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Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study.

ABSTRACT: INTRODUCTION:The aim of this study was to objectively analyze the correlation between dietary components and blood glucose variation by means of continuous glucose monitoring (CGM). METHODS:Patients with type 1 diabetes mellitus (T1DM) who received CGM to manage their blood glucose levels were enrolled into the study, and the components of their total caloric intake were analyzed. Glycemic variation parameters were calculated, and dietary components, including percentages of carbohydrate, protein and fat in the total dietary intake, were analyzed by a dietitian. The interaction between parameters of glycemic variability and dietary components was analyzed. RESULTS:Sixty-one patients with T1DM (33 females, 28 males) were enrolled. The mean age of the participants was 34.7 years, and the average duration of diabetes was 14 years. Glycated hemoglobin before CGM was 8.54%. Participants with a carbohydrate intake that accounted for < 50% of their total caloric intake had a longer DM duration and a higher protein and fat intake than did those with a carbohydrate intake that accounted for ≥ 50% of total caloric intake, but there was no between-group difference in total caloric intake per day. The group with a carbohydrate intake that accounted for < 50% of their total caloric intake also had lower nocturnal continuous overlapping net glycemic action (CONGA) 1, - 2 and - 4 values. The percentage of protein intake had a slightly negative correlation with mean amplitude of glycemic excursions (MAGE) (r = - 0.286, p < 0.05) and a moderately negative correlation with coefficient of variation (CV) (r = 0.289, p < 0.05). One additional percentage of protein calories of total calories per day decreased the MAGE to 4.25 mg/dL and CV to 0.012 (p < 0.05). The optimal dietary protein percentage for MAGE < 140 mg/dL was 15.13%. The performance of predictive models revealed the beneficial effect of adequate carbohydrate intake on glucose variation when combined with protein consumption. CONCLUSIONS:Adequate carbohydrate consumption-but not more than half the daily total calories-combined with protein calories that amount to approximately 15% of the daily caloric intake is important for glucose stability and beneficial for patients with T1DM.

SUBMITTER: Lin YH

PROVIDER: S-EPMC6848334 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Aim: Patients with Type 2 Diabetes Mellitus (T2DM) have increased risk of developing vascular complications due to chronic hyperglycemia. Glycemic variability (GV) has been suggested to play an even more important role in the risk of developing diabetic complications than sustained hyperglycemia. Physical activity (PA) has shown reducing effects on mean plasma glucose; however, the effect on GV in T2DM needs further description. The objective of this review is to evaluate the effect of PA on GV, assessed by continuous glucose monitoring (CGM) in people with T2DM. Methods: A systematic literature search was conducted on MEDLINE and Embase to find randomized controlled trials (RCTs) covering the aspects T2DM, PA, and CGM. Following eligibility screening, variables of population characteristics, PA interventions, and GV outcomes were extracted and processed through qualitative synthesis. Risk of bias (ROB) was assessed using Cochrane ROB tool v2.0. Results: Of 1,825 identified articles, 40 full texts were screened. In the ten included RCTs matching the eligibility criteria, sample sizes ranged from nine to 63, mean age from 51 (SD 11) to 65 (SD 2) years and mean T2DM duration from four (SD 3) to ten (SD 6) years. Eight RCTs examined GV following single bouts of exercise, while two RCTs examined GV following training interventions. One RCT applied parallel group design, while nine RCTs applied crossover design. Numeric reductions in GV following acute exercise were seen, with four RCTs reaching statistical significance. Numeric reductions in GV were seen following training interventions, with one RCT reaching statistical significance. Numeric reductions of GV after PA appeared independently of intensity and T2DM progression but higher in participants with high baseline HbA1c and GV than with low. 80% of the trials were evaluated as uncertain/high ROB. Conclusion: The systematic literature search revealed limited and biased evidence showing that acute PA numerically reduced GV in patients with T2DM. PA reduced GV independently of PA intensity and T2DM progression. Prolonged RCTs with low ROB are needed to confirm reducing effects of PA on GV and to assess the influence of patient- and intervention characteristics on the effect of PA on GV.

Project description:BackgroundContinuous 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.ObjectivesTo 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.DesignGlycaemic 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.ResultsData 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).ConclusionsA 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.

Project description:AimsCVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis.MethodsWe included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SDCGM] and CGM-assessed CV [CVCGM]) and time in range (TIRCGM) with carotid-femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima-media thickness, ankle-brachial index and circumferential wall stress via multiple linear regression.ResultsHigher SDCGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SDCGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSGCGM), SDCGM and MSGCGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [-0.018, 0.167], p = 0.160; and 0.059 [-0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CVCGM (B [95% CI] per 10% CVCGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIRCGM (B [95% CI] per 10% TIRCGM: -0.145 m/s [-0.252, -0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures.ConclusionsOur findings show that greater daily glucose variability and lower TIRCGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIRCGM to prevent CVD.

