Metabolic and lifestyle determinants of glycemic variability in persons at high risk of type 2 diabetes: a study with continuous monitoring of diet, physical activity, and glucose level. (Preprint)

2021 ◽  
Author(s):  
Su Hyun Park ◽  
Jiali Yao ◽  
Clare Whitton ◽  
Xin Hui Chua ◽  
Suresh Rama Chandran ◽  
...  
Keyword(s):  
Physical Activity ◽  
High Risk ◽  
Glycemic Variability ◽  
Tolerance Test ◽  
Glucose Levels ◽  
Intensity Physical Activity ◽  
Time In Range ◽  
Vigorous Intensity ◽  
Mean Glucose

BACKGROUND Frequent and large fluctuations in blood glucose concentration during the day may increase risk of type 2 diabetes. It remains unclear how diet and physical activity affect glycemic variability in real-world conditions in persons without diabetes. OBJECTIVE We examined metabolic and lifestyle determinants (diet, physical activity, and sleep) of blood glucose levels over a seven-day period in people at high risk for diabetes METHODS Twenty-eight participants with a mean age of 46.0 (SD 9.9) years and a mean body mass index (BMI) of 27.5 (SD 1.8) kg/m2 underwent a mixed meal tolerance test to assess glucose homeostasis at baseline. Subsequently, they wore an accelerometer to assess movement behaviors, recorded their dietary intakes through a mobile phone application, and wore a flash glucose monitoring device that measured glucose levels every 15 min for seven days. Generalized estimating equation models were used to assess the associations of metabolic and lifestyle risk factors with daily mean glucose levels (mmol/L), the coefficient of variation (CV%) of glucose levels, and time-in-range (3.0 to 7.8 mmol/L, %). RESULTS A higher BMI (β = 0.12 per kg/m2; P = 0.01), body fat (β = 0.03 per kg; P = 0.01), and selected markers of hyperglycemia and insulin resistance from the meal tolerance test were associated with higher mean glucose levels during the seven days. Moderate-to-vigorous intensity physical activity (β = -1.77 per hr./d, P = 0.008) and polyunsaturated fat intake (β = -2.23 per 5 energy %, P < 0.001) were independently associated with less variation in glucose levels (CV%). Higher protein (β = 0.90, P = 0.007) and polyunsaturated fatty acid (β = 3.21, P = 0.02) intakes were associated with more time-in-range. In contrast, higher carbohydrates intake was associated with less time-in-range (β = -0.59, P = 0.04). Sleep, sedentary behavior, or light intensity physical activity were not independently associated with glucose measures. CONCLUSIONS Body fatness was associated with higher mean glucose levels, and moderate-to-vigorous intensity physical activity was associated with less glycemic variability throughout a week. Diets with higher protein and polyunsaturated fat, and lower carbohydrates were associated with more time in normal glucose range. Physical activity and dietary composition can substantially influence glucose variation in people at high risk of diabetes.

scholarly journals Diet and Physical Activity as Determinants of Continuously Measured Glucose Levels in Persons at High Risk of Type 2 Diabetes

Nutrients ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 366
Author(s):  
Su Hyun Park ◽  
Jiali Yao ◽  
Xin Hui Chua ◽  
Suresh Rama Chandran ◽  
Daphne S. L. Gardner ◽  
...  
Keyword(s):  
Physical Activity ◽  
High Risk ◽  
Vigorous Physical Activity ◽  
Glycemic Variability ◽  
Tolerance Test ◽  
Dietary Intakes ◽  
Polyunsaturated Fat ◽  
Meal Tolerance Test ◽  
Glucose Levels ◽  
Time In Range

We examined how dietary and physical activity behaviors influence fluctuations in blood glucose levels over a seven-day period in people at high risk for diabetes. Twenty-eight participants underwent a mixed meal tolerance test to assess glucose homeostasis at baseline. Subsequently, they wore an accelerometer to assess movement behaviors, recorded their dietary intakes through a mobile phone application, and wore a flash glucose monitoring device that measured glucose levels every 15 min for seven days. Generalized estimating equation models were used to assess the associations of metabolic and lifestyle risk factors with glycemic variability. Higher BMI, amount of body fat, and selected markers of hyperglycemia and insulin resistance from the meal tolerance test were associated with higher mean glucose levels during the seven days. Moderate- to vigorous-intensity physical activity and polyunsaturated fat intake were independently associated with less variation in glucose levels (CV%). Higher protein and polyunsaturated fatty acid intakes were associated with more time-in-range. In contrast, higher carbohydrate intake was associated with less time-in-range. Our findings suggest that dietary composition (a higher intake of polyunsaturated fat and protein and lower intake of carbohydrates) and moderate-to-vigorous physical activity may reduce fluctuations in glucose levels in persons at high risk of diabetes.

scholarly journals Comparison Between Glycemic Variability Values Calculated From Professional-CGM and Glycemic Variability Values Calculated From Personal-CGM

