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

Exercise-induced improvement of glycemic fluctuation and its relationship with fat and muscle distribution in type 2 diabetes

AIMS

Management of blood glucose fluctuation is essential for diabetes. Exercise is a key therapeutic strategy for diabetes patients, although little is known about determinants of glycemic response to exercise training. We aimed to investigate the effect of combined aerobic and resistance exercise training on blood glucose fluctuation in type 2 diabetes patients and explore the predictors of exercise-induced glycemic response.

MATERIALS AND METHODS

Fifty sedentary diabetes patients were randomly assigned to control or exercise group. Participants in the control group maintained sedentary lifestyle for 2 weeks, and those in the exercise group specifically performed combined exercise training for 1 week. All participants received dietary guidance based on a recommended diet chart. Glycemic fluctuation was measured by flash continuous glucose monitoring. Baseline fat and muscle distribution were accurately quantified through magnetic resonance imaging (MRI).

RESULTS

Combined exercise training decreased SD of sensor glucose (SDSG, exercise-pre vs exercise-post, mean 1.35 vs 1.10 mmol/L, p = .006) and coefficient of variation (CV, mean 20.25 vs 17.20%, p = .027). No significant change was observed in the control group. Stepwise multiple linear regression showed that baseline MRI-quantified fat and muscle distribution, including visceral fat area (β = -0.761, p = .001) and mid-thigh muscle area (β = 0.450, p = .027), were significantly independent predictors of SDSG change in the exercise group, as well as CV change.

CONCLUSIONS

Combined exercise training improved blood glucose fluctuation in diabetes patients. Baseline fat and muscle distribution were significant factors that influence glycemic response to exercise, providing new insights into personalized exercise intervention for diabetes.

Dietary composition and time in range in population with type 2 diabetes mellitus-exploring the association using continuous glucose monitoring device

AIM

To understand the association between macronutrient composition of a diet with Time in Range (TIR), Time above Range (TAR) and Time below Range (TBR) derived using a Continuous Glucose Monitoring (CGM) device for 14 days.

METHODOLOGY

An exploratory analysis on the baseline data of 50 Type 2 Diabetes Mellitus participants with age 25-55 years, HbA1c upto 8% and on Metformin only) enrolled for an interventional clinical trial was performed.

RESULTS

Participants consuming adequate carbohydrates (CHO) of 55 to 60% of total calories had better Average Blood Glucose of 142.0 ± 24.0 mg/dL with a significance of p = 0.03 and Glucose Management Indicator (GMI) of 6.6 ± 0.7% significant at p = 0.01, than those with high CHO intake >60% of the total calories, with Average Blood Glucose - 155.0 ± 13.4 mg/dL and GMI - 7.06 ± 0.4%. Similarly, TIR - 68.2 ± 5.1% and TAR - 23.0 ± 10.8% was significantly better (p = 0.00) among those consuming adequate protein (12-15%) as compared to low protein (≤ 10%) with TIR- 61.0 ± 5.1% & TAR- 32.9 ± 10.3%. A correlation (r = -0.482 & p = 0.00) and simple linear regression analysis (R² = 0.33, F = 7.72, p = 0.000) revealed that when CHO intake increases the TIR decreases whereas TAR increases (r = 0.380 & p = 0.006). We did not find any significant relation between fat intake and TIR, TAR or TBR.

CONCLUSION

Our results suggest that lowering CHO, while increasing protein in the diet may help improve TIR. Further in-depth studies are needed to confirm these findings.

Continuous glucose monitoring for automatic real-time assessment of eating events and nutrition: a scoping review

Background

Accurate dietary assessment remains a challenge, particularly in free-living settings. Continuous glucose monitoring (CGM) shows promise in optimizing the assessment and monitoring of ingestive activity (IA, i.e., consumption of calorie-containing foods/beverages), and it might enable administering dietary Just-In-Time Adaptive Interventions (JITAIs).

Objective

In a scoping review, we aimed to answer the following questions: (1) Which CGM approaches to automatically detect IA in (near-)real-time have been investigated? (2) How accurate are these approaches? (3) Can they be used in the context of JITAIs?

Methods

We systematically searched four databases until October 2023 and included publications in English or German that used CGM-based approaches for human (all ages) IA detection. Eligible publications included a ground-truth method as a comparator. We synthesized the evidence qualitatively and critically appraised publication quality.

