|
AI-Driven Nutritional Letter Grade and Grade Point Average (GPA) The University-Style Framework to Evaluate the Healthiness of foods.
Daniel S. L. Roberts, Ph.D. Summary The AI-Driven Food Grading System is a university-style A-B-C-D-F grading framework and Nutritional Grade Point Average (GPA) model designed to help consumers evaluate food products, meals, and dietary patterns in a clearer, more systematic, and more meaningful way. This approach is important because the modern food environment contains thousands of products that differ widely in nutritional value, ingredient quality, degree of processing, and potential health effects. Without a simple evaluative tool, consumers may find it difficult to compare products accurately and make informed dietary decisions. An AI-assisted grading system translates nutritional complexity into an understandable score, letter grade, and GPA value, thereby supporting more informed food choices in grocery stores, restaurants, and home meal preparation. Rather than relying only on isolated nutrition facts, marketing claims, or general impressions of whether a food is “healthy,” the grading process provides a structured framework for assessing multiple dimensions of food quality. These may include nutrient density, degree of processing, added sugar, sodium content, fat quality, additive profile, ingredient quality, glycemic impact, fiber quality, and broader metabolic and neurocognitive relevance. The broader significance of this system lies in its potential contribution to physiological health, mental health, cognitive functioning, metabolic regulation, and longevity. Foods with higher nutritional grades and GPA scores are generally more likely to provide essential vitamins, minerals, high-quality proteins, healthy fats, fiber, antioxidants, and bioactive compounds that support cellular function, immune balance, gut health, cardiovascular stability, blood sugar regulation, and brain function. Conversely, foods with consistently low-grade scores contribute to nutrient displacement, metabolic dysregulation, inflammation, poor satiety, excessive energy intake, and increased long-term health risk. Therefore, selecting foods with higher grades and maintaining a higher Nutritional GPA may help individuals build dietary patterns that are more supportive of optimal health. Several lines of evidence support the association between higher-quality dietary patterns and improved health outcomes. In general, individuals who regularly consume foods and follow dietary patterns corresponding to A-range letter grades and high Nutritional GPA scores tend to show more favorable health profiles than individuals whose dietary patterns fall predominantly in the C or D range. These favorable profiles may include better physical health, improved cardiometabolic function, stronger cognitive performance, greater emotional stability, healthier aging trajectories, and lower risk of chronic disease and premature mortality. Preliminary and emerging studies have identified positive associations between higher-quality dietary patterns and better physical health, cardiometabolic function, cognitive performance, mental well-being, healthy aging, and reduced mortality risk. Similarly, research on optimal dietary patterns for healthy aging has found that stronger adherence to healthy dietary patterns is associated with greater odds of reaching older age with preserved cognitive, physical, and mental health. These findings support the validity of using Universal Food Letter Grades and Nutritional GPA scores as summary indicators of dietary patterns that are more likely to promote long-term health. In conclusion, an AI-driven A–F Food Grading System and Nutritional GPA model can serve as a valuable tool for identifying foods and dietary patterns that support health. By combining numerical scoring, letter grades, and GPA-style dietary tracking, the model offers a simple but powerful way to evaluate food quality, compare meals, and monitor long-term dietary patterns. Ultimately, foods and meals with higher grades are more likely to support a stronger overall health profile, while lower-grade foods can be identified and reduced where appropriate. In this sense, the Nutritional GPA system transforms food selection into a clearer, more measurable, and more health-oriented process, helping individuals make choices that better support lifelong physiological, mental, cognitive, and metabolic well-being.
