Keywords: Continuous Glucose Monitoring, Type 2 Diabetes, Glycemic Variability, HbA1c, Ambulatory Glucose Profile, Personalized Care
Introduction
Type 2 diabetes mellitus (T2DM) is one of the most prevalent and economically burdensome non-communicable diseases globally, affecting an estimated 537 million adults worldwide as of 2021, with projections approaching 784 million by 2045. Chronic hyperglycaemia, the hallmark of T2DM, drives the development of microvascular and macrovascular complications, including diabetic retinopathy, nephropathy, peripheral neuropathy, and cardiovascular disease that collectively impose enormous morbidity, premature mortality, and healthcare costs [1,2,3].
For decades, glycated haemoglobin (HbA1c) has served as the gold-standard biomarker for assessing long-term glycaemic control and guiding therapeutic decision-making in T2DM. The landmark United Kingdom Prospective Diabetes Study (UKPDS) established the foundational relationship between HbA1c reduction and decreased microvascular risk, demonstrating that every 1% (11 mmol/mol) reduction in HbA1c is associated with a 37% reduction in microvascular complications. However, HbA1c reflects only a 2- to 3-month average of blood glucose concentration and is inherently insensitive to the moment-to-moment glycaemic fluctuations , including post-prandial spikes, nocturnal hypoglycaemia, and glycaemic variability that may independently contribute to vascular damage and impair quality of life [4,5,6].
Traditional self-monitoring of blood glucose (SMBG) via capillary finger-prick testing, while providing discrete glucose data points, is intermittent, painful, and poorly tolerated with long-term adherence. Moreover, SMBG captures only momentary snapshots and fails to reveal glucose trends, directional changes, or comprehensive 24-hour glycaemic patterns. These fundamental limitations have created a compelling need for more sophisticated glycaemic monitoring technologies [6,7].
Continuous glucose monitoring (CGM) represents a technological leap beyond both HbA1c and SMBG, providing real-time, continuous glucose readings typically every 1–5 minutes throughout the day and night. CGM systems measure glucose concentrations in the interstitial fluid and transmit data to a display device or smartphone application, enabling individuals and clinicians to visualise glucose patterns, identify hypoglycaemia and hyperglycaemia, and track responses to meals, medication, and physical activity in real time [8,9].
While CGM adoption was initially led by type 1 diabetes management, where the technology demonstrated unequivocal benefits in randomised controlled trials, there has been a rapid and significant expansion of evidence supporting CGM use in T2DM across the full spectrum of treatment intensity. The availability of standardised CGM-derived metrics, particularly time in range (TIR) and the ambulatory glucose profile (AGP), has provided a new framework for glycaemic assessment and a richer vocabulary for clinician-patient communication [10,11,12,13].
This comprehensive narrative review synthesises current evidence regarding the clinical utility of CGM in T2DM, with a particular focus on glycaemic outcomes including HbA1c reduction, TIR improvement, hypoglycaemia detection, glucose variability, and the practical application of the AGP report in clinical practice. We further examine patient selection criteria, digital readiness considerations, current national and international guideline recommendations, and future directions for CGM technology in T2DM management.
CGM Technology: Principles and Device Landscape
Mechanism of Glucose Sensing
All currently available CGM systems employ a minimally invasive electrochemical glucose sensor inserted into the subcutaneous tissue of the upper arm or abdomen. The sensor contains a glucose oxidase enzyme that catalyses the oxidation of interstitial fluid glucose, generating an electrical current proportional to glucose concentration. This current is converted to a glucose reading and transmitted wirelessly, via Bluetooth or near-field communication (NFC) to a paired smartphone application or proprietary reader device [8,9].
A critical physiological concept underpinning CGM interpretation is the lag between blood (capillary) glucose and interstitial fluid (ISF) glucose. Studies have demonstrated a physiological lag of approximately 5–10 minutes during which glucose diffuses across the transcapillary membrane into the ISF. This lag is clinically important during periods of rapidly changing glucose levels such as following a meal or during exercise, where ISF glucose may transiently lag behind capillary glucose. For this reason, confirmatory finger-prick testing remains recommended when symptoms do not match CGM readings, when glucose is changing rapidly, or when a hypoglycaemic reading does not correlate with clinical presentation [1,14].
