Associations of the HEI, Mediterranean, and MIND Dietary Patterns with Nutrient Intake and Adequacy in Older Adults

Associations of the HEI, Mediterranean, and MIND Dietary Patterns with Nutrient Intake and Adequacy in Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Brief Communication Associations of the HEI, Mediterranean, and MIND Dietary Patterns with Nutrient Intake and Adequacy in Older Adults Samitinjaya Dhakal, Nirajan Ghimire This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7489726/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diet quality is essential to healthy aging, yet the effectiveness of common dietary indices in predicting nutritional adequacy in older adults remains unclear. We address the gap by comparing popular indices: Healthy-Eating-Index, Mediterranean-score, and MIND-score. Using linear and multivariate-logistic regression models on a cohort of 72 older adults (mean age:77.5 years), we evaluated associations of the dietary indices with nutrient intake and the likelihood of meeting EAR/AI. Higher HEI-scores predicted greater intake of 12 nutrients (e.g., vitamin D, B12, potassium; all p < 0.05) and increased odds of nutrient adequacy for two. The Mediterranean-score had mixed associations, with higher vitamin C intake but lower adequacy of thiamin and selenium. The MIND score consistently outperformed both, predicting adequacy for 11 essential nutrients and showing the strongest predictive power. Therefore, we highlight HEI and MIND scores as effective for nutritional assessment in older adults, while the Mediterranean score may be limited by cultural context. Health sciences/Health care/Nutrition Health sciences/Medical research/Epidemiology Introduction Diet quality plays an important role in supporting healthy aging[ 1 ]. While the concept of a “healthy diet” is widely accepted, its measurement remains complex. Researchers and clinicians use dietary scores, like i) Healthy Eating Index (HEI)[ 2 ], ii) Mediterranean Diet (MED)-score[ 3 ], and iii) MIND-score[ 4 ]. However, these indices are built from different frameworks, and their effectiveness in predicting actual nutritional status is not uniform. The existing literature lacks a direct, comparative analysis to determine which of these scores is the strongest predictor of micronutrient adequacy in older adults. Using both linear and multivariate logistic regression models, we address this gap by evaluating the independent predictive value of each diet score for both overall nutrient intake and the likelihood of meeting established guidelines based on Estimated Average Requirements (EAR) and Adequate Intakes (AI). Methods Dietary Assessment Participants completed a structured 24-hour dietary recall interview, reporting all foods and beverages consumed the previous day, including portion sizes, preparation methods, and brand information. Each reported item was assigned an 8-digit USDA food code, which was then linked to nutrient composition data from the Food and Nutrient Database for Dietary Studies using a custom Python script. Dietary pattern scores The HEI-score was calculated for each participant using dietary assessment data[ 2 ]; MED-score was assessed using an adaptation of the PREDIMED[ 3 ] scoring system, excluding the sofrito component, as it is not common among Americans; and the MIND-score was calculated following the scoring system described by Morris et al. 2015[ 4 ]. Statistical Analysis The associations between dietary scores and nutrient intake were estimated with a series of linear regression models. Each model included one dietary score as the primary predictor, with nutrient intake as the dependent variable and age as a covariate. The results were summarized using unstandardized coefficients (β), p-values, and adjusted R 2 . $$\:NutrientIntake=\beta\:0+\beta\:1\left(DietaryScore\right)+\beta\:2\left(Age\right)+ϵ$$ To assess the independent predictive value of each dietary score for meeting micronutrient adequacy, we used multivariate logistic regression models. Adequacy was defined as meeting or exceeding EAR for nutrients with established values, or AI for those without an EAR. Adequacy was coded as a binary outcome. For direct comparison of effect sizes, all dietary scores were standardized (z-scored). Results were reported using standardized coefficients, odds ratios, and p-values. $$\:Logit\left(Adequacy\right)=\beta\:0+\beta\:1(z\_HEI)+\beta\:2(z\_MED)+\beta\:3(z\_MIND)+\beta\:4\left(Age\right)+\beta\:5\left(Sex\right)+ϵ$$ Results and Discussion Study Population : The study included 72 older adults from the parent study [ 5 ] (mean age 77.5 years), predominantly female (64%) and mostly White, non-Hispanic (98.6%). The overall mean dietary scores showed moderate overall diet quality, with an average HEI-score of 54.39 (SD = 9.40), MED-score of 4.32 (SD = 1.17), and MIND-score of 3.65 (SD = 1.60). These levels are below the recommendation and are consistent with prior reports of suboptimal nutrient intake among older adults [ 6 ]. Dietary Patterns and Nutrient Intake From Linear Regression ( Table 1 ) : Higher HEI-scores were consistently and positively associated with greater intake of a several nutrients: retinol (β = 11.33,p < 0.01), vitamin A (RAE) (β = 16.24,p < 0.05), β-cryptoxanthin (β = 12.46,p < 0.05), lutein-zeaxanthin (β = 123.69,p < 0.01), riboflavin (β = 0.03,p < 0.05), choline (β = 5.09,p < 