Chayakrit Krittanawong

Future Physicians in the Era of Precision Cardiovascular Medicine

Cardiovascular diseases (CVDs) are complex and heterogeneous in nature and are caused by multiple genetic, environmental, and behavioral factors. Precision cardiovascular medicine transitions from a one-size-fits-all approach to focusing on the prevention of a single patient’s CVD instead. Moreover, it uses tailored preventive, diagnostic, prognostic, and therapeutic strategies based on specifics derived from an individual’s genome, clinical features, biomarkers, cardiovascular imaging results, behavioral idiosyncrasies, and environmental factors. The exponential growth of big data analytics in CVD and advances in bioinformatics are revolutionizing cardiovascular clinical care, necessitating the expertise of bioinformatics-literate physicians. To date, the field of oncology has seen advancements with respect to more personalized, target-driven trial designs, because tumor biopsies are easily available to detect genetic mutations. However, precision cardiovascular medicine has only just been introduced; and it has the potential to improve cardiovascular outcomes, quality of patient care, and cost-effectiveness, and reduce hospital admission and mortality rates. This article highlights (1) the insights of precision cardiovascular medicine, (2) the challenges that the next generation of physicians will face, and (3) the way in which the new paradigm might lead to better individualized cardiovascular care. The path to adoption of precision cardiovascular medicine requires access to large-scale, well-designed, and standardized electronic health records or precision medicine platforms to connect the omic fields (ie, genomics, metagenomics, metabolomics, and proteomics) with clinical features, biomarkers, human microbiome sequencing, genome editing, cardiovascular imaging, and …

Practical Pharmacogenomic Approaches to Heart Failure Therapeutics.

Opinion statementThe major challenge in applying pharmacogenomics to everyday clinical practice in heart failure (HF) is based on (1) a lack of robust clinical evidence for the differential utilization of neurohormonal antagonists in the management of HF in different subgroups, (2) inconsistent results regarding appropriate subgroups that may potentially benefit from an alternative strategy based on pharmacogenomic analyses, and (3) a lack of clinical trials that focused on testing gene-guided treatment in HF. To date, all pharmacogenomic analyses in HF have been conducted as post hoc retrospective analyses of clinical trial data or of observational patient series studies. This is in direct contrast with the guideline-directed HF therapies that have demonstrated their safety and efficacy in the absence of pharmacogenomic guidance. Therefore, the future of clinical applications of pharmacogenomic testing will largely depend on our ability to incorporate gene-drug interactions into the prescribing process, requiring that preemptive and cost-effective testing be paired with decision-support tools in a value-based care approach.

DPP-4 inhibitors and heart failure: a potential role for pharmacogenomics

There remains an ongoing controversy regarding the safety of dipeptidyl peptidase-4 (DPP-4) inhibitors and the risk of developing heart failure (HF). In addition, none of the animal studies suggested a mechanism for the DPP-4 inhibitors and HF risk. To date, advances in pharmacogenomics have enabled the identification of genetic variants in DPP-4 gene. Studies have shown that genetic polymorphisms in the gene encoding DPP-4 may be associated with potential pathways involved in HF risk. This review discusses the contradictory findings of DPP-4 inhibitors and HF and a potential role for pharmacogenomics. Pharmacogenomics of DPP-4 inhibitors is promising, and genetic information from randomized control trials is urgently needed to gain a full understanding of the safety of DPP-4 inhibitors and the risk of HF.

Current Management and Future Directions of Heart Failure With Preserved Ejection Fraction: a Contemporary Review

Heart failure with preserved ejection fraction (HFpEF), a complex and debilitating syndrome, is commonly seen in elderly populations. Exacerbation of HFpEF is among the most common reasons for hospital admission in the USA. The high rate of morbidity and mortality from this condition underscores the fact that HFpEF is heterogeneous, complex, and poorly characterized. Randomized, controlled trials have been very successful at identifying treatments for HF with reduced ejection fraction (HFrEF), but effective treatment options for HFpEF are lacking. Here, we discuss (1) the pathophysiology of HFpEF, (2) a standardized diagnostic and therapeutic approach, (3) a comparison of the management of recent guidelines, and (4) challenges and future directions for HFpEF management. The authors believe that it is important to identify new subtypes of HFpEF to better classify genotypes and phenotypes of HFpEF and to develop novel targeted therapies. It is our hypothesis that big data analytics will shine new light on unique HFpEF phenotypes that better respond to treatment modalities.

Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

Purpose of ReviewEvidence that artificial intelligence (AI) is useful for predicting risk factors for hypertension and its management is emerging. However, we are far from harnessing the innovative AI tools to predict these risk factors for hypertension and applying them to personalized management. This review summarizes recent advances in the computer science and medical field, illustrating the innovative AI approach for potential prediction of early stages of hypertension. Additionally, we review ongoing research and future implications of AI in hypertension management and clinical trials, with an eye towards personalized medicine.Recent FindingsAlthough recent studies demonstrate that AI in hypertension research is feasible and possibly useful, AI-informed care has yet to transform blood pressure (BP) control. This is due, in part, to lack of data on AI’s consistency, accuracy, and reliability in the BP sphere. However, many factors contribute to poorly controlled BP, including biological, environmental, and lifestyle issues. AI allows insight into extrapolating data analytics to inform prescribers and patients about specific factors that may impact their BP control.SummaryTo date, AI has been mainly used to investigate risk factors for hypertension, but has not yet been utilized for hypertension management due to the limitations of study design and of physician’s engagement in computer science literature. The future of AI with more robust architecture using multi-omics approaches and wearable technology will likely be an important tool allowing to incorporate biological, lifestyle, and environmental factors into decision-making of appropriate drug use for BP control.

Meta-Analysis Comparing Frequency of Overweight Versus Normal Weight in Patients With New-Onset Heart Failure

Association between obesity and new-onset heart failure (HF) has repeatedly been established. Less is known about the risk of overweight with the development of HF. The aim of this systematic review and meta-analysis was to explore the association between overweight, obesity, and the incidence of new-onset HF. In this study, we systematically searched MEDLINE, MEDLINE In-Process & Other Non-Indexed Citations, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials from database inception through June 2017. Studies were included if they reported the association between overweight or obesity and new-onset HF compared with normal weight. DerSimonian and Laird random effect meta-analyses were used, and subgroup analyses were performed to explore the potential sources of heterogeneity. Of 2,184 retrieved articles, we identified 21 relevant studies with a total of 525,656 participants with 18,948 HF cases. Compared with the normal body weight index (body mass index < 25 kg/m2), overweight (body mass index 25 to 29.9 kg/m2) was associated with a 33% higher risk of developing HF (pooled risk ratios 1.33; 95% confidence interval 1.16 to 1.52; p <0.001), with substantial heterogeneity among studies (I2 = 83.6%). In addition, class I, II, and III obesity were stepwise-associated with an increase in the risk of developing HF as 73%, 85% and 189%, respectively (all p <.001) compared with normal weight. In conclusion, compared with healthy normal-weight patients, these results show that both overweight patients were independently associated with a significantly higher incidence of HF. These results highlight the need for a better understanding of the potential mechanisms of overweight and HF.

ASSOCIATION BETWEEN SHORT AND LONG SLEEP DURATION AND CARDIOVASCULAR OUTCOMES? A SYSTEMATIC REVIEW AND META-ANALYSIS

Background: Short sleep duration has been identified as a risk factor for cardiovascular diseases (CVD) and mortality. It has been hypothesized that short sleep duration may be linked to changes in ghrelin/leptin production leading to alteration of stress hormone production. Here, we conducted

Tweeting influenza vaccine to cardiovascular health community

Globally, among infectious diseases, influenza is one of the leading causes of morbidity and mortality. Individuals with chronic conditions, including cardiovascular disease and diabetes, are particularly vulnerable to complications of an influenza infection. The European Society of Cardiology recommended annual influenza vaccinations for patients with cardiovascular disease. Numerous studies have suggested a link between influenza and increased risk of cardiovascular events. Despite its proven benefits, little is known about the reason for the underutilization of influenza vaccination. We present the findings of an assessment of patients’ perception of the influenza vaccine and the reason for its underutilization, by data mining from Twitter.

Crowdfunding for cardiovascular research

The competition for public cardiovascular research grants has recently increased. Independent researchers are facing increasing competition for public research grant support and ultimately may need to seek alternative funding sources. Crowdfunding, a financing method of raising funds online by pooling together small donations from the online community to support a specific initiative, seems to have significant potential. However, the feasibility of crowdfunding for cardiovascular research remains unknown. Here, we performed exploratory data analysis of the feasibility of online crowdfunding in cardiovascular research.

Big data, artificial intelligence, and cardiovascular precision medicine

ABSTRACT Introduction: Cardiovascular diseases (CVDs) are chronic, heterogeneous diseases which are generally classified according to clinical presentation. However, the arrival of big data and analytical methods presents an opportunity to better understand these disease entities. Areas covered: This review article highlights: (1) the potential of a big data approaches with emerging technology to explore the heterogeneity of CVDs; (2) current challenges of a big data approach; and (3) the future of precision cardiovascular medicine. Expert commentary: Overall, most of the current data utilizing big data techniques remain largely descriptive and retrospective. Precision medicine, or N-of-1, approaches have not yet allowed for consistent interpretation since there is no ‘standard’ of how to best apply treatment approaches in a field where evidence-based medicine is based largely on randomized controlled trials. The risk score and biomarker-based approaches have been utilized with some ‘validation’ studies, but more in-depth biomarkers (i.e. pharmacogenomic biomarkers) have failed to demonstrate incremental benefits. Exploring novel CVD phenotypes by integrating existing medical variables, multi-omics, lifestyle, and environmental data using artificial intelligence is vitally important and may allow us to digitize future clinical trials, potentially leading to novel therapies.