle and the triglyceride-glucose (TyG) index as potential non-invasive biomarkers.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Patients with type 1 diabetes mellitus (diagnosed).
  • Aged between 10 and 20 years.
  • With or without a continuous glucose monitor.

Exclusion criteria

  • Not being hospitalized.
  • Not being pregnant, in the case of female patients.

Treatment and study plan

There is no intervention

Other

This is an observational, cross-sectional study with no intervention. Data were collected through a single body composition measurement without any treatment or manipulation. Biochemical, clinical and anthropometric data was collected directly from the patient's clinical records. No intervention was needed.

Primary outcomes

  1. Phase angle

    Time frame: Day 1.

    Measurement of the phase angle using bioelectrical impedance analysis to assess body composition and cellular health in adolescents with type 1 diabetes by using the portable device Biody-Xpert (Aminogram SAS, La Ciotat, France).

  2. Triglyceride-glucose index (TyG index)

    Time frame: Day 1

    Calculation of the TyG index from recent fasting blood samples to evaluate insulin resistance and glycaemic control. The TyG index was calculated as Ln[fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2].

Secondary outcomes

  1. Predictive variable: Fat mass

    Time frame: Day 1.

    Includes body fat mass (total amount of fat tissue in the body) in two measurements: % and Kg. These variables were measured using a bioelectrical impedance device and analyzed for their correlation with primary outcomes (phase angle and TyG index).

  2. Predictive variable: Skeletal muscle mass

    Time frame: Day 1.

    Includes skeletal muscle mass (mass of muscles attached to bones for movement) in two measurements: % and Kg. These variables were measured using a bioelectrical impedance device and analyzed for their correlation with primary outcomes (phase angle and TyG index).

  3. Predictive variable: Fat free mass

    Time frame: Day 1.

    Includes body fat-free mass (total body mass excluding all fat tissue) in two measurements: % and Kg. These variables were measured using a bioelectrical impedance device and analyzed for their correlation with primary outcomes (phase angle and TyG index).

  4. Predictive variable: Lean mass

    Time frame: Day 1.

    Includes lean body mass (all non-fat components, including muscles and organs) in two measurements: % and Kg. These variables were measured using a bioelectrical impedance device and analyzed for their correlation with primary outcomes (phase angle and TyG index).

  5. Predictive variable: Total body water

    Time frame: Day 1.

    Total body water (total amount of water in the body) was assessed in liters (L) using a bioelectrical impedance device and analyzed for its correlation with the primary outcomes: phase angle and TyG index.

  6. Predictive variable: Intracellular water

    Time frame: Day 1

    Intracellular water (water contained within cells) was assessed inn liters (L) using a bioelectrical impedance device and analyzed for its correlation with the primary outcomes: phase angle and TyG index

  7. Predictive variable: Extracellular water

    Time frame: Day 1

    Extracellular water (water found outside cells, including plasma and interstitial fluid) was assessed in liters (L) using a bioelectrical impedance device and analyzed for its correlation with the primary outcomes: phase angle and TyG index

  8. Predictive variables: Biochemical Variables

    Time frame: Day 1

    Includes HbA1c, total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides and fasting blood glucose. These values were obtained from the participant's most recent clinical laboratory results and analyzed for correlation with the primary outcomes (phase angle and TyG index).

  9. Predictive variable: Weight

    Time frame: Day 1.

    Wight (body mass measured in kilograms (kg)) was measured using a Seca brand scale.

  10. Predictive variable: Height

    Time frame: Day 1.

    Height (body length measured in centimeters (cm)) was measured using a Seca brand scale.

  11. Predictive variable: Body Mass Index (BMI)

    Time frame: Day 1.

    Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²) and serves as an indicator of body fatness. This measurement was obtained at the study visit and analyzed as a potential predictor of the primary outcomes (phase angle and TyG index).

  12. Predictive variable: BMI Percentile.

    Time frame: Day 1.

    Relative position of BMI compared to a reference population, expressed as a percentile (%). This measurement was obtained at the study visit and analyzed as a potential predictor of the primary outcomes (phase angle and TyG index).

  13. Predictive variable: BMI Z-score

    Time frame: Day 1

    Standard deviation score indicating how far BMI deviates from the population mean. This measurement was obtained at the study visit and analyzed as a potential predictor of the primary outcomes (phase angle and TyG index).

  14. Predictive variable: Time in Range (TIR)

    Time frame: Day 1

    Average percentage of time blood glucose levels remained within the target range over the past 3 months. This measurement was obtained at the study visit and analyzed as a potential predictor of the primary outcomes (phase angle and TyG index).

  15. Predictive variable: Coefficient of Glycaemic Variation (CV)

    Time frame: Day 1

    Average measure of fluctuations in blood glucose levels over the past 3 months. This measurement was obtained at the study visit and analyzed as a potential predictor of the primary outcomes (phase angle and TyG index).

Sponsors and collaborators

Lead sponsor

University of Alicante

Other

Registry information

Official study title

Phase Angle and Triglyceride-glucose Index as Glycaemic Control, Body Composition and Cardiovascular Risk Biomarkers in Adolescents With Type 1 Diabetes: a Cross-sectional Study.

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Jun 13, 2025
Registry last updated
Jun 13, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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