Monitoring of Cardiac Autonomic Neuropathy

Multimodal Monitoring of Cardiac Autonomic Neuropathy in Type 1 Diabetes - Multimodal Monitoring of Cardiac Autonomic Neuropathy in Type 1 Diabetes

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

NL-OMON

Registry ID

NL-OMON58287

Enrollment

120

Registered

2025-08-25

Start date

2025-12-01

Completion date

Unknown

Last updated

2026-01-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Type 1 diabetes mellitus & Cardiac autonomic neuropathy Type 1 diabetes & Nerve damage of the heart’s autonomic system

Interventions

None listed

Eligibility

Age

18 Years to 99 Years

Inclusion criteria

Inclusion criteria: Diagnosed with type 1 diabetes mellitusAge 18 years or olderHave at least six-month of CGM recordsHave at least one HbA1c record Physically able to perform the active tests, as assessed by the physician or researcherWritten informed consent

Exclusion criteria

Exclusion criteria: Patients with Raynaud’s phenomenon.Patients who currently have atrial fibrillation or presence of a pacemaker/ICD;Patients are currently taking medicine that will affect autonomic function, including ß-blockers,  tricyclic antidepressants (TCAs), anticholinergic agents, centrally acting antihypertensives

Design outcomes

Primary

Measure	Time frame
In this study there are two primary objectives:1. To evaluate whether glycemic metrics from different time scales are associated with impaired autonomic cardiac regulation in patients with T1DM.2. To assess the feasibility of integrating multi-modal wearable sensing technology for the early diagnosis of CAN in T1DM.To answer the first research question, continuous blood pressure and heart rate measured by Finapres Nova during the experiment (deep breathing, VM, and supine-to-stand test) will be used to quantify the risk level of CAN for each patient as ground truth. In case the continuous BP fails given patients might have impaired peripheral circulation, an arm cuff will measure the BP in each minute. Then a score will be calculate based on the BP drop after standing up, the Valsalva ratio, and the R-R interval change during deep breathing. Each patient’s risk will be assessed by the score.Patients retrospective CGM recordings will be used. Firstly, different glycemia features will be extracted. While some glycemia features have shown correlation with CAN. They mostly focus on using shorter recordings such as within two weeks. By taking advantages of longer recordings from the current data (>6 months), more meaningful robust and sensitive markers can be evaluated and feasibility tested. Multivariate regression analysis will be performed to study the relationship between CAN risks and features extracted from glucose recordings. Confounders will also be considered in the analysis. These endpoints were chosen based on prior evidence that glycemic variability and impaired autonomic reflexes (quantified by CARTs such as DB, VM, SS) are early hallmarks of CAN. Therefore, our design integrates both established physiological tests and digital markers derived from CGM and wearable sensing.Meanwhile, to answer the second research question, multiple kinds of signals will be recorded during the experiment:1. NeuECG (simultaneously measured ECG and skin sympathetic nerv	—

Measure

Time frame

In this study there are two primary objectives:1. To evaluate whether glycemic metrics from different time scales are associated with impaired autonomic cardiac regulation in patients with T1DM.2. To assess the feasibility of integrating multi-modal wearable sensing technology for the early diagnosis of CAN in T1DM.To answer the first research question, continuous blood pressure and heart rate measured by Finapres Nova during the experiment (deep breathing, VM, and supine-to-stand test) will be used to quantify the risk level of CAN for each patient as ground truth. In case the continuous BP fails given patients might have impaired peripheral circulation, an arm cuff will measure the BP in each minute. Then a score will be calculate based on the BP drop after standing up, the Valsalva ratio, and the R-R interval change during deep breathing. Each patient’s risk will be assessed by the score.Patients retrospective CGM recordings will be used. Firstly, different glycemia features will be extracted. While some glycemia features have shown correlation with CAN. They mostly focus on using shorter recordings such as within two weeks. By taking advantages of longer recordings from the current data (>6 months), more meaningful robust and sensitive markers can be evaluated and feasibility tested. Multivariate regression analysis will be performed to study the relationship between CAN risks and features extracted from glucose recordings. Confounders will also be considered in the analysis. These endpoints were chosen based on prior evidence that glycemic variability and impaired autonomic reflexes (quantified by CARTs such as DB, VM, SS) are early hallmarks of CAN. Therefore, our design integrates both established physiological tests and digital markers derived from CGM and wearable sensing.Meanwhile, to answer the second research question, multiple kinds of signals will be recorded during the experiment:1. NeuECG (simultaneously measured ECG and skin sympathetic nerv

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Secondary

Measure	Time frame
As autonomic function can be affected by many factors and thus cofound the results. To minimize bias in the analysis, several categories of relevant confounding variables will be extracted from participants’ medical records and by asking patients (smoking habits) before the experiment (smoking and other conditions that might affect autonomic function). These variables will be used either for covariate adjustment in regression models or for subgroup stratification where appropriate. The selected confounders include:•           Demographic factors: age, sex, and body mass index (BMI) •           Glycemic control factors: glycated hemoglobin (HbA1c); duration of diabetes.•           Treatment-related factors: type of glucose-lowering therapy (insulin, oral hypoglycemic agents, or both) and use of antihypertensive medication.•           Lifestyle factors: smoking habits (yes/no)•           Metabolic and renal biomarkers: lipid profile (triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol), and estimated glomerular filtration rate (22, 25) These confounders were selected because they have been consistently identified as independent determinants of autonomic dysfunction in large epidemiological cohorts. Therefore, adjusting for them will help isolate the specific contribution of glycemic metrics to CAN risk detection.	—

Measure

Time frame

As autonomic function can be affected by many factors and thus cofound the results. To minimize bias in the analysis, several categories of relevant confounding variables will be extracted from participants’ medical records and by asking patients (smoking habits) before the experiment (smoking and other conditions that might affect autonomic function). These variables will be used either for covariate adjustment in regression models or for subgroup stratification where appropriate. The selected confounders include:•           Demographic factors: age, sex, and body mass index (BMI) •           Glycemic control factors: glycated hemoglobin (HbA1c); duration of diabetes.•           Treatment-related factors: type of glucose-lowering therapy (insulin, oral hypoglycemic agents, or both) and use of antihypertensive medication.•           Lifestyle factors: smoking habits (yes/no)•           Metabolic and renal biomarkers: lipid profile (triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol), and estimated glomerular filtration rate (22, 25) These confounders were selected because they have been consistently identified as independent determinants of autonomic dysfunction in large epidemiological cohorts. Therefore, adjusting for them will help isolate the specific contribution of glycemic metrics to CAN risk detection.

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Countries

Netherlands

Contacts

Public ContactR. L. Lin

University of Twente

r.lin@utwente.nl+31534894311

Outcome results