Suitability of continuous glucose monitoring in healthy subjects to detect effects of meal sequences and nutritional content of meals on postprandial glycemic responses

doi:10.21203/rs.3.rs-1992681/v1

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Suitability of continuous glucose monitoring in healthy subjects to detect effects of meal sequences and nutritional content of meals on postprandial glycemic responses

https://doi.org/10.21203/rs.3.rs-1992681/v1

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Background

Continuous glucose monitoring (CGM) systems have initially been developed for diabetes patients but are also increasingly used by healthy people in order to monitor individual eating behaviors and the glucose responses to different foods, e.g. to support weight loss. The aim of the study was to assess the suitability of this technology to detect effects of meal sequences and nutritional content of meals on postprandial glycemic responses. In addition, the effect of meal sequences on the subsequent eating behavior was evaluated.

Subjects/Methods

On two consecutive days, 36 participants without diabetes received standardized test meals (TM) for breakfast and lunch, as well as a free-choice dinner. Both TM contained equal amounts of carbohydrates with different absorption characteristics and differing fat and protein content. Participants consumed TM “fast” for breakfast and “slow” for lunch on one day, and in reverse order on the other day. Dinner was selected from a buffet; meal content and amount were free-choice. Participants rated their feeling of satiety directly before dinner intake. Glucose profiles were assessed with a CGM device.

Results

CGM was able to distinguish postprandial glucose responses according to the nutritional content of the TM. When TM were consumed for lunch, median glucose increase was higher than when consumed for breakfast (TM “fast”: 72.7 mg/dL vs. 56.5 mg/dL; TM “slow”: 38.3 mg/dL; vs. 22.1 mg/dL). Satiety before dinner was lower and energy intake for dinner was higher after TM “fast” for lunch than after TM “slow” for lunch (5 058.3 ± 1 787.8 kJ vs. 4 429.8 ± 1 205.4 kJ).

Conclusions

Data collected in this evaluation with the use of CGM firstly supports its use under everyday life conditions in people without diabetes and secondly could contribute to identify beneficial dietary patterns that may be considered in the management and prevention of metabolic disorders.

Regular blood glucose measurements are mandatory for people with diabetes to manage their disease. In addition to self-monitoring of blood glucose (SMBG), continuous glucose monitoring (CGM) has become widely accepted as a method for assessing glucose levels in recent years and has primarily been developed for people with diabetes. CGM enables a comprehensive assessment of glycemic excursions in real time and several CGM studies focusing on CGM accuracy or the glycemic course of people with diabetes exist (1–4). Due to the development of new low-cost CGM devices combined with their simple application, CGM systems are also attractive for individuals without diabetes as a health and lifestyle application, for example to monitor nutritional behavior during weight loss (5–7). Many people are not aware of the different effects of several foods on glucose levels and thus, data obtained by CGM can be used to guide dietary adjustment (8).

In particular elevated postprandial glucose levels have been shown to be associated with an increased risk in developing cardiovascular or metabolic diseases (9–11). Thus, a long-term change in dietary habits remains a cornerstone for the prevention and management of metabolic disorders like overweight, obesity or diabetes (11–14).

Some studies examining postprandial glucose profiles in terms of meal composition in individuals without diabetes using CGM are available (15–18). However, CGM data on postprandial glucose excursions in individuals without diabetes that also consider the impact of meal order in relation to the nutritional composition of meals are limited.

Current guidelines for type 2 diabetes mellitus therapy and prevention primarily recommend lifestyle interventions that support weight loss (12, 13). No general eating pattern recommendations (e.g. low carbohydrate diet) exist and meal planning should be individualized according to current eating patterns, preferences and metabolic goals (13, 19). Thus, the identification of food and meals that suppress hunger and promote satiety would be useful for therapy and prevention of obesity and type 2 diabetes mellitus (20).

The aim of this study was to evaluate the capability of CGM to monitor the effect of different orders of meals containing standardized amounts of carbohydrates (CHO) and differing protein and fat contents on postprandial glucose profiles and subsequent eating behavior in individuals without diabetes.

The presented open, mono-center study was conducted between January and March 2018 at the Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm (IfDT) in Germany. All requirements of local regulations and Good Clinical Practice were met. The study protocol was approved by the responsible Ethics Committee and the study was registered at clinicaltrials.gov (NCT03405415). Prior to commencing study procedures, all participants gave informed consent.

