GLU: a software package for analysing continuously measured glucose levels in epidemiology

Short-term intermittent fasting and energy restriction do not impair rates of muscle protein synthesis: A randomised, controlled dietary intervention

BACKGROUND

Intermittent fasting (IF) is an effective energy restricted dietary strategy to reduce body and fat mass and improve metabolic health in individuals with either an overweight or obese status. However, dietary energy restriction may impair muscle protein synthesis (MPS) resulting in a concomitant decline in lean body mass. Due to periods of prolonged fasting combined with irregular meal intake, we hypothesised that IF would reduce rates of MPS compared to an energy balanced diet with regular meal patterns.

AIMS

We assessed the impact of a short-term, ten days, alternate day fasting or a continuous energy restricted diet to a control diet on integrated rates of skeletal MPS in middle-aged males with overweight or obesity.

METHODS

Twenty-seven middle-aged males with overweight or obesity (age: 44.6 ± 5.4 y; BMI: 30.3 ± 2.6 kg/m2) consumed a three-day lead-in diet, followed by a ten-day controlled dietary intervention matched for protein intake, as alternate day fasting (ADF: 62.5 energy (En)%, days of 25 En% alternated with days of 100 En% food ingestion), continuous energy restriction (CER: 62.5 En%), or an energy balanced, control diet (CON: 100 En%). Deuterated water (D2O) methodology with saliva, blood, and skeletal muscle sampling were used to assess integrated rates of MPS over the ten-day intervention period. Secondary measures included fasting plasma glucose, insulin, and gastrointestinal hormone concentrations, continuous glucose monitoring, and assessment of body composition.

RESULTS

There were no differences in daily rates of MPS between groups (ADF: 1.18 ± 0.13, CER: 1.13 ± 0.16, and CON: 1.18 ± 0.18 %/day, P > 0.05). The reductions in body mass were greater in ADF and CER compared to CON (P < 0.001). Lean and fat mass were decreased by a similar magnitude across groups (main time effect, P < 0.001; main group effect, P > 0.05). Fasting plasma leptin concentrations decreased in ADF and CER (P < 0.001), with no differences in fasting plasma glucose or insulin concentrations between groups.

CONCLUSION

Short-term alternate day fasting does not lower rates of MPS compared to continuous energy restriction or an energy balanced, control diet with matched protein intake. The prolonged effects of IF and periods of irregular energy and protein intake patterns on muscle mass maintenance remain to be investigated. This trial was registered under Australian New Zealand Clinical Trial Registry (https://www.anzctr.org.au), identifier no. ACTRN12619000757112.

AGATA: A Toolbox for Automated Glucose Data Analysis

BACKGROUND

Analyzing continuous glucose monitoring (CGM) data is a mandatory step for multiple purposes spanning from reporting clinical trial outcomes to developing new algorithms for diabetes management. This task is repetitive, and scientists struggle in computing literature glucose control metrics and waste time in reproducing possibly complex plots and reports. For this reason, to provide the diabetes technology community a unified tool, here we present Automated Glucose dATa Analysis (AGATA), an automated glucose data analysis toolbox developed in MATLAB/Octave.

METHODS

Automated Glucose dATa Analysis is an open-source software program to visualize and preprocess CGM data, compute glucose control metrics, detect adverse events, evaluate the effectiveness of users' prediction algorithms, and compare study arms. Automated Glucose dATa Analysis can be used as a standalone computer application accessible through a dedicated graphical user interface, particularly suitable for clinicians, or by integrating its functionalities in user-defined MATLAB/Octave scripts, which fits the need of researchers and developers. To demonstrate its features, we used AGATA to analyze CGM data of two subjects extracted from a publicly available data set of individuals with type one diabetes. Finally, AGATA's features are compared against those of 12 noncommercial software programs for CGM data analysis.

RESULTS

Using AGATA, we easily preprocessed, analyzed, and visualized CGM data in a handy way, in compliance with the requirements and the standards defined in the literature. Compared to the other considered software programs, AGATA offers more functionalities and capabilities.

CONCLUSION

Automated Glucose dATa Analysis is easy to use and reduces the burden of CGM data analysis. It is freely available in GitHub at https://github.com/gcappon/agata.