eChapter
The AI-Driven University-Style Nutritional Grading of Foods and Meals The modern food supply contains thousands of products that differ substantially in nutrient density, degree of processing, ingredient quality, added sugar content, sodium level, fat composition, fiber content, and additive profile. Conventional nutrition labels provide important information, but they often require substantial nutritional literacy for consumers to interpret them accurately. An AI-assisted grading system can synthesize multiple nutritional and health-related variables into a single evaluative score. This score may help consumers identify foods and meals that are more likely to support physiological health, metabolic stability, cognitive function, mental well-being, and long-term disease prevention. The proposed model is conceptually similar to the way academic achievement is summarized through individual course grades and an overall GPA. Just as grades from multiple courses contribute to a student’s cumulative academic GPA, individual food scores may contribute to an overall Nutritional GPA. A single food product may receive a letter grade, a complete meal may receive a composite score based on its ingredients, and all foods consumed during a day may be combined to generate a daily Nutritional GPA. Over longer periods, daily scores may also be aggregated to estimate weekly, monthly, or long-term dietary quality. This framework moves food assessment beyond isolated nutrient values and toward a more integrated evaluation of dietary healthfulness. Each food or meal may be assessed across several domains, including whole-food quality, nutrient density, protein quality, fiber content, carbohydrate quality, glycemic impact, fat composition, sodium burden, degree of processing, additive exposure, and overall ingredient quality. Each domain may be assigned a score, converted into a grade-point value, weighted according to its relative nutritional importance, and averaged to produce a final grade. The resulting score provides a simplified but multidimensional estimate of the food’s probable contribution to health. Letter-Grade and GPA Classification Systems Across Industries Letter-grade classification systems are already widely used across many sectors to communicate differences in quality, performance, safety, or value. In education, the A–B–C–D–F framework is used to summarize levels of achievement and to calculate cumulative GPA values. In the food industry, grading systems are used to classify agricultural products and commodities such as meat, eggs, dairy products, and fresh produce according to standardized quality criteria. In consumer product evaluation, automobiles, household appliances, electronics, and industrial materials are commonly rated according to reliability, durability, safety, efficiency, and performance. In entertainment, terms such as “B movie” or “A-level production” reflect the broader cultural familiarity of letter grades as shorthand indicators of relative quality. The use of an A–F framework in nutrition therefore represents a logical extension of an already familiar evaluative model. Within this system, foods, meals, and diets can be classified in a manner that is intuitive to consumers while still grounded in multidimensional nutritional assessment. For example, an individual could evaluate a whole food such as an avocado, a packaged product such as canned baked beans, or a homemade meal such as beef and vegetable soup using the same core criteria. This approach allows diverse foods to be compared across categories while still accounting for differences in processing, nutrient composition, ingredient quality, and meal balance. For illustrative purposes, a homemade beef and vegetable soup prepared with beef cubes, canned diced tomatoes, frozen mixed vegetables, potatoes, and garlic may receive an estimated grade of A−, corresponding to a GPA of approximately 3.7. This grade indicates that the soup is nutritionally strong because it contains whole-food ingredients, high-quality protein, vegetables, complex carbohydrates, and micronutrient-rich components. However, the score may be limited by the potential sodium content of canned or packaged ingredients and by the saturated fat content of the beef, depending on the cut used. The grade could be improved further by using lower-sodium ingredients, leaner beef, and additional high-fiber foods such as beans, lentils, barley, or leafy greens. A breakfast composed of oats, flaxseed, walnuts, blueberries, and plain milk would likely receive a high grade because it provides slow-digesting carbohydrates, dietary fiber, plant-based omega-3 fatty acids, minerals, antioxidants, and protein. Such a meal supports satiety, glycemic stability, gut health, and cardiometabolic health. Depending on portion size, milk type, and overall nutrient balance, this breakfast may fall within the A to A+ range. By contrast, a can of Heinz Deep-Browned Beans with pork and tomato sauce may receive an estimated grade of B+ and a GPA of approximately 3.3. This grade reflects