Types of CGM Systems
Three principal categories of CGM system are currently available, differentiated by their scanning mechanism, wear duration, and connectivity features:
| System Type | Examples | Wear Duration | Scan Requirement | Alarms/Alerts |
| Real-Time CGM (rtCGM) | Dexcom G7, Medtronic Guardian 4 | 7–14 days | Automatic (passive) | Yes, customisable glucose alarms |
| Intermittently Scanned CGM (isCGM / Flash) | Abbott FreeStyle Libre 2/3 | 14 days | Active scan required (≤8 hours) | Optional (Libre 2/3 with optional alerts) |
| Implantable CGM | Eversense E3 | Up to 180 days | Automatic | Vibratory on-body alerts |
Table 1. System Time of CGM
Real-time CGM systems continuously and automatically transmit glucose data and can alert the user to impending hypoglycaemia or hyperglycaemia, even during sleep. Intermittently scanned CGM (isCGM), also referred to as flash glucose monitoring, requires the user to actively scan the sensor to obtain a reading. Landmark trials have demonstrated clinical benefits for both rtCGM and isCGM in T2DM. Sensor accuracy has improved markedly over successive generations, with mean absolute relative difference (MARD) values of 8–10% now achievable for leading devices [11,15,16].
Limitations of Conventional Glycaemic Monitoring in T2DM
The HbA1c Problem
HbA1c remains the cornerstone biomarker for diagnosing and monitoring T2DM, and its association with microvascular complication risk is well established. However, a growing body of evidence has identified fundamental limitations of HbA1c that reduce its utility as a sole glycaemic metric [4]:
First, HbA1c provides only a weighted time-average of glycaemia over approximately 90 days, without any information about intraday glucose patterns, post-prandial excursions, or episodes of hypoglycaemia. Crucially, two individuals with identical HbA1c values may have radically different glucose profiles, one with stable, in-range glucose and the other with wild oscillations between severe hypoglycaemia and extreme hyperglycaemia [12,13].
Second, numerous clinical conditions alter HbA1c independently of actual glycaemia, complicating its interpretation. Conditions that reduce red blood cell (RBC) lifespan, including chronic haemolytic anaemia, sickle cell disease, glucose-6-phosphate dehydrogenase deficiency, recent blood transfusion, and chronic kidney disease with erythropoietin use, falsely lower HbA1c. Conversely, conditions that increase RBC lifespan, such as iron deficiency anaemia, vitamin B12 deficiency, and certain haemoglobinopathies, can falsely elevate HbA1c [5,17].
Third, HbA1c has a 2- to 3-month temporal resolution, precluding its use for detecting acute glycaemic deterioration, assessing response to medication changes over short timeframes, or capturing the dynamic day-to-day glucose variability that is increasingly recognised as an independent cardiovascular risk factor [5,6].
Glucose Variability as an Emerging Risk Biomarker
Glycaemic variability (GV), encompassing both within-day and between-day oscillations in glucose concentration, has attracted considerable scientific interest as a potential independent contributor to diabetic vascular complications. Proposed mechanisms include oxidative stress from recurrent glucose spikes, endothelial dysfunction, platelet activation, and activation of inflammatory pathways. Prospective studies have demonstrated associations between high GV and increased risk of hypoglycaemia, cardiovascular events, and impaired quality of life in T2DM [18,19].
CGM-derived measures of GV, principally the coefficient of variation (CV), are now incorporated into international consensus guidelines for CGM data interpretation. A CV of ≤36% is recommended as indicative of acceptable glycaemic stability in people with T2DM. The ability of CGM to characterise GV comprehensively represents one of its most significant clinical advantages over conventional monitoring [13].
Clinical Evidence for CGM in Insulin-Treated T2DM
Randomised Controlled Trials
The strongest evidence base for CGM in T2DM derives from randomised controlled trials (RCTs) in insulin-treated populations. The MOBILE trial, published in JAMA (2021), was a landmark multicentre RCT that evaluated the effect of continuous glucose monitoring in 175 adults with T2DM managed with basal insulin, with or without additional oral agents. Participants randomised to CGM experienced a significantly greater reduction in HbA1c compared with the SMBG control group (−0.4% vs −0.1%; p < 0.001) at 8 months. Additionally, CGM users demonstrated significant increases in TIR (+26 percentage points, equivalent to approximately 6.2 additional hours per day in the target range) and reductions in time below range (TBR) [20].
