Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

Abnormalities in functional limbic-anterior cingulate-prefrontal circuits associated with emotional reactivity, evaluation and regulation have been implicated in the pathophysiology of major depressive disorder (MDD). However, existing knowledge about structural alterations in depression is equivocal and based on cohorts of limited sample size. This study used voxel-based morphometry (VBM) and surface-based cortical thickness to investigate the structure of these circuits in a large and well-characterized patient cohort with MDD. Non-geriatric MDD outpatients (n = 102) and age- and gender-matched healthy control participants (n = 34) provided T1-weighted magnetic resonance imaging data during their baseline visit as part of the International Study to Predict Optimized Treatment for Depression. Whole-brain VBM volumetric and surface-based cortical thickness assessments were performed voxel-wise and compared (at p < 0.05 corrected for multiple comparisons) between the MDD and control groups. MDD participants had reduced gray matter volume in the anterior cingulate cortex, regions of the prefrontal circuits, including dorsolateral and dorsomedial prefrontal cortices, and lateral and medial orbitofrontal cortices, but not in limbic regions. Additional reductions were observed cortically in the posterior temporal and parieto-occipital cortices and, subcortically in the basal ganglia and cerebellum. Focal cortical thinning in the medial orbitofrontal cortex was also observed for the MDD group. These alterations in volume and cortical thickness were not associated with severity of depressive symptoms. The findings demonstrate that widespread gray matter structural abnormalities are present in a well-powered study of patients with depression. The patterns of gray matter loss correspond to the same brain functional network regions that were previously established to be abnormal in MDD, which may support an underlying structural abnormality for these circuits.