Keywords: Continuous Glucose Monitoring, Type 2 Diabetes, GRADE Guideline, Glycemic Management
Introduction
Type 2 diabetes is a progressive metabolic disorder characterized by insulin resistance and inadequate insulin secretion, resulting in persistent hyperglycaemia that drives microvascular and macrovascular complications and contributes substantially to morbidity and premature mortality. The most recent global estimates indicate that more than 589 million adults are living with diabetes, of whom over 90% have T2D, and that diabetes accounted for nearly half a million premature deaths in Europe alone in 2024. Despite advances in pharmacological therapy and models of care, achieving and sustaining glycaemic targets remains difficult: a pooled analysis across 100 countries found that only 63% of people with diagnosed diabetes achieved an HbA1c below 8% (64 mmol/mol) in 2021. This persistent gap between guideline-recommended targets and real-world outcomes underlines the need for additional tools to support glycaemic management and self-care [1-5].
Continuous glucose monitoring, a wearable sensor technology that measures interstitial glucose continuously and reports trends, rates of change, and hyper- or hypoglycaemia alerts has been transformative in type 1 diabetes care, but its role in T2D has historically been less clearly defined. Unlike SMBG, which depends on intermittent finger-prick sampling, CGM provides a continuous data stream that may inform both clinical decision-making and day-to-day behavioral choices around diet, activity, and medication timing. Clinical guidance has traditionally reserved glucose monitoring via SMBG or CGM for people with T2D treated with insulin or other agents that carry meaningful hypoglycaemia risk, while leaving its role in non-insulin-treated T2D unresolved. Given the accelerating clinical adoption of CGM and accumulating trial evidence, the EASD prioritized this topic via a formal membership poll for a dedicated, GRADE-based clinical practice guideline addressing the effectiveness of CGM across the T2D population and its principal subgroups and modalities [4,6-9].
This review summarizes that guideline for a longevity- and prevention-oriented audience, situating its recommendations within the broader context of metabolic disease management and highlighting the implications for digital health tools designed to support glucose-related behavior change.
Methods: How the Guideline was Developed
The guideline was developed in accordance with the EASD standard operating procedure for clinical practice guidelines, the National Academy of Medicine and Guidelines International Network standards, and the GRADE framework. A twelve-member multidisciplinary Guideline Development Panel (GDP), comprising two co-chairs, four clinical experts, two methodologists, two people with lived experience of diabetes, an evidence synthesis lead, and a research associate formulated nine clinical questions using the PICO (Population, Intervention, Comparator, Outcome) framework. These questions addressed: CGM use in the overall adult T2D population; CGM use stratified by background glucose-lowering therapy (non-insulin agents, basal insulin only, and intensified insulin regimens); the effectiveness of specific CGM modalities (real-time, intermittently scanned, and masked/professional CGM); and differing patterns of access (continuous versus periodic use) [9-13,18].
An independent Evidence Synthesis Team conducted a de novo systematic review and meta-analysis of randomized controlled trials for each clinical question, extracting data on glycated haemoglobin (a critical outcome), time below range below 3.0 mmol/L (a critical outcome), health-related quality of life (a critical outcome), time in range, time below range below 3.9 mmol/L, severe hypoglycaemia, diabetic ketoacidosis, body weight, diabetes treatment satisfaction, and diabetes distress. Minimal clinically important difference thresholds were pre-specified for each outcome to support interpretation of clinical relevance, for example, an HbA1c difference of at least 0.4 percentage points and a health-related quality of life standardized mean difference of at least 0.3 [9,20,21,22,28]. Certainty of evidence was assessed using GRADE across five domains (risk of bias, inconsistency, indirectness, imprecision, and other considerations), and recommendations were formulated through GRADE Evidence-to-Decision frameworks that incorporated the balance of benefits and harms, patient values and preferences, resource implications, equity, and feasibility, alongside formal input from panel members with lived experience of T2D. The completed guideline underwent internal GDP review, independent AGREE II appraisal, EASD Guideline Oversight Committee review, and a two-week public consultation before submission to Diabetologia [9,12-15,17,19,20-22,28,31,32].
