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FRI622 Impact Of Personalized Diet Using Digital Twin On Body Composition Parameters: 180 Day Analysis Of 209 Participants

Disclosure: P. Shamanna: None. J. Mohammed: None. M. Mohamed: None. T. Poon: None. M. Dharmalingam: None. B. Saboo: None. S. Damodharan: None. A. Vadavi: None. M. Thajudeen: None. A. Keshavamurthy: None. S. Bhonsley: None. B. R: None. S. Joshi: None. Background and Aims: The aim was to study the eff...

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Detalles Bibliográficos
Autores principales: Shamanna, Paramesh, Mohammed, Jahangir, Mohamed, Maluk, Poon, Terrence, Dharmalingam, Mala, Saboo, Banshi, Damodharan, Suresh, Vadavi, Arun, Thajudeen, Mohamed, Keshavamurthy, Ashok, Bhonsley, Suchitra, R, Balasubramaniam, Joshi, Shashank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10555446/
http://dx.doi.org/10.1210/jendso/bvad114.842
Descripción
Sumario:Disclosure: P. Shamanna: None. J. Mohammed: None. M. Mohamed: None. T. Poon: None. M. Dharmalingam: None. B. Saboo: None. S. Damodharan: None. A. Vadavi: None. M. Thajudeen: None. A. Keshavamurthy: None. S. Bhonsley: None. B. R: None. S. Joshi: None. Background and Aims: The aim was to study the effect of a personalized diet using the Digital Twin (DT) on the various parameters of body composition. The personalized food recommendations were provided through the mobile app. Materials and Methods: Data from 209 participants (176 males and 33females) who had been on DT for 6 months or more were analyzed. DT uses a machine learning algorithm to integrate clinical and sensor data. USFDA approved Powermax BCA-130 Bluetooth Smart Scale, was used for bioimpedance analysis. Results: We analyzed 209 participants (176 males, 33 females) to study bioimpedance changes over 180 days. Weight significantly decreased in both males and females, with mean decreases of 10.6 kg (80 to 69.4kg) and 11.5kg (75.8 to 64.3kg), respectively, (p<0.001). BMI (kg/m2) also significantly decreased in both genders, with mean decreases of 3.8 (27.2 to 23.4) and 4.3(29.6 to 25.3), respectively, (p<0.001). Body fat percentage decreased in both genders, with mean decreases of 7.7% (28.4 to 20.7%) and 8.3% (41 to 32.7), respectively, (p<0.001). BMR decreased significantly in males 1585.4 to1544.0 kcal/day (p<.001), decreased by 2.6%, but not in females, (1297.7 to1273.2 kcal/day), p=0.095. Total Body Water % significantly increased in both males (51.7% to 57.2%, p<.001) and females (40.5% to 46.2%, p<0.001). Bone mass and lean body weight decreased significantly in both males (2.8 kg to2.7 kg, p<0.001) and females (2.6 kg to 2.5 kg, p=.171). Metabolic age decreased significantly in both genders, with a mean decrease from 44.1 to 41.0years for males (p<0.001) and from 48.9 to 46.2 years for females(p<0.001). Muscle mass decreased significantly in both genders, males 53.5to 51.7 kg, females 40.4 to 39.3 kg, p=0.095 but skeletal mass % increased significantly, 40.8% to 45.2% for males (p<0.001) and 34.4% to 39.2% for females (p<0.001). Protein levels increased significantly in both genders,16.3 g/dL to 18.1 g/dL for males (p<0.001) and from 13.0 g/dL to 15.5 g/dL for females (p<0.001). Subcutaneous fat decreased significantly in both genders, 25.2% to 18.6% for males (p<0.001) and 36.6% to 29.8% for females(p<0.001). Visceral fat decreased significantly from a mean of 9.9 to 6.1for males (p<.001) and from 12.2 to 7.7 for females (p<0.001). Conclusions: Digital Twin (DT) technology through personalized diet interventions is a successful approach for weight and body fat reduction, while maintaining BMR at a stable level. There is a positive impact on metabolic age, and an increase in skeletal muscle mass. DT algorithms and personalized nutrition recommendations are beneficial for improving body composition parameters. Presentation: Friday, June 16, 2023