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BackgroundPrograms targeting the standard modifiable cardiovascular risk factors (SMuRFs: hypertension, diabetes mellitus, hypercholesterolemia, smoking) are critical to tackling coronary heart disease at a community level. However, myocardial infarction in SMuRF‐less individuals is not uncommon. This study uses 2 sequential large, multicenter registries to examine the proportion and outcomes of SMuRF‐less ST‐segment–elevation myocardial infarction (STEMI) patients.Methods and ResultsWe identified 3081 STEMI patients without a prior history of cardiovascular disease in the Australian GRACE (Global Registry of Acute Coronary Events) and CONCORDANCE (Cooperative National Registry of Acute Coronary Syndrome Care) registries, encompassing 42 hospitals, between 1999 and 2017. We examined the proportion that were SMuRF‐less as well as outcomes. The primary outcome was in‐hospital mortality, and the secondary outcome was major adverse cardiovascular events (death, myocardial infarction, or heart failure, during the index admission). Multivariate regression models were used to identify predictors of major adverse cardiovascular events. Of STEMI patients without a prior history of cardiovascular disease 19% also had no history of SMuRFs. This proportion increased from 14% to 23% during the study period (P=0.0067). SMuRF‐less individuals had a higher in‐hospital mortality rate than individuals with 1 or more SMuRFs. There were no clinically significant differences in major adverse cardiovascular events at 6 months between the 2 groups.ConclusionsA substantial and increasing proportion of STEMI presentations occur independently of SMuRFs. Discovery of new markers and mechanisms of disease beyond standard risk factors may facilitate novel preventative strategies. Studies to assess longer‐term outcomes of SMuRF‐less STEMI patients are warranted.

Aims Identification and management of the Standard Modifiable Cardiovascular Risk Factors (SMuRFs; hypercholesterolaemia, hypertension, diabetes and smoking) has substantially improved cardiovascular disease outcomes. However, cardiovascular disease remains the leading cause of death worldwide. Suspecting an evolving pattern of risk factor profiles in the ST elevation myocardial infarction (STEMI) population with the improvements in primary care, we hypothesized that the proportion of 'SMuRFless' STEMI patients may have increased. Methods/results We performed a single centre retrospective study of consecutive STEMI patients presenting from January 2006 to December 2014. Over the study period 132/695 (25%) STEMI patients had 0 SMuRFs, a proportion that did not significantly change with age, gender or family history. The proportion of STEMI patients who were SMuRFless in 2006 was 11%, which increased to 27% by 2014 (odds ratio 1.12 per year, 95% confidence interval: 1.04-1.22). The proportion of patients with hypercholesterolaemia decreased (odds ratio 0.92, 95% confidence interval 0.86-0.98), as did the proportion of current smokers (odds ratio 0.93, 95% confidence interval 0.86-0.99), with no significant change in the proportion of patients with diabetes and hypertension. SMuRF status was not associated with extent of coronary disease; in-hospital outcomes, or discharge prescribing patterns. Conclusion The proportion of STEMI patients with STEMI poorly explained by SMuRFs is high, and is significantly increasing. This highlights the need for bold approaches to discover new mechanisms and markers for early identification of these patients, as well as to understand the outcomes and develop new targeted therapies.

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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.

IntroductionCoronary artery disease (CAD) persists as a major cause of morbidity and mortality worldwide despite intensive identification and treatment of traditional risk factors. Data emerging over the past decade show a quarter of patients have disease in the absence of any known risk factor, and half have only one risk factor. Improvements in quantification and characterisation of coronary atherosclerosis by CT coronary angiography (CTCA) can provide quantitative measures of subclinical atherosclerosis—enhancing the power of unbiased ‘omics’ studies to unravel the missing biology of personal susceptibility, identify new biomarkers for early diagnosis and to suggest new targeted therapeutics.Methods and analysisBioHEART-CT is a longitudinal, prospective cohort study, aiming to recruit 5000 adult patients undergoing clinically indicated CTCA. After informed consent, patient data, blood samples and CTCA imaging data are recorded. Follow-up for all patients is conducted 1 month after recruitment, and then annually for the life of the study. CTCA data provide volumetric quantification of total calcified and non-calcified plaque, which will be assessed using established and novel scoring systems. Comprehensive molecular phenotyping will be performed using state-of-the-art genomics, metabolomics, proteomics and immunophenotyping. Complex network and machine learning approaches will be applied to biological and clinical datasets to identify novel pathophysiological pathways and to prioritise new biomarkers. Discovery analysis will be performed in the first 1000 patients of BioHEART-CT, with validation analysis in the following 4000 patients. Outcome data will be used to build improved risk models for CAD.Ethics and disseminationThe study protocol has been approved by the human research ethics committee of North Shore Local Health District in Sydney, Australia. All findings will be published in peer-reviewed journals or at scientific conferences.Trial registration numberACTRN12618001322224.

Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)-diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking-has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the individual level rendering some individuals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large-scale multi-omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.