The REPLACE trial examined intermittently scanned CGM (FreeStyle Libre) versus SMBG in 224 adults with T2DM on intensive insulin therapy. Although the primary endpoint of HbA1c reduction did not reach statistical significance, the trial demonstrated a significant 38% reduction in time spent in hypoglycaemia (<3.9 mmol/L) in the CGM group compared with controls, along with improvements in patient-reported outcomes including quality of life and diabetes treatment satisfaction [21].
A further RCT by Ehrhardt et al. (2011), one of the earlier trials investigating rtCGM in insulin-treated T2DM, demonstrated a significant HbA1c reduction of 0.8% in participants assigned to real-time CGM versus 0.2% in the SMBG arm over 12 weeks. Notably, greater benefit was observed in participants with baseline HbA1c ≥ 8.0% (64 mmol/mol), suggesting that CGM may have the greatest absolute glycaemic impact in those with sub optimally controlled T2DM [22].
Systematic Reviews and Meta-Analyses
Multiple systematic reviews and meta-analyses have synthesised evidence across CGM trials in T2DM. A comprehensive systematic review and meta-analysis by Aronson et al. (2025), incorporating data from 30 RCTs and 5,423 participants, evaluated CGM in both insulin-treated and non-insulin-treated T2DM. In insulin-treated participants, CGM use was associated with a weighted mean HbA1c reduction of 0.53% (95% CI: 0.39–0.67%) compared with SMBG (p < 0.001), alongside significant improvements in TIR and reductions in hypoglycaemia [10].
An earlier meta-analysis by Poolsup et al. demonstrated consistent HbA1c reductions of 0.3–0.5% in insulin-treated T2DM populations, with the greatest benefits observed in those using CGM for ≥12 weeks and with higher baseline HbA1c values. These data collectively reinforce the clinical relevance of CGM for glycaemic optimisation in insulin-managed T2DM [23].
Clinical Evidence for CGM in Non-Insulin-Treated T2DM
The Emerging Evidence Base
The application of CGM in non-insulin-treated T2DM, including individuals managed with oral hypoglycaemic agents, GLP-1 receptor agonists, or lifestyle modification alone represents a rapidly evolving area of research. Historically, clinical guidelines were more circumspect about recommending CGM in this population, given a perceived lower risk of hypoglycaemia and questions about cost-effectiveness. However, this landscape is changing rapidly [1,10].
The prospective PEARL study examined the effect of FreeStyle Libre isCGM in 120 adults with T2DM treated with oral agents or no pharmacotherapy over 10 months. Participants demonstrated significant reductions in HbA1c and reported increased motivation to make dietary and physical activity modifications after reviewing their glucose data. The real-time visualisation of post-prandial glucose responses to specific foods was identified as a particularly impactful motivational driver [24].
Behavioural and Educational Utility
A compelling application of CGM in non-insulin-treated T2DM is its role as a behavioural feedback tool. By providing immediate, visible feedback on how specific foods, meals, physical activity, stress, and sleep affect glucose levels, CGM enables individuals to make informed and timely lifestyle adjustments that static HbA1c monitoring cannot facilitate [2,6].
A prospective cohort study by Ritholz et al. demonstrated that CGM users in non-insulin-treated T2DM reported enhanced understanding of the relationship between their dietary choices and glycaemic responses, leading to sustained improvements in dietary adherence and physical activity levels at 6-month follow-up. Similarly, observational data from a UK primary care cohort found that short-term (2-week) CGM use in newly diagnosed T2DM led to significant reductions in post-prandial glucose excursions and improved self-efficacy scores [2,25].
Episodic and Short-Term CGM Use
A pragmatic approach emerging in clinical practice is the use of intermittent or episodic CGM, defined as short-duration CGM use at periodic intervals (e.g., 2 weeks every 3–6 months), rather than continuous, indefinite CGM wear. This strategy may be particularly appropriate for non-insulin-treated individuals in whom ongoing CGM may not be clinically necessary but where periodic glycaemic profiling can inform therapeutic decisions, motivate behaviour change, or detect occult glycaemic deterioration [2].
A UK-based consensus statement on CGM in primary care (Fernando et al., 2025) recommended that episodic CGM could be considered in: individuals with prediabetes (as an educational tool), newly diagnosed T2DM (for baseline glycaemic profiling and therapeutic guidance), non-insulin-treated T2DM (to support self-management and lifestyle change), and individuals not meeting HbA1c targets (to overcome therapeutic inertia) [2].