Participants

All subjects signed informed consent prior to any study procedures and were included after a screening visit if they fulfilled the eligibility criteria (age ≥ 18 years and willingness to abstain from medications containing salicylic acid or ascorbic acid during the study period). Exclusion criteria included history of known diabetes; acute or severe chronic illness; pregnancy or lactation period or barriers that might preclude sufficient understanding of the study procedures. For this evaluation, 36 adult participants without diabetes were included.

Study design and procedures

The results of this evaluation do not represent the outcomes of the primary endpoint of the study; they are published elsewhere (21).

Each participant wore two sensors of an intermittent-scanning CGM system (FreeStyle® Libre; Abbott Diabetes Care Ltd., Witney, UK) in parallel. This CGM system measures glucose levels every minute in the interstitial fluid and continuously stored one value every 15 minutes for up to 14 days. To obtain continuous glucose data over this period, the CGM system needs to be actively scanned using a handheld reader device. Participants were asked to perform regular scans to retain the whole daily glycemic data.

For ambulatory dietary interventions, participants visited the study site on two consecutive days from 07:30 to 19:30 (days 4 and 5 after CGM sensor insertion) (Fig. 1).

The dietary intervention consisted of standardized meals for breakfast (08:00) and lunch (13:00), and a free-choice dinner (18:00) from a buffet. Additional meal intake, CHO-containing beverages and physical exertion were not allowed. The test meals (TM; TM “fast” and TM “slow”) were already investigated in previous study settings to assess glucose profiles in healthy individuals (18) and people with type 1 and type 2 diabetes (not published). Both meals contained equal amounts of CHO (50 g), but TM “fast” was designed to induce fast glucose absorption (80% CHO, 8% fat, 12% protein, 1 048.4 kJ (250.4 kcal); wheat toast, honey, jam, cream cheese, orange juice), whereas TM “slow” was designed to induce slow absorption (27% CHO, 56% fat, 17% protein, 3 090.0 kJ (738.0 kcal); whole-meal bread, liver sausage, gouda, salami, butter, kidney beans with olive oil and cream, tomatoes) (Fig. 2). Participants received TM “fast” for breakfast and TM “slow” for lunch on one day, and TMs “slow” for breakfast and TM “fast” for lunch on the other day. The meal sequence for the first intervention day was randomly assigned to the participants, and the other meal sequence was assigned for the second intervention day. Dinner was selected from a buffet by the participants themselves. The meal content and amount were free-choice, and nutrient content was determined by a dietician prior to consumption. In addition, participants rated their feeling of satiety directly before every meal on a scale ranging from − 3 (= extremely hungry) to + 3 (= extremely satisfied) based on the visual analogue scale by Holt et al. (22). In the evening of the intervention days, participants left the study site and were asked to refrain from any further carbohydrate consumption or strenuous physical activities.

In two cases, subjects arrived late at the study site. All study procedures were delayed by 30 minutes for these two subjects.

Data preparation

To increase the reliability of glucose data and reduce the influence of measurement errors, the CGM data of simultaneously worn sensors were re-calibrated based on SMBG data and subsequently combined to yield a single glucose trace per subject. The resulting SMBG-adjusted CGM glucose concentration traces per subject, are hereinafter referred as glucose traces. Detailed information on the CGM data adjustment procedure is available in (21).

Data analysis

A 2-hour window after meal consumption was used to calculate postprandial glucose peak concentrations, time to peak and glucose increase compared to pre-meal glucose levels. Postprandial glucose peaks were defined as a rise in glucose concentration of at least 15 mg/dL within 2 hours after meal consumption compared to pre-prandial glucose levels. Pre-prandial glucose levels were defined as glucose concentration 8 minutes before meal intake.

Results for glucose concentrations across all patients are given as medians with interquartile ranges (IQR). Hourly postprandial areas under curves (AUC) above pre-prandial values were calculated for 5 hours after meal intake using the trapezoidal rule, and mean values ± standard errors of mean values (SEM) are provided (23).

For each meal energy and macronutrient intake was calculated as mean ± standard deviation (SD). An explorative t-test was performed for comparison of energy intake for dinner to establish whether a clinically relevant difference was also statistically significant.

Demographic data

Of the 36 analysed participants without diabetes, 21 were female and 15 were male. On average, participants were 23.7 ± 5.7 years old (mean ± SD; range: 18 to 52 years) with an average HbA1c of 5.2 ± 0.2% (33.0 ± 2.2 mmol/mol), and a mean BMI of 23.8 ± 3.5 kg/m² (range: 19.4 to 30.6 kg/m²). The estimated daily energy requirement during the meal intervention days (sitting life style; physical activity level 1.2) was 8 337.2 ± 1 446.6 kJ (1 991.2 ± 345.5 kcal) (24).

