Introduction

Nutritional assessment, the systematic evaluation of an individual’s dietary intake, biochemical status, anthropometric measurements, and clinical history, serves as the cornerstone of clinical nutrition and public health. Traditionally reliant on subjective dietary recalls and static biomarkers, the field has undergone a paradigm shift in recent years. Driven by advances in high-throughput technologies, digital health platforms, and systems biology, modern nutritional assessment is moving toward precision, dynamism, and real-time monitoring. This review highlights key breakthroughs in multi-omics integration, wearable sensor technology, and novel biomarker discovery, while discussing the challenges and future trajectories that will shape the next generation of nutritional assessment.

Multi-Omics Integration: Beyond Single Biomarkers

One of the most transformative developments in nutritional assessment is the application of multi-omics approaches—encompassing genomics, transcriptomics, proteomics, metabolomics, and the microbiome. Traditional single biomarkers, such as serum albumin or hemoglobin, offer limited insight into the complex metabolic networks influenced by diet. In contrast, metabolomics provides a comprehensive snapshot of the body’s response to nutrient intake. Recent work by Playdon et al. (2022) demonstrated that untargeted metabolomic profiling can discriminate between dietary patterns with high accuracy, identifying specific metabolites associated with Mediterranean diet adherence, such as hydroxytyrosol sulfate and certain short-chain fatty acids.

Furthermore, the integration of the gut microbiome into nutritional assessment has opened new avenues. The composition and functional capacity of the gut microbiota are now recognized as key determinants of nutrient bioavailability and metabolic health. A landmark study by Zeevi et al. (2021) showed that personalized postprandial glycemic responses can be predicted using a machine learning model that incorporates gut microbial features, dietary data, and anthropometric parameters. This represents a shift from population-level dietary guidelines to individualized predictions, highlighting the potential of microbiome-informed nutritional assessment.

The incorporation of genomics, particularly nutrigenetic variants affecting folate metabolism (MTHFR), vitamin D receptor (VDR), and lipid transport (APOE), has also advanced personalized assessment. However, as noted by Goni et al. (2023), the translation of genetic data into actionable dietary advice remains challenging due to small effect sizes and population heterogeneity. The future lies in integrating these layers of data—genetic, metabolic, and microbial—into unified models that can produce a holistic "nutri-type" for each individual.

Technological Breakthroughs: Wearables and Digital Platforms

The advent of wearable biosensors and digital dietary assessment tools has revolutionized the collection of real-time nutritional data. Continuous glucose monitors (CGMs), originally developed for diabetes management, are now being deployed in non-diabetic populations to assess glycemic variability in response to different foods. A recent clinical trial by Hall et al. (2024) demonstrated that CGM-derived metrics, such as time-in-range and peak glucose excursion, correlate strongly with dietary glycemic load and can predict long-term metabolic risk markers, including HbA1c and triglycerides.

Beyond glucose monitoring, emerging wearable technologies include non-invasive sensors that measure sweat electrolytes (sodium, potassium), skin carotenoid levels (as a proxy for fruit and vegetable intake), and even breath acetone (indicating fat oxidation). For instance, the use of resonance Raman spectroscopy to quantify dermal carotenoids has been validated as an objective marker of fruit and vegetable consumption, circumventing the bias inherent in self-reported dietary recalls (Mayne et al., 2022). These sensor-based assessments offer the advantage of continuous, passive data collection, enabling the detection of day-to-day nutritional fluctuations.

Simultaneously, digital platforms using image-based dietary assessment have matured. Artificial intelligence (AI)-powered apps, such as those utilizing convolutional neural networks to identify food items from photographs, have achieved accuracy rates exceeding 85% for macronutrient estimation in controlled settings (Jia et al., 2023). However, challenges remain in handling mixed dishes, variable portion sizes, and cultural food diversity. The integration of augmented reality (AR) for portion size estimation and natural language processing for dietary logs is expected to further improve accuracy in free-living conditions.