Time in Range and CGM-Derived Glycaemic Metrics
The International Consensus on Time in Range
The publication of the International Consensus on Time in Range by Battelino et al. in Diabetes Care (2019) represented a pivotal moment in the standardisation of CGM-derived metrics for clinical practice. The consensus established validated targets for a suite of CGM metrics applicable across T2DM populations, replacing ad hoc metric interpretation with evidence-informed benchmarks [13].
The primary metric, TIR, is defined as the percentage of CGM readings within the standard target glucose range of 3.9–10.0 mmol/L (70–180 mg/dL). For most adults with T2DM, the recommended TIR target is >70%, equating to ≥16.8 hours per day within the target glucose range. Importantly, the consensus recognised the need for individualised targets in specific populations:
| Population | TIR Target | TBR Target (<3.9 mmol/L) | TAR Target (>10.0 mmol/L) |
| Most adults with T2DM | >70% | <4% | <25% |
| Older adults / high hypoglycaemia risk / frailty | >50% | <1% | <50% |
| T2DM in pregnancy | >70% (3.5–7.8 mmol/L) | <4% | <25% |
Table 2. Time in Range Target
The clinical importance of TBR is particularly noteworthy. International consensus recommends that TBR below 3.9 mmol/L should be maintained at <4% and TBR below 3.0 mmol/L (the “very low” threshold) at <1%. The prioritisation of hypoglycaemia avoidance , especially in insulin-treated T2DM is a cornerstone of CGM-guided therapy [13].
TIR, HbA1c Correlation
A key validation of TIR as a clinically meaningful metric came from the demonstration of its robust correlation with HbA1c. Vigersky and McMahon (2019) evaluated 18 studies comprising 3,111 patients and found that each 10 percentage-point increase in TIR was associated with an approximate 0.5–0.8% (5.5–8.7 mmol/mol) reduction in HbA1c.26 Beck et al. (2019) further confirmed that a TIR of 70% corresponds to an estimated HbA1c of approximately 6.7–7.0% (50–53 mmol/mol), providing clinicians with a clinically intuitive translation between CGM and laboratory metrics [27].
However, as previously discussed, the TIR-HbA1c correlation is not perfect, and TIR provides additional information not captured by HbA1c, particularly regarding glucose stability, hypoglycaemia burden, and post-prandial excursions. This complementarity, rather than competition, between the two metrics is well recognised in contemporary guidance [1,13].
Glucose Management Indicator
The glucose management indicator (GMI) is a CGM-derived estimate of the laboratory HbA1c value, calculated from the average (mean) sensor glucose over a minimum of 14 days. The GMI formula, GMI (%) = 3.31 + 0.02392 × mean glucose (mg/dL) was derived from a large dataset of CGM users and validated in both type 1 and type 2 diabetes. GMI is particularly useful for detecting discordance between CGM-estimated and laboratory HbA1c, which may signal conditions affecting RBC lifespan or haemoglobin structure [28].
Glucose Variability Metrics
In addition to TIR, TBR, and TAR, the CGM-derived coefficient of variation (CV) provides a dimensionless measure of glycaemic variability relative to mean glucose. A CV of ≤36% is recommended as the target for glycaemic stability in T2DM. High CV (>36%) indicates significant glycaemic lability and is associated with increased hypoglycaemia risk, impaired quality of life, and potentially adverse cardiovascular outcomes [13,18,19].
CGM and Hypoglycaemia Detection and Prevention in T2DM
Burden and Consequences of Hypoglycaemia in T2DM
Hypoglycaemia in T2DM, while traditionally perceived as less frequent than in type 1 diabetes, carries significant clinical consequences, particularly in insulin-treated and sulfonylurea-treated individuals. Severe hypoglycaemia is associated with acute cardiovascular events (including cardiac arrhythmias and myocardial ischaemia), cognitive impairment (especially in older adults), accidental injury, emergency healthcare utilisation, and paradoxically, increased all-cause mortality [29,30].
A major limitation of SMBG and HbA1c monitoring is their near-complete insensitivity to asymptomatic and nocturnal hypoglycaemia. Studies utilising blinded CGM in T2DM populations on sulfonylurea therapy have revealed a surprisingly high prevalence of asymptomatic nocturnal hypoglycaemia, occurring in up to 39% of patients that would otherwise go undetected with conventional monitoring. These findings have significant implications for medication dosing, cardiovascular risk management, and patient safety [31].
