Abstract
Nutrition science has experienced a paradigm shift during the last decade. As evidenced by much of modern nutritional history, dietary recommendations and guidelines were inherently developed utilizing broad-ranging population averages, with the intended benefit geared towards the masses in general. Despite their obvious public health value as accessible baselines for preventing and addressing widespread nutritional deficiencies, the approach fails to take into account the individual variability in terms of genetics, diet, metabolic responses, gut microbiome composition, and environmental and lifestyle-associated factors. The emergence of precision/personalized nutrition (PN) as a specialized area of nutrition research is a reflection of the growing evidence that metabolic responses to identical foods vary substantially between individuals, and a “one-size-fits-all” model is not a realistic strategy anymore (Asghar and Khalid, 2025).
Continuous glucose monitors (CGM) systems are increasingly emerging as one of the most significant tools in this context. Initially regarded as a sophisticated alternative to the traditional fingerstick/finger-prick blood draws for diabetes management, the technology is increasingly finding application as a mainstream wellness tool and PN gateway for healthy individuals. The real-time dynamic biofeedback (through conversion of glucose responses into a feedback loop) provided by CGM informs users regarding exact postprandial glucose (PPG) spikes and crashes (glucose excursion) (Howard et al., 2020). This allows them to pinpoint dietary components acting as sugar triggers, tailor their diets for sustained energy release, and facilitate more individualized nutritional counseling (Arshad et al., 2025). More importantly, CGM has greatly influenced the transition of PN from theory to actionable practice solutions, with many commercial programs increasingly utilizing CGM data in combination with dietary tracking and behavioral coaching for developing personalized dietary recommendations (Jospe et al., 2026).
However, amidst all the excitement and expectations, a few key questions remain: Are we overlooking other metabolic biomarkers (such as overall diet quality, insulin sensitivity, and lipid profiles) with this single-metric focus approach? Does more data actually translate into better health outcomes? These questions are highly pertinent, as in the latter case scenario, the assumption, as yet, remains unproven in the context of the general population (Hengist et al., 2025).
Glucose, undoubtedly, is of paramount importance as a metabolic biomarker, whereby its elevated concentrations have a direct and significant influence on both the metabolic dysfunction and pathophysiology of diabetes mellitus. However, it represents essentially only one component/dimension of a highly intricate and complex metabolic network that involves a vast interplay of insulin dynamics, lipid metabolism, hormonal signaling (e.g., hunger and satiety), gut microbiome, inflammatory pathways, circadian rhythms, and energy expenditure (Abdalla, 2025). Overreliance, solely, on glucose curves, therefore, is akin to oversimplification of the inherent complexity of routine human metabolic responses. A favorable glucose curve does not automatically represent an optimal metabolic response. Conversely, an unfavorable glucose excursion does not necessarily signify an underlying health issue.
Another mitigating factor could be the CGM sensor limitations. Since CGMs measure glucose levels in the interstitial fluid (ISF), there is a physiological time lag (5–15 min time delay in most individuals under periods of rapid glycemic fluctuations, such as after a high-carb meal consumption, or post intense exercise sessions), whereby the glucose travels from the blood, crosses the capillary walls, and enters ISF (Ahmad Tarar et al., 2020). Therefore, variables such as sensor placement, hydration levels, compression artifacts/compression lows during sleeping (false data drops induced by physical pressure at the sensor site), as well as sensor drift (progressive and systematic changes in a sensor's electrochemical sensitivity gradient over its intended lifespan) can contribute to significant skewness in the data (Barton et al., 2025). Clinical data overload, commonly termed as “glucose fatigue,” is another emerging outcome stemming from CGM use. For many individuals, every glucose spike serves as a warning sign, although transient PPG elevations are a normal physiological phenomenon, rather than an indication of metabolic disorder. The constant stream of CGM data and the compulsive tendency of frequent over-analysis can contribute to mental exhaustion, anxiety, and mental fatigue (Xiao, 2025), distracting the user from actual lifestyle management.
There is a growing concern among nutrition and healthcare experts that PN (in conjunction with CGM) adoption may inadvertently promote the phenomenon of “glucocentrism,” a glucose-centric approach that involves evaluating metabolic health exclusively through analysis of glucose dynamics (Kesavadev et al., 2026). The approach dictates that the choice of foods depends on their ability to flatten glucose curves, rather than their overall nutritional makeup, creating some significant paradoxes. This may lead to the restriction of many nutrient-dense foods (such as whole foods) in the diet, which may generate temporary glucose spikes, but are richly supplied with many vital micronutrients and phytochemicals (e.g., antioxidants), central to maintenance of long-term health. Conversely, there could be increased inclusion of highly processed foods engineered with artificial sweeteners and modified starches, which, on the flip side, are designed to suppress glucose spikes and flatten the glucose curves, but offer no or little in the way of nutritional value (the “diet food” trap), or the potential to improve health outcomes (Menezes et al., 2025).
The major challenge currently faced by PN, therefore, is not centered primarily around the measurement of the data, but rather the interpretation of it. More data does not automatically equate to more actionable insights. Although CGMs offer the advantage of a continuous influx of numbers associated with metabolic processes, there is an increased risk of consumer preoccupation with numerical optimization while failing to take into account many broader determinants of long-term health, such as dietary patterns, physical activity, exposure to stressors, sleep quality (and quantity), medication use, gut microbiome health, environmental factors, and other indicators of psychosocial well-being (Bailey and Stover, 2023). The prime objective for PN, therefore, should not be the achievement of a perfect glucose curve (through addressing only one metabolic marker), but to lay down the foundation for improved health outcomes on a holistic and sustainable basis.
The future of PN, and proactive health management, therefore, will most likely rely on the integration of multiple layers of biological data (for instance, genetics, epigenetics, microbiomics, and metabolomics), and a shift away from a single, reductionist biomarker to holistic biomarker signatures (Nourazarain and Vaziri, 2025). In this regard, CGMs should serve as a viable point of entry, paving the way for the paradigm movement towards multi-dimensional phenotyping. Artificial intelligence (AI) and machine learning (ML)-assisted models, as well as multi-omics integration platforms, are increasingly being sought, developed, and optimized for bringing together and integrating the aforementioned complex data streams into predictive frameworks capable of formulating truly bespoke nutritional/dietary interventions (Barrera-Suarez et al., 2026). Of equal importance is the need for these technological advances to be clinically validated. This is highly imperative in the context of bridging the transitory gap between the narrative that “personalization is feasible,” to “personalization is beneficial,” and would serve to strengthen the rhetoric that these sophisticated, data-driven PN approaches can produce sustained improvements in health outcomes, rather than generating more precise numerical descriptors only.