Glycemic Excursions in Type 1 Diabetes in Pregnancy: A Semiparametric Statistical Approach to Identify Sensitive Time Points during Gestation

Aim.To examine the gestational glycemic profile and identify specific times during pregnancy that variability in glucose levels, measured by change in velocity and acceleration/deceleration of blood glucose fluctuations, is associated with delivery of a large-for-gestational-age (LGA) baby, in women with type 1 diabetes.Methods.Retrospective analysis of capillary blood glucose levels measured multiple times daily throughout gestation in women with type 1 diabetes was performed using semiparametric mixed models.Results.Velocity and acceleration/deceleration in glucose levels varied across gestation regardless of delivery outcome. Compared to women delivering LGA babies, those delivering babies appropriate for gestational age exhibited significantly smaller rates of change and less variation in glucose levels between 180 days of gestation and birth.Conclusions.Use of innovative statistical methods enabled detection of gestational intervals in which blood glucose fluctuation parameters might influence the likelihood of delivering LGA baby in mothers with type 1 diabetes. Understanding dynamics and being able to visualize gestational changes in blood glucose are a potentially useful tool to assist care providers in determining the optimal timing to initiate continuous glucose monitoring.

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Advantages and Disadvantages of Realtime Continuous Glucose Monitoring in People with Type 2 Diabetes

US Endocrinology ◽

10.17925/use.2012.08.01.22 ◽

2012 ◽

Vol 08 (01) ◽

pp. 22 ◽

Cited By ~ 3

Author(s):

M Susan Walker ◽

Stephanie J Fonda ◽

Sara Salkind ◽

Robert A Vigersky ◽

◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Continuous Glucose Monitoring ◽

Glucose Monitoring ◽

Glucose Levels ◽

Advantages And Disadvantages ◽

Blood Glucose Levels

Previous research has shown that realtime continuous glucose monitoring (RT-CGM) is a useful clinical and lifestyle aid for people with type 1 diabetes. However, its usefulness and efficacy for people with type 2 diabetes is less known and potentially controversial, given the continuing controversy over the efficacy of self-monitoring of blood glucose (SMBG) in this cohort. This article reviews theextantliterature on RT-CGM for people with type 2 diabetes, and enumerates several of the advantages and disadvantages of this technology from the perspective of providers and patients. Even patients with type 2 diabetes who are not using insulin and/or are relatively well controlled on oral medications have been shown to spend a significant amount of time each day in hyperglycemia. Additional tools beyond SMBG are necessary to enable providers and patients to clearly grasp and manage the frequency and amplitude of glucose excursions in people with type 2 diabetes who are not on insulin. While SMBG is useful for measuring blood glucose levels, patients do not regularly check and SMBG does not enable many to adequately manage blood glucose levels or capture marked and sustained hyperglycemic excursions. RT-CGM systems, valuable diabetes management tools for people with type 1 diabetes or insulin-treated type 2 diabetes, have recently been used in type 2 diabetes patients. Theextantstudies, although few, have demonstrated that the use of RT-CGM has empowered people with type 2 diabetes to improve their glycemic control by making and sustaining healthy lifestyle choices.

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Blood Glucose Levels and Performance in a Sports Camp for Adolescents with Type 1 Diabetes Mellitus: A Field Study

International Journal of Pediatrics ◽

10.1155/2010/216167 ◽

2010 ◽

Vol 2010 ◽

pp. 1-8 ◽

Cited By ~ 22

Author(s):

Dylan Kelly ◽

Jill K. Hamilton ◽

Michael C. Riddell

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Type 1 Diabetes Mellitus ◽

Glucose Monitoring ◽

Sports Performance ◽

Nocturnal Hypoglycemia ◽

Glucose Levels ◽

Skill Performance

Background. Acute hypo- and hyperglycemia causes cognitive and psychomotor impairment in individuals with type 1 diabetes mellitus (T1DM) that may affect sports performance.Objective. To quantify the effect of concurrent and antecedent blood glucose concentrations on sports skills and cognitive performance in youth with T1DM attending a sports camp.Design/Methods. 28 youth (ages 6–17 years) attending a sports camp carried out multiple skill-based tests (tennis, basketball, or soccer skills) with glucose monitoring over 4 days. Glucose levels at the time of testing were categorized as (a) hypoglycemic (<3.6 mM); (b) within an acceptable glycemic range (3.6–13.9 mM); or (c) hyperglycemic (>13.9 mM).Results. Overall, sports performance skill was~20% lower when glucose concentrations were hypoglycemic compared to either acceptable or hyperglycemic at the time of skill testing (). During Stroop testing, “reading” and “color recognition” also degraded during hypoglycemia, while “interference” scores improved (). Nocturnal hypoglycemia was present in 66% of subjects, lasting an average of 84 minutes, but this did not affect sports skill performance the following day.Conclusions. Mild hypoglycemia markedly reduces sports skill performance and cognition in young athletes with T1DM.

