Dexcom Smart Basal wins FDA Clearance for Type 2 diabetes insulin support

DexCom Inc. said the Food and Drug Administration has cleared Dexcom Smart Basal, a basal insulin dosing optimizer designed to integrate with the Dexcom G7 15 Day continuous glucose monitoring system and support adults with Type 2 diabetes using glargine U-100 long-acting insulin.

The company said the tool is the first of its kind to integrate CGM data directly into basal insulin dose calculations. By combining 15 days of CGM readings with user-logged insulin doses, Dexcom Smart Basal generates personalized daily dosing recommendations intended to help patients and clinicians reach appropriate basal insulin levels more efficiently.

“We understand that many people with Type 2 diabetes are apprehensive about starting basal insulin due to fears of hypoglycemia and the reality that it often takes months of trial and error to reach the right dose,” said Peter Simpson, senior vice president of innovation and sensor technology at Dexcom. “Dexcom Smart Basal is designed to remove barriers to insulin initiation–giving health care providers the confidence to start their patients on basal therapy sooner and with greater ease. We’re excited about the opportunity to improve this experience for users and their healthcare providers by making it simpler and more personalized.”

According to Dexcom, clinicians set the initial basal dose, after which users receive daily recommendations and reminders in the G7 app. The system evaluates full CGM data—not just a single fasting glucose reading—to adjust dose guidance. If low-glucose events occur, the system automatically lowers dose recommendations by a provider-defined amount. Users who maintain stable glucose within their target range receive an optimal dose, completing the adjustment process.

Dexcom said Smart Basal will join the G7 15 Day system shortly after its U.S. launch, with international expansion planned. Providers will be able to prescribe and configure the feature through Dexcom Clarity.

Recent advances in digital diabetes management

The clearance of a CGM-integrated basal insulin optimizer reflects broader momentum in diabetes care toward individualized, data-driven tools. Several advances over the past few years have centered on improving glucose visibility, simplifying insulin decision-making and reducing patient burden.

A major trend is the evolution of continuous glucose monitoring systems themselves. Sensors have become smaller, more accurate and longer-lasting, enabling near-real-time glucose insights with minimal user intervention. Many newer sensors require no fingerstick calibration and offer streamlined onboarding, making them easier for people with Type 2 diabetes to adopt. Alongside hardware gains, software platforms have become more sophisticated, using pattern recognition and adaptive algorithms to flag trends, guide lifestyle adjustments and support clinical decision-making.

Another area of rapid growth is automated insulin support. While fully closed-loop systems are primarily used in Type 1 diabetes, algorithm-based tools aimed at Type 2 populations are expanding. These tools help providers fine-tune long-acting insulin, adjust doses based on glucose trends and reduce the trial-and-error phase that often discourages patients. Daily dosing guidance, risk-based alerts and visualization dashboards are becoming more common features, especially as developers work to tailor systems to adults using basal insulin only.

Decision-support platforms for clinicians are also advancing. Newer systems integrate CGM data, medication histories and behavioral metrics into unified dashboards that highlight patients at risk for hypoglycemia or persistent hyperglycemia. These platforms aim to streamline encounters, reduce administrative tasks and support proactive outreach. Many incorporate telehealth and remote monitoring capabilities, which gained widespread adoption during the pandemic and have remained a core component of diabetes management.

Finally, digital therapeutics continue to mature. App-based programs increasingly pair glucose data with personalized education, coaching and behavior-change strategies. Some use machine-learning models to recommend dietary adjustments, activity plans or medication timing. Others focus on reducing therapeutic inertia by providing structured pathways for insulin initiation and titration.

Together, these developments illustrate a growing emphasis on personalization, automation and continuous support, with the goal of reducing complications and making diabetes management less burdensome for both patients and clinicians.