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A330-A330
Author(s):  
Soichi Takeishi ◽  
Tatsuo Inoue
Keyword(s):  
Glycemic Variability ◽  
Glucose Levels ◽  
Blood Glucose Levels ◽  
Time Calibration ◽  
Sensor Glucose ◽  
Time In Range ◽  
The Individual ◽  
A Prospective Study ◽  
Relative Differences ◽  
Mean Glucose

Abstract It is important whether the differences between glycemic variability (GV) values calculated from professional CGM and GV values calculated from personal CGM are acceptable when using professional CGM data and personal CGM data together in studies. This is a prospective study. 41 inpatients with type 2 diabetes wore professional CGM (iPro2) and personal CGM (GUARDIAN CONNECT) in parallel for 6 days (CGM attachment: day 1). Each CGM were calibrated 22 times from day 2 to day 5, on the same timing in all patients (n of calibration = 902). Four 24 h GV from day 2 to day 5 per patient were evaluated (n of 24 h GV = 164) on each CGM. There were no significant differences between the standard deviation on professional CGM and that on personal CGM (32.2 mg/dL vs 32.8 mg/dL, p = 0.21). For time in range (70–180 mg/dL) [TIR 70–180], mean glucose levels and coefficient of variation (CV), the values were numerically almost equal between GV on professional CGM and that on personal CGM. However, for TIR 70–180 and CV, the GV on professional CGM was statistically significantly lower than that on personal CGM (70.8 % vs 72.5 %, p = 0.002, 20.6 % vs 21.3 %, p = 0.04); and for mean glucose levels, the GV on professional CGM was statistically significantly higher than that on personal CGM (157.7 mg/dL vs 155.2 mg/dL, p &lt; 0.001). For mean absolute glucose (MAG) and glycemic variability percentage (GVP), the GV on professional CGM was significantly lower than that on personal CGM (25.3 mg/dL/h vs 41.0 mg/dL/h, p &lt; 0.001, 14.4 % vs 31.1 %, p &lt; 0.001). The calibrations, whose blood glucose levels (BG) were higher than ‘sensor glucose levels just before the BG’ (SGjb), were more than those, whose BG were lower than SGjb (482 times vs 420 times). The mean absolute relative differences on professional CGM were significantly lower than those on personal CGM (5.0 % vs 6.3 %, p &lt; 0.001). The study patients correlated to distributions of ‘ratio of CV on personal CGM to CV on professional CGM on the same day’ (correlation ratio: η 2 = 0.48, p &lt; 0.001). The results of TIR 70–180 and mean glucose levels may have been caused by the fact that the calibration BG were high dissociated from SGjb and algorithm of professional CGM is easier to allow the dissociation than that of personal CGM. The results of CV may have been caused by the individual differences in sensor accuracy between professional CGM and personal CGM. The results of MAG and GVP may have been caused by the fact that personal CGM can reveal detailed glycemic variability which cannot be revealed by professional CGM, due to real time calibration. According to purpose of studies, it should be determined whether using professional CGM data and personal CGM data together is acceptable.

scholarly journals Type 2 diabetes patients’ views on prevention of hypoglycaemia – a mixed methods study investigating self-management issues and self-identified causes of hypoglycaemia

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stijn Crutzen ◽  
Tessa van den Born-Bondt ◽  
Petra Denig ◽  
Katja Taxis
Keyword(s):  
Physical Activity ◽  
Type 2 Diabetes ◽  
Mixed Methods ◽  
Food Intake ◽  
Mixed Methods Study ◽  
Self Management ◽  
Hypoglycaemic Event ◽  
Glucose Levels ◽  
Management Issues

Abstract Background Hypoglycaemia is a common and potentially avoidable adverse event in people with type 2 diabetes (T2D). It can reduce quality of life, increase healthcare costs, and reduce treatment success. We investigated self-management issues associated with hypoglycaemia and self-identified causes of hypoglycaemia in these patients. Methods In this mixed methods study qualitative semi-structured interviews were performed, which informed a subsequent quantitative survey in T2D patients. All interviews were audio recorded, transcribed verbatim and coded independently by two coders using directed content analysis, guided by the Theoretical Domains Framework. Descriptive statistics were used to quantify the self-management issues and causes of hypoglycaemia collected in the survey for the respondents that had experienced at least one hypoglycaemic event in the past. Results Sixteen participants were interviewed, aged 59–84 years. Participants perceived difficulties in managing deviations from routine, and they sometimes lacked procedural knowledge to adjust medication, nutrition or physical activity to manage their glucose levels. Grief and loss of support due to the loss of a partner interfered with self-management and lead to hypoglycaemic events. Work ethic lead some participant to overexerting themselves, which in turn lead to hypoglycaemic events. The participants had difficulties preventing hypoglycaemic events, because they did not know the cause, suffered from impaired hypoglycaemia awareness and/or did not want to regularly measure their blood glucose. When they did recognise a cause, they identified issues with nutrition, physical activity, stress or medication. In total, 40% of respondents reported regular stress as an issue, 24% reported that they regularly overestimated their physical abilities, and 22% indicated they did not always know how to adjust their medication. Around 16% of patients could not always remember whether they took their medication, and 42% always took their medication at regular times. Among the 83 respondents with at least one hypoglycaemic event, common causes for hypoglycaemia mentioned were related to physical activity (67%), low food intake (52%), deviations from routine (35%) and emotional burden (28%). Accidental overuse of medication was reported by 10%. Conclusion People with T2D experience various issues with self-managing their glucose levels. This study underlines the importance of daily routine and being able to adjust medication in relation to more physical activity or less food intake as well as the ability to reduce and manage stress to prevent hypoglycaemic events.