the product’s beneficial content of legumes, plant-based protein, fiber, carbohydrates, and minerals. However, the score is limited by its degree of processing, sodium content, added sugars, and reliance on canned packaging. When consumed as part of a more complete meal—such as with steamed spinach, brown rice or barley, a boiled egg, a side salad, and an olive oil-based dressing—the overall meal grade may increase to approximately A−. In this context, the canned beans serve as the foundation of a more balanced plate rather than as a complete meal on their own. Overall, an AI-driven university-style food-grading system may serve as an accessible decision-support tool for consumers, educators, clinicians, and researchers. By combining the simplicity of letter grades with the analytical capacity of multidimensional nutritional assessment, the framework helps communicate dietary quality more clearly and support healthier food choices. Although such a system should not replace individualized dietary guidance or clinical nutrition assessment, it provides a useful complementary method for interpreting food quality and promoting long-term health. Using a University-Style A–F Framework to Evaluate the Healthfulness of Dietary Patterns The university-style A–B–C–D–F grading framework may provide a practical and accessible method for evaluating the healthfulness of established dietary models and dietary patterns. Such a framework can be applied to comprehensive menu plans based on models such as the AI-Driven All-Substances Inclusive Diet (AI-ASID), the Mediterranean diet, Blue Zones dietary patterns, and the Paleo diet. For instance, the following prompt was used to evaluate the healthiness of the AI-Driven All-Substances Inclusive Diet (AI-ASID. Using the university-style A-B-C-D-F framework traditionally applied to student performance, and incorporating additional food-grading categories, provide a comprehensive menu plan based for the AI-Driven All-Substances Inclusive Diet (AI-ASID) The AI-ASID scores of A corresponding to a GPA 4.0 indicates that the menu plan closely approximates an ideal dietary pattern. It emphasizes vegetables, fruits, whole grains, legumes, nuts, seeds, high-quality proteins, healthy fats, fermented foods, herbs, spices, and adequate hydration, while minimizing ultra-processed foods, excess sodium, added sugars, poor-quality fats, artificial additives, and avoidable contaminants. This is consistent with major public-health guidance emphasizing dietary adequacy, balance, moderation, diversity, minimally processed foods, and limits on sodium, unhealthy fats, and added sugars. A similar prompt may be used to evaluate other dietary patterns, including the Loma Linda Blue Zone diet, also associated with the Seventh-day Adventist dietary pattern in California, United States. Using the university-style A-B-C-D-F framework applied to grade student performance, and incorporating additional categories of food grading categories, provide a letter grade and a comprehensive menu plan based on the Loma Linda Blue Zone Diet or Seventh-day Adventist Diet (California, US). Using the university-style A–B–C–D–F framework, a comprehensive menu plan based on the Seventh-day Adventist Blue Zone diet, especially the Loma Linda pattern, generally receive a high nutritional grade, approximately A−, with an estimated Nutritional GPA of 3.74. This high score reflects the diet’s emphasis on whole grains, legumes, vegetables, fruits, nuts, seeds, and minimally processed foods. The Adventist Health Study-2 (Ortich et al., 2013), classified dietary patterns as vegan, lacto-ovo vegetarian, pesco-vegetarian, semi-vegetarian, and no vegetarian, and found that vegetarian dietary patterns were associated with lower all-cause mortality compared with no vegetarian patterns. Thus, within the proposed university-style grading model, the Seventh-day Adventist Blue Zone diet may be classified as an A− dietary pattern, indicating excellent overall nutritional quality with minor limitations related primarily to sodium control in packaged foods and the need to monitor nutrients such as vitamin B12, vitamin D, iodine, calcium, iron, zinc, selenium, and long-chain omega-3 fatty acids in stricter vegetarian or vegan versions. This framework may also be applied to other dietary models, including the MIND diet, a Mediterranean-DASH hybrid focused on brain health; the DASH diet, which emphasizes blood-pressure control; the ketogenic diet, which is low in carbohydrates and high in fat; the carnivore diet, which is highly restrictive and based almost entirely on animal foods; the flexitarian diet, which is mostly plant-based with occasional meat; the low-FODMAP diet, which is used primarily for irritable bowel syndrome; and the vegan diet, which excludes all animal products. Table 1: Estimated Letter Grades and Nutritional GPA Scores for Selected Popular Dietary Patterns
