Correlation of serum HDL level with HRV indices using multiple linear regression analysis in patients with type 2 diabetes mellitus

Balikai, Fareedabanu A; Javali, Shivalingappa B; Shindhe, Varsha M; Deshpande, Neeta; Benni, Jyoti M; Shetty, Darshit P; Kapoor, Nitin; Jaalam, Kamaruddin

doi:10.1016/j.diabres.2022.109988

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…In comparison with studies of correlations between short-term linear HRV analysis and metabolic function, conducted among T2DM patients (Bhati et al, 2019), correlations found in this study were extensively stronger and broadened with a similar sample size, suggesting the potential of HRV analysis during sleep cycles in T2DM patients. However, when compared with a study with larger sample size (Balikai et al, 2022), correlations found in this study were more comprehensive but slightly weaker, which may be due to the limited sample size, exclusion of female subjects, differences in the physiological status of the subjects including duration and severity of diabetes since our subjects were all patients during hospitalization, the bias introduced by non-linear metrics including ApEn and MSE via the contamination of variance and suboptimal filtering procedure (Pincus and Huang, 1992;Nikulin and Brismar, 2004;Porta et al, 2007a;Valencia et al, 2009;Delgado-Bonal and Marshak, 2019) and the selection of long-term HRV sequences since metrics including LF, HF and SDNN would be affected by length of the sequences (Saul et al, 1988;Furlan et al, 1990; Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology, 1996).…”

Section: Discussionmentioning

confidence: 99%

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

et al. 2023

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Introduction: Autonomic nervous system (ANS) plays an important role in the exchange of metabolic information between organs and regulation on peripheral metabolism with obvious circadian rhythm in a healthy state. Sleep, a vital brain phenomenon, significantly affects both ANS and metabolic function.Objectives: This study investigated the relationships among sleep, ANS and metabolic function in type 2 diabetes mellitus (T2DM), to support the evaluation of ANS function through heart rate variability (HRV) metrics, and the determination of the correlated underlying autonomic pathways, and help optimize the early prevention, post-diagnosis and management of T2DM and its complications.Materials and methods: A total of 64 volunteered inpatients with T2DM took part in this study. 24-h electrocardiogram (ECG), clinical indicators of metabolic function, sleep quality and sleep staging results of T2DM patients were monitored.Results: The associations between sleep quality, 24-h/awake/sleep/sleep staging HRV and clinical indicators of metabolic function were analyzed. Significant correlations were found between sleep quality and metabolic function (|r| = 0.386 ± 0.062, p < 0.05); HRV derived ANS function showed strengthened correlations with metabolic function during sleep period (|r| = 0.474 ± 0.100, p < 0.05); HRV metrics during sleep stages coupled more tightly with clinical indicators of metabolic function [in unstable sleep: |r| = 0.453 ± 0.095, p < 0.05; in stable sleep: |r| = 0.463 ± 0.100, p < 0.05; in rapid eye movement (REM) sleep: |r| = 0.453 ± 0.082, p < 0.05], and showed significant associations with glycemic control in non-linear analysis [fasting blood glucose within 24 h of admission (admission FBG), |r| = 0.420 ± 0.064, p < 0.05; glycated hemoglobin (HbA1c), |r| = 0.417 ± 0.016, p < 0.05].Conclusions: HRV metrics during sleep period play more distinct role than during awake period in investigating ANS dysfunction and metabolism in T2DM patients, and sleep rhythm based HRV analysis should perform better in ANS and metabolic function assessment, especially for glycemic control in non-linear analysis among T2DM patients.

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Section: Discussionmentioning

confidence: 99%

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

et al. 2023

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“…Schober & Schwarte (2018) conducted a study and found a classification correlation coefficient of 0.53 classified as a Moderate Correlation. The correlation coefficient is 0.5-0.7, with both positive and negative values, concluding that those variables depend on each other (Balikai et al 2022). An interesting finding recently by another study stated correlation coefficient of multiple R is overinterpreting if it is the only one that indicates the "strength" and "weakness" dependence of variables (Rusakov 2022).…”

Section: Correlation Coefficientmentioning

confidence: 90%

“…Many studies determined a correlation between multiple variables with the Pearson correlation coefficient equation (Balikai et al 2022, Zeng et al 2021. In this study, the two variables were rice production and emission from rice cultivation, determined with the same means.…”

Section: Methodsmentioning

confidence: 99%

Correlation of Rice Production and Greenhouse Gas Emissions in North Sulawesi Province

Suli¹,

Damanik²

2023

J. Ilmu. Pertan. Indones.

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This study aimed to reveal correlation, calculate significance, and discover the regression equation of rice production to Green House Gas (GHG) emission in North Sulawesi Province. The data on GHG emissions from rice cultivation (Gg CO2eq) was obtained from the Ministry of Environment and Forestry of Indonesia. Data on rice production from wetland and dryland (Gg) was from the BP Statistical Review annual period of 2000-2021, both for North Sulawesi Province. Data analysis of correlation coefficient, F-test for Regression, and Simple Regression Analysis will be processed with the help application of MS Excel. The results show that the correlation between rice production and emission of rice cultivation in North Sulawesi Province is 0.53 and classified as a moderate correlation. The coefficient of determination stated that the emission of rice cultivation could be explained by about 28.6% from rice production. Therefore, rice production is statistically significant to the emission of rice cultivation with a 5% confidence level for North Sulawesi Province. Furthermore, this study found a regression equation, emission of rice cultivation is 112.67 + 0.516 times rice production. Keywords: correlation coefficient, green house gas emission, rice production

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Correlation of serum HDL level with HRV indices using multiple linear regression analysis in patients with type 2 diabetes mellitus

Cited by 2 publications

References 32 publications

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Heart rate variability in different sleep stages is associated with metabolic function and glycemic control in type 2 diabetes mellitus

Correlation of Rice Production and Greenhouse Gas Emissions in North Sulawesi Province

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