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The 'Digital Twin' to enable the vision of precision cardiology.

ABSTRACT: Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

SUBMITTER: Corral-Acero J

PROVIDER: S-EPMC7774470 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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The 'Digital Twin' to enable the vision of precision cardiology.

Corral-Acero Jorge J Margara Francesca F Marciniak Maciej M Rodero Cristobal C Loncaric Filip F Feng Yingjing Y Gilbert Andrew A Fernandes Joao F JF Bukhari Hassaan A HA Wajdan Ali A Martinez Manuel Villegas MV Santos Mariana Sousa MS Shamohammdi Mehrdad M Luo Hongxing H Westphal Philip P Leeson Paul P DiAchille Paolo P Gurev Viatcheslav V Mayr Manuel M Geris Liesbet L Pathmanathan Pras P Morrison Tina T Cornelussen Richard R Prinzen Frits F Delhaas Tammo T Doltra Ada A Sitges Marta M Vigmond Edward J EJ Zacur Ernesto E Grau Vicente V Rodriguez Blanca B Remme Espen W EW Niederer Steven S Mortier Peter P McLeod Kristin K Potse Mark M Pueyo Esther E Bueno-Orovio Alfonso A Lamata Pablo P

European heart journal 20201201 48

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to cu ...[more]

PMID: 32128588

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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.

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The 'Digital Twin' to enable the vision of precision cardiology.

Publications

The 'Digital Twin' to enable the vision of precision cardiology.

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