Introduction
Continuous glucose monitoring (CGM) systems are well established in diabetes care, where strong evidence demonstrates improvements in glycaemic variability, time in range, and hypoglycaemia prevention in both type 1 and insulin-treated type 2 diabetes populations (Battelino et al., 2019; Beck et al., 2017). In recent years, CGMs have increasingly been adopted by individuals without diabetes for purposes such as dietary optimisation, metabolic health tracking, and performance monitoring. This expansion reflects interest in precision nutrition, although evidence supporting clinical benefit in healthy populations remains limited (Liao et al., 2026). The key question is whether additional metabolic data improves outcomes in already well-regulated physiology.
Physiological Basis of CGM Technology
CGMs measure glucose in interstitial fluid using enzymatic sensors. Interstitial glucose is physiologically linked to blood glucose but is not identical. A consistent limitation is the time lag between blood and interstitial compartments, typically 5–15 minutes depending on metabolic state and perfusion (Torimoto and Okada, 2021). This lag becomes more pronounced during rapid changes such as postprandial absorption or exercise. Accuracy is also influenced by sensor kinetics, calibration algorithms, and tissue-level variability. Facchinetti (2016) notes that CGM accuracy is generally acceptable in diabetic ranges but is reduced at lower glucose levels and during rapid glycaemic shifts, which are more typical in healthy individuals. This means CGM outputs in normoglycaemic populations should be interpreted as trend-based estimates rather than precise biochemical measurements.
Inter-Individual Variability in Glycaemic Response
A key rationale for CGM use in personalised nutrition is inter-individual variability in postprandial glycaemic responses (PPGRs). Zeevi et al. (2015) demonstrated that identical meals produce highly variable glucose responses driven by factors including microbiome composition, insulin sensitivity, sleep, and anthropometrics. Their machine-learning model was able to predict PPGRs and showed that personalised dietary interventions could reduce postprandial glucose excursions. Mendes-Soares et al. (2019) replicated these findings in an independent cohort, confirming that glycaemic responses are highly individualised. Mechanistically, variability reflects differences in gastric emptying, insulin secretion dynamics, hepatic glucose output, and peripheral glucose uptake. However, variability in physiological response does not necessarily imply that reducing all glucose excursions improves long-term health outcomes.
CGM Effects in Non-Diabetic Populations
The most comprehensive synthesis of evidence is provided by Liao et al. (2026), who conducted a systematic review and meta-analysis of CGM use in non-diabetic populations. They reported small reductions in mean glucose and improved dietary awareness, but no consistent improvements in BMI, glycaemic variability, or long-term metabolic outcomes in healthy individuals. Benefits were more evident in individuals with impaired glucose regulation, suggesting CGM utility may be dependent on baseline metabolic status. These findings indicate CGMs function more effectively as behavioural feedback tools than as metabolic intervention devices in healthy populations.
Do Postprandial Glucose Spikes Matter?
Postprandial increases in glucose are a normal physiological response to carbohydrate ingestion. In healthy individuals, glucose homeostasis is maintained through coordinated insulin secretion, hepatic regulation, and peripheral uptake. DeFronzo et al. (2015) describe these responses as central to metabolic flexibility rather than pathological dysfunction. While glycaemic variability has been associated with adverse outcomes in diabetic populations, causality in healthy individuals is not established. No randomised controlled trials demonstrate that reducing physiological glucose excursions improves cardiovascular outcomes, body composition, or longevity in normoglycaemic populations.
CGM Accuracy and Interpretation Limitations
CGM interpretation is limited by both physiological and technical factors. Interstitial lag introduces temporal discrepancy between blood and tissue glucose (Torimoto and Okada, 2021). Sensor accuracy decreases during rapid glucose fluctuations and at lower glucose ranges (Facchinetti, 2016). In healthy individuals, where glucose variability is relatively small, these limitations may disproportionately influence interpretation, increasing the risk of misclassifying normal physiological variation as meaningful metabolic disturbance.
Behavioural and Psychological Considerations
CGMs provide continuous physiological feedback, which can influence behaviour. Vettoretti et al. (2020) highlight that while CGMs may improve awareness of dietary patterns, continuous monitoring can also increase cognitive load and attention bias toward short-term fluctuations. This may lead to over-interpretation of normal glucose variability, increased dietary restriction, and reduced dietary flexibility in some individuals. Importantly, there is no evidence that focusing on minimising all glucose excursions improves dietary quality or long-term health outcomes in healthy populations.
CGMs in Sport and Exercise
CGMs are increasingly used in athletic populations to monitor carbohydrate availability, fuelling strategies, and recovery nutrition. However, exercise significantly alters glucose kinetics through catecholamine-mediated hepatic glucose output, increased skeletal muscle uptake, and changes in insulin sensitivity. These physiological responses complicate interpretation of CGM data during training and recovery. Jeukendrup (2017) notes that while carbohydrate availability is central to performance, there is no strong evidence that CGM-guided nutrition improves athletic performance outcomes in controlled trials.
Future Directions: Precision Nutrition
CGMs are being integrated into precision nutrition models alongside microbiome and dietary data. Zeevi et al. (2015) and Mendes-Soares et al. (2019) demonstrated that machine-learning approaches can predict individual glycaemic responses with moderate accuracy, supporting the concept of metabolic phenotyping. However, translation into clinical practice remains limited due to lack of long-term outcome data, limited external validity, and absence of large-scale randomised controlled trials demonstrating clinical benefit.
Practical Implications
Current evidence suggests CGMs may improve short-term dietary awareness and engagement behaviours (Liao et al., 2026). Individual variability in glycaemic response is well established (Zeevi et al., 2015), but does not justify routine intervention in healthy populations. Physiological glucose excursions are not inherently harmful (DeFronzo et al., 2015). CGM data must be interpreted cautiously due to physiological lag and measurement limitations (Facchinetti, 2016). Behavioural effects may be beneficial or maladaptive depending on the individual context (Vettoretti et al., 2020).
Are CGMs Worth Using in Healthy Individuals?
In healthy individuals, CGMs are not currently supported as a routine metabolic optimisation tool. Evidence suggests their primary value is educational and behavioural rather than clinical. They may help increase awareness of dietary patterns and individual variability but do not currently demonstrate improvements in body composition, performance, or long-term health outcomes (Liao et al., 2026). In individuals with impaired glucose regulation, CGMs may have greater utility as part of lifestyle intervention strategies. In athletes, CGMs may provide descriptive insights into fuelling responses but lack evidence for performance enhancement. Overall, CGMs should be considered informational rather than interventional tools in healthy populations.
Conclusion
Continuous glucose monitoring is a well-established clinical tool in diabetes management and an emerging technology in personalised nutrition. However, current evidence does not support routine use in healthy individuals for improving metabolic health, performance, or body composition. While CGMs provide valuable insight into inter-individual variability in glycaemic responses, physiological glucose excursions in healthy individuals are not inherently pathological, and the clinical significance of modifying them remains unproven. CGMs are best viewed as research and educational tools rather than essential health optimisation devices in normoglycaemic populations.
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References
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Liao, X., et al. (2026) ‘Continuous glucose monitoring in non-diabetic individuals: systematic review and meta-analysis’, European Journal of Medical Research, 31, pp. 1–15.
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Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., et al. (2015) ‘Personalized nutrition by prediction of glycemic responses’, Cell, 163(5), pp. 1079–1094










