Glycemic variability: Importance, relationship with physical activity, and the influence of exercise

Sparks, Joshua R.; Kishman, Erin E.; Sarzynski, Mark A.; Davis, John M.; Grandjean, Peter W.; Durstine, J. Larry; Wang, Xuewen

doi:10.1016/j.smhs.2021.09.004

Cited by 19 publications

(15 citation statements)

References 86 publications

(165 reference statements)

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“…T2DM is a common disease in older adults; although determination of glycated hemoglobin (HbA1c) is considered to be the standard for evaluation of glycemic control in diabetic patients, it does not consider changes in blood glucose levels, that is, blood glucose variability (GV)[ 6 ]. GV, the cause of glycemic fluctuations, is a sensitive indicator of blood sugar, and advances in continuous blood glucose monitoring (CGM) technology have become an established evaluation tool[ 7 ]. CGM technology can further understand daily fluctuations and changes in the daily glucose concentration.…”

Section: Introductionmentioning

confidence: 99%

Relationship between glycemic variability and cognitive function in lacune patients with type 2 diabetes

Meng¹,

Wang²,

Li³

et al. 2023

World J Clin Cases

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BACKGROUND Lacunes are the manifestations of lacunar infarction which can lead many patients to the clinical outcome of disability or dementia. However, the relationship between lacune burden, cognitive function and blood glucose fluctuation in patients with type 2 diabetes mellitus (T2DM) complicated with lacunes is not very clear. AIM To explore the correlation between glucose variability, lacune burden and cognitive function in patients with lacunes complicated with T2DM. METHODS The clinical and imaging data of 144 patients with lacunes combined with T2DM were reviewed retrospectively. 72 h continuous glucose monitoring was performed. The Montreal Cognitive Assessment was used to assess cognitive function. The burden of lacunes was evaluated using magnetic resonance imaging performance. Multifactorial logistic regression analysis was used to study the affecting the lacune load and cognitive impairment in patients. To predict the value of patients' cognitive impairment with lacunes complicated with T2DM, a receiver operating characteristic (ROC) curve and a nomogram prediction model were constructed. RESULTS The standard deviation (SD) of the average blood glucose concentration, percentage coefficient of variation (%CV) and time of range (TIR) were significantly different between the low and the high load groups ( P < 0.05). The SD, %CV and TIR of the cognitive impairment group and non-cognitive impairment group were significantly different ( P < 0.05). SD (odds ratio (OR): 3.558, 95% confidence interval (CI): 1.268-9.978, P = 0.006), and %CV (OR: 1.192, 95%CI: 1.081-1.315, P < 0.05) were the risk factors for an increased infarct burden in lacunes patients complicated with T2DM. TIR (OR: 0.874, 95%CI: 0.833-0.928, P < 0.05) is a protective factor. In addition, an increased SD (OR: 2.506, 95%CI: 1.008-6.23, P = 0.003), %CV (OR: 1.163, 95%CI: 1.065-1.270, P < 0.05) were the risk factors for cognitive impairment in patients with lacunes complicated with T2DM, TIR (OR: 0.957, 95%CI: 0.922-0.994, P < 0.05) is a protective factor. A nomogram prediction model of the risk of cognitive impairment was established based on SD, %CV and TIR. Decision curve analysis and the internal calibration analysis were used for internal verification and showed that the model was clinical benefit. The area under the ROC curves for predicting cognitive impairment in patients with lacunes complicated with T2DM was drawn were %CV: 0.757 (95%CI :0.669-0.845, P < 0.05), TIR: 0.711 (95%CI: 0.623-0.799, P < 0.05). CONCLUSION Blood glucose variability is closely associated with the level of lacune burden and cognitive dysfunction in lacune patients...

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Section: Introductionmentioning

confidence: 99%

Relationship between glycemic variability and cognitive function in lacune patients with type 2 diabetes

Meng¹,

Wang²,

Li³

et al. 2023

World J Clin Cases

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“…Greater activity energy expenditure (AEE; kcal) was associated with lower TIR. This was surprising given the established effects of physical activity on improving TIR and GV in individuals with T2D 63,64 . However there are several potential explanations for a lack of effect in the current study.…”

Section: Discussionmentioning

confidence: 97%

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes.

Berry,

Bermingham,

Smith

et al. 2023

Preprint

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Background Continuous glucose monitors (CGMs) provide high-frequency information regarding daily glucose variation and are recognised as effective for improving glycaemic control in individuals living with diabetes. Despite increased use in individuals with non-diabetic blood glucose concentrations (euglycemia), their utility as a health tool in this population remains unclear. Objectives To characterise variation in time in range (TIR) and glycaemic variability in large populations without diabetes or impaired glucose tolerance; describe associations between CGM-derived glycaemic metrics and metabolic and cardiometabolic health traits; identify key diet and lifestyle factors associated with TIR and glycaemic variability. Design Glycaemic variability (coefficient of variation) and time spent in both the ADA secondary target range (TIRADA; 3.9-7.8 mmol/L) and a more stringent range (TIR3.9-5.6; 3.9-5.6 mmol/L) were calculated during free-living in PREDICT 1, PREDICT 2, and PREDICT 3 euglycaemic community-based volunteer cohorts. Associations between CGM derived glycaemic metrics, markers of cardiometabolic health, diet (food frequency questionnaire and logged diet records), diet-habits, and lifestyle were explored. Results Data from N=4135 participants (Mean ± SD; Age: 47 ± 12 y; Sex: 83% Female, BMI: 27 ± 6 kg/m2). Median glycaemic variability was 14.8% (IQR 12.6-17.6%), median TIRADA was 95.8% (IQR 89.6-98.6%) and TIR3.9-5.6 was 75.0% (IQR 64.6-82.8%). Greater TIR3.9-5.6 was associated with lower HbA1c, ASCVD 10y risk and HOMA-IR (all p < 0.05). Lower glycaemic variability was associated with lower % energy derived from carbohydrate (rs: 0.17, p < 0.01), ultra-processed foods (NOVA 4, % EI; rs: 0.12, p = 0.01) and a longer overnight fasting duration (rs: -0.10, p = 0.01). Conclusions A stringent TIR target provides sensitivity to detect changes in HOMA-IR, ASCVD 10 y risk and HbA1c that were not detected using ADA secondary targets. Associations among TIR, glycaemic variability, dietary intake (e.g. carbohydrate and protein) and habits (e.g. nocturnal fasting duration) highlight potential strategic targets to improve glycaemic metrics derived from continuous glucose monitors.

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“…High sedentary behavior is linked to increased fasting glucose concentrations, impaired glucose metabolism, and decreased glucose tolerance [53]. Physical activity is an effective way to decrease glucose values and, in particular, postprandial glucose excursions [54][55][56][57][58][59].…”

Section: Physical Activitymentioning

confidence: 99%

Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports

Holzer

Bloch

Brinkmann

2022

Sensors

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Introduction: Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports. Results: CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise. Conclusions: CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users.

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Glycemic variability: Importance, relationship with physical activity, and the influence of exercise

Cited by 19 publications

References 86 publications

Relationship between glycemic variability and cognitive function in lacune patients with type 2 diabetes

Relationship between glycemic variability and cognitive function in lacune patients with type 2 diabetes

Glycaemic variability, assessed with continuous glucose monitors, is associated with diet, lifestyle and health in people without diabetes.

Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports

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