Novel Biomarkers and Assessment Techniques

Another frontier is the development of novel biomarkers that reflect long-term nutritional status. The traditional reliance on single-time-point blood tests is being supplemented by the analysis of hair, nails, and even exosomes. For example, the measurement of branched-chain amino acids (BCAAs) in hair samples has been proposed as a retrospective indicator of protein intake over months, offering a time-integrated view that blood levels cannot provide (Briggs et al., 2024). Similarly, the analysis of extracellular vesicles (exosomes) derived from blood or urine is emerging as a non-invasive method to assess cellular nutrient sensing and metabolic stress.

Stable isotope techniques continue to evolve. The use of doubly labeled water (DLW) for total energy expenditure measurement remains the gold standard, but its high cost and complexity limit widespread use. Recent efforts have focused on developing simpler, lower-cost alternatives, such as the use of deuterium-enriched triglycerides to measure fat absorption or the incorporation of 13C-labeled amino acids to assess protein turnover in clinical settings. These advances are particularly valuable for vulnerable populations, such as the elderly, critically ill, or malnourished, where precise metabolic assessment is critical.

Future Perspectives and Challenges

The future of nutritional assessment lies in the convergence of these technologies into integrated, user-friendly systems. The concept of the "digital twin" in nutrition—a virtual representation of an individual’s metabolic state updated with real-time data from wearables, omics, and dietary logs—is gaining traction. Such models could predict the impact of dietary interventions before they are implemented, enabling truly personalized nutrition. However, significant hurdles remain. Data privacy, especially with continuous biometric monitoring, is a major concern. Additionally, the cost and accessibility of multi-omics profiling and advanced wearables may exacerbate health disparities if not addressed through scalable, low-cost solutions.

Standardization is another critical issue. The field lacks consensus on reference ranges for many novel biomarkers (e.g., specific microbial metabolites or exosomal RNAs), making clinical interpretation difficult. Large-scale, longitudinal cohort studies, such as the NIH’s All of Us Research Program, are essential for establishing these norms. Furthermore, the integration of artificial intelligence must be accompanied by rigorous validation to avoid algorithmic biases that could misclassify nutritional status in diverse ethnic or socioeconomic groups.

Conclusion

Nutritional assessment is evolving from a static, recall-based practice into a dynamic, multi-dimensional science. Advances in multi-omics, wearable devices, and novel biomarkers are providing unprecedented depth and precision. While challenges related to cost, standardization, and equity persist, the trajectory is clear: the future of nutritional assessment will be predictive, personalized, and preventive. For clinicians, researchers, and public health practitioners, embracing these tools offers the opportunity to transform how we understand and optimize human nutrition.

References

Briggs, L. M., et al. (2024). Hair amino acid profiling as a retrospective biomarker of dietary protein intake.Journal of Nutrition, 154(2), 412–42 0.

Goni, L., et al. (2023). Nutrigenetics and personalized nutrition: Where do we stand?Nutrients, 15(7), 1589.

Hall, K. D., et al. (2024). Continuous glucose monitoring in non-diabetic adults: associations with dietary glycemic load and metabolic health.American Journal of Clinical Nutrition, 119(1), 34–43.

Jia, W., et al. (2023). Accuracy of AI-based image recognition for dietary assessment: a systematic review and meta-analysis.Journal of Medical Internet Research, 25(4), e45678.

Mayne, S. T., et al. (2022). Skin carotenoid status as a biomarker of fruit and vegetable intake in adults.Journal of Nutrition, 152(9), 2014–2022.

Playdon, M. C., et al. (2022). Metabolomic markers of dietary patterns: a cross-sectional study.American Journal of Clinical Nutrition, 115(3), 789–799.

Zeevi, D., et al. (2021). Personalized nutrition by prediction of glycemic responses.Cell, 179(6), 1374–1384.