Artificial Intelligence-Generated Diet Plans for Hypertension and Dyslipidemia: Adherence and Nutritional Insights
Abstract
Background: We evaluated diet plans generated by ChatGPT for hypertension and dyslipidaemia.
Methods: In October 2024, ChatGPT was used to generate meal plans for 24 simulated patients with different cardiovascular health problems. Data were used from men (n=12) and women (n=12), aged 56 yr, with mean heights of 176 cm and 161 cm respectively. Weight categories were based on BMI: normal, overweight, and obese, using weights of 56, 71, and 84 kg for women and 67, 85, and 101 kg for men. Four health conditions were assessed: hypertension stages 1 and 2 (systolic BP 130-139 mm Hg and ≥140 mm Hg; diastolic BP 80-89 mm Hg and ≥90 mm Hg), and elevated LDL levels (≥130 mg/dL and ≥160 mg/dL). Menus were evaluated for adherence to Mediterranean and DASH diets, including recommendations.
Results: Adherence to the Mediterranean and DASH diets was low across all groups, with median scores below 9 and 4.5, respectively. Common recommendations included weight loss, physical activity, reduced salt intake, stress management, and omega-3s for both hypertension and LDL reduction. Plant sterols/stanols were suggested only for LDL. No advice was given on smoking or alcohol use. Nutrient content did not differ significantly between hypertension and LDL menus (P>0.05).
Conclusion: This pioneering study found that AI-generated dietary models had low adherence to DASH and Mediterranean diets, though most recommendations were generally appropriate. Since the prompts only requested basic nutrition plans, future research should use more specific, personalized prompts to better assess AI's role in managing chronic diseases.
2. Maida CD, Daidone M, Pacinella G, et al (2022). Diabetes and ischemic stroke: an old and new relationship an overview of the close interaction between these dis-eases. Int J Mol Sci, 23:2397.
3. Joseph P, Leong D, McKee M, et al (2017). Reducing the global burden of cardio-vascular disease, part 1: the epidemiolo-gy and risk factors. Circ Res, 121:677-694.
4. Kaminsky LA, German C, Imboden M, et al (2022). The importance of healthy lifestyle behaviors in the prevention of cardiovascular disease. Prog Cardiovasc Dis, 70:8-15.
5. Valenzuela PL, Carrera-Bastos P, Gálvez BG, et al (2021). Lifestyle interventions for the prevention and treatment of hy-pertension. Nat Rev Cardiol, 18:251-275.
6. Lichtenstein AH, Appel LJ, Vadiveloo M, et al (2021). 2021 Dietary Guidance to Im-prove Cardiovascular Health: A Scien-tific Statement From the American Heart Association. Circulation, 144:e472-e487
7. Billingsley HE, Hummel SL, Carbone S (2020). The role of diet and nutrition in heart failure: A state-of-the-art narrative review. Prog Cardiovasc Dis, 63:538-551.
8. Kim DW, Park JS, Sharma K, et al (2024). Qualitative evaluation of artificial intel-ligence-generated weight management diet plans. Front Nutr, 11:1374834.
9. Islam MR, Urmi TJ, Al Mosharrafa R, Rahman MS, Kadir MF (2023). Role of ChatGPT in health science and re-search: A correspondence addressing potential application. Health Sci Rep, 6(10):e1625.
10. Ponzo V, Goitre I, Favaro E, et al (2024). Is ChatGPT an effective tool for provid-ing dietary advice? Nutrients, 16:469.
11. Papastratis I, Stergioulas A, Konstantinidis D, et al (2024). Can ChatGPT provide appropriate meal plans for NCD pa-tients? Nutrition, 121:112291.
12. Whelton PK, Carey RM, Aronow WS, et al (2018). 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guide-line for the prevention, detection, evalu-ation, and management of high blood pressure in adults: a report of the Amer-ican College of Cardiology/American Heart Association Task Force on Clini-cal Practice Guidelines. J Am Coll Cardiol, 71(19):e127-e248.