Interpretation of Selected Dietary Patterns The Paleo diet receives an estimated grade of B+ to A− because it is generally nutrient-dense, whole-food based, and low in ultra-processed foods. A well-designed Paleo menu typically emphasizes vegetables, fruits, nuts, seeds, eggs, fish, lean meats, and healthy fats, which may support protein quality, micronutrient intake, glycemic stability, and reduced additive exposure. However, the Paleo diet may lose points because it commonly excludes legumes, whole grains, and dairy foods. These exclusions may reduce dietary fiber diversity, calcium intake, prebiotic exposure, and long-term microbiome support if the plan is not carefully designed. The Mediterranean diet receives an estimated grade of A to A+ because it is consistently associated with high nutritional quality, strong cardiometabolic protection, reduced inflammatory burden, and favorable longevity outcomes. Its emphasis on vegetables, fruits, whole grains, legumes, nuts, seeds, olive oil, fish, herbs, and minimally processed foods makes it highly compatible with the proposed university-style nutritional grading framework. The Sardinia Blue Zone dietary pattern receives an estimated grade of A+ because it is exceptionally aligned with principles of longevity nutrition, metabolic stability, cardiovascular protection, and low cumulative dietary burden. Its emphasis on plant foods, legumes, whole grains, traditional preparation methods, moderate energy intake, and minimally processed foods supports its classification as a very high-quality dietary pattern. The vegetarian diet receives an estimated grade of A− to A when it is well planned. This grade reflects a dietary pattern that is generally nutrient-dense, fiber-rich, and associated with favorable cardiometabolic and longevity outcomes. Within the university-style A-B-C-D-F nutritional grading framework and the AI-ASID model, a comprehensive vegetarian diet performs strongly across multiple nutritionals, metabolic, microbiome, and exposomic domains. However, stricter vegetarian and vegan versions require careful attention to vitamin B12, vitamin D, iodine, iron, zinc, calcium, selenium, and long-chain omega-3 fatty acids. Overall, the university-style A to F framework provides an accessible method for comparing the healthfulness of diverse dietary patterns. By translating complex nutritional characteristics into letter grades and GPA-style scores, the model allows consumers, educators, clinicians, and researchers to evaluate dietary quality in a familiar, systematic, and practical way. The Application of Letter Grades and GPA to Academic and Nutritional Assessment Frameworks In an academic-style grading framework, the letter grades represent different levels of overall quality or performance. In colleges and university systems, these letter grades are commonly converted into grade points, which are then used to calculate a Grade Point Average (GPA). A standard 4.0-scale conversion is often structured as follows:
The Grade Point Average (GPA) is a cumulative numerical index representing overall performance across multiple courses or evaluated units. GPA is calculated by weighting the grade points earned in each course according to the number of credit hours or units assigned to that course. The standard GPA formula is:
For example:
The total weighted points equal 29.3, and the total credits equal 8. The GPA is therefore calculated as:
The Application of Letter Grades and GPA to Nutritional Assessment In evaluative systems outside education—such as nutritional assessment frameworks—the same logic is applied. Foods, meals, dietary patterns, or food products may receive letter grades based on criteria such as nutrient density, degree of processing, additive exposure, metabolic impact, or long-term associations with health outcomes. These grades can then be converted into numerical scores and averaged to produce an overall dietary quality index or nutritional GPA, thereby providing a standardized and intuitive method for comparing dietary quality across individuals, populations, or food systems. Defining How Letter Grade and GPA are Calculated for Nutrition A letter grade represents the overall nutritional quality of a food item or dietary pattern based on predefined evaluation criteria. These criteria may include nutrient density, caloric balance, fiber content, glycemic load, fatty acid composition, sodium and added sugar content, degree of processing, presence of additives or contaminants, and associations with chronic disease risk. Foods or diets that strongly support physiological and metabolic health receive higher grades, whereas those associated with nutritional imbalance or adverse health outcomes receive lower grades. A representative grading structure may be defined as follows. To permit quantitative comparison, each letter grade is converted into a nutritional grade point value. A standard 4.0-scale system may be adapted as follows:
The Nutritional GPA (NGPA) is then calculated by averaging the weighted nutritional scores of foods, meals, or dietary components consumed over a defined period. Weighting may be based on factors such as caloric contribution, serving size, frequency of consumption, or metabolic significance.