0.05), vitamin B12 (β = 0.11,p < 0.001), vitamin C (β = 3.79,p < 0.01), vitamin D (β = 0.25,p < 0.001), vitamin K (β = 3.98,p < 0.05), calcium (β = 17.87,p < 0.01), and potassium (β = 43.01,p < 0.01). We did not observe any negative associations of HEI-scores with nutrients. On the other hand, MED-score showed conflicting associations, including both beneficial and inverse relationships. Higher scores were positively associated with α-carotene (β = 118.25,p < 0.05), theobromine (β = 7.26,p < 0.05), and vitamin C (β = 12.36,p < 0.05). This can be attributed to the focus of the MED on plant-based food intake. In contrast, higher adherence predicted lower intakes of thiamin (β=−0.1,p < 0.05), folic acid (β=−19.65,p < 0.01), and added vitamin E (β=−0.27,p < 0.05). The inverse associations for nutrients are noteworthy because they suggest that a higher adherence to a traditional, whole-food-focused MED may reduce consumption of modern fortified foods and supplements, which are common sources of these nutrients. This highlights a potential limitation of using the MED-scoring in U.S. populations that may not have access to the same variety of fresh foods as in the traditional Mediterranean region. Table 1 Significant Associations Between Dietary Scores and Nutrient Intake Unstandardized Beta (p-value) Nutrients HEI Score MED Score MIND Score Calcium (mg) (17.87, 0.01) – – Alpha-carotene (mcg) – (118.25, 0.03) – Choline (mg) (5.09, 0.02) – (16.60, 0.01) Copper (mg) – – (0.06, 0.02) Beta-cryptoxanthin (mcg) (12.46, 0.05) – (47.11, 0.01) Food folate (mcg) – – (11.64, 0.01) Folic acid (mcg) – (-19.65, 0.00) – Lutein + zeaxanthin (mcg) (123.69, 0.01) – – Potassium (mg) (43.01, 0.01) – (154.63, < 0.001) Retinol (mcg) (11.33, 0.01) – – Riboflavin (mg) (0.03, 0.02) – (0.08, 0.03) Theobromine (mg) – (7.26, 0.03) (7.70, 0.00) Thiamin (mg) – (-0.10, 0.01) – Vitamin A RAE (mcg) (16.24, 0.03) – – Vitamin B12 (mcg) (0.11, < 0.001) – (0.24, 0.01) Vitamin B6 (mg) – – (0.07, 0.02) Vitamin C (mg) (3.79, 0.00) (12.36, 0.02) (13.21, < 0.001) Vitamin D (mcg) (0.25, < 0.001) – (0.65, < 0.001) Vitamin E (added, mg) – (-0.27, 0.03) – Vitamin K (mcg) (3.98, 0.02) – – Results from linear regression models showing unstandardized Beta coefficients and p-values. Only statistically significant associations (p < 0.05) are displayed. Notably, MIND-score showed the most consistent association with nutrient density. Higher scores were linked to greater intake of sugars (β = 12.09,p < 0.05), fiber (β = 1.67,p < 0.05), which is likely attributable to the recommendations for berries, and other fruits[ 4 ]; as well as riboflavin (β = 0.08,p < 0.05), vitamin B6 (β = 0.07,p < 0.05), food folate (β = 11.64,p < 0.05), choline (β = 16.60,p < 0.01), vitamin B12 (β = 0.24,p < 0.01), vitamin C (β = 13.21,p < 0.001), vitamin D (β = 0.65,p < 0.001), theobromine (β = 7.70,p < 0.01), copper (β = 0.06,p < 0.05), and potassium (β = 154.63,p < 0.001). The MIND diet's strong performance is likely due to its design, which combines brain-healthy elements from the Mediterranean and DASH diets, and therefore, suggests its potential as an effective tool for promoting nutritional status in aging populations. Dietary Patterns and Nutrient Adequacy from Logistic Regression ( Table 2 ): Each standard deviation increase in HEI-score was associated with 3.0 times higher odds of meeting the EAR for phosphorus (p < 0.05), with a negative association observed for sodium adequacy (β=−0.88,p = 0.05). The negative association with sodium adequacy may reflect a positive outcome, as higher HEI-scores indicate lower sodium intake—a key target of dietary guidelines. On the other hand, the MED-score was associated with reduced adequacy of some nutrients. Each standard deviation increase predicted significantly lower odds of meeting the EAR for thiamin (β=−1.60,p < 0.01), niacin (β=−1.22,p < 0.01), phosphorus (β=−1.52,p < 0.01), zinc (β=−1.13,p < 0.05), and selenium (β=−1.97,p < 0.01). These findings mirror the intake-level associations, suggesting strict adherence to this score, outside its native cultural context, may underestimate the adequacy of some nutrients. Furthermore, MIND-score again emerged as the most consistent positive predictor of nutrient adequacy. Higher adherence was associated with 2.6 times greater odds of meeting vitamin C requirements (p < 0.05) and was also a significant predictor of thiamin, iron, selenium, and potassium adequacies (all,p < 0.05). The MIND diet’s superior prediction of nutrient adequacy is notable, as its design appears to overcome limitations observed with the MED. Table 2 Significant Associations Between Dietary Patterns and the Odds of Meeting Nutrient Guidelines Odds Ratio (p-value) Nutrient Reference MED Score MIND Score HEI Score Iron (mg) EAR (0.15, 0.00) (3.27, 0.02) – Niacin (mg) EAR (0.30, 0.01) – – Phosphorus (mg) EAR (0.22, 0.01) – (3.00, 0.01) Potassium (mg) AI – (3.15, 0.04) – Selenium (mcg) EAR (0.14, 0.00) (3.54, 0.02) – Sodium (mg) AI (0.15, 0.00) (4.22, 0.01) (0.41, 0.05) Thiamin (mg) EAR (0.19, 0.00) (3.12, 0.02) – Vitamin B12 (mcg) EAR (0.41, 0.03) – – Vitamin C (mg) EAR – (2.62, 0.02) – Zinc (mg) EAR (0.32, 0.01) – – Results from a multivariate logistic regression model showing odds ratios and p-values. Odds ratios > 1 indicate a higher likelihood of meeting the guideline. Only statistically significant associations (p < 0.05) are displayed. In conclusion, this