Postprandial glucose traces after breakfast and lunch depending on different meal sequences

Figure 3 presents participants’ glucose traces on days with standardized meal intake for breakfast and lunch and a free-choice meal for dinner. Fasting glucose values were comparable on both days (median 89.3 mg/dL and 86.2 mg/dL). Detailed results with IQRs are provided in Table 1. When TM “fast” was consumed for breakfast (Fig. 3A), glucose traces reached their peak after a median duration of 36 minutes with a median increase of 56.5 mg/dL. In contrast, TM “slow” for breakfast with the same CHO content induced a median increase of glucose traces of 22.1 mg/dL after a median duration of 44 minutes (Fig. 3B).

Similarly, TM “fast” for lunch induced a higher increase of glucose traces compared to TM “slow” for lunch (increase of 72.7 mg/dL vs. 38.3 mg/dL) (Table 1).

When TM “slow” was consumed for lunch, glucose increase was higher than when it was consumed for breakfast (median 38.3 mg/dL vs. 22.1 mg/dL) leading to a peak glucose level of 127.5 mg/dL. For TM “fast”, the corresponding differences in glucose increase for lunch and breakfast were even greater (median 72.7 mg/dL vs. 56.5 mg/dL), which led to a peak glucose concentration of 163.9 mg/dL after lunch (Table 1).

Figure 4 shows the AUCs over 5 hours after meals on the two days with different meal sequences. When TM “fast” was consumed for breakfast, pre-prandial glucose levels were already reached after less than one hour (Fig. 4A). After lunch, TM “fast” led to a more pronounced and longer-lasting increase and lower glucose levels in the following hours (Fig. 4B). TM “slow” showed constantly elevated glucose concentrations for the following 5 h when consumed for breakfast (Fig. 4B) and a more curved course when consumed for lunch (Fig. 4A).

The mean feeling of satiety before lunch was higher when TM “slow” was consumed for breakfast as compared to when TM “fast” was consumed for breakfast (-1.0 vs -2.1; (-3 = extremely hungry; +3 = extremely satisfied)).

Energy intake and postprandial glucose traces after dinner depending on different meal sequences

Combining the TMs “fast” and “slow”, subjects consumed a total of 4138.4 kJ (988.4 kcal) for breakfast and lunch. After the TM sequence “fast” for breakfast and “slow” for lunch, the mean feeling of satiety directly before dinner was − 1.5. Participants were free to choose the type and the amount of food for dinner, which lead to a mean energy intake of 4 429.8 ± 1 205.4 kJ (1 058.0 ± 287.9 kcal) (mean CHO intake: 89.3 ± 28.6 g). With the opposite TM sequence (“slow” for breakfast, “fast” for lunch), participants’ satiety before dinner intake was − 2.4 and, on average, 628.8 ± 582.4 kJ (150.2 ± 139.1 kcal) more were consumed for dinner (5 058.3 ± 1 787.8 kJ (1 208.1 ± 427.0 kcal); p = 0.0013) (mean CHO intake: 98.0 ± 34.6 g). The higher feeling of hunger after the TM sequence “slow-fast” was associated with a slightly lower glucose concentration directly before dinner (86.8 mg/dL vs 89.6 mg/dL for TM sequence “fast-slow”; Table 1). The calculated daily energy requirement (8 337.2 ± 1 466.6 kJ (1 991.2 ± 350.4 kcal)) was exceeded with both TM sequences (104% and 111%, respectively). The nutrient ratio for dinner did not differ (33.9%/34.4% CHO, 47.8%/47.2% fat, 18.3%/18.4% protein).

With TM sequence TM “fast” for breakfast and TM “slow” for lunch, the median postprandial peak glucose increase after dinner was 49.8 mg/dL and a median glucose peak of 142.2 mg/dL was observed (mean CHO intake was 89.3 ± 28.6 g). With opposite TM order (“slow” for breakfast and “fast” for lunch), the median glucose increase and peak after dinner were higher (glucose increase: 65.5 mg/dL; glucose peak: 156.2 mg/dL; mean CHO intake was 98.0 ± 34.6 g) (Table 1).

In the present study, we have shown that CGM enabled the visualization of postprandial glucose profiles in people without diabetes after consumption of different sequences of meals with standardized nutritional composition supporting its use in non-diabetic people. As is widely known, the application of CGM in people with diabetes to assess glucose data has been shown to be useful in optimizing glucose management and achieving glucose targets (13, 25, 26). Although CGM systems were initially developed for people with diabetes, this investigation implies that this technology could also be valuable when integrated in the everyday life of people without diabetes as it allowed distinguishing between meals of different nutritional content and illustrated the impact of a previous meal on the following eating behavior. Being aware of glucose responses of different foods might be helpful for example for obese people in weight loss programs to individually adjust their nutritional behavior according to the recorded glucose profiles.

