Identification and performance of multiple clinical and laboratorial risk factors for diagnosis of cardiac autonomic neuropathy in type 1 diabetes patients

Riguetto, Cínthia Minatel; Takano, Caroline Rigoleto; Admoni, Sharon Nina; Parisi, Maria Cândida Ribeiro; Côrrea-Giannella, Maria Lúcia; Pavin, Elizabeth João; Moura, Arnaldo Vieira

doi:10.1007/s40200-019-00467-2

Cited by 5 publications

(7 citation statements)

References 29 publications

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“…Finally, a study by Ang et al [ 95 ] explored the potential of using ESC as a measure of sudomotor function for the early recognition of CAN but found no significant correlations. However, Riguetto et al [ 96 ] showed that postprandial sweating, as well as hypertension, diastolic blood pressure, retinopathy, and nephropathy were independent predictors of CAN. The methods and findings of the above studies are presented in Table S6 in Multimedia Appendix 4 .…”

Section: Resultsmentioning

confidence: 99%

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Daskalaki¹,

Parkinson²,

Brew‐Sam³

et al. 2022

J Med Internet Res

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Background Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. Objective The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. Methods A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. Results On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Conclusions Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.

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Section: Resultsmentioning

confidence: 99%

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Daskalaki¹,

Parkinson²,

Brew‐Sam³

et al. 2022

J Med Internet Res

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show abstract

“…A single study computed a numerical score to predict the presence of CAN in participants with type 1 diabetes and included in a complex formula post-prandial sweating, retinopathy, nephropathy, monofilament test, total cholesterol, triglycerides, and diastolic BP. 14 This CAN score showed an excellent AUC of 0.90 and at the cut-off of 16.88, high sensitivity and specificity (89.5% and 82.3%, respectively), although this performance was not confirmed in the external validation study in a quite different population (Table 1). Compared to that study, the present CAN risk score is more easily computable, uses binary variables, includes only accessible features, and does not require information or procedures like post-prandial sweating and monofilament test not usually collected and recorded in routine visits.…”

Section: Type 1 Diabetesmentioning

confidence: 86%

“…A single study computed a numerical score to predict the presence of CAN in participants with type 1 diabetes and included in a complex formula post‐prandial sweating, retinopathy, nephropathy, monofilament test, total cholesterol, triglycerides, and diastolic BP 14 . This CAN score showed an excellent AUC of 0.90 and at the cut‐off of 16.88, high sensitivity and specificity (89.5% and 82.3%, respectively), although this performance was not confirmed in the external validation study in a quite different population (Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…Riguetto et al found a lower diagnostic performance of their CAN score in a validation study in a different population with type 1 diabetes. However, the population considered in their exploratory set had a high percentage of retinopathy (47%), nephropathy (38%, and few participants under dialysis), and CAN (51.5%), and seemed to represent a selected population 14 . Our type 1 and type 2 study populations included participants who were regularly followed for diabetes at our hospital and reflected unselected population in diabetes centres in Italy.…”

Section: Discussionmentioning

confidence: 99%

“…9 Until now, five studies have developed appropriate scoring systems for the risk of CAN. Three were population-based studies, which included both people without diabetes and patients with type 2 diabetes and diagnosed CAN using HRV indexes, [11][12][13] one study concerned patients with type 1 diabetes and put in the scoring system also a test of diabetic polyneuropathy (DPN), 14 and a recent study based on electronic health records of a large cohort of patients with type 1 diabetes elaborated a nomogram for risk calculation of complications including CAN 15 (Table 1).…”

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confidence: 99%

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Clinical scoring systems for the risk of cardiovascular autonomic neuropathy in type 1 and type 2 diabetes: A simple tool

Menduni

D’Amato

Leoni

et al. 2022

J Peripheral Nervous Sys

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This study was aimed at developing a clinical risk score for cardiovascular autonomic neuropathy (CAN) for type 1 and type 2 diabetes. In a retrospective cross-sectional one-centre study in an unselected population, 115 participants with type 1 diabetes (age 41.1 ± 12.2 years) and 161 with type 2 diabetes (age 63.1 ± 8.9 years), wellcharacterized for clinical variables, underwent standard cardiovascular reflex tests (CARTs). Strength of associations of confirmed CAN (based on 2 abnormal CARTs) with clinical variables was used to build a CAN risk score. CAN risk score was based on resting heart rate, HbA1c, retinopathy, nephropathy, cardiovascular disease in both type 1 and type 2 diabetes, and on HDL cholesterol, systolic blood pressure, and smoking in type 1 diabetes or insulin treatment and physical activity in type 2 diabetes (range 0-10). In type 1 diabetes, CAN risk score showed an area under the ROC curve (AUC) of 0.890 ± 0.034, and at cut-off of 4 sensitivity of 88%, specificity of 74.4%, and negative predictive value (NPV) of 95.7% for confirmed CAN. In type 2 diabetes, CAN risk score showed an AUC of 0.830 ± 0.051 and at the cut-off of 4 sensitivity and specificity of 78.6% and 73.5%, respectively, and NPV of 97.3% for confirmed CAN. These newly developed CAN risk scores are accessible in clinical practice and, if confirmed in a validation study, they might identify asymptomatic individuals with diabetes at greater risk of CAN to be referred to CARTs, thus limiting the burden of a universal screening.

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Diabetic neuropathy: Current issues in diagnosis and prevention

Spallone

2024

Chronic Complications of Diabetes Mellitus

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Identification and performance of multiple clinical and laboratorial risk factors for diagnosis of cardiac autonomic neuropathy in type 1 diabetes patients

Cited by 5 publications

References 29 publications

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey

Clinical scoring systems for the risk of cardiovascular autonomic neuropathy in type 1 and type 2 diabetes: A simple tool

Diabetic neuropathy: Current issues in diagnosis and prevention

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