13. Nantsupawat N, Booncharoen A, Wi-setborisut A, et al (2019). Appropriate to-tal cholesterol cut-offs for detection of abnormal LDL cholesterol and non-HDL cholesterol among low cardiovas-cular risk population. Lipids Health Dis, 18:28.
14. Ruggeri S, Buonocore P, Amoriello T (2022). New validated short question-naire for the evaluation of the adher-ence of Mediterranean diet and nutri-tional sustainability in all adult popula-tion groups. Nutrients, 14:5177.
15. Mellen PB, Gao SK, Vitolins MZ, et al (2008). Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004. Arch Intern Med, 168:308-314.
16. Cohen J. (1988). Set correlation and contin-gency tables. Applied Psychological Measure-ment, 12: 425–434.
17. Filippou CD, Tsioufis CP, Thomopoulos CG, et al (2020). Dietary approaches to stop hypertension (DASH) diet and blood pressure reduction in adults with and without hypertension: a systematic review and meta-analysis of randomized controlled trials. Adv Nutr, 11:1150-1160.
18. Mehrabani S, Gerami S, Nouri M, et al (2023). Association of Mediterranean and DASH diets adherence with dyslipidemia: A cross-sectional study. J Iran Med Council, 6:469-478.
19. Antoniazzi L, Arroyo-Olivares R, Bitten-court MS, T et al (2021). Adherence to a Mediterranean diet, dyslipidemia and inflammation in familial hypercholes-terolemia. Nutr Metab Cardiovasc Dis, 31:2014-2022.
20. Karataş Ö, Demirci S, Pota K, Tuna S (2025). Assessing ChatGPT’s Role in Sar-copenia and Nutrition: Insights from a Descriptive Study on AI-Driven Solu-tions. J Clin Med, 14(5): 1747.
21. Li Q, Liu C, Zhang S, et al (2021). Dietary carbohydrate intake and new-onset hy-pertension: a nationwide cohort study in China. Hypertension, 78:422-430.
22. Chawla S, Tessarolo Silva F, Amaral Medei-ros S, et al (2020). The effect of low-fat and low-carbohydrate diets on weight loss and lipid levels: a systematic review and meta-analysis. Nutrients, 12:3774.
23. Maki KC, Dicklin MR, Kirkpatrick CF (2021). Saturated fats and cardiovascular health: current evidence and controver-sies. J Clin Lipidol, 15:765-772.
24. Yuan S, Yu HJ, Liu MW, et al (2020). Fat in-take and hypertension among adults in China: the modifying effects of fruit and vegetable intake. Am J Prev Med, 58:294-301.
25. World Health Organization (2023) Saturat-ed fatty acid and trans-fatty acid intake for adults and children: WHO guideline.
26. Nagao T, Nogawa K, Sakata K, et al (2021). Effects of alcohol consumption and smoking on the onset of hypertension in a long-term longitudinal study in a male workers’ cohort. Int J Environ Res Public Health, 18:11781.
27. Lee K, Kim J (2020). The effect of smoking on the association between long-term alcohol consumption and dyslipidemia in a middle-aged and older population. Alcohol Alcohol, 55:531-539.
28. Abroms LC, Yousefi A, Wysota CN, et al (2025). Assessing the Adherence of ChatGPT Chatbots to Public Health Guidelines for Smoking Cessation: Con-tent Analysis. J Med Internet Res, 27:e66896.
29. Amin S, Kawamoto CT, Pokhrel P (2025). Exploring the ChatGPT platform with scenario-specific prompts for vaping cessation. Tob Control, 34(2):251-253.
30. Guo P, Liu G, Xiang X, An R (2025). From AI to the Table: A Systematic Review of ChatGPT’s Potential and Performance in Meal Planning and Dietary Recom-mendations. Dietetics, 4(1): 7.
31. Kaçar HK, Kaçar ÖF, Avery A (2025). Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots. Nutrients, 17(2):206.
| Files | ||
| Issue | Vol 54 No 6 (2025) | |
| Section | Original Article(s) | |
| Keywords | ||
| Hypertension Dyslipidaemia Artificial intelligence Nutrition | ||
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