Two-week continuous glucose monitoring-derived metrics and degree of hepatic steatosis: a cross-sectional study among Chinese middle-aged and elderly participants

BACKGROUND

Continuous glucose monitoring (CGM) devices provide detailed information on daily glucose control and glycemic variability. Yet limited population-based studies have explored the association between CGM metrics and fatty liver. We aimed to investigate the associations of CGM metrics with the degree of hepatic steatosis.

METHODS

This cross-sectional study included 1180 participants from the Guangzhou Nutrition and Health Study. CGM metrics, covering mean glucose level, glycemic variability, and in-range measures, were separately processed for all-day, nighttime, and daytime periods. Hepatic steatosis degree (healthy: n = 698; mild steatosis: n = 242; moderate/severe steatosis: n = 240) was determined by magnetic resonance imaging proton density fat fraction. Multivariate ordinal logistic regression models were conducted to estimate the associations between CGM metrics and steatosis degree. Machine learning models were employed to evaluate the predictive performance of CGM metrics for steatosis degree.

RESULTS

Mean blood glucose, coefficient of variation (CV) of glucose, mean amplitude of glucose excursions (MAGE), and mean of daily differences (MODD) were positively associated with steatosis degree, with corresponding odds ratios (ORs) and 95% confidence intervals (CIs) of 1.35 (1.17, 1.56), 1.21 (1.06, 1.39), 1.37 (1.19, 1.57), and 1.35 (1.17, 1.56) during all-day period. Notably, lower daytime time in range (TIR) and higher nighttime TIR were associated with higher steatosis degree, with ORs (95% CIs) of 0.83 (0.73, 0.95) and 1.16 (1.00, 1.33), respectively. For moderate/severe steatosis (vs. healthy) prediction, the average area under the receiver operating characteristic curves were higher for the nighttime (0.69) and daytime (0.66) metrics than that of all-day metrics (0.63, P < 0.001 for all comparisons). The model combining both nighttime and daytime metrics achieved the highest predictive capacity (0.73), with nighttime MODD emerging as the most important predictor.

CONCLUSIONS

Higher CGM-derived mean glucose and glycemic variability were linked with higher steatosis degree. CGM-derived metrics during nighttime and daytime provided distinct and complementary insights into hepatic steatosis.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

BACKGROUND

Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

METHODS

A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

RESULTS

A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

CONCLUSION

This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Time-restricted eating improves measures of daily glycaemic control in people with type 2 diabetes

AIMS

Examine the effect of 5 d/wk, 9-h time-restricted eating (TRE) protocol on 24-h glycaemic control in adults with type 2 diabetes (T2D).

METHODS

Nineteen adults with T2D (10 F/9 M; 50 ± 9 y, HbA1c 7.6% (60 mmol/mol), BMI ∼34 kg/m²) completed a pre-post non-randomised trial comprising of a 2-wk Habitual monitoring period followed by 9-h (10:00-19:00 h) TRE for 4-wk. Glycaemic control was assessed via continuous glucose monitoring (CGM; for mean 24-h glucose concentrations, 24-h total area under the curve (AUC) and glucose variability metrics), with dietary records and physical activity monitoring. Changes in CGM measures, dietary intake and physical activity were assessed with linear mixed-effects models.

RESULTS

TRE did not alter dietary energy intake, macronutrient composition or physical activity, but reduced the daily eating window (-2 h 35 min, P < 0.001). Compared to the Habitual period, 24-h glucose concentrations (mean, SD) and AUC decreased in the 4-wk TRE period (mean: -0.7 ± 1.2 mmol/L, P = 0.02; SD: -0.2 ± 0.3 mmol/L, P = 0.01; 24-h AUC: -0.9 ± 1.4 mmol/L⋅h^-1 P = 0.01). During TRE, participants spent 10% more time in range (3.9-10.0 mmol/L; P = 0.02) and 10% less time above range (>10.0 mmol/L; P = 0.02).

CONCLUSIONS

Adhering 5 d/wk. to 9-h TRE improved glycaemic control in adults with T2D, independent of changes in physical activity or dietary intake.

CLINICAL TRIAL REGISTRATION

Australia New Zealand Clinical Trial Registry, ACTRN12618000938202.

Eight-hour time-restricted eating does not lower daily myofibrillar protein synthesis rates: A randomized control trial

OBJECTIVE

This study aimed to assess the impact of time-restricted eating (TRE) on integrated skeletal muscle myofibrillar protein synthesis (MyoPS) rates in males with overweight/obesity.

