Obesity and the Bidirectional Risk of Cancer and Cardiovascular Diseases in African Americans: Disparity vs. Ancestry.

Cardiovascular disease (CVD) and cancer often occur in the same individuals, in part due to the shared risk factors such as obesity. Obesity promotes adipose inflammation, which is pathogenically linked to both cardiovascular disease and cancer. Compared with Caucasians, the prevalence of obesity is significantly higher in African Americans (AA), who exhibit more pronounced inflammation and, in turn, suffer from a higher burden of CVD and cancer-related mortality. The mechanisms that underlie this association among obesity, inflammation, and the bidirectional risk of CVD and cancer, particularly in AