TY - JOUR

T1 - Optimizing Weight Loss with Artificial Intelligence: A Randomized Controlled Trial of Dietary-Supplement Prescriptions in Adults with Overweight and Obesity

AU - Pokushalov, Evgeny

AU - Garcia, Claire

AU - Ponomarenko, Andrey

AU - Samoilova, Iuliia

AU - Smith, John

AU - Johnson, Michael

AU - Pak, Inessa

AU - Shrainer, Evgenya

AU - Kudlay, Dmitry

AU - Romanova, Anastasia

AU - Miller, Richard

N1 - In this study, the pharmaceutical company S.Lab (Soloways TM) contributed solely by manufacturing the necessary active supplements used in the research. S.Lab (Soloways TM) did not participate in the design, conduct, or funding of the experiment, except for providing the required supplements. Similarly, Triangel Scientific did not participate in the design, conduct, or funding of the experiment, except for providing the AI software. The entire study was independently conducted by the research team from the Center for New Medical Technologies. This arrangement ensured that the study results were not influenced by commercial interests, maintaining the integrity and independence of our research.

PY - 2025/10

Y1 - 2025/10

N2 - BackgroundObesity is a complex, multifactorial chronic disease that poses significant health risks. Recent advancements in artificial intelligence (AI) offer the potential for more personalized and effective dietary-supplement (DS) regimens to promote weight loss. This randomized controlled trial evaluated the efficacy of AI-guided DS prescriptions compared with standard physician-guided DS prescriptions in adults with obesity.MethodsThis randomized, parallel-group pilot study enrolled 60 individuals aged 40–60 years with a body-mass index (BMI) ≥ 25 kg m−2. Participants were randomized to receive either AI-guided DS prescriptions generated on the basis of each patient's individualized genetic, metabolic, and behavioral data (n = 30) or physician-guided DS prescriptions (n = 30) for 180 days. The primary endpoints were the percentage change in body weight and the proportion of participants achieving a ≥5 % weight reduction. Secondary endpoints included changes in BMI, fat mass, visceral-fat rating, systolic and diastolic blood pressure, lipid profiles, fasting plasma glucose, hsCRP levels, and postprandial appetite ratings. Adverse events were monitored throughout the study.ResultsBaseline characteristics were well balanced between groups. Mean weight loss was −12.3 % (95 % CI: −13.1 to −11.5) in the AI-guided group vs. −7.2 % (95 % CI: −8.1 to −6.3) in the physician-guided group, giving a treatment difference of −5.1 % (95 % CI: −6.4 to −3.8; p < 0.01). At day 180, 25/30 (83.3 %) AI-guided participants achieved ≥5 % weight reduction compared with 16/30 (53.3 %) in the physician-guided arm (OR 4.4; 95 % CI: 1.3 to 14.5; p = 0.01). Significant improvements were also seen in BMI, fat mass and visceral-fat rating in the AI-guided group (p < 0.01 for all).Postprandial appetite suppression was greater in the AI-guided group, with significant reductions in hunger and prospective food consumption and increases in fullness and satiety (p < 0.01 for all). Adverse events were generally mild to moderate, with higher incidences of gastrointestinal symptoms in the AI-guided group, but these were manageable and did not affect adherence.ConclusionThe AI-guided dietary-supplement regimen was more effective in promoting weight loss, improving body composition, and suppressing appetite than the physician-guided regimen. These findings suggest that AI-guided, personalized supplement prescriptions—grounded in genetic, metabolic, and behavioral profiling—could provide a more effective approach to obesity management. Larger studies are warranted to confirm these results and further refine AI-based interventions for weight loss.

AB - BackgroundObesity is a complex, multifactorial chronic disease that poses significant health risks. Recent advancements in artificial intelligence (AI) offer the potential for more personalized and effective dietary-supplement (DS) regimens to promote weight loss. This randomized controlled trial evaluated the efficacy of AI-guided DS prescriptions compared with standard physician-guided DS prescriptions in adults with obesity.MethodsThis randomized, parallel-group pilot study enrolled 60 individuals aged 40–60 years with a body-mass index (BMI) ≥ 25 kg m−2. Participants were randomized to receive either AI-guided DS prescriptions generated on the basis of each patient's individualized genetic, metabolic, and behavioral data (n = 30) or physician-guided DS prescriptions (n = 30) for 180 days. The primary endpoints were the percentage change in body weight and the proportion of participants achieving a ≥5 % weight reduction. Secondary endpoints included changes in BMI, fat mass, visceral-fat rating, systolic and diastolic blood pressure, lipid profiles, fasting plasma glucose, hsCRP levels, and postprandial appetite ratings. Adverse events were monitored throughout the study.ResultsBaseline characteristics were well balanced between groups. Mean weight loss was −12.3 % (95 % CI: −13.1 to −11.5) in the AI-guided group vs. −7.2 % (95 % CI: −8.1 to −6.3) in the physician-guided group, giving a treatment difference of −5.1 % (95 % CI: −6.4 to −3.8; p < 0.01). At day 180, 25/30 (83.3 %) AI-guided participants achieved ≥5 % weight reduction compared with 16/30 (53.3 %) in the physician-guided arm (OR 4.4; 95 % CI: 1.3 to 14.5; p = 0.01). Significant improvements were also seen in BMI, fat mass and visceral-fat rating in the AI-guided group (p < 0.01 for all).Postprandial appetite suppression was greater in the AI-guided group, with significant reductions in hunger and prospective food consumption and increases in fullness and satiety (p < 0.01 for all). Adverse events were generally mild to moderate, with higher incidences of gastrointestinal symptoms in the AI-guided group, but these were manageable and did not affect adherence.ConclusionThe AI-guided dietary-supplement regimen was more effective in promoting weight loss, improving body composition, and suppressing appetite than the physician-guided regimen. These findings suggest that AI-guided, personalized supplement prescriptions—grounded in genetic, metabolic, and behavioral profiling—could provide a more effective approach to obesity management. Larger studies are warranted to confirm these results and further refine AI-based interventions for weight loss.

UR - https://www.mendeley.com/catalogue/b18f3905-9a93-3d22-b7f9-9d58e9706c20/

UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105009689661&origin=inward

U2 - 10.1016/j.clnesp.2025.06.035

DO - 10.1016/j.clnesp.2025.06.035

M3 - Article

C2 - 40562096

JO - Clinical Nutrition ESPEN

JF - Clinical Nutrition ESPEN

SN - 2405-4577

ER -