The glycemic profiles reported in this study after consumption of standardized meals with identical carbohydrate amounts (50 g) showed that not only the CHO content but also other meal components, like fat and protein content, have to be considered when focusing on postprandial glucose (27, 28). In this study, highest postprandial glucose traces were seen after TM “fast”. This TM had fast absorbable character consisting mainly of simple CHO and a low content of fat and protein. The AUCs after consumption of TM “fast” were largest within the first hours followed by a rapid decline. TM “slow” was characterized by complex CHO, a high fat content and slightly higher protein content than TM “fast”. Compared to TM “fast”, TM “slow” caused a low and prolonged postprandial glucose increase; even 5 hours after meal intake, glucose values did not return to baseline. These results are in line with other study results, demonstrating that complexity of CHOs and meal composition itself influence the magnitude and extent of postprandial glucose changes, due to differing ability to be digested and absorbed in the intestine. Thus, meals with rapidly absorbed carbohydrates and low fiber content induce a rapid increase in postprandial glucose (16, 29, 30).

For TM “slow”, the AUC was larger and glucose traces were higher after lunch than after breakfast. This finding is in contradiction to a previous study we conducted in 2007 (18), where TM “slow” for breakfast led to higher glucose excursions and AUC in comparison to lunch. A possible explanation for this effect could be that the subjects of the present study arrived at the study site in the morning, which required at least a low level of physical activity. In the 2007 study, however, subjects stayed overnight so that they only had negligible levels of activity between getting up and having breakfast. As reported by Büsing et al. and Fencher et al., low-intensity physical activity can improve postprandial glycemic response of individuals without diabetes (31, 32).

The delayed glycemic response and low peak after TM “slow” with high fat content matches with our previous analysis in 2007 (18) and another study result focusing on the impact of dietary fat in people with type 1 diabetes mellitus (27). Moreover, compared to TM “fast”, TM “slow” contained more fiber, which is reported to increase gastrointestinal content, decrease the gastric emptying rate and reduce the glucose absorption rate (15, 33).

Additionally, protein preload was reported to blunt the glycemic response to a subsequent meal with high CHO amount (34–36). In the current analysis, this effect cannot be judged because proteins were not separately consumed beforehand.

Besides the effect of single meals, meal sequences seem to be relevant for the glycemic profile as well in our study. When comparing the glycemic data after TM “fast” for breakfast and TM “slow” for lunch and with opposite order, the glycemic courses differ although the same TM were consumed and pre-meal glucose levels were comparable. Previous studies indicate that the meal composition of breakfast can influence the glycemic response of the following meal (29, 37, 38). Our results show that when TM “slow” was consumed before TM “fast”, an elevated glycemic response (on average + 17 mg/dL) was seen compared to the glucose course after TM “fast” consumption without a pre-meal intake. Ando et al. also reported 20 mg/dL higher blood glucose concentrations when a previous high fat meal was consumed (29). Additionally, Fechner et al. presented significantly higher glucose responses of a rice meal challenge after a low glycemic load diet in overweight and slightly obese participants without diabetes which might be due to a physiological adaptation to a low glycemic eating pattern which only becomes visible when challenged with high glycemic foods (32).

Similarly, another study demonstrated that a high-fat breakfast can lead to an elevated glycemic response at lunchtime (29).

Moreover, the circadian rhythm of glucose metabolism can influence the postprandial glucose course as well. As nutrient metabolism exhibits a day-night variation, the postprandial response of metabolic functions may depend on the time of meal intake. Due to the time-dependent regulation of glucose utilization, a higher glucose tolerance is reported in the morning than in the evening in healthy individuals (39–41). As reported by Sutton et al. (42) eating in alignment with circadian rhythms in metabolism, e.g. by increased food intake at breakfast time and reduced intake at dinnertime, improves glycemic control. Their trial of early time-restricted feeding indicated that in men with prediabetes an early time-restricted feeding improves aspects of cardio metabolic health like insulin sensitivity and satiety. Thus, meal timing may have an impact on the development of metabolic disorders as well (42).