Evidence for Hypoglycaemia Reduction with CGM
The REPLACE trial (Haak et al., 2017) demonstrated a significant 38% reduction in time spent in hypoglycaemia (<3.9 mmol/L) in insulin-treated T2DM patients randomised to FreeStyle Libre isCGM versus SMBG. Real-time CGM systems, by virtue of their proactive glucose alarms, provide an additional layer of protection against severe hypoglycaemia by alerting users, including during nocturnal periods to impending glucose lows [21].
A systematic review and meta-analysis by Yeh et al. (2012) examined hypoglycaemia outcomes with CGM across insulin-treated diabetes populations and found significant reductions in hypoglycaemic event rates, particularly in individuals with prior hypoglycaemia unawareness.32 These data informed the incorporation of CGM into hypoglycaemia prevention strategies in multiple national and international guidelines [1,8].
Impaired Hypoglycaemia Awareness
Impaired awareness of hypoglycaemia (IAH), defined as a diminished or absent symptomatic response to hypoglycaemia, affects approximately 20–25% of insulin-treated T2DM patients and substantially amplifies the risk of severe hypoglycaemia. CGM, particularly rtCGM with customisable low-glucose alerts and predictive hypoglycaemia alarms, is recommended by international guidelines as a priority intervention for individuals with T2DM and IAH [1,13,30].
Glucose Variability as a Therapeutic Target in T2DM
Pathophysiological Mechanisms Linking GV to Vascular Risk
Emerging evidence suggests that glycaemic variability, characterised by acute glucose swings and high peak-to-trough amplitude may exert harmful effects on the vasculature that are independent of and additive to those of sustained hyperglycaemia.Proposed mechanisms include: (1) oxidative stress from transient hyperglycaemia activating protein kinase C and advanced glycation end-product (AGE) formation; (2) endothelial dysfunction mediated by reduced nitric oxide bioavailability; (3) pro-inflammatory cytokine release; and (4) platelet hyperreactivity and coagulation pathway activation [18,19].
In vitro studies have demonstrated that oscillating glucose concentrations induce greater endothelial apoptosis than sustained hyperglycaemia at equivalent mean glucose levels, providing a mechanistic basis for the hypothesis that GV contributes independently to vascular injury [18].
CGM as the Primary Tool for GV Assessment
Prior to CGM, quantification of GV was limited to sparse SMBG data or laboratory measures such as mean amplitude of glycaemic excursions (MAGE), which required hospitalised glucose monitoring series. CGM has democratised GV assessment by enabling continuous, ambulatory measurement of multiple GV indices, including CV, standard deviation (SD) of glucose, mean of daily differences (MODD), and continuous overlapping net glycaemic action (CONGA) in patients’ everyday environments [13].
The clinical applicability of CGM-derived GV data is exemplified by its utility in medication selection. For individuals with high GV, therapeutic strategies targeting post-prandial glucose excursions, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs), dipeptidyl peptidase-4 (DPP-4) inhibitors, or sodium-glucose cotransporter-2 (SGLT2) inhibitors, may be preferentially indicated, and CGM provides a direct means to assess their effectiveness in reducing GV [1].
The Ambulatory Glucose Profile in Clinical Practice
Structure of the AGP Report
The Ambulatory Glucose Profile (AGP) is a standardised, single-page visualisation of CGM data developed by the International Diabetes Center and endorsed by international diabetes organisations. The AGP report integrates multiple layers of glucose information including: summary glucose statistics (mean glucose, GMI, glucose variability/CV); time-in-range breakdown (TIR, TBR, TAR, with colour-coded proportional display); the modal AGP curve (superimposed median and interquartile range of glucose across 24 hours); and individual daily glucose trace overlays [12,13].
The standardisation of the AGP format across CGM manufacturers has been a critical step in enabling consistent and reproducible CGM data interpretation across clinical settings. Bergenstal et al. (2013) described the rationale and evidence base for the AGP format, emphasising that consistent data presentation reduces the cognitive burden of CGM data interpretation for clinicians and enhances clinical communication with patients [12].
