The impact of continuous glucose monitoring (CGM) on fasting glucose variability (FG) fluctuation in non-diabetic adults on diabetes categorization and its correlation with clinical markers were evaluated in a recent study that was published in the journal Nature Medicine.

Background

There are serious health hazards and financial burdens associated with the rise in prediabetes and diabetes worldwide. The oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c) values, high plasma fasting glucose (PFG), or random plasma glucose in symptomatic people are the mainstays of diagnosis. Nonetheless, because of its expense and inconvenience, the OGTT is frequently disregarded, making PFG and HbA1c the primary diagnostic instruments, particularly in individuals with no symptoms. The daily variability of PFG in people without diabetes is still poorly understood, which could result in incorrect diagnoses despite its critical role in diagnosis. Interstitial glucose monitors, or CGMs, have become more accurate over time and are currently utilized either alone or in combination with other closed-loop systems to provide insulin doses. To create CGM-based diagnostic standards that appropriately capture intrapersonal variability in FG levels and their clinical consequences, more study is required.

Concerning the study

The current study focused on people between the ages of 40 and 70 and examined data from the 10K trial. During two weeks, the FreeStyle Libre Pro Flash system was used to collect particular tests, such as blood tests, electrocardiography, and CGM, in addition to a variety of baseline measurements, such as lifestyle, nutritional habits, vital signs, and medical history. Participants in this study included those who actively tracked their meals in addition to their CGM data, even though they did not self-report having type 2 diabetes or any related diseases. Strict exclusion criteria applied, such as irregular CGM readings and insufficient meal recording.

In particular, the study focused on FG measurements in the morning and used CGM data to evaluate intrapersonal variability and its possible influence on the diagnosis of diabetes. The approach, which depended on at least 8 hours of no calorie intake before the measurement windows and strict meal reporting criteria, guaranteed a realistic simulation of fasting conditions. The analysis of FG variability and its relationship to several clinical parameters, including as anthropometry, vital signs, and sleep monitoring, was performed on 8,315 individuals with 59,565 fasting morning windows. The FDA-approved WatchPAT-300 gadget was utilized for sleep monitoring, and other health indicators and detailed retinal imaging were thoroughly examined for any correlations with FG fluctuation. Additionally, taking age and gender into consideration, the study used statistical analysis to investigate the association between FG variability and clinical parameters.

Study results

The nuances of FG variability and its consequences for diabetes classification were investigated by researchers through an analysis of FG measurements from 8,315 people over 59,565 morning periods. The mean body mass index (BMI) of the study participants, who were 51.3 years old on average, was 25.92 ± 4.07 kg m−2. The morning FG measures were collected between 6:00 and 09:00, after a minimum of 8 hours of fasting, however the average fasting period was more than 10 hours. This was part of a strict data collection process. Notably, there was no discernible relationship between FG levels and the length of the fast.

The technique employed in the study was comprehensive in determining the FG of each participant, guaranteeing genuine morning windows by means of stringent criteria, such as active meal logging. An average FG value of 96.2 mg dl−1 was found in this extensive analysis. It was noted to increase somewhat with age, suggesting a progressive rise in glucose levels with time. Significant daily variability in FG values between individuals was also emphasized by the research, a finding that emphasizes how intricate glucose metabolism is and how sensitive it is to different stimuli.

The study found notable variations in the possibility of misclassifying diabetes and prediabetes based on FG levels. Throughout the course of the trial, a significant number of patients had changes in the classification of their glycemic state, highlighting the drawbacks of using a single FG test to diagnose diabetes. This variability indicates the need for more precise diagnostic criteria to better account for individual changes in glucose readings, especially given the small range that defines normal and diabetic FG levels.

Additionally, the study looked at the clinical relationships between FG variability and a number of health parameters, including blood pressure, liver function, and body composition. It’s interesting to note that FG variability revealed strong correlations with a number of clinical parameters, suggesting that it may be used as a marker for metabolic health. In particular, associations with daily caloric intake and body composition imply that FG fluctuation may represent more extensive metabolic processes than glucose regulation alone.

Conclusions

In summary, this study examined FG data from 8,315 people who were not diabetics using CGM. It found substantial FG variability, which calls into question the validity of the current PFG-based diabetes diagnostic criteria. The majority of subjects had normal FG levels according to the initial classifications, but additional measurements revealed a significant movement towards prediabetes, emphasizing the possibility of incorrect classification. According to the study, there may be a considerable decrease of misdiagnosis if there were more FG tests performed. A more accurate method of diagnosing diabetes that takes into account the dynamic and variable nature of FG levels is required, as evidenced by the study’s significant correlations between FG levels and other clinical parameters within normal glucose ranges.