The General Formula is:
For example:
An NGPA of 2.78 would correspond approximately to a “B−/C+” dietary profile, suggesting moderate nutritional quality with substantial room for improvement. Within more advanced nutritional frameworks—including precision nutrition, exposomic, nutrigenomics, metabolomics, microbiome science, and longevity science dietary models —the grading system may extend beyond classical nutrient analysis to include broader dimensions of dietary exposure. These may include ultra-processed foods, food additives, environmental contaminants, endocrine-disrupting compounds, microplastics, inflammatory potential, microbiome effects, and cumulative metabolic burden. In this context, the nutritional GPA functions as a multidimensional index that summarizes the overall quality and potential health impact of an individual’s total dietary exposure. Note: When additional food-grading categories are provided, an AI-assisted system can help estimate the final letter grade and Grade Point Average (GPA) of packaged foods, canned foods, individual meals, specific menu plans, and broader dietary patterns. These may include dietary models such as the AI-Driven All-Substances Inclusive Diet (AI-ASID), the Mediterranean diet or pale diet, and other established or emerging nutritional models. The same grading framework may be applied to a canned food product, a homemade meal, a seven-day menu plan, or an entire dietary pattern to produce a standardized nutritional letter grade and GPA-style summary score. Additional Categories to the University-Style Food-Grading Criteria In addition to core nutritional criteria, the Food Grade Point Assessment System may incorporate broader evaluative domains, including glycemic impact, fiber quality, protein completeness, micronutrient diversity, phytonutrient content, inflammatory potential, contaminant risk, cooking method, digestive tolerance, sustainability, and cost-effectiveness. These expanded categories allow food products and meals to be assessed not only according to basic nutrient composition, but also according to their broader metabolic, neurocognitive, gastrointestinal, environmental, and practical health implications. Table 2 below presents a list of additional food-grading criteria for multidimensional nutritional evaluation. The following prompt may be used to apply these criteria: Using a university-style A–B–C–D–F grading framework, evaluate the nutritional quality of an avocado across each of the additional food-grading criteria listed below. For each category, assign a numerical score, a corresponding letter grade, and a brief justification. Present the results in table format.” Table 2: Food-Grading Criteria forSelected Multidimensional Nutritional Evaluation
Other Criteria to Evaluate the Healthfulness of Foods In addition to academic-style letter grades and Nutritional GPA systems, numerous nutritional evaluation frameworks have been developed to assess the healthfulness of foods, meals, and dietary patterns. These systems vary in complexity, purpose, scientific methodology, and target audience. Some are designed primarily for consumer guidance and food labeling, whereas others are intended for clinical research, public health policy, or precision nutrition applications. Below is an overview of major nutritional evaluation systems currently used in nutrition science, public health, and the food industry. You may use these criteria to evaluate the healthiness of different foods. Including a standard avocado, one cup of cooked black beans, or an apple.
Table 3: Major Nutritional Evaluation Systems and Criteria for Assessing Food Healthfulness
Conceptual Hierarchy of Nutritional Evaluation Systems Compared with more advanced frameworks—such as the Health Quality Index, exposomic dietary models, precision nutrition systems, and other multidimensional assessment approaches—academic-style letter grades and Nutritional GPA systems provide accessible summary tools for communicating dietary quality. Their strength lies in translating complex nutritional information into familiar, easily interpretable categories and numerical scores. However, these systems are necessarily simplified and cannot, by themselves, fully capture the complexity of diet–health interactions across biological systems or over the lifespan. Rather, they may function as consumer-friendly summary measures derived from more comprehensive models that integrate nutritional composition, metabolic effects, food processing, environmental exposures, microbiome interactions, inflammatory potential, and long-term health outcomes. The Benefits of Healthy Dietary Patterns Healthy dietary patterns provide benefits that extend well beyond disease prevention. A high-quality diet supports physical health, mental well-being, energy regulation, cognitive performance, emotional stability, healthy aging, and overall quality of life. Rather than focusing only on isolated nutrients, contemporary nutrition science emphasizes whole dietary patterns composed of vegetables, fruits, legumes, whole grains, nuts, seeds, lean proteins, fermented foods, and healthy fats (Cena & Calder, 2020; U.S. Department of Agriculture & U.S. Department of Health and Human Services, 2020; World