Results

Of 1,561 potentially relevant publications identified, 19 publications (17 studies, total N = 311; for 2 studies, 2 publications each were relevant) were included. Most publications included individuals with diabetes, often using meal announcements and/or insulin boluses accompanying meals. Inpatient and free-living settings were used. CGM-only approaches and CGM combined with additional inputs were deployed. A broad range of algorithms was tested. Performance varied among the reviewed methods, ranging from unsatisfactory to excellent (e.g., 21% vs. 100% sensitivity). Detection times ranged from 9.0 to 45.0 min.

Conclusion

Several CGM-based approaches are promising for automatically detecting IA. However, response times need to be faster to enable JITAIs aimed at impacting acute IA. Methodological issues and overall heterogeneity among articles prevent recommending one single approach; specific cases will dictate the most suitable approach.

Clinical and Lifestyle Determinants of Continuous Glucose Monitoring Metrics in Insulin-Treated Patients with Type 2 Diabetes Mellitus

BACKGRUOUND

There was limited evidence to evaluate the association between lifestyle habits and continuous glucose monitoring (CGM) metrics. Thus, we aimed to depict the behavioral and metabolic determinants of CGM metrics in insulin-treated patients with type 2 diabetes mellitus (T2DM).

METHODS

This is a prospective observational study. We analyzed data from 122 insulin-treated patients with T2DM. Participants wore Dexcom G6 and Fitbit, and diet information was identified for 10 days. Multivariate-adjusted logistic regression analysis was performed for the simultaneous achievement of CGM-based targets, defined by the percentage of time in terms of hyper, hypoglycemia and glycemic variability (GV). Intake of macronutrients and fiber, step counts, sleep, postprandial C-peptide-to-glucose ratio (PCGR), information about glucose lowering medications and metabolic factors were added to the analyses. Additionally, we evaluated the impact of the distribution of energy and macronutrient during a day, and snack consumption on CGM metrics.

RESULTS

Logistic regression analysis revealed that female, participants with high PCGR, low glycosylated hemoglobin (HbA1c) and daytime step count had a higher probability of achieving all targets based on CGM (odds ratios [95% confidence intervals] which were 0.24 [0.09 to 0.65], 1.34 [1.03 to 1.25], 0.95 [0.9 to 0.99], and 1.15 [1.03 to 1.29], respectively). And participants who ate snacks showed a shorter period of hyperglycemia and less GV compared to those without.

CONCLUSION

We confirmed that residual insulin secretion, daytime step count, HbA1c, and women were the most relevant determinants of adequate glycemic control in insulin-treated patients with T2DM. In addition, individuals with snack consumption were exposed to lower times of hyperglycemia and GV.

Evaluating the Effects of Rewards and Schedule Length on Response Rates to Ecological Momentary Assessment Surveys: Randomized Controlled Trials

BACKGROUND

Ecological momentary assessments (EMAs) are short, repeated surveys designed to collect information on experiences in real-time, real-life contexts. Embedding periodic bursts of EMAs within cohort studies enables the study of experiences on multiple timescales and could greatly enhance the accuracy of self-reported information. However, the burden on participants may be high and should be minimized to optimize EMA response rates.

OBJECTIVE

We aimed to evaluate the effects of study design features on EMA response rates.

METHODS

Embedded within an ongoing cohort study (Health@NUS), 3 bursts of EMAs were implemented over a 7-month period (April to October 2021). The response rate (percentage of completed EMA surveys from all sent EMA surveys; 30-42 individual EMA surveys sent/burst) for each burst was examined. Following a low response rate in burst 1, changes were made to the subsequent implementation strategy (SMS text message announcements instead of emails). In addition, 2 consecutive randomized controlled trials were conducted to evaluate the efficacy of 4 different reward structures (with fixed and bonus components) and 2 different schedule lengths (7 or 14 d) on changes to the EMA response rate. Analyses were conducted from 2021 to 2022 using ANOVA and analysis of covariance to examine group differences and mixed models to assess changes across all 3 bursts.

