Hypoglycemia and Body Temperature

Molnár, George

doi:10.1001/jama.1974.03230210028006

Cited by 25 publications

(7 citation statements)

References 32 publications

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“…A number of measurable factors influence blood glucose levels, including diet 34 , 47 – 57 , physical activity and exercise 35 , 58 – 62 , stress 36 , 63 – 69 , circadian rhythm 37 , 55 , 70 – 73 , and biological sex 38 , 74 – 80 . Additionally, physiological parameters like vital signs are associated with glycemic health and glucose fluctuations, including heart rate 39 , 81 , 82 , core body temperature 40 , 83 , and autonomic functions 41 , 84 – 86 like the sudomotor response 42 . These relationships suggest that it may be possible to estimate glucose values from novel modes of measurement.…”

Section: Resultsmentioning

confidence: 99%

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

et al. 2021

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Prediabetes affects one in three people and has a 10% annual conversion rate to type 2 diabetes without lifestyle or medical interventions. Management of glycemic health is essential to prevent progression to type 2 diabetes. However, there is currently no commercially-available and noninvasive method for monitoring glycemic health to aid in self-management of prediabetes. There is a critical need for innovative, practical strategies to improve monitoring and management of glycemic health. In this study, using a dataset of 25,000 simultaneous interstitial glucose and noninvasive wearable smartwatch measurements, we demonstrated the feasibility of using noninvasive and widely accessible methods, including smartwatches and food logs recorded over 10 days, to continuously detect personalized glucose deviations and to predict the exact interstitial glucose value in real time with up to 84% and 87% accuracy, respectively. We also establish methods for designing variables using data-driven and domain-driven methods from noninvasive wearables toward interstitial glucose prediction.

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Section: Resultsmentioning

confidence: 99%

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

et al. 2021

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“…Similarly, hypoglycemia and glucopenia can reduce body temperature in humans [60, 140] and other species [169] [20, 31, 153]. Reducing temperature dramatically increases lifespan in poikilothermic (cold-blooded) animals [40, 84, 112–116, 206, 220] but the effect in mammals has been challenging to determine.…”

Section: Examples Of Hypothalamic Top-down Control Of Two Specificmentioning

confidence: 99%

Role of the hypothalamus in mediating protective effects of dietary restriction during aging

Dacks

Moreno

Kim

et al. 2013

Frontiers in Neuroendocrinology

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Dietary restriction (DR) can extend lifespan and reduce disease burden across a wide range of animals and yeast but the mechanisms mediating these remarkably protective effects remain to be elucidated despite extensive efforts. Although it has generally been assumed that protective effects of DR are cell-autonomous, there is considerable evidence that many whole-body responses to nutritional state, including DR, are regulated by nutrient-sensing neurons. In this review, we explore the hypothesis that nutrient sensing neurons in the ventromedial hypothalamus hierarchically regulate the protective responses of dietary restriction. We describe multiple peripheral responses that are hierarchically regulated by the hypothalamus and we present evidence for non-cell autonomous signaling of dietary restriction gathered from a diverse range of models including invertebrates, mammalian cell culture, and rodent studies.

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“…For example, increased heart rate or tachycardia has been correlated in the literature with increased glucose concentration [24]. Furthermore, research has substantiated that body temperature is an indicator of glucose concentration specifically the occurrence of hypoglycemia [25]. Other factors/inputs such as dextrose solutions (D5, D5W, etc.)…”

Section: Resultsmentioning

confidence: 99%

Evaluation of a Model for Glycemic Prediction in Critically Ill Surgical Patients

et al. 2013

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We evaluated a neural network model for prediction of glucose in critically ill trauma and post-operative cardiothoracic surgical patients. A prospective, feasibility trial evaluating a continuous glucose-monitoring device was performed. After institutional review board approval, clinical data from all consenting surgical intensive care unit patients were converted to an electronic format using novel software. This data was utilized to develop and train a neural network model for real-time prediction of serum glucose concentration implementing a prediction horizon of 75 minutes. Glycemic data from 19 patients were used to “train” the neural network model. Subsequent real-time simulated testing was performed in 5 patients to whom the neural network model was naive. Performance of the model was evaluated by calculating the mean absolute difference percent (MAD%), Clarke Error Grid Analysis, and calculation of the percent of hypoglycemic (≤70 mg/dL), normoglycemic (>70 and <150 mg/dL), and hyperglycemic (≥150 mg/dL) values accurately predicted by the model; 9,405 data points were analyzed. The models successfully predicted trends in glucose in the 5 test patients. Clark Error Grid Analysis indicated that 100.0% of predictions were clinically acceptable with 87.3% and 12.7% of predicted values falling within regions A and B of the error grid respectively. Overall model error (MAD%) was 9.0% with respect to actual continuous glucose modeling data. Our model successfully predicted 96.7% and 53.6% of the normo- and hyperglycemic values respectively. No hypoglycemic events occurred in these patients. Use of neural network models for real-time prediction of glucose in the surgical intensive care unit setting offers healthcare providers potentially useful information which could facilitate optimization of glycemic control, patient safety, and improved care. Similar models can be implemented across a wider scale of biomedical variables to offer real-time optimization, training, and adaptation that increase predictive accuracy and performance of therapies.

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Hypoglycemia and Body Temperature

Cited by 25 publications

References 32 publications

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

Engineering digital biomarkers of interstitial glucose from noninvasive smartwatches

Role of the hypothalamus in mediating protective effects of dietary restriction during aging

Evaluation of a Model for Glycemic Prediction in Critically Ill Surgical Patients

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