study provides new evidence on the associations between three dietary indices and nutrient adequacy in older adults. The MIND- and HEI-scores were the strongest predictors, suggesting their utility for nutritional assessment, whereas the MED-score showed limitations when applied outside its cultural context. While interpretation is constrained by modest sample size, limited diversity, and reliance on recall-based dietary assessment, these findings highlight the importance of index selection and provide a foundation for future, larger longitudinal studies. Declarations The study was done in accordance with Declaration of Helsinki and was approved by the Institutional Review Board (IRB) at South Dakota State University (IRB-2024-56). All participants provided written informed consent for the study and its publication before any study procedures. Acknowledgements: The authors would like to express their sincere gratitude to the participants of this study for their time and valuable contributions. We would also like to thank the Brookings Activity Center for allowing us to use their facilities for surveys Funding sources: This research received no external funding. This work was supported by the South Dakota State University’s College of Education and Human Sciences Pilot Study Funding Program Authorship contribution statement: SD: Conceptualization, Funding acquisition, Methodology, Supervision, Project administration, Resources, Software, Data curation, Formal analysis, Writing – original draft, Writing – review & editing; NG: Investigation, Data curation, Software, Formal analysis Declaration of Generative AI and AI-assisted technologies in the writing process: During the preparation of this work, the authors used LLM to assist with sentence structure and grammar. All content was subsequently reviewed and edited by the authors, who take full responsibility for the final version of the manuscript. Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Bandayrel, K. and S. Wong, Systematic literature review of randomized control trials assessing the effectiveness of nutrition interventions in community-dwelling older adults. J Nutr Educ Behav, 2011. 43 (4): p. 251-62. Shams-White, M.M., et al., Healthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans,2020-2025. J Acad Nutr Diet, 2023. 123 (9): p. 1280-1288. Martínez-González, M.A., et al., A 14-item Mediterranean diet assessment tool and obesity indexes among high-risk subjects: the PREDIMED trial. PLoS One, 2012. 7 (8): p. e43134. Morris, M.C., et al., MIND diet slows cognitive decline with aging. Alzheimers Dement, 2015. 11 (9): p. 1015-22. Dhakal, S. and S. Bass, Association of Food Groups and Healthy Eating Index Scores With Domains of Cognitive Function in Older Adults From the Upper-Midwest: A Cross-Sectional Study. Current Developments in Nutrition, 2025. 9 . Choi, Y.J., et al., Food and nutrient intake and diet quality among older Americans. Public Health Nutr, 2021. 24 (7): p. 1638-1647. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7489726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":508492903,"identity":"764c8796-7bcd-4b52-b10f-40d9a1be806e","order_by":0,"name":"Samitinjaya Dhakal","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0246-9203","institution":"South Dakota State University","correspondingAuthor":true,"prefix":"","firstName":"Samitinjaya","middleName":"","lastName":"Dhakal","suffix":""},{"id":508492904,"identity":"f7d76bd7-677f-4187-96e3-9878cc6e3199","order_by":1,"name":"Nirajan Ghimire","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nirajan","middleName":"","lastName":"Ghimire","suffix":""}],"badges":[],"createdAt":"2025-08-29 15:05:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7489726/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7489726/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90870872,"identity":"15914dec-e794-4dd8-8448-3afa7b3f7f85","added_by":"auto","created_at":"2025-09-09 08:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":693851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7489726/v1/ca420104-10d5-4460-8729-e8c1c67cf8f0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Associations of the HEI, Mediterranean, and MIND Dietary Patterns with Nutrient Intake and Adequacy in Older Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiet quality plays an important role in supporting healthy aging[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While the concept of a “healthy diet” is widely accepted, its measurement remains complex. Researchers and clinicians use dietary scores, like i) Healthy Eating Index (HEI)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], ii) Mediterranean Diet (MED)-score[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and iii) MIND-score[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, these indices are built from different frameworks, and their effectiveness in predicting actual nutritional status is not uniform. The existing literature lacks a direct, comparative analysis to determine which of these scores is the strongest predictor of micronutrient adequacy in older adults. Using both linear and multivariate logistic regression models, we address this gap by evaluating the independent predictive value of each diet score for both overall nutrient intake and the likelihood of meeting established guidelines based on Estimated Average Requirements (EAR) and Adequate Intakes (AI).