An Eight-Step approach to AGP Interpretation
A practical, structured approach to AGP interpretation has been proposed in clinical guidance documents and consensus publications, designed to be applicable in busy primary care and general practice settings. The following framework synthesises recommendations from multiple sources [2,33]:
| Step | Action | Clinical Rationale |
| 1 | Confirm data sufficiency: ≥14 days of data, >70% sensor activity | Incomplete datasets may not represent habitual glycaemia; avoid therapeutic changes based on insufficient data |
| 2 | Review key summary metrics: mean glucose, GMI, CV | Establishes overall glycaemic burden and stability at a glance; flags discordance with laboratory HbA1c |
| 3 | Assess TIR, TBR, and TAR against consensus targets | Identifies the relative contributions of hyperglycaemia vs hypoglycaemia to overall glycaemic profile |
| 4 | Identify hypoglycaemia patterns: timing, frequency, depth | Hypoglycaemia should always be prioritised before addressing hyperglycaemia; identifies unsafe medication regimens |
| 5 | Identify hyperglycaemia patterns: post-prandial, fasting, nocturnal, dawn phenomenon | Informs selection of specific therapeutic interventions targeting the dominant driver of hyperglycaemia |
| 6 | Assess glycaemic variability: width of IQR bands on AGP curve | Wide bands indicate high day-to-day variability; explore dietary inconsistency, activity variation, or medication adherence |
| 7 | Review individual daily glucose traces | Confirms patterns observed on the modal curve; identifies outlier days (illness, social events, shift work) |
| 8 | Agree on one focused action with the patient | Prevents information overload; positive, collaborative framing enhances self-management engagement and therapeutic alliance |
Table 3. Recomendation
This structured approach enables efficient, focused AGP review within a standard consultation timeframe and avoids the “CGM data paralysis” that can occur when clinicians attempt to act on all aspects of a glucose profile simultaneously [2,33].
Patient Selection, Guideline Recommendations, and Digital Readiness
International Guideline Recommendations
The American Diabetes Association (ADA) Standards of Care in Diabetes 2026 strongly recommend CGM for adults with T2DM on intensive insulin regimens and offer conditional recommendations for broader use in non-insulin-treated populations. Specifically, the ADA recommends offering CGM to patients on non-insulin therapies that carry hypoglycaemia risk (e.g., sulfonylureas), as well as to any individual for whom CGM data would assist diabetes management [1].
The National Institute for Health and Care Excellence (NICE) NG28 guideline (updated February 2026) supports CGM use in people with T2DM on multiple daily insulin injections if any of the following applies: recurrent or severe hypoglycaemia; impaired hypoglycaemia awareness; a physical or cognitive disability that precludes reliable SMBG; a requirement for ≥8 SMBG checks per day; or dependence on a carer for SMBG.8 The NICE guidance also acknowledges the potential value of short-term CGM in broader T2DM populations as an educational tool [8].
A consensus statement by the European Association for the Study of Diabetes (EASD) and the ADA on hyperglycaemia management in T2DM similarly endorsed CGM as an adjunct to optimise glycaemic management, particularly in individuals with uncontrolled glucose despite therapy [34].
Digital Readiness Assessment
CGM systems are fundamentally digital health technologies, requiring users to interact with smartphone applications, Bluetooth connectivity, software platforms, and cloud-based data-sharing tools. The ability to successfully adopt and use CGM is therefore contingent on a degree of digital literacy and access to appropriate technology [2].
Fernando et al. (2025) proposed a validated digital readiness framework comprising four domains: digital access (smartphone and internet availability); digital usage (ability to use connectivity features and mobile applications); digital literacy (ability to navigate online health information); and digital learnability (motivation to learn digital health skills).2 Healthcare providers offering CGM should incorporate digital readiness assessment into the pre-initiation consultation to identify individuals who may require additional support, simplified CGM options, or caregiver involvement [2].
Special Populations
Older adults and individuals living with frailty represent a population for whom CGM has particular clinical value but also unique considerations. The risk of hypoglycaemia-associated falls, cardiac events, and cognitive deterioration is disproportionately high in this group, yet HbA1c monitoring may be further unreliable due to coexisting anaemia and CKD. CGM enables the setting of individualised, less-stringent glycaemic targets (e.g., TIR >50%) with a strong priority on minimising TBR (<1%) [1,13].
People with T2DM and chronic kidney disease (CKD) present a particular challenge for glycaemic monitoring, as CKD independently alters both HbA1c (typically lowering it due to reduced RBC lifespan) and fructosamine. CGM provides a CKD-independent measure of glycaemia and is increasingly recognised as the preferred monitoring modality in this population [1,17].