Health Organization, 2026). Healthy dietary patterns help preserve metabolic and cardiovascular health by supporting blood glucose regulation, lipid balance, blood pressure control, and healthy body composition. Diets rich in fiber, unsaturated fats, minimally processed foods, and diverse plant foods are associated with reduced risk of obesity, type 2 diabetes, cardiovascular disease, certain cancers, and premature mortality (Cena & Calder, 2020; Shan et al., 2023; World Health Organization, 2026). These effects are important across the lifespan because dietary habits influence not only present health, but also long-term functional capacity, independence, and healthy aging. Healthy diets also support energy, endurance, sleep quality, immune function, skin health, and physical performance. By providing adequate protein, essential fatty acids, vitamins, minerals, antioxidants, and phytochemicals, a nutrient-dense diet helps the body repair tissues, regulate inflammation, maintain hormonal balance, and sustain daily activity (Cena & Calder, 2020; U.S. Department of Agriculture & U.S. Department of Health and Human Services, 2020). In this sense, healthy eating is not merely a preventive medical practice; it is a foundation for vitality, productivity, and resilience. Diet also plays an important role in brain and mental health. Healthy dietary patterns, particularly those similar to the Mediterranean diet, have been associated with better mood, lower risk of depressive symptoms, improved quality of life, and stronger cognitive functioning (Firth et al., 2020). Nutrients such as omega-3 fatty acids, B vitamins, magnesium, zinc, iron, iodine, choline, and polyphenols contribute to neurotransmitter synthesis, neuroplasticity, mitochondrial function, and regulation of inflammation. Therefore, a healthy diet may support concentration, memory, motivation, emotional regulation, and stress tolerance. The benefits of healthy eating also have social and psychological dimensions. When individuals experience better energy, clearer thinking, improved physical comfort, and greater confidence in their health, they may also experience stronger self-efficacy and self-esteem. Improved health can support participation in education, work, family life, and personal relationships. Although diet alone does not determine success, attractiveness, self-worth, or social acceptance, it can contribute to the biological and psychological conditions that help people participate more fully in life. Finally, healthy dietary patterns may help individuals preserve their health long enough to enjoy meaningful relationships and later-life responsibilities, including caring for children or grandchildren. Long-term adherence to high-quality dietary patterns has been associated with healthier aging, including better physical, cognitive, and mental health outcomes (Tessier et al., 2025). Good nutrition can support longevity, mobility, independence, and continued engagement in family and community life. In this broader sense, healthy eating contributes not only to living longer, but also to living better—with more energy, dignity, connection, and capacity to love and be loved. Relationships between AI-ASID Dietary Quality, High Letter Grades, Nutritional GPA, and Health Outcomes Several lines of evidence support an association between higher-quality dietary patterns and improved health outcomes. Within the proposed AI-Driven All-Substances Inclusive Diet (AI-ASID) grading model, foods and dietary patterns that receive letter grades in the A range and high Nutritional Grade Point Average (GPA) scores reflect stronger overall dietary quality than foods or dietary patterns receiving grades in the C or D range. In general, higher-grade dietary patterns are characterized by greater intake of vegetables, fruits, whole grains, legumes, nuts, seeds, high-quality proteins, healthy fats, and minimally processed foods, together with lower intake of ultra-processed foods, excess sodium, added sugars, poor-quality fats, and avoidable contaminants. The relationship between dietary quality and health outcomes is typically modest to moderate at the level of single biomarkers but becomes stronger when multiple health domains are evaluated together. Across nutritional epidemiology and diet-quality research, higher scores on dietary quality indices such as the Healthy Eating Index (HEI), Alternative Healthy Eating Index (AHEI), Mediterranean diet scores, DASH-style scores, and Dietary Inflammatory Index (DII) are consistently associated with more favorable health indicators, including lower adiposity, improved cardiometabolic risk profiles, reduced inflammatory burden, lower depressive-symptom risk, and reduced chronic disease incidence or mortality (Chiuve et al., 2005; Lassale et al., 2019; Onvani et al., 2017). For individual outcomes—such as body mass index, blood pressure, lipid profile, glycemic markers, inflammatory markers, depressive symptoms, or cognitive performance—the observed associations are often small to moderate in magnitude and vary substantially by population, dietary assessment method, covariate adjustment, follow-up period, and outcome definition (Chiuve et al., 2005; Hua et al., 2024; Lassale et al., 2019). For