Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis

Khandoker, Ahsan H.; Jelinek, Herbert F.; Palaniswami, Marimuthu

doi:10.1186/1475-925x-8-3

Cited by 110 publications

(94 citation statements)

References 36 publications

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“…[35] We assume that the same standard to how it is done applied in both studies. [15,16] IHR Approximate Entropy is not notionally different from IHR Sample Entropy, and so qualitatively same result is expected. It appears though that IHR Sample Entropy is much worse on its own in determining T2DM outcomes, as seen from Table 6, which correlates with other results obtained for CAN diagnostic in the DiScRi space.…”

Section: Cardiac Autonomic Neuropathysupporting

confidence: 66%

“…Utility of Sample Entropy, a particular measure engaging IHR for identification of CAN was previously demonstrated. [16] Sample Entropy "was developed to reduce the bias caused by the self matching in approximate entropy which is a mathematical approach to quantifying the complexity and regularity of a system," as claimed. [16] So, we shall assume the two measures are similar; however, only Sample Entropy is discussed in the concerned paper [16] where an expression for it can be found.…”

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

“…The rules have 'under' orientation corresponding to the original data statistics for these variables in Table 6, although a desired clearance between the two classes is not guaranteed. In the study behind the paper [16] data for a small number of T2DM patients with and without CAN was analysed to evaluate various IHR measures. CAN was diagnosed using a single component of the Ewing system out of the consideration of data availability.…”

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

“…Our effort to have any missing entries filled, particularly in all variables the Ewing system draws on, pays with the ability of relying on all data, and we find the threshold to be at 1.54 which is very close to 1.56 reported in the mentioned study. [16] Of course, the rule in Table 1 is just an enhanced HbA1c rule for T2DM diagnostic. It is only because of the similarity between our and the other party results for IHR Sample Entropy we can hypothesise that CAN is involved and that at least some of CAN is explained by T2DM.…”

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

“…[12] This may explain that varied results are obtained world-wide for HbA1c as a clinical marker in T2DM diagnostic. [8] Beyond protein glycation, DM progression is linked to markers of endothelial [13,14] and autonomic nervous system (ANS) dysfunction, [15,16] inflammatory [17,18] and oxidative stress [19,20] biomarkers. Recent literature reports that presence of 8-hydroxy-2'-deoxy-guanosine (8-OHdG) in blood or urine samples is indicative of oxidative stress and deoxyribonucleic acid (DNA) damage due to minor increases in the PG level.…”

Section: Introductionmentioning

confidence: 99%

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Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Stranieri

Yatsko

Jelinek

et al. 2015

AIR

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Glycated haemoglobin (HbA1c) is now more commonly used as an alternative test to the fasting plasma glucose and oral glucose tolerance tests for the identification of Type 2 Diabetes Mellitus (T2DM) because it is easily obtained using the point-of-care technology and represents long-term blood sugar levels. According to WHO guidelines, HbA1c values of 6.5% or above are required for a diagnosis of T2DM. However outcomes of a large number of trials with HbA1c have been inconsistent across the clinical spectrum and further research is required to determine the efficacy of HbA1c testing in identification of T2DM. Medical records from a diabetes screening program in Australia illustrate that many patients could be classified as diabetics if other clinical indicators are included, even though the HbA1c result does not exceed 6.5%. This suggests that a cutoff for the general population of 6.5% may be too simple and miss individuals at risk or with already overt, undiagnosed diabetes. In this study, data mining algorithms have been applied to identify markers that can be used with HbA1c. The results indicate that T2DM is best classified by HbA1c at 6.2% -a cutoff level lower than the currently recommended one, which can be even less, having assumed the threshold flexibility, if additionally to HbA1c being high the rule is conditioned on oxidative stress or inflammation being present, atherogenicity or adiposity being high, or hypertension being diagnosed, etc.

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Section: Cardiac Autonomic Neuropathysupporting

confidence: 66%

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

Section: Cardiac Autonomic Neuropathymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Stranieri

Yatsko

Jelinek

et al. 2015

AIR

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Decreased heart rate variability as a predictor for diabetes—A prospective study of the Brazilian longitudinal study of adult health

Hoshi

Santos

Dantas

et al. 2019

Diabetes Metabolism Res

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Aim To investigate whether heart rate variability (HRV) is a predictor for the incidence of diabetes in a 4‐year follow‐up. Materials and methods The HRV of 9192 participants free of diabetes was analysed in time and frequency domains and stratified based on the reference values presented in the literature. The participants were then allocated to one of three groups, according to age‐specific value distributions for each HRV domain: lower than the 25th percentile, between the 25th and 75th percentiles, and higher than the 75th percentile. The association between HRV and diabetes incidence at 4‐year follow‐up was analysed using Poisson regression models with robust estimator. Results Six hundred thirty‐four participants (6.90%) developed diabetes within 4 years and five out of six HRV analysed indices showed increased relative risk of developing diabetes associated with low HRV: SDNN (RR = 1.29; 95% CI, 1.09‐1.52; .003), pNN50 (RR = 1.33; 95% CI, 1.11‐1.58; .001), RMSSD (RR = 1.29; 95% CI, 1.09‐1.53; .004), LF (RR = 1.25; 95% CI, 1.05‐1.48; .012), and HF (RR = 1.39; 95% CI, 1.16‐1.63; .001). Conclusions This study suggests that both overall variability and changes in parasympathetic modulation precede the incidence of diabetes. For four HRV indices below the 25th percentile, the risk for incident diabetes was 68% higher than for those participants who presented none. We concluded that HRV is an independent risk predictor of diabetes in a 4‐year period.

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Reminder: RMSSD and SD1 are identical heart rate variability metrics

et al. 2017

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Assessment of heart rate variability (HRV) is a common approach to examine cardiac autonomic nervous system modulation that has been employed in a variety of settings. Frequently, both the root mean square of successive differences (RMSSD) and SD1, which is a Poincaré plot component, have been used to quantify short-term heart rate variability. It is not typically appreciated, however, that RMSSD and SD1 are identical metrics of HRV. As a reminder to clinicians and researchers who use and study HRV, we show both empirically and mathematically that RMSSD and SD1 are identical metrics. Because the homology between RMSSD and SD1 is not commonly known, the inclusion of both measures has been reported in many recent publications. The inappropriate use of such redundant data may affect the interpretation of HRV studies. Muscle Nerve 56: 674-678, 2017.

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Identifying diabetic patients with cardiac autonomic neuropathy by heart rate complexity analysis

Cited by 110 publications

References 36 publications

Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Data-analytically derived flexible HbA1c thresholds for type 2 diabetes mellitus diagnostic

Decreased heart rate variability as a predictor for diabetes—A prospective study of the Brazilian longitudinal study of adult health

Reminder: RMSSD and SD1 are identical heart rate variability metrics

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