METHODS

A total of 18 healthy males (age 46 ±  5 years; BMI: 30 ± 2 kg/m² ) completed this exploratory, parallel, randomized dietary intervention after a 3-day lead-in diet. Participants then consumed an isoenergetic diet (protein: ~1.0 g/kg body mass per day) following either TRE (10:00 a.m. to 6:00 p.m.) or an extended eating control (CON; 8:00 a.m. to 8:00 p.m.) protocol for 10 days. Integrated MyoPS rates were measured using deuterated water administration with repeated saliva, blood, and muscle sampling. Secondary measures included continuous glucose monitoring and body composition (dual-energy x-ray absorptiometry).

RESULTS

There were no differences in daily integrated MyoPS rates (TRE: 1.28% ± 0.18% per day, CON: 1.26% ± 0.22% per day; p = 0.82) between groups. From continuous glucose monitoring, 24-hour total area under the curve was reduced following TRE (-578 ± 271 vs. CON: 12 ± 272 mmol/L × 24 hours; p = 0.001). Total body mass declined (TRE: -1.6 ± 0.9 and CON: -1.1 ± 0.7 kg; p < 0.001) with no differences between groups (p = 0.22). Lean mass loss was greater following TRE compared with CON (-1.0 ± 0.7 vs. -0.2 ± 0.5 kg, respectively; p = 0.01).

CONCLUSION

Consuming food within an 8-hour time-restricted period does not lower daily MyoPS rates when compared with an isoenergetic diet consumed over 12 hours. Future research should investigate whether these results translate to free-living TRE.

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Software Packages and Tools for the Analysis of Continuous Glucose Monitoring Data

The advancement of technology in the field of glycemic control has led to the widespread use of continuous glucose monitoring (CGM), which can be nowadays obtained from wearable devices equipped with a minimally invasive sensor, that is, transcutaneous needle type or implantable, and a transmitter that sends information to a receiver or smart device for data storage and display. This work aims to review the currently available software packages and tools for the analysis of CGM data. Based on the purposes of this work, 12 software packages have been identified from the literature, published until December 2021, namely: GlyCulator, EasyGV (Easy Glycemic Variability), CGM-GUIDE^© (Continuous Glucose Monitoring Graphical User Interface for Diabetes Evaluation), GVAP (Glycemic Variability Analyzer Program), Tidepool, CGManalyzer, cgmanalysis, GLU, CGMStatsAnalyser, iglu, rGV, and cgmquantify. Comparison of available software packages and tools has been done in terms of main characteristics (i.e., publication year, presence of a graphical user interface, availability, open-source code, number of citations, programming language, supported devices, supported data format and organization of the data structure, documentation, presence of a toy example, video tutorial, data upload and download, measurement-units conversion), preprocessing procedures, data display options, and computed metrics; also, each of the computed metrics has been analyzed in terms of its adherence to the American Diabetes Association (ADA) 2017 international consensus on CGM data analysis and the ADA 2019 international consensus on time in range. Eventually, the agreement between metrics computed by different software and tools has been investigated. Based on such comparison, usability and complexity of data management, as well as the possibility to perform customized or patients-group analyses, have been discussed by highlighting limitations and strengths, also in relation to possible different user categories (i.e., patients, clinicians, researchers). The information provided could be useful to researchers interested in working in the diabetic research field as to clinicians and endocrinologists who need tools capable of handling CGM data effectively.

A New Analysis Tool for Continuous Glucose Monitor Data

BACKGROUND

With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools.

METHODS

In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual's CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies.

RESULTS

In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability.

CONCLUSIONS

We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Perspective: A Framework for Addressing Dynamic Food Consumption Processes

The study of food consumption, diet, and related concepts is motivated by diverse goals, including understanding why food consumption impacts our health, and why we eat the foods we do. These varied motivations can make it challenging to define and measure consumption, as it can be specified across nearly infinite dimensions-from micronutrients to carbon footprint to food preparation. This challenge is amplified by the dynamic nature of food consumption processes, with the underlying phenomena of interest often based on the nature of repeated interactions with food occurring over time. This complexity underscores a need to not only improve how we measure food consumption but is also a call to support theoreticians in better specifying what, how, and why food consumption occurs as part of processes, as a prerequisite step to rigorous measurement. The purpose of this Perspective article is to offer a framework, the consumption process framework, as a tool that researchers in a theoretician role can use to support these more robust definitions of consumption processes. In doing so, the framework invites theoreticians to be a bridge between practitioners who wish to measure various aspects of food consumption and methodologists who can develop measurement protocols and technologies that can support measurement when consumption processes are clearly defined. In the paper we justify the need for such a framework, introduce the consumption process framework, illustrate the framework via a use case, and discuss existing technologies that enable the use of this framework and, by extension, more rigorous study of consumption. This consumption process framework demonstrates how theoreticians could fundamentally shift how food consumption is defined and measured towards more rigorous study of what, how, and why food is eaten as part of dynamic processes and a deeper understanding of linkages between behavior, food, and health.