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Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques

Bioengineering ◽

10.3390/bioengineering8060072 ◽

2021 ◽

Vol 8 (6) ◽

pp. 72

Author(s):

Benedetta De Paoli ◽

Federico D’Antoni ◽

Mario Merone ◽

Silvia Pieralice ◽

Vincenzo Piemonte ◽

...

Keyword(s):

Physical Activity ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Mean Squared Error ◽

Glucose Monitoring ◽

Online Training ◽

Machine Learning Algorithms ◽

Glucose Levels ◽

Blood Glucose Levels

Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.

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Increased Second Trimester Maternal Glucose Levels Are Related to Extremely Large-for-Gestational-Age Infants in Women With Type 1 Diabetes

Diabetes Care ◽

10.2337/dc06-1985 ◽

2007 ◽

Vol 30 (5) ◽

pp. 1069-1074 ◽

Cited By ~ 28

Author(s):

A. Kerssen ◽

H. W. de Valk ◽

G. H.A. Visser

Keyword(s):

Type 1 Diabetes ◽

Gestational Age ◽

Large For Gestational Age ◽

Second Trimester ◽

Glucose Levels ◽

Maternal Glucose Levels ◽

Maternal Glucose

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Use of real-time continuous glucose monitoring versus traditional self-monitoring of blood glucose levels improves glycaemic control in patients with type 1 diabetes

Evidence-Based Nursing ◽

10.1136/ebn.2011.100247 ◽

2011 ◽

Vol 15 (1) ◽

pp. 7-8 ◽

Cited By ~ 1

Author(s):

Cynthia Fritschi

Keyword(s):

Type 1 Diabetes ◽

Blood Glucose ◽

Glycaemic Control ◽

Real Time ◽

Continuous Glucose Monitoring ◽

Glucose Monitoring ◽

Self Monitoring ◽

Glucose Levels ◽

Blood Glucose Levels

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Evaluating the Infuence of Mood and Stress on Glycemic Variability in T1DM Patients using Glucose Monitoring Sensors and Pools

10.1101/2020.09.03.20186940 ◽

2020 ◽

Author(s):

Sergio Contador Pachón ◽

Marta Botella Serrano ◽

Aranzazu Aramendi Zurimendi ◽

Remedios Rodríguez Martínez ◽

Esther Maqueda Villaizán ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Blood Glucose ◽

Glycemic Control ◽

Glucose Monitoring ◽

Glycemic Variability ◽

P Value ◽

Glucose Levels ◽

Blood Glucose Levels

Objective: Assess in a sample of patients with type 1 diabetes mellitus whether mood and stress influence blood glucose levels and variability. Material and Methods: Continuous glucose monitoring was performed on 10 patients with type 1 diabetes, where interstitial glucose values were recorded every 15 minutes. A daily survey was conducted through Google Forms, collecting information on mood and stress. The day was divided into 6 slots of 4-hour each, asking the patient to assess each slot in relation to mood (sad, normal or happy) and stress (calm, normal or nervous). Different measures of glycemic control (arithmetic mean and percentage of time below/above the target range) and variability (standard deviation, percentage coefficient of variation, mean amplitude of glycemic excursions and mean of daily differences) were calculated to relate the mood and stress perceived by patients with blood glucose levels and glycemic variability. A hypothesis test was carried out to quantitatively compare the data groups of the different measures using the Student's t-test. Results: Statistically significant differences (p-value < 0.05) were found between different levels of stress. In general, average glucose and variability decrease when the patient is calm. There are statistically significant differences (p-value < 0.05) between different levels of mood. Variability increases when the mood changes from sad to happy. However, the patient's average glucose decreases as the mood improves. Conclusions: Variations in mood and stress significantly influence blood glucose levels, and glycemic variability in the patients analyzed with type 1 diabetes mellitus. Therefore, they are factors to consider for improving glycemic control. The mean of daily differences does not seem to be a good indicator for variability. Keywords: Diabetes mellitus, continuous glucose monitoring, glycemic variability, average glycemia, glycemic control, stress, mood.

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