Circadian Periodicity in Some Physicochemical Parameters of Circulating Blood

Seaman, G. V. F.; Engel, R. W.; Swank, Roy L.; Hissen, W

doi:10.1038/207833a0

Cited by 85 publications

(42 citation statements)

References 19 publications

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“…The time lag was calculated as the cross correlation between the smoothed signal and its predictions. The lag corresponding to the peak of the cross-correlation function provides an accurate estimate of the prediction time lag [12], provided the length of the time series being compared is longer than one 24-h period of the human circadian rhythm [16].…”

Section: Metricsmentioning

confidence: 99%

Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

Gani

Gribok

Rajaraman

et al. 2009

IEEE Trans. Biomed. Eng.

118

106

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Abstract-The combination of predictive data-driven models with frequent glucose measurements may provide for an early warning of impending glucose excursions and proactive regulatory interventions for diabetes patients. However, from a modeling perspective, before the benefits of such a strategy can be attained, we must first be able to quantitatively characterize the behavior of the model coefficients as well as the model predictions as a function of prediction horizon. We need to determine if the model coefficients reflect viable physiologic dependencies of the individual glycemic measurements and whether the model is stable with respect to small changes in noise levels, leading to accurate near-future predictions with negligible time lag. We assessed the behavior of linear autoregressive data-driven models developed under three possible modeling scenarios, using continuous glucose measurements of nine subjects collected on a minute-by-minute basis for approximately 5 days. Simulation results indicated that stable and accurate models for near-future glycemic predictions (<60 min) with clinically acceptable time lags are attained only when the raw glucose measurements are smoothed and the model coefficients are regularized. This study provides a starting point for further needed investigations before real-time deployment can be considered.

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Section: Metricsmentioning

confidence: 99%

Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

Gani

Gribok

Rajaraman

et al. 2009

IEEE Trans. Biomed. Eng.

118

106

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show abstract

“…Urinary calcium excretion has been found to be highest during the day (2-5), but several investigators have failed to demonstrate a rhythm in serum calcium levels (2,5,6). Serum phosphate levels are higher during the night than during the day (2).…”

Section: Introductionmentioning

confidence: 99%

“…Serum phosphate levels are higher during the night than during the day (2). Total serum protein concentration falls during the night and early morning hours (6).…”

Section: Introductionmentioning

confidence: 99%

Circadian rhythm in serum parathyroid hormone concentration in human subjects: correlation with serum calcium, phosphate, albumin, and growth hormone levels

Jubiz¹,

Canterbury²,

Reiss³

et al. 1972

J. Clin. Invest.

248

122

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“…We developed a new method for measuring blood glucose noninvasively, based on a method named metabolic heat conformation (MHC), 4 which takes into account these matrix effects. The principles (Fig.…”

confidence: 99%

Noninvasive Measurement of Glucose by Metabolic Heat Conformation Method

Cho¹,

Kim²,

Mitsumaki

et al. 2004

Clinical Chemistry

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Background:We developed a method, called the metabolic heat conformation (MHC) method, for the noninvasive measurement of blood glucose. The MHC method involves the measurement of physiologic indices related to metabolic heat generation and local oxygen supply, which correspond to the glucose concentration in the local blood supply. Results: Regression analysis was performed to compare the noninvasive method with the hexokinase method, using 127 data points (109 data points from diabetic patients, 18 data points from nondiabetic patients) with glucose concentrations ranging from 3.0 to 22.5 mmol/L (54 -405 mg/dL). The correlation coefficient (r) was 0.91. Reproducibility was measured for healthy fasting persons; the CV was 6% at 5.56 mmol/L (100 mg/dL). Conclusions: These data provide preliminary evidence that the MHC method can be used to estimate blood glucose concentrations noninvasively.

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Circadian Periodicity in Some Physicochemical Parameters of Circulating Blood

Cited by 85 publications

References 19 publications

Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

Predicting Subcutaneous Glucose Concentration in Humans: Data-Driven Glucose Modeling

Circadian rhythm in serum parathyroid hormone concentration in human subjects: correlation with serum calcium, phosphate, albumin, and growth hormone levels

Noninvasive Measurement of Glucose by Metabolic Heat Conformation Method

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