Differences in Body Temperature Variability Between Subjects With and Without Diabetes and Predictive Value for Cardiovascular Events

Amiya, Eisuke; Watanabe, Masafumi; Takata, Masayuki; Watanabe, Shozo; Ozeki, Atsuko; Watanabe, Aya; Kawarasaki, Shuichi; Nakao, Tomoko; Hosoya, Yumiko; Omori, K.; Maemura, Koji; Hirata, Yasunobu; Nagai, Ryozo; Komuro, Issei

doi:10.1253/circj.cj-12-1591

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…These findings showed that peak onset of AMI for early chronotypes occurred in the morning whereas the peak in late chronotypes was later in the day. Further, in support of these epidemiological findings, evidence indicates that the circadian pattern of core body temperature is altered depending on chronotype (Bailey and Heitkemper, 2001) and has been linked to endothelial function (Amiya et al, 2013). This may result in different temporal windows of enhanced susceptibility to cardiovascular events thus provide an explanation as to why chronotype influences the time-of-day pattern in occurrences of such events.…”

Section: Introductionmentioning

confidence: 70%

Diurnal Variations in Vascular Endothelial Vasodilation Are Influenced by Chronotype in Healthy Humans

et al. 2019

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Introduction: The time of day when cardiovascular events are most likely to occur is thought to be aligned with the circadian rhythm of physiological variables. Chronotype has been shown to influence the time of day when cardiovascular events happen, with early chronotypes reported to be more susceptible in the morning and late chronotypes in the evening. However, no studies have investigated the influence of chronotype on physiological variables responsible for cardiovascular regulation in healthy individuals. Methods: 312 individuals completed the Munich ChronoType Questionnaire to assess chronotype. Twenty participants were randomly selected to continue into the main study. In a repeated-measures experiment, participants were tested between 08:00 and 10:00 h and again between 18:00 and 20:00 h. Measurements of mean arterial pressure, heart rate and vascular endothelial vasodilation via flow-mediated dilatation (FMD) were obtained at each session. Results: Individual diurnal differences in mean arterial pressure and heart rate show no significant relationship with chronotype. Diurnal differences in FMD showed a significant correlation ( p = 0.010), driven by a clear significant relationship in the evening and not the morning ( p < 0.001). Conclusion: These preliminary data indicate that chronotype influences the diurnal variation of endothelial vasodilation measured using flow-mediated dilatation. Furthermore, we show that the influence of chronotype is much stronger in the evening, highlighting an increased susceptibility for later types. These findings are consistent with the diurnal rhythm in cardiovascular events and uncover potential mechanisms of local mediators that may underpin the influence of chronotype in the onset of these events.

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Section: Introductionmentioning

confidence: 70%

Diurnal Variations in Vascular Endothelial Vasodilation Are Influenced by Chronotype in Healthy Humans

et al. 2019

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“…There are several variables which can be obtained from the patient's medical history, for example admission type (33.3%), length of stay in ICU (38.1%), length of stay in hospital (23.8%), mortality rate in ICU (14.2%) and mortality rate in hospital (28.5%). 2013) mentioned that the magnitude of body temperature fluctuation is linked with cardiovascular events in ischemic heart disease (IHD) patients with diabetes [26]. According to Figure 3, 23.8% of the studies used temperature and 33.33% of studies used systolic blood pressure (SBP) as variables in CDSS.…”

Section: Methodsmentioning

confidence: 99%

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

Rahman

Tumian

2019

AMM

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Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.

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“…Similar relationships between intra-individual variability and poor health have also been shown for biological parameters other than blood-based biomarkers. For example, variability in blood pressure (BP) 5 , 6 , heart rate (HR) 7 , gait 8 – 10 , body temperature 11 , emotion 12 , and sleep 13 have been reported to be associated with adverse outcomes. Therefore, whatever the type of biomarker, intra-individual variability may represent its dysregulation.…”

Section: Introductionmentioning

confidence: 99%

Intercorrelated variability in blood and hemodynamic biomarkers reveals physiological network in hemodialysis patients

Nakazato

Shimoyama

Cohen

et al. 2023

Sci Rep

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Increased intra-individual variability of a variety of biomarkers is generally associated with poor health and reflects physiological dysregulation. Correlations among these biomarker variabilities should then represent interactions among heterogeneous biomarker regulatory systems. Herein, in an attempt to elucidate the network structure of physiological systems, we probed the inter-variability correlations of 22 biomarkers. Time series data on 19 blood-based and 3 hemodynamic biomarkers were collected over a one-year period for 334 hemodialysis patients, and their variabilities were evaluated by coefficients of variation. The network diagram exhibited six clusters in the physiological systems, corresponding to the regulatory domains for metabolism, inflammation, circulation, liver, salt, and protein. These domains were captured as latent factors in exploratory and confirmatory factor analyses (CFA). The 6-factor CFA model indicates that dysregulation in each of the domains manifests itself as increased variability in a specific set of biomarkers. Comparison of a diabetic and non-diabetic group within the cohort by multi-group CFA revealed that the diabetic cohort showed reduced capacities in the metabolism and salt domains and higher variabilities of the biomarkers belonging to these domains. The variability-based network analysis visualizes the concept of homeostasis and could be a valuable tool for exploring both healthy and pathological conditions.

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Differences in Body Temperature Variability Between Subjects With and Without Diabetes and Predictive Value for Cardiovascular Events

Cited by 7 publications

References 31 publications

Diurnal Variations in Vascular Endothelial Vasodilation Are Influenced by Chronotype in Healthy Humans

Diurnal Variations in Vascular Endothelial Vasodilation Are Influenced by Chronotype in Healthy Humans

Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review

Intercorrelated variability in blood and hemodynamic biomarkers reveals physiological network in hemodialysis patients

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