The global burden of noncommunicable diseases (NCDs) such as diabetes, cardiovascular disease, and others have risen to account for 74% of all deaths globally [https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases]. The World Health Organization identifies nutrition as a key contributor to NCD deaths, which have grown despite the adoption and refinement of population-based dietary guidance over the last four decades. Because broad dietary recommendations have not blunted these trends, public and private investment in precision nutrition strategies has risen dramatically. In 2021, the National Institutes of Health launched the largest precision nutrition research program of its kind—The Nutrition for Precision Health Study [https://nutritionforprecisionhealth.org/].

The field of precision nutrition has been challenged by the inability to accurately predict how a specific dietary pattern may impact an individual’s metabolic health and prevent or decrease risk for chronic disease. The work of Drs. Mitchell, Stinson, Chang, and Krakoff [1] published in this issue of Nutrition & Diabetes is, to our knowledge, the first study to identify predictors of incident type 2 diabetes using a mixed meal tolerance test (MMTT). This research lends support to the use of an MMTT for diagnosing risk of diabetes, with the potential for predicting risk of other disease states.

Currently, fasting blood tests are considered the standard of care for determining chronic disease risk. However, evidence shows that post-prandial measures of insulin, glucose, and lipids are independent risk factors for chronic disease, in particular for diabetes and cardiovascular disease [2,3,4,5,6]; post-prandial glucose increases the risk of cardiometabolic diseases even in individuals with normal fasted glucose levels [3, 6]. These observations highlight the importance of measuring multiple metabolites in both the fasted and non-fasted state. The oral glucose tolerance test (OGTT), traditionally used for the clinical diagnosis of diabetes, considers post-prandial glucose and insulin dynamics in addition to fasted measures. However, the composition of the OGTT (glucose) does not reflect the composition of a mixed meal, nor the resulting metabolites in the human post-prandial state. Further, the OGTT may not adequately challenge multiple metabolic systems beyond those involved in glucose metabolism.

The study by Mitchell et al. lends support to the use of an MMTT in place of the gold standard OGTT for diagnosing type 2 diabetes. The study uses valuable longitudinal cohort data (median follow-up time: 9.6 [inter-quartile range: 5.6-13.5] years) in adult Indigenous Americans without diabetes, in whom detailed inpatient baseline measures for MMTT and OGTT—as well as the more intensive intravenous glucose tolerance test—were obtained. Using in-depth modeling and analysis, Mitchell et al. demonstrated that glucose responses to their MMTT predicted the development of type 2 diabetes. While this work focuses on glycemic outcomes and type 2 diabetes, its use of an MMTT as a prognostic tool offers the possibility to examine other measures and metabolites without compromising the accuracy of glycemic endpoints.

The field of precision nutrition would benefit from a robust metabolic challenge test with biomarkers of phenotypic flexibility in addition to glycemic control. Phenotypic flexibility, or metabolic resilience, refers to the body’s ability to cope with or adapt to a nutritional challenge; it serves as an important indicator of metabolic health. Therefore, a challenge test, preferably an MMTT, that can interrogate multiple organ systems and serve as a prognostic marker for multiple chronic disease endpoints should be developed such that timely intervention can be provided to prevent or delay the development of disease. This test also should be standardized and validated, with the potential to be scaled and commercially manufactured for wider use and dissemination. The PhenFlex challenge, a liquid MMTT developed by Dr. Wopereis and colleagues [7], held promise of becoming one such test. As reported in a 2017 study, this test enabled the detection of metabolic disturbances in individuals with type 2 diabetes—disturbances not found in healthy individuals. While the PhenFlex challenge was validated in a small sample of 40 males and successfully measured phenotypic flexibility, whether the test has been scaled for widespread use in nutrition research or clinical practice remains unclear. Given the limitations in this study and others, the current work by Mitchell et al. is a timely and much needed contribution to the literature. Importantly, the prognostic capacity of the MMTT examined by Mitchell et al. suggests potential future use not only in clinical care but also for informing precision nutrition research.

The nutrition field is in urgent need of guidance for an established/standardized methodology for measuring phenotypic flexibility. It is critical that the scientific community works to identify an MMTT that shows diagnostic and prognostic potential. By standardizing, validating, and scaling such a test, we may be able find a new gold standard for identifying biomarkers that (a) illuminate interindividual variability in response to diets and (b) inform precision nutrition approaches that target chronic disease. The potential for this test to aid in the clinical diagnosis and monitoring of common chronic diseases also could be explored [https://nutritionforprecisionhealth.org/].