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDietary Assessment\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eParticipants completed a structured 24-hour dietary recall interview, reporting all foods and beverages consumed the previous day, including portion sizes, preparation methods, and brand information. Each reported item was assigned an 8-digit USDA food code, which was then linked to nutrient composition data from the Food and Nutrient Database for Dietary Studies using a custom Python script.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDietary pattern scores\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe HEI-score was calculated for each participant using dietary assessment data[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; MED-score was assessed using an adaptation of the PREDIMED[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] scoring system, excluding the sofrito component, as it is not common among Americans; and the MIND-score was calculated following the scoring system described by Morris et al. 2015[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe associations between dietary scores and nutrient intake were estimated with a series of linear regression models. Each model included one dietary score as the primary predictor, with nutrient intake as the dependent variable and age as a covariate. The results were summarized using unstandardized coefficients (β), p-values, and adjusted R\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:NutrientIntake=\\beta\\:0+\\beta\\:1\\left(DietaryScore\\right)+\\beta\\:2\\left(Age\\right)+ϵ$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the independent predictive value of each dietary score for meeting micronutrient adequacy, we used multivariate logistic regression models. Adequacy was defined as meeting or exceeding EAR for nutrients with established values, or AI for those without an EAR. Adequacy was coded as a binary outcome. For direct comparison of effect sizes, all dietary scores were standardized (z-scored). Results were reported using standardized coefficients, odds ratios, and p-values.\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Logit\\left(Adequacy\\right)=\\beta\\:0+\\beta\\:1(z\\_HEI)+\\beta\\:2(z\\_MED)+\\beta\\:3(z\\_MIND)+\\beta\\:4\\left(Age\\right)+\\beta\\:5\\left(Sex\\right)+ϵ$$\u003c/div\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy Population\u003c/span\u003e: The study included 72 older adults from the parent study [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (mean age 77.5 years), predominantly female (64%) and mostly White, non-Hispanic (98.6%). The overall mean dietary scores showed moderate overall diet quality, with an average HEI-score of 54.39 (SD\u0026thinsp;=\u0026thinsp;9.40), MED-score of 4.32 (SD\u0026thinsp;=\u0026thinsp;1.17), and MIND-score of 3.65 (SD\u0026thinsp;=\u0026thinsp;1.60). These levels are below the recommendation and are consistent with prior reports of suboptimal nutrient intake among older adults [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDietary Patterns and Nutrient Intake From Linear Regression (\u003c/span\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e: Higher HEI-scores were consistently and positively associated with greater intake of a several nutrients: retinol (β\u0026thinsp;=\u0026thinsp;11.33,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), vitamin A (RAE) (β\u0026thinsp;=\u0026thinsp;16.24,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), β-cryptoxanthin (β\u0026thinsp;=\u0026thinsp;12.46,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), lutein-zeaxanthin (β\u0026thinsp;=\u0026thinsp;123.69,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), riboflavin (β\u0026thinsp;=\u0026thinsp;0.03,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), choline (β\u0026thinsp;=\u0026thinsp;5.09,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), vitamin B12 (β\u0026thinsp;=\u0026thinsp;0.11,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vitamin C (β\u0026thinsp;=\u0026thinsp;3.79,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), vitamin D (β\u0026thinsp;=\u0026thinsp;0.25,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vitamin K (β\u0026thinsp;=\u0026thinsp;3.98,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), calcium (β\u0026thinsp;=\u0026thinsp;17.87,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and potassium (β\u0026thinsp;=\u0026thinsp;43.01,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). We did not observe any negative associations of HEI-scores with nutrients. On the other hand, MED-score showed conflicting associations, including both beneficial and inverse relationships. Higher scores were positively associated with α-carotene (β\u0026thinsp;=\u0026thinsp;118.25,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), theobromine (β\u0026thinsp;=\u0026thinsp;7.26,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and vitamin C (β\u0026thinsp;=\u0026thinsp;12.36,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This can be attributed to the focus of the MED on plant-based food intake. In contrast, higher adherence predicted lower intakes of thiamin (β=\u0026minus;0.1,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), folic acid (β=\u0026minus;19.65,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and added vitamin E (β=\u0026minus;0.27,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The inverse associations for nutrients are noteworthy because they suggest that a higher adherence to a traditional, whole-food-focused MED may reduce consumption of modern fortified foods and supplements, which are common sources of these nutrients. This highlights a potential limitation of using the MED-scoring in U.S. populations that may not have access to the same variety of fresh foods as in the traditional Mediterranean region.