The specific and rather uncommon composition of the TM represents a limitation of the study. However, the TM were chosen based on previous analyses assessing glucose profiles in healthy (18) and people with type 1 and type 2 diabetes after consumption of the same TM. To be able to compare the results of each analysis in a next step, it was decided to use the identical TM for the present evaluation. Another limitation of the study is the use of a first generation intermittent-scanning CGM which may not be as accurate as current generation devices. Sufficient accuracy of CGMs in response to meals in healthy individuals is indispensable to reliably predict glycemic postprandial responses and needs to be taken into account when interpreting CGM values. One study compared postprandial CGM accuracy against plasma glucose measurements in healthy subjects showing that CGM significantly underestimates glucose values during 8 hours after a meal and CGM accuracy was dependent on different macronutrient compositions (43). However, only ten individuals were included in this study and the population consisted of older and overweight subjects so that further validation with a larger cohort is needed.

An energy intake exceeding the energy requirement contributes to the progression of obesity and associated complications, like type 2 diabetes mellitus (13). Thus, the identification of food and meals that suppress hunger and promote satiety is one of the main interests for therapy and prevention of obesity and type 2 diabetes mellitus (20, 44). The glucostatic theory describes a causal relation between the short time appetite regulation and the blood glucose level. According to this theory, high blood glucose levels signal satiety, whereas low glucose levels trigger food intake (45). In this study, the meal sequence or previous meal may have influenced the feeling of satiety and energy intake of the subsequent meal. Irrespective of the TM sequence, the same energy content was consumed for breakfast and lunch combined; however, the feeling of satiety before dinner was lower when TM “fast” was consumed for lunch. Consistently, the energy intake for dinner was higher as well. Likewise, satiety was higher before lunch when TM “slow” was consumed in advance. TM “slow” promoted the feeling of satiety for a longer time and decreased the risk of an increased energy intake at a subsequent meal. The results support previous studies, describing positive correlations between fat and fiber content and the feeling of fullness (20).

In conclusion, we showed that CGM can be a valuable tool for individuals without diabetes to monitor nutritional behaviors as it was able to reliably reflect postprandial glucose profiles according to the nutritional content of different meals in our study as well as after different sequences of predefined meals suggesting a potential application of this technology also in non-diabetic people as health and lifestyle application. The glycemic response data as well as the feeling of satiety in relation to specific meals and sequences in individuals without diabetes collected in this evaluation may further contribute to identify beneficial dietary patterns with the use of CGM that could be considered in the management of metabolic disorders.

Acknowledgements:

The authors would like to thank the study personnel and all volunteers who contributed to the study, as well as Dr. Manuel Eichenlaub (IfDT) for his help and feedback in creating the manuscript.

Conflict of Interest:

G.F. is general manager of the Institut für Diabetes-Technologie Forschungs- und Entwicklungsgesellschaft mbH an der Universität Ulm (IfDT, Ulm, Germany), which carries out clinical studies on the evaluation of blood glucose meters and medical devices for diabetes therapy on its own initiative and on behalf of various companies. G.F./IfDT have received grants, speakers' honoraria or consulting fees from Abbott, Agamatrix, Ascensia, Berlin Chemie, Beurer, Boydsens, CRF Health, Dexcom, i-SENS, LifeScan, Lilly, Metronom Health, Medtronic, Menarini, MySugr, Novo Nordisk, PharmaSens, Roche, Sanofi, Sensile and Ypsomed. All other authors are employees of IfDT.

Study funding

None

Author contributions

D.W. contributed substantially to study design, interpretation of data, reviewed/edited the manuscript and approved the final version. S.B. contributed substantially to analysis and interpretation of data, wrote and edited the manuscript and approved the final version. S.B. had full access to the data in the study and takes responsibility for the decision to submit for publication. S.P. contributed substantially to study design, analysis and interpretation of data, reviewed/edited the manuscript and approved the final version. A.B. contributed substantially to analysis and interpretation of data, wrote and edited the manuscript and approved the final version. S.S. contributed substantially to analysis and interpretation of data, reviewed the manuscript and approved the final version. M.L. contributed substantially to study design, acquisition of data, reviewed the manuscript and approved the final version. E.Z. and N.J. contributed substantially to acquisition of data, reviewed the manuscript and approved the final version. C.H. and G.F. contributed substantially to study design, interpretation of data, reviewed the manuscript and approved the final version.

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Table 1

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Suitability of continuous glucose monitoring in healthy subjects to detect effects of meal sequences and nutritional content of meals on postprandial glycemic responses

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Abstract

Background

Subjects/Methods

Results

Conclusions

Figures

Introduction

Materials/subjects And Methods

Participants

Study design and procedures

Data preparation

Data analysis

Results

Demographic data

Postprandial glucose traces after breakfast and lunch depending on different meal sequences

Energy intake and postprandial glucose traces after dinner depending on different meal sequences

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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