Project description:BackgroundThe association between glucose variability and the progression of atherosclerosis is not completely understood. We aimed to evaluate the associations of glucose variability with the progression of atherosclerosis in the early stages.MethodsWe conducted a cross-sectional analysis to investigate the associations of glucose variability, assessed by continuous glucose monitoring, with intima-media thickness (IMT) and gray-scale median (GSM) of the carotid arteries, which are different indicators for the progression of atherosclerosis. We used baseline data from a hospital-based multicenter prospective observational cohort study among Japanese patients with type 2 diabetes without a history of cardiovascular diseases aged between 30 and 80 years. Continuous glucose monitoring was performed by Freestyle Libre Pro, and glucose levels obtained every 15 min for a maximum of eight days were used to calculate the metrics of glucose variability. IMT and GSM were evaluated by ultrasonography, and the former indicates thickening of intima-media complex in the carotid artery wall, while the latter indicates tissue characteristics.ResultsAmong 600 study participants (age: 64.9 ± 9.2 (mean ± SD) years; 63.2%: men; HbA1c: 7.0 ± 0.8%), participants with a larger intra- and inter-day glucose variability had a lower GSM and most of these associations were statistically significant. No trend based on glucose variability was shown regarding IMT. Standard deviation of glucose (regression coefficient, β = - 5.822; 95% CI - 8.875 to - 2.768, P < 0.001), glucose coefficient of variation (β = - 0.418; - 0.685 to - 0.151, P = 0.002), mean amplitude of glycemic excursion (β = - 1.689; - 2.567 to - 0.811, P < 0.001), mean of daily differences (β = - 6.500; - 9.758 to - 3.241, P < 0.001), and interquartile range (β = - 4.289; - 6.964 to - 1.614, P = 0.002) had a statistically significant association with mean-GSM after adjustment for conventional cardiovascular risk factors, including HbA1c. No metrics of glucose variability had a statistically significant association with IMT.ConclusionsContinuous glucose monitoring-assessed glucose variability was associated with the tissue characteristics of the carotid artery wall in type 2 diabetes patients without cardiovascular diseases.

Project description:BackgroundType 2 diabetes mellitus (T2DM) is a leading cause of morbidity and mortality globally, with adverse health consequences largely related to hyperglycemia. Despite clinical practice guideline recommendations, effective pharmacotherapy, and interventions to support patients and providers, up to 60% of patients diagnosed with T2DM are estimated to have hemoglobin A1c (HbA1c) levels above the recommended targets owing to multilevel barriers hindering optimal glycemic control.ObjectiveThe aim of this study is to compare changes in HbA1c levels among patients with suboptimally controlled T2DM who were offered the opportunity to use an intermittently viewed continuous glucose monitor and receive personalized low-carbohydrate nutrition counseling (<100 g/day) versus those who received usual care (UC).MethodsThis was a 12-month, pragmatic, randomized quality improvement program. All adult patients with T2DM who received primary care at a university-affiliated primary care clinic (N=1584) were randomized to either the UC or the enhanced care (EC) group. Within each program arm, we identified individuals with HbA1c >7.5% (58 mmol/mol) who were medically eligible for tighter glycemic control, and we defined these subgroups as UC-high risk (UC-HR) or EC-HR. UC-HR participants (n=197) received routine primary care. EC-HR participants (n=185) were invited to use an intermittently viewed continuous glucose monitor and receive low-carbohydrate nutrition counseling. The primary outcome was mean change in HbA1c levels from baseline to 12 months using an intention-to-treat difference-in-differences analysis comparing EC-HR with UC-HR groups. We conducted follow-up semistructured interviews to understand EC-HR participant experiences with the intervention.ResultsHbA1c decreased by 0.41% (4.5 mmol/mol; P=.04) more from baseline to 12 months among participants in the EC-HR group than among those in UC-HR; however, only 61 (32.9%) of 185 EC-HR participants engaged in the program. Among the EC-HR participants who wore continuous glucose monitors (61/185, 32.9%), HbA1c was 1.1% lower at 12 months compared with baseline (P<.001). Interviews revealed themes related to EC-HR participants' program engagement and continuous glucose monitor use.ConclusionsAmong patients with suboptimally controlled T2DM, a combined approach that includes continuous glucose monitoring and low-carbohydrate nutrition counseling can improve glycemic control compared with the standard of care.

Project description:BackgroundDeterioration of sleep quality has been reported to contribute to the incidence of diabetes and may be responsible for glycemic status in diabetes. The present study explored the relationship between sleep quality and glycemic variability in patients with type 2 diabetes (T2D).MethodsWe recruited 111 patients with T2D for this cross-sectional study. Each patient underwent flash glucose monitoring for 14 days to obtain glycemic variability parameters, such as standard deviation of glucose (SD), coefficient of variation of glucose (CV), mean amplitude of glycemic excursions (MAGE), mean of daily differences (MODD), and time in glucose range of 3.9-10 mmol/L (TIR3.9-10). After 14 days of flash glucose monitoring, each patient received a questionnaire on the Pittsburgh Sleep Quality Index (PSQI) to evaluate subjective sleep quality. HbA1c was also collected to assess average glucose.ResultsHbA1c was comparable among the subgroups of PSQI score tertiles. Across ascending tertiles of PSQI scores, SD, CV and MAGE were increased, while TIR3.9-10 was decreased (p for trend < 0.05), but not MODD (p for trend = 0.090). Moreover, PSQI scores were positively correlated with SD, CV, MODD and MAGE (r = 0.322, 0.361, 0.308 and 0.354, respectively, p < 0.001) and were inversely correlated with TIR3.9-10 (r = - 0.386, p < 0.001). After adjusting for other relevant data by multivariate linear regression analyses, PSQI scores were independently responsible for SD (β = 0.251, t = 2.112, p = 0.041), CV (β = 0.286, t = 2.207, p = 0.033), MAGE (β = 0.323, t = 2.489, p = 0.018), and TIR3.9-10 (β = - 0.401, t = - 3.930, p < 0.001) but not for MODD (β = 0.188, t = 1.374, p = 0.177).ConclusionsIncreased glycemic variability assessed by flash glucose monitoring was closely associated with poor subjective sleep quality evaluated by the PSQI in patients with T2D.

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Impact of Carbohydrate on Glucose Variability in Patients with Type 1 Diabetes Assessed Through Professional Continuous Glucose Monitoring: A Retrospective Study.

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