scholarly journals Enlarged glycemic variability in sulfonylurea-treated well-controlled type 2 diabetics identified using continuous glucose monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fumi Uemura ◽  
Yosuke Okada ◽  
Keiichi Torimoto ◽  
Yoshiya Tanaka
Keyword(s):  
Glucose Level ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
High Dose ◽  
Type 2 Diabetics ◽  
Time In Range ◽  
The Difference ◽  
Glycated Hemoglobin Level

AbstractTime in range (TIR) is an index of glycemic control obtained from continuous glucose monitoring (CGM). The aim was to compare the glycemic variability of treatment with sulfonylureas (SUs) in type 2 diabetes mellitus (T2DM) with well-controlled glucose level (TIR > 70%). The study subjects were 123 patients selected T2DM who underwent CGM more than 24 h on admission without changing treatment. The primary endpoint was the difference in glycemic variability, while the secondary endpoint was the difference in time below range < 54 mg/dL; TBR < 54, between the SU (n = 63) and non-SU (n = 60) groups. The standard deviation, percentage coefficient of variation (%CV), and maximum glucose level were higher in the SU group than in the non-SU group, and TBR < 54 was longer in the high-dose SU patients. SU treatment was identified as a significant factor that affected %CV (β: 2.678, p = 0.034). High-dose SU use contributed to prolonged TBR < 54 (β: 0.487, p = 0.028). Our study identified enlarged glycemic variability in sulfonylurea-treated well-controlled T2DM patients and high-dose SU use was associated with TBR < 54. The results highlight the need for careful adjustment of the SU dose, irrespective of glycated hemoglobin level or TIR value.

scholarly journals Glycemic variability and cardiovascular disease in patients with type 2 diabetes

2021 ◽  
Vol 9 (1) ◽  
pp. e002032
Author(s):  
Marcela Martinez ◽  
Jimena Santamarina ◽  
Adrian Pavesi ◽  
Carla Musso ◽  
Guillermo E Umpierrez
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Glycated Hemoglobin ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Cardiovascular Complications ◽  
Glucose Levels ◽  
Increased Risk ◽  
Patients With Diabetes

Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.

Moderate-to-vigorous intensity physical activity from young adulthood to middle age and metabolic disease: a 30-year population-based cohort study

2021 ◽  
pp. bjsports-2021-104231
Author(s):  
Jason M Nagata ◽  
Eric Vittinghoff ◽  
Kelley Pettee Gabriel ◽  
Andrea K Garber ◽  
Andrew E Moran ◽  
...  
Keyword(s):  
Physical Activity ◽  
Metabolic Disease ◽  
Life Course ◽  
Young Adulthood ◽  
Disease Risk ◽  
Density Lipoprotein ◽  
Active Group ◽  
Diabetes Incidence ◽  
Intensity Physical Activity ◽  
Vigorous Intensity

ObjectivesTo determine the association between moderate-to-vigorous intensity physical activity (MVPA) trajectories (course over age and time) through the adult life course and onset of metabolic disease (diabetes and dyslipidaemia).MethodsWe analysed prospective community-based cohort data of 5115 participants in the Coronary Artery Risk Development in Young Adults study, who were black and white men and women aged 18–30 years at baseline (1985–1986) at four urban sites, collected through 30 years of follow-up. Individualised MVPA trajectories were developed for each participant using linear mixed models.ResultsLower estimated MVPA score at age 18 was associated with a 12% (95% CI 6% to 18%) higher odds of incident diabetes, a 4% (95% CI 1% to 7%) higher odds of incident low high-density lipoprotein (HDL) and a 6% (95% CI 2% to 11%) higher odds of incident high triglycerides. Each additional annual 1-unit reduction in the MVPA score was associated with a 6% (95% CI 4% to 9%) higher annual odds of diabetes incidence and a 4% (95% CI 2% to 6%) higher annual odds of high triglyceride incidence. Analysing various MVPA trajectory groups, participants who were in the most active group at age 18 (over 300 min/week), but with sharp declines in midlife, had higher odds of high low-density lipoprotein and low HDL incidence, compared with those in the most active group at age 18 with subsequent gains.ConclusionGiven recent trends in declining MVPA across the life course and associated metabolic disease risk, young adulthood is an important time period for interventions to increase and begin the maintenance of MVPA.

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