Clinical Evidence: CGM Across Population and Modalities
Overall T2D Population
Across the general adult T2D population, meta-analysis demonstrated a small but consistent reduction in HbA1c with CGM added to usual care (mean difference approximately −0.32 percentage points), alongside improvements in time in range and diabetes treatment satisfaction, without an associated increase in hypoglycaemia. On this basis, and reflecting people with T2D’s frequently reported preference for CGM over finger-prick testing, the panel suggested offering CGM as an option added to usual care, albeit as a conditional recommendation given the low certainty of evidence and the cost implications of wider CGM access [9].
Populations Defined by Background Therapy
For people treated with non-insulin glucose-lowering agents, CGM was associated with small reductions in HbA1c, clinically meaningful improvements in health-related quality of life, and gains in time in range, body weight, and treatment satisfaction, with minimal harms, supporting a conditional recommendation in favor of offering CGM in this group, historically the population in whom monitoring technology has been least routinely used. In people using basal insulin only, evidence was sparser, but CGM was associated with trivial improvements in HbA1c and quality of life and improved time in range, with the panel noting CGM’s potential to help identify inadequate basal dosing and reduce the emotional burden of hypoglycaemic events. For people on intensified insulin regimens (multiple daily injections or continuous subcutaneous insulin infusion), CGM produced small HbA1c reductions and improved treatment satisfaction, though certainty of evidence was rated very low [9].
CGM Modality: Real-Time, Intermittently Scanned, and Masked CGM
Real-time CGM produced the largest observed HbA1c reduction among the modalities assessed (approximately −0.46 percentage points), together with improvements in health-related quality of life, time in range, and treatment satisfaction, supporting a recommendation in its favor. Intermittently scanned CGM (flash glucose monitoring) also showed small HbA1c benefits and improved time in range and treatment satisfaction, albeit with very low certainty of evidence; the panel noted its typically lower acquisition cost may improve accessibility relative to real-time systems. No head-to-head comparison between real-time and intermittently scanned CGM was available within this evidence base. Masked (professional or ‘blind’) CGM, in which glucose data are hidden from the user and reviewed later with a clinician, did not produce a significant HbA1c reduction and offered no additional clinical benefit beyond a small quality-of-life improvement; the panel therefore recommended against its routine clinical use, while affirming its continued value as a research and diagnostic tool for characterizing glycaemic patterns without influencing behavior [9].
Pattern of Access: Continuous Versus Periodic Use
Continuous (uninterrupted) CGM use was associated with small improvements in HbA1c, time in range, time below range, and treatment satisfaction. Periodic (intermittent) CGM use produced comparable small HbA1c benefits and improved time in range. No trials directly compared continuous against periodic access, leaving the relative merits of each pattern an open empirical question; the panel suggested periodic use may be particularly useful during periods of change such as new diagnosis, medication titration, illness, or pregnancy, but cautioned it may be unsuitable for those at higher hypoglycaemia risk, including people treated with insulin or sulphonylureas [9].
Person-Centered Benefits and Patient Experience
A distinguishing feature of this guideline is its explicit incorporation of the perspectives of people with lived experience of T2D throughout its development, including in the foreword and within the Evidence-to-Decision deliberations for every recommendation. Beyond glycaemic metrics, panel members with lived experience emphasized that CGM data, used well, can increase confidence in self-management, reduce uncertainty about glucose patterns, and support more informed day-to-day decisions around food, activity, and medication. Across nearly every clinical question, CGM was associated with meaningful gains in the Diabetes Treatment Satisfaction Questionnaire, reinforcing that patient-reported experience, not solely HbA1c, is central to judging CGM’s value [9].
