Application of FreeStyle Libre 2 for Evaluating Glycemic Variability Characteristics in Patients With Extreme Glucose Metabolism Phenotypes

Application of FreeStyle Libre 2 for Evaluating Glycemic Variability Characteristics in Patients With Extreme Glucose Metabolism Phenotypes

This cross-sectional study aims to further subdivide diabetes mellitus into more homogeneous subgroups by focusing on extreme glucose metabolism phenotypes, including monogenic diabetes with β cell dysfunction, hyperinsulinemia caused by excessive β cell secretion, and postprandial hypoglycemia phenotypes. By utilizing continuous glucose monitoring (CGM) technology and the FreeStyle Libre 2 glucose monitoring device, this study will evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes and perform comparative analyses using existing CGM data from healthy populations and patients with type 2 diabetes in our center's database. The study aims to address current gaps in understanding glycemic variability characteristics under extreme β cell functional states, provide novel dynamic monitoring evidence to support early identification, precise classification, and personalized management of these special metabolic states, and simultaneously screen for biomarkers to enable more accurate disease identification, thereby offering potential avenues for improving personalized treatment of diabetes mellitus.

Diabetes mellitus is one of the leading causes of death and disability worldwide, affecting individuals regardless of race, sex, or age. Over the past decade, the prevalence of diabetes mellitus in China has increased markedly. Statistics indicate that there were 140.9 million adults with diabetes mellitus in China in 2021, and this number is projected to rise to 174.4 million by 2045.

Monogenic diabetes refers to diabetes mellitus caused by mutations in a single gene and accounts for approximately 1%-5% of all diabetes mellitus cases. Monogenic diabetes results from a single pathogenic defect in one of more than 40 genes. Since the type 2 diabetes-like presentation in young individuals was termed maturity-onset diabetes of the young (MODY) by Fajans and characterized by an autosomal dominant inheritance pattern, understanding of the phenotypic and genetic heterogeneity of monogenic diabetes has continued to expand. The main categories of monogenic diabetes include MODY, neonatal diabetes mellitus (NDM), and syndromic diabetes. In monogenic diabetes, high-penetrance variants predominantly cause severe impairment of β cell development and insulin secretion, leading to diabetes mellitus independent of other risk factors. In recent years, substantial progress has been made in elucidating the genetic defects underlying monogenic diabetes, improving diagnostic accuracy for rare subtypes, deepening understanding of patients' clinical courses, and contributing to the identification of optimal treatment strategies through precision medicine approaches. However, many aspects of this disease remain insufficiently characterized, including characteristic glycemic profiles and objective, quantifiable indicators applicable to clinical differential diagnosis. Therefore, further research is urgently needed.

Type 2 diabetes mellitus (T2DM) is a multifactorial disease resulting from the combined effects of genetic and environmental factors and accounts for approximately 96% of diabetes mellitus cases worldwide. The pathophysiology of T2DM is characterized by insulin resistance, pancreatic β cell dysfunction, and chronic inflammation. Hyperinsulinemia and insulin resistance may occur several years before the clinical onset of T2DM. Previous studies have demonstrated that more than 75% of individuals in the United States exhibit increased insulin secretion during oral glucose tolerance testing (OGTT) despite normal glucose clearance. This finding suggests that in a substantial proportion of the population, hyperinsulinemia may represent the earliest warning signal of metabolic disease risk, even in the presence of normal glucose tolerance. Targeted lifestyle interventions aimed at hyperinsulinemia, such as increased resistance training, nutritional strategies, and improved sleep, have been shown to produce immediate and sustained improvements in insulin resistance. However, within this gray zone spanning the progression from normal glucose tolerance to overt diabetes mellitus, characteristic glycemic profiles have not yet been clearly defined. Therefore, exploring glycemic variability characteristics is essential for elucidating the onset and progression of insulin resistance and type 2 diabetes mellitus, as well as for enabling early intervention.

The oral glucose tolerance test (OGTT), as the most widely used diagnostic gold standard for assessing glycemic characteristics, employs a standardized 75 g glucose load to evaluate early-phase and second-phase β cell secretory capacity following glucose stimulation. As an artificially constructed experimental simulation, OGTT does not reflect daily physiological conditions and can capture only short-term, single-day glycemic variability, thus failing to represent true blood glucose trajectories. Continuous glucose monitoring (CGM) can dynamically and continuously reflect interstitial fluid glucose levels in real time, providing critical information on the amplitude, frequency, and patterns of glycemic variability that cannot be obtained through traditional point blood glucose testing. CGM has become animportant tool for refined diabetes mellitus management. This technology provides technical support for delineating characteristic glycemic variability patterns under different insulin secretion states.

Accordingly, this study will use CGM to objectively and quantitatively compare glycemic variability parameters and patterns among four groups: patients with β cell dysfunction monogenic diabetes versus patients with type 2 diabetes mellitus, and patients with hyperinsulinemia versus healthy controls. This approach will reveal the effects of extreme β cell function on diurnal glycemic variability patterns and characterize distinctive dynamic glycemic profiles. In addition, this study will screen for biomarkers to facilitate early identification, diagnosis, and treatment of this specific type of diabetes mellitus, while simultaneously deepening understanding of glycemic characteristics in the early stages of insulin resistance and providing a theoretical basis for subsequent precise prevention and intervention.

Primary study objective: To evaluate glycemic variability patterns in patients with extreme glucose metabolism phenotypes, including β-MND, hyperinsulinemia, and postprandial hypoglycemia phenotypes.

Secondary study objective:

1. To assess differences in blood glucose profiles between individuals with extreme glucose metabolism phenotypes and healthy populations, as well as patients with type 2 diabetes mellitus (T2DM), particularly among comparable T2DM subgroups.
2. To screen for biomarkers that enable more accurate identification of this disease.

1. Inclusion Criteria:

   1. Group A1:

      • Age ≥ 18 years;
      • Patients with β cell dysfunction monogenic diabetes confirmed by DNA sequencing or other diagnostic testing.

   2. Group A2:

      • Age ≥ 18 years;
      • Patients with confirmed type 2 diabetes mellitus;
      • Derived from this center's existing continuous glucose monitoring (CGM) database.

   3. Group B1:

      • Age ≥ 18 years;
      • Normal fasting plasma glucose (≥ 3.6 and < 6.1 mmol/L) and normal 2-hour plasma glucose during OGTT (≥ 3 and < 7.8 mmol/L);
      • Fasting insulin ≥ 25 µU/mL and/or 2-hour insulin during OGTT greater than 10 times the fasting insulin level.

   4. Group B2:

      • Age ≥ 18 years;
      • Normal glucose tolerance meeting the 2024 ADA criteria: fasting plasma glucose < 5.6 mmol/L, 2-hour plasma glucose during OGTT < 7.8 mmol/L;
      • According to laboratory reference standards, fasting insulin ≥ 2.6 and < 25 µU/mL, and 2-hour insulin during OGTT 5-10 times the fasting insulin level.
      • Derived from this center's existing continuous glucose monitoring (CGM) database.

2. Exclusion Criteria:

   1. Neonates younger than 4 months of age (congenital diabetes);
   2. Pregnancy;
   3. Patients with positive pancreatic autoantibody test results;
   4. Patients with severe cardiovascular or cerebrovascular diseases, hepatic disease, or renal disease;
   5. Patients who have participated in other clinical trials.