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant Associations Between Dietary Scores and Nutrient Intake\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnstandardized Beta (p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutrients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHEI Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMED Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMIND Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalcium (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(17.87, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAlpha-carotene (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(118.25, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCholine (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5.09, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(16.60, 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCopper (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.06, 0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBeta-cryptoxanthin (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(12.46, 0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(47.11, 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFood folate (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(11.64, 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFolic acid (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-19.65, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLutein\u0026thinsp;+\u0026thinsp;zeaxanthin (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(123.69, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePotassium (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(43.01, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(154.63, \u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRetinol (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(11.33, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRiboflavin (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.03, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.08, 0.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTheobromine (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(7.26, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.70, 0.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThiamin (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.10, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin A RAE (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(16.24, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin B12 (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.11, \u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.24, 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin B6 (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.07, 0.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin C (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.79, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(12.36, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(13.21, \u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin D (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.25, \u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.65, \u0026lt;\u0026thinsp;0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin E (added, mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-0.27, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin K (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(3.98, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eResults from linear regression models showing unstandardized Beta coefficients and p-values. Only statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are displayed.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNotably, MIND-score showed the most consistent association with nutrient density. Higher scores were linked to greater intake of sugars (β\u0026thinsp;=\u0026thinsp;12.09,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), fiber (β\u0026thinsp;=\u0026thinsp;1.67,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which is likely attributable to the recommendations for berries, and other fruits[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; as well as riboflavin (β\u0026thinsp;=\u0026thinsp;0.08,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), vitamin B6 (β\u0026thinsp;=\u0026thinsp;0.07,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), food folate (β\u0026thinsp;=\u0026thinsp;11.64,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), choline (β\u0026thinsp;=\u0026thinsp;16.60,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), vitamin B12 (β\u0026thinsp;=\u0026thinsp;0.24,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), vitamin C (β\u0026thinsp;=\u0026thinsp;13.21,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), vitamin D (β\u0026thinsp;=\u0026thinsp;0.65,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), theobromine (β\u0026thinsp;=\u0026thinsp;7.70,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), copper (β\u0026thinsp;=\u0026thinsp;0.06,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and potassium (β\u0026thinsp;=\u0026thinsp;154.63,p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The MIND diet's strong performance is likely due to its design, which combines brain-healthy elements from the Mediterranean and DASH diets, and therefore, suggests its potential as an effective tool for promoting nutritional status in aging populations.