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The "Virtual Digital Twins" Concept in Precision Nutrition.

ABSTRACT: Nutritional and lifestyle changes remain at the core of healthy aging and disease prevention. Accumulating evidence underscores the impact of genetic, metabolic, and host gut microbial factors on individual responses to nutrients, paving the way for the stratification of nutritional guidelines. However, technological advances that incorporate biological, nutritional, lifestyle, and health data at an unprecedented scale and depth conceptualize a future where preventative dietary interventions will exceed stratification and will be highly individualized. We herein discuss how genetic information combined with longitudinal metabolomic, immune, behavioral, and gut microbial parameters, and bioclinical variables could define a digital replica of oneself, a "virtual digital twin," which could serve to guide nutrition in a personalized manner. Such a model may revolutionize the management of obesity and its comorbidities, and provide a pillar for healthy aging.

SUBMITTER: Gkouskou K

PROVIDER: S-EPMC7666894 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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The "Virtual Digital Twins" Concept in Precision Nutrition.

Gkouskou Kalliopi K Vlastos Ioannis I Karkalousos Petros P Chaniotis Dimitrios D Sanoudou Despina D Eliopoulos Aristides G AG

Advances in nutrition (Bethesda, Md.) 20201101 6

Nutritional and lifestyle changes remain at the core of healthy aging and disease prevention. Accumulating evidence underscores the impact of genetic, metabolic, and host gut microbial factors on individual responses to nutrients, paving the way for the stratification of nutritional guidelines. However, technological advances that incorporate biological, nutritional, lifestyle, and health data at an unprecedented scale and depth conceptualize a future where preventative dietary interventions wil ...[more]

PMID: 32770212

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Project description:BackgroundAs global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.ObjectiveThis study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.MethodsRelevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.ResultsSince 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved. However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment decision-making, prognosis prediction, and surgical planning.ConclusionDigital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.

Project description:INTRODUCTION:The objective of this study was to examine changes in hemoglobin A1c (HbA1c), anti-diabetic medication use, insulin resistance, and other ambulatory glucose profile metrics between baseline and after 90 days of participation in the Twin Precision Nutrition (TPN) Program enabled by Digital Twin Technology. METHODS:This was a retrospective study of patients with type 2 diabetes who participated in the TPN Program and had at least 3 months of follow-up. The TPN machine learning algorithm used daily continuous glucose monitor (CGM) and food intake data to provide guidelines that would enable individual patients to avoid foods that cause blood glucose spikes and to replace them with foods that do not produce spikes. Physicians with access to daily CGM data titrated medications and monitored patient conditions. RESULTS:Of the 89 patients who initially enrolled in the TPN Program, 64 patients remained in the program and adhered to it for at least 90 days; all analyses were performed on these 64 patients. At the 90-day follow-up assessment, mean (±?standard deviation) HbA1c had decreased from 8.8?±?2.2% at baseline by 1.9 to 6.9?±?1.1%, mean weight had decreased from 79.0?±?16.2 kg at baseline to 74.2?±?14.7 kg, and mean fasting blood glucose had fallen from 151.2 ±?45.0 mg/dl at baseline to 129.1?±?36.7 mg/dl. Homeostatic model assessment of insulin resistance (HOMA-IR) had decreased by 56.9% from 7.4?±?3.5 to 3.2?±?2.8. At the 90-day follow-up assessment, all 12 patients who were on insulin had stopped taking this medication; 38 of the 56 patients taking metformin had stopped metformin; 26 of the 28 patients on dipeptidyl peptidase-4 (DPP-4) inhibitors discontinued DPP-4 inhibitors; all 13 patients on alpha-glucosidase inhibitors discontinued these inhibitors; all 34 patients on sulfonylureas were able to stop taking these medications; two patients stopped taking pioglitazone; all ten patients on sodium-glucose cotransporter-2 (SGLT2) inhibitors stopped taking SGLT2 inhibitors; and one patient stopped taking glucagon-like peptide-1 analogues. CONCLUSION:The results provide evidence that daily precision nutrition guidance based on CGM, food intake data, and machine learning algorithms can benefit patients with type 2 diabetes. Adherence for 3 months to the TPN Program resulted in patients achieving a 1.9 percentage point decrease in HbA1c, a 6.1% drop in weight, a 56.9% reduction in HOMA-IR, a significant decline in glucose time below range, and, in most patients, the elimination of diabetes medication use.

Dataset Information

The "Virtual Digital Twins" Concept in Precision Nutrition.

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The "Virtual Digital Twins" Concept in Precision Nutrition.

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