broader composite outcomes—such as overall cardiometabolic health, healthy aging, major chronic disease risk, or cumulative disease-risk profiles—associations tend to be stronger because these outcomes capture the combined influence of diet across multiple biological pathways rather than a single biomarker (Chiuve et al., 2012; Onvani et al., 2017). Stronger associations may also be observed in model-based or composite scoring systems when dietary quality scores are compared with aggregated health-quality indices. Accordingly, individuals who regularly consume foods and follow dietary patterns with A-range letter grades and high Nutritional GPA scores have more favorable health profiles than individuals whose dietary patterns fall predominantly in the C or D range. These favorable profiles include better physical health, improved cardiometabolic function, stronger cognitive performance, greater emotional stability, healthier aging trajectories, and lower risk of chronic disease and premature mortality. However, these associations should not be interpreted as deterministic, because health outcomes are also shaped by genetics, physical activity, sleep, socioeconomic conditions, environmental exposures, medication use, stress, and access to health care. In summary, dietary quality id an important determinant of health, but it operates as part of a broader network of biological, behavioral, environmental, and social influences. Preliminary and emerging studies have identified positive associations between higher-quality dietary patterns and better physical health, cardiometabolic function, cognitive performance, mental well-being, healthy aging, and reduced mortality risk. For example, evidence from studies of ultra-processed food exposure indicates that poorer dietary quality is associated with higher risks of cardiovascular disease mortality, type 2 diabetes, anxiety, and common mental disorder outcomes (Lane et al., 2024). Similarly, research on optimal dietary patterns for healthy aging has found that stronger adherence to healthy dietary patterns is associated with greater odds of reaching older age with preserved cognitive, physical, and mental health (Tessier et al., 2025). These findings support the plausibility of using high AI-ASID letter grades and Nutritional GPA scores as summary indicators of dietary patterns that are more likely to promote long-term health. References Cena, H., & Calder, P. C. (2020). Defining a healthy diet: Evidence for the role of contemporary dietary patterns in health and disease. Nutrients, 12(2), 334. https://doi.org/10.3390/nu12020334 Chiuve, S. E., Fung, T. T., Rimm, E. B., Hu, F. B., McCullough, M. L., Wang, M., Stampfer, M. J., & Willett, W. C. (2005). Alternative dietary indices both strongly predict risk of chronic disease. The Journal of Nutrition, 142(6), 1009–1018. https://doi.org/10.3945/jn.111.157222 Firth, J., Gangwisch, J. E., Borsini, A., Wootton, R. E., & Mayer, E. A. (2020). Food and mood: How do diet and nutrition affect mental wellbeing? BMJ, 369, m2382. https://doi.org/10.1136/bmj.m2382 Hua, R., Liang, G., & Yang, F. (2024). Meta-analysis of the association between dietary inflammation index and C-reactive protein level. Medicine, 103(19), e38196. https://doi.org/10.1097/MD.0000000000038196 Lane, M. M., Gamage, E., Du, S., Ashtree, D. N. et al. (2024). Ultra-processed food exposure and adverse health outcomes: Umbrella review of epidemiological meta-analyses. BMJ, 384, e077310. https://doi.org/10.1136/bmj-2023-077310 Lassale, C., Batty, G. D., Baghdadli, A., Jacka, F., Sánchez-Villegas, A., Kivimäki, M., &Akbaraly, T. (2019). Healthy dietary indices and risk of depressive outcomes: A systematic review and meta-analysis of observational studies. Molecular Psychiatry, 24, 965–986. https://doi.org/10.1038/s41380-018-0237-8 Onvani, S., Haghighatdoost, F., Surkan, P. J., Larijani, B., &Azadbakht, L. (2017). Adherence to the Healthy Eating Index and Alternative Healthy Eating Index dietary patterns and mortality from all causes, cardiovascular disease and cancer: A meta-analysis of observational studies. Journal of Human Nutrition and Dietetics, 30(2), 216–226. https://doi.org/10.1111/jhn.12415 Orlich, M. J., Singh, P. N., Sabaté, J., et al. (2013). Vegetarian dietary patterns and mortality in Adventist Health Study 2. JAMA Internal Medicine, 173(13), 1230–1238. https://doi.org/10.1001/jamainternmed.2013.6473 Shan, Z., Wang, F., Li, Y., Baden, M. Y. et al. (2023). Healthy eating patterns and risk of total and cause-specific mortality. JAMA Internal Medicine, 183(2), 142–153. https://doi.org/10.1001/jamainternmed.2022.6117 Tessier, A.-J., Wang, F., Ardisson Korat, A. A., Eliassen, A. H., Chavarro, J., Grodstein, F., Li, J., Liang, L., Willett, W. C., Sun, Q., Stampfer, M. J., Hu, F. B., & Guasch-Ferré, M. (2025). Optimal dietary patterns for healthy aging. Nature Medicine, 31, 1644–1652. https://doi.org/10.1038/s41591-025-03570-5 U.S. Department of Agriculture, & U.S. Department of Health and Human Services. (2020). Dietary guidelines for Americans, 2020–2025 (9th ed.). https://www.dietaryguidelines.gov/ World Health Organization. (2026, January 26). Healthy diet. https://www.who.int/news-room/fact-sheets/detail/healthy-diet
|