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDietary Patterns and Nutrient Adequacy from Logistic Regression (\u003c/span\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Each standard deviation increase in HEI-score was associated with 3.0 times higher odds of meeting the EAR for phosphorus (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a negative association observed for sodium adequacy (β=\u0026minus;0.88,p\u0026thinsp;=\u0026thinsp;0.05). The negative association with sodium adequacy may reflect a positive outcome, as higher HEI-scores indicate lower sodium intake\u0026mdash;a key target of dietary guidelines. On the other hand, the MED-score was associated with reduced adequacy of some nutrients. Each standard deviation increase predicted significantly lower odds of meeting the EAR for thiamin (β=\u0026minus;1.60,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), niacin (β=\u0026minus;1.22,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), phosphorus (β=\u0026minus;1.52,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), zinc (β=\u0026minus;1.13,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and selenium (β=\u0026minus;1.97,p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These findings mirror the intake-level associations, suggesting strict adherence to this score, outside its native cultural context, may underestimate the adequacy of some nutrients. Furthermore, MIND-score again emerged as the most consistent positive predictor of nutrient adequacy. Higher adherence was associated with 2.6 times greater odds of meeting vitamin C requirements (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and was also a significant predictor of thiamin, iron, selenium, and potassium adequacies (all,p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The MIND diet\u0026rsquo;s superior prediction of nutrient adequacy is notable, as its design appears to overcome limitations observed with the MED.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant Associations Between Dietary Patterns and the Odds of Meeting Nutrient Guidelines\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eOdds Ratio (p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutrient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMED Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMIND Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHEI Score\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIron (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.15, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.27, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNiacin (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.30, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhosphorus (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.22, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(3.00, 0.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePotassium (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.15, 0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelenium (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.14, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.54, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSodium (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.15, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.22, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.41, 0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThiamin (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.19, 0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.12, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin B12 (mcg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.41, 0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVitamin C (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(2.62, 0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZinc (mg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.32, 0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eResults from a multivariate logistic regression model showing odds ratios and p-values. Odds ratios\u0026thinsp;\u0026gt;\u0026thinsp;1 indicate a higher likelihood of meeting the guideline. Only statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are displayed.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides new evidence on the associations between three dietary indices and nutrient adequacy in older adults. The MIND- and HEI-scores were the strongest predictors, suggesting their utility for nutritional assessment, whereas the MED-score showed limitations when applied outside its cultural context. While interpretation is constrained by modest sample size, limited diversity, and reliance on recall-based dietary assessment, these findings highlight the importance of index selection and provide a foundation for future, larger longitudinal studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan\u003eThe study was done in accordance with Declaration of Helsinki and was approved by the Institutional Review Board (IRB) at South Dakota State University (IRB-2024-56). All participants provided written informed consent for the study and its publication before any study procedures.