Future Directions and Emerging Developments
Integration with Closed-Loop and Automated Insulin Delivery Systems
The most technologically advanced application of CGM is its integration into automated insulin delivery (AID) systems, commonly referred to as “artificial pancreas” or closed-loop systems. In these systems, CGM glucose readings are fed continuously into a control algorithm that automatically adjusts insulin delivery from a connected insulin pump. While AID systems are currently approved predominantly for type 1 diabetes management, clinical trials are underway evaluating closed-loop systems in insulin-treated T2DM, with preliminary data suggesting significant improvements in TIR and hypoglycaemia reduction compared with sensor-augmented pump therapy [35].
CGM for Metabolic Health Beyond Diabetes
There is growing interest in the use of CGM beyond clinical diabetes management , including in individuals with prediabetes, obesity, non-alcoholic fatty liver disease (NAFLD)/metabolic-associated steatohepatitis (MASH), and those pursuing general metabolic health optimisation. Commercial wearable CGM devices have entered the consumer health market, providing glucose data to non-diabetic individuals seeking real-time feedback on dietary patterns and metabolic responses. While this use of CGM holds significant potential for population-level T2DM prevention, it also raises important questions regarding data interpretation, psychological wellbeing, and healthcare resource utilisation in non-clinical populations [36].
Artificial Intelligence and CGM Data Analytics
The integration of machine learning and artificial intelligence (AI) algorithms with CGM data streams represents a frontier in personalised diabetes management. AI-driven pattern recognition can identify complex glycaemic patterns that may elude human interpretation, such as subtle sleep-related glucose dysregulation or the impact of specific dietary combinations on post-prandial excursions. Predictive algorithms embedded within CGM platforms are already providing predictive hypoglycaemia alerts up to 20–30 minutes before a low glucose event, enabling proactive rather than reactive hypoglycaemia management [1,36].
Expanding CGM Access and Health Equity
Despite compelling clinical evidence, CGM uptake in T2DM remains limited by factors including device cost, variable insurance coverage, digital access disparities, and implicit clinical biases regarding which patients are “suitable” for CGM. Addressing these systemic barriers is critical for ensuring that the benefits of CGM are equitably accessible across socioeconomic, cultural, and geographic populations. Policymakers, healthcare systems, and device manufacturers must collaborate to develop sustainable reimbursement frameworks that reflect the clinical and economic evidence for CGM in T2DM [2,37].
Conclusion
Continuous glucose monitoring represents a paradigm shift in the management and monitoring of type 2 diabetes mellitus that extends far beyond the limitations of HbA1c or intermittent self-monitoring of blood glucose. By providing continuous, dynamic, and actionable glycaemic data, CGM enables a richer understanding of the glucose landscape in T2DM , capturing hypoglycaemia, post-prandial excursions, glycaemic variability, and nocturnal glucose patterns that would otherwise remain clinically invisible.
A robust and growing evidence base, including multiple RCTs and meta-analyses, confirms that CGM use in insulin-treated T2DM significantly reduces HbA1c, increases time in range, and decreases hypoglycaemic burden. Evidence in non-insulin-treated T2DM is rapidly expanding, with CGM demonstrating valuable roles as an educational tool, a motivational intervention for lifestyle change, and a mechanism for detecting occult glycaemic deterioration.
The standardisation of CGM-derived metrics, particularly time in range, glucose variability (CV), and the ambulatory glucose profile has provided a practical and clinically actionable framework for CGM data interpretation that complements and extends conventional HbA1c monitoring. International consensus targets (TIR >70%, TBR <4%, CV ≤36%) offer clinicians and patients shared, evidence-based goals for glycaemic management.
As CGM technology continues to evolve , with improving sensor accuracy, longer wear durations, automated insulin delivery integration, and AI-driven analytics, its role in T2DM management will only continue to expand. Clinicians involved in the care of people living with T2DM should develop proficiency in CGM data interpretation, incorporating CGM into individualised, patient-centred management strategies that address the full complexity of glycaemic control.
The ultimate goal of CGM in T2DM is not simply to generate data, but to translate that data into meaningful, sustained improvements in glycaemic outcomes, quality of life, and long-term complication prevention for the millions of people living with this condition worldwide.
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