The guideline is equally candid about CGM’s risks: being overwhelmed by continuous data, frustration at persistently out-of-range readings, shame or stigma that discourages engagement with the device, and the potential, in vulnerable individuals, to contribute to disordered eating, exercise, or a form of data preoccupation. The panel’s response was not to restrict access but to recommend that CGM be embedded within person-centred care, with decisions about whether to offer CGM, which modality to use, and how frequently to use it tailored to an individual’s preferences, digital confidence, treatment goals, and available support [9].
Equity, Access, and Implementation Considerations
Access to CGM varies substantially between and within countries, shaped by reimbursement policy, health system structure, insurance coverage, and socioeconomic status. The guideline warns that recommending CGM more broadly risks disproportionately benefiting people who are already digitally literate and better resourced, while those with the greatest clinical need remain underserved, a risk the panel identified as a central equity concern requiring deliberate mitigation. Successful implementation was judged to depend on healthcare infrastructure, clinician training, integration of CGM data into routine workflows, device and software costs, and ongoing educational support for both clinicians and patients. The panel called for context-specific implementation strategies that balance clinical benefit, economic feasibility, equitable access, and environmental impact, and noted that trial evidence remains sparse for socioeconomically deprived populations, rural and remote communities, and people with lower digital literacy [9].
Strengths and Limitations of the Evidence Base
The guideline’s principal strengths are its rigorous, GRADE-based methodology; its multidisciplinary panel composition, including formal representation of lived experience; and its grounding in a comprehensive, de novo systematic review and meta-analysis spanning glycaemic, patient-reported, safety, and resource outcomes [9]. Its principal limitation, acknowledged throughout, is that certainty of evidence was low or very low for the majority of clinical questions, reflecting substantial clinical and statistical heterogeneity, variable comparator definitions, and imprecision, particularly for rare safety outcomes such as severe hypoglycaemia and diabetic ketoacidosis, for which event rates were too low to permit confident effect estimates. Direct comparative trials between CGM modalities (real-time versus intermittently scanned) and between patterns of access (continuous versus periodic) were not identified, leaving important practical questions about relative effectiveness unanswered. The evidence base was also geographically concentrated, with 40% of trials conducted in North America, 27% in East and South-East Asia, 19% in Western Europe, and comparatively little representation from the Middle East and other regions, which may limit generalizability to some healthcare settings [9,26].
Future Research Priorities
The panel identified several priorities for future research directly relevant to organizations developing longevity- and prevention-focused metabolic tools. These include evaluating CGM in populations underrepresented in current trials such as younger and older adults, people with cognitive impairment, those receiving renal replacement therapy, and people at elevated hypoglycaemia risk; determining whether CGM meaningfully supports sustained lifestyle and behavioral change, including within diabetes remission pathways; establishing whether short-term glycaemic and quality-of-life gains persist over the long term, and whether CGM affects hard outcomes such as microvascular and macrovascular complications, hospitalization, and mortality; and generating robust, context-specific cost-effectiveness and implementation research, including comparative studies against other advanced technologies such as automated insulin delivery systems [9].
Conclusion: Implications for Longevity and Preventive Metabolic Care
The 2026 EASD guideline establishes that CGM, added to usual care, provides modest but broadly consistent glycaemic and person-reported benefits across most adults with T2D and across real-time, intermittently scanned, continuous, and periodic use patterns, with masked CGM reserved for research and diagnostic use rather than routine practice. For organizations working at the intersection of AI-enabled health technology and metabolic disease prevention, the guideline offers two central lessons. First, CGM’s value in T2D lies as much in supporting understanding, confidence, and behavior change as in incremental HbA1c reduction, a finding that aligns closely with the goals of longevity and wellness platforms built around continuous, actionable physiological data. Second, the low-to-very-low certainty of much of the underlying evidence, together with pronounced gaps in long-term outcomes, cost-effectiveness, and equitable access, means that responsible deployment of CGM-based tools should be paired with rigorous outcome measurement, transparent communication of uncertainty, and explicit design attention to equity of access. As CGM adoption continues to expand beyond type 1 diabetes and into broader metabolic health and prevention contexts, this guideline provides a methodologically rigorous, person-centered evidence base from which to build [9].
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