RESULTS

Participants (N=384) were university students (n=232, 60.4% female; mean age 23, SD 1.3 y) in Singapore. Changing the reward structure did not significantly change the response rate (F3,380=1.75; P=.16). Changing the schedule length did significantly change the response rate (F1,382=6.23; P=.01); the response rate was higher for the longer schedule (14 d; mean 48.34%, SD 33.17%) than the shorter schedule (7 d; mean 38.52%, SD 33.44%). The average response rate was higher in burst 2 and burst 3 (mean 50.56, SD 33.61 and mean 48.34, SD 33.17, respectively) than in burst 1 (mean 25.78, SD 30.12), and the difference was statistically significant (F2,766=93.83; P<.001).

CONCLUSIONS

Small changes to the implementation strategy (SMS text messages instead of emails) may have contributed to increasing the response rate over time. Changing the available rewards did not lead to a significant difference in the response rate, whereas changing the schedule length did lead to a significant difference in the response rate. Our study provides novel insights on how to implement EMA surveys in ongoing cohort studies. This knowledge is essential for conducting high-quality studies using EMA surveys.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05154227; https://clinicaltrials.gov/ct2/show/NCT05154227.

Associations between oral processing, saliva, and bolus properties on daily glucose excursions amongst people at risk of type-2 diabetes

Background: A greater time spent with glucose above the normal range (TAR) has been associated with poorer glycaemic control amongst pre-diabetic individuals. Individual differences in oral processing behaviours and saliva amylase activity have been shown to influence glucose responses. Objective: The current study is a preliminary exploration of the associations of oral processing behaviours, bolus characteristics, and salivary amylase activity with the variability in daily glucose excursions within a free-living setting in populations with an elevated risk of type-2 diabetes. Method: Participant oral processing behaviour was derived from video recordings while they consumed a test meal. Post-meal bolus characteristics and saliva properties were measured. Participants were fitted with a continuous glucose monitor (CGM) which monitored blood glucose fluctuation over 7 consecutive free-living days. Dietary intake was recorded through a smartphone application and physical activity was monitored using a wrist worn accelerometer. Results: Participants varied in daily time spent with glucose above the normal range (>7.8 mmol l^-1) from 0% to 15%. Greater saliva uptake in the bolus was associated with a higher time spent above the normal range for glucose (β = 0.067 [95% CI = 0.015, 0.120]; p < 0.05), which remained significant after adjustment for dietary carbohydrate intake and BMI. Salivary amylase and saliva flow rate were not significantly associated with the time spent above the normal range. Conclusion: In addition to conventional dietary factors, more research is needed to understand how eating behaviours such as oro-sensory exposure, bolus surface area, and saliva uptake contribute to daily variations in postprandial glucose excursions among populations with a higher risk of developing type-2 diabetes.

Advancing understanding of dietary and movement behaviours in an Asian population through real-time monitoring: Protocol of the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA)

Background

Modifiable risk factors for non-communicable diseases, including eating an unhealthy diet and being physically inactive, are influenced by complex and dynamic interactions between people and their social and physical environment. Therefore, understanding patterns and determinants of these risk factors as they occur in real life is essential to enable the design of precision public health interventions.

Objective

This paper describes the protocol for the Continuous Observations of Behavioural Risk Factors in Asia study (COBRA). The study uses real-time data capture methods to gain a comprehensive understanding of eating and movement behaviours, including how these differ by socio-demographic characteristics and are shaped by people's interaction with their social and physical environment.

Methods

COBRA is an observational study in free-living conditions. We will recruit 1500 adults aged 21-69 years from a large prospective cohort study. Real-time data capture methods will be used for nine consecutive days: an ecological momentary assessment app with a global positioning system enabled to collect location data, accelerometers to measure movement, and wearable sensors to monitor blood glucose levels. Participants receive six EMA surveys per day between 8 a.m. and 9.30 p.m. to capture information on behavioural risk factors including eating behaviours and diet composition movement behaviours (physical activity, sedentary behaviour, sleep), and related contextual factors. The second wave of ecological momentary assessment surveys with a global positioning system enabled will be sent 6 months later. Data will be analysed using generalised linear models to examine associations between behavioural risk factors and contextual determinants.

Discussion

Findings from this study will advance our understanding of dietary and movement behaviours as they occur in real-life and inform the development of personalised interventions to prevent chronic diseases.