Preliminary prospective study of real-time post-gastrectomy glycemic fluctuations during dumping symptoms using continuous glucose monitoring

BACKGROUND

Although dumping symptoms constitute the most common post-gastrectomy syndromes impairing patient quality of life, the causes, including blood sugar fluctuations, are difficult to elucidate due to limitations in examining dumping symptoms as they occur.

AIM

To investigate relationships between glucose fluctuations and the occurrence of dumping symptoms in patients undergoing gastrectomy for gastric cancer.

METHODS

Patients receiving distal gastrectomy with Billroth-I (DG-BI) or Roux-en-Y reconstruction (DG-RY) and total gastrectomy with RY (TG-RY) for gastric cancer (March 2018-January 2020) were prospectively enrolled. Interstitial tissue glycemic profiles were measured every 15 min, up to 14 d, by continuous glucose monitoring. Dumping episodes were recorded on 5 patient-selected days by diary. Within 3 h postprandially, dumping-associated glycemic changes were defined as a dumping profile, those without symptoms as a control profile. These profiles were compared.

RESULTS

Thirty patients were enrolled (10 DG-BI, 10 DG-RY, 10 TG-RY). The 47 early dumping profiles of DG-BI showed immediately sharp rises after a meal, which 47 control profiles did not (P < 0.05). Curves of the 15 late dumping profiles of DG-BI were similar to those of early dumping profiles, with lower glycemic levels. DG-RY and TG-RY late dumping profiles (7 and 13, respectively) showed rapid glycemic decreases from a high glycemic state postprandially to hypoglycemia, with a steeper drop in TG-RY than in DG-RY.

CONCLUSION

Postprandial glycemic changes suggest dumping symptoms after standard gastrectomy for gastric cancer. Furthermore, glycemic profiles during dumping may differ depending on reconstruction methods after gastrectomy.

Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools.

Postprandial Asymptomatic Glycemic Fluctuations after Gastrectomy for Gastric Cancer Using Continuous Glucose Monitoring Device

Purpose

Although dumping symptoms are thought to involve postprandial glycemic changes, postprandial glycemic variability without dumping symptoms remains poorly understood due to the lack of a method that allows the easy and continuous measurement of blood glucose levels.

Materials and Methods

Patients having undergone distal gastrectomy with Billroth-I (DG-BI) or Roux-en-Y reconstruction (DG-RY), total gastrectomy with RY (TG-RY) and pylorus preserving gastrectomy (PPG) for gastric cancer 3 months to 3 years prior, diagnosed as pathological stage I or II, were prospectively enrolled from March 2018 to January 2020. The interstitial tissue glycemic levels were measured every 15 min, up to 14 days by continuous glucose monitoring. Moreover, using a diary recording the diet and symptoms, asymptomatic glucose profiles without sugar supplementation within 3 h postprandially were compared among the four procedures.

Results

A total of 40 patients were enrolled, 10 patients for each of the four procedures. There were 47 glucose profiles with DG-BI, 46 profiles with DG-RY, 38 profiles with TG-RY, and 46 profiles with PPG. PPG showed the slowest increase with a subsequent gradual decrease in glucose fluctuations, without hyperglycemia or hypoglycemia, among the four procedures. In contrast, TG-RY and DG-RY showed spike-like glycemic variability, sharp rises during meals, and rapid drops. The glucose profiles of DG-BI were milder than those of RY.

Conclusions

The asymptomatic glycemic changes after meals differ among the types of surgical procedures for gastric cancer. Given the mild glycemic fluctuations in PPG and the glucose spikes in TG-RY and DG-RY, pylorus preservation and physiological reconstruction without changes in food pathways may optimize postprandial glucose profiles after gastrectomy.