Continuous Glucose Monitoring (CGM) provides real-time glycemic variability data, surpassing traditional methods like HbA1c. CGM data, known as glucometrics, offers a comprehensive assessment of glycemic variability rather than a single point estimate like HbA1c. By tracking glucose levels continuously, CGM enables clinicians to better understand dysglycemia patterns, allowing for individualized adjustments to antidiabetic therapy. While costly, CGM enables long-distance monitoring, addressing healthcare inaccessibility in rural areas.

This case study examines a 24-year-old Indonesian patient diagnosed with young onset diabetes with limited access to specialized care, a history of macrosomia birth, high blood glucose and the body mass index (BMI) was 27.7. The patient's abdominal circumference was 86 cm, which is above normal for women and within the range of obesity

In this patient, CGM recorded a mean glucose level of 145 mg/dL. Studies indicate that when at least 70% of CGM data is available over a 10–14-day period, an estimated HbA1c can be calculated. This patient intolerant to metformin which presented as nausea after its administration and responded well to sulfonylurea, maintaining her CGM result 92.2% time in range. Despite a high MODY probability (62.4%), genetic testing is currently being conducted using Whole Genome Sequencing (WGS) to identify potential genetic variants contributing to the clinical presentation

CGM is vital for diabetes management in rural settings, and integrating telemedicine can bridge healthcare gaps. Expanding CGM and genetic testing access is crucial to improving outcomes in underserved communities.