\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e The authors would like to express their sincere gratitude to the participants of this study for their time and valuable contributions. We would also like to thank the Brookings Activity Center for allowing us to use their facilities for surveys\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources:\u003c/strong\u003e This research received no external funding. This work was supported by the South Dakota State University\u0026rsquo;s College of Education and Human Sciences Pilot Study Funding Program\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution statement: SD:\u003c/strong\u003e Conceptualization, Funding acquisition, Methodology, Supervision, Project administration, Resources, Software, Data curation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing; \u003cstrong\u003eNG:\u003c/strong\u003e Investigation, Data curation, Software, Formal analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-assisted technologies in the writing process:\u0026nbsp;\u003c/strong\u003eDuring the preparation of this work, the authors used LLM to assist with sentence structure and grammar. All content was subsequently reviewed and edited by the authors, who take full responsibility for the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBandayrel, K. and S. Wong, \u003cem\u003eSystematic literature review of randomized control trials assessing the effectiveness of nutrition interventions in community-dwelling older adults.\u003c/em\u003e J Nutr Educ Behav, 2011. \u003cstrong\u003e43\u003c/strong\u003e(4): p. 251-62.\u003c/li\u003e\n\u003cli\u003eShams-White, M.M., et al., \u003cem\u003eHealthy Eating Index-2020: Review and Update Process to Reflect the Dietary Guidelines for Americans,2020-2025.\u003c/em\u003e J Acad Nutr Diet, 2023. \u003cstrong\u003e123\u003c/strong\u003e(9): p. 1280-1288.\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Gonz\u0026aacute;lez, M.A., et al., \u003cem\u003eA 14-item Mediterranean diet assessment tool and obesity indexes among high-risk subjects: the PREDIMED trial.\u003c/em\u003e PLoS One, 2012. \u003cstrong\u003e7\u003c/strong\u003e(8): p. e43134.\u003c/li\u003e\n\u003cli\u003eMorris, M.C., et al., \u003cem\u003eMIND diet slows cognitive decline with aging.\u003c/em\u003e Alzheimers Dement, 2015. \u003cstrong\u003e11\u003c/strong\u003e(9): p. 1015-22.\u003c/li\u003e\n\u003cli\u003eDhakal, S. and S. Bass, \u003cem\u003eAssociation of Food Groups and Healthy Eating Index Scores With Domains of Cognitive Function in Older Adults From the Upper-Midwest: A Cross-Sectional Study.\u003c/em\u003e Current Developments in Nutrition, 2025. \u003cstrong\u003e9\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eChoi, Y.J., et al., \u003cem\u003eFood and nutrient intake and diet quality among older Americans.\u003c/em\u003e Public Health Nutr, 2021. \u003cstrong\u003e24\u003c/strong\u003e(7): p. 1638-1647.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7489726/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7489726/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiet quality is essential to healthy aging, yet the effectiveness of common dietary indices in predicting nutritional adequacy in older adults remains unclear. We address the gap by comparing popular indices: Healthy-Eating-Index, Mediterranean-score, and MIND-score. Using linear and multivariate-logistic regression models on a cohort of 72 older adults (mean age:77.5 years), we evaluated associations of the dietary indices with nutrient intake and the likelihood of meeting EAR/AI. Higher HEI-scores predicted greater intake of 12 nutrients (e.g., vitamin D, B12, potassium; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and increased odds of nutrient adequacy for two. The Mediterranean-score had mixed associations, with higher vitamin C intake but lower adequacy of thiamin and selenium. The MIND score consistently outperformed both, predicting adequacy for 11 essential nutrients and showing the strongest predictive power. Therefore, we highlight HEI and MIND scores as effective for nutritional assessment in older adults, while the Mediterranean score may be limited by cultural context.\u003c/p\u003e","manuscriptTitle":"Associations of the HEI, Mediterranean, and MIND Dietary Patterns with Nutrient Intake and Adequacy in Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-08 11:52:08","doi":"10.21203/rs.3.rs-7489726/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"651dbb57-7698-4297-b7e6-e7157a8b47f3","owner":[],"postedDate":"September 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54003244,"name":"Health sciences/Health care/Nutrition"},{"id":54003245,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2025-09-09T08:02:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-08 11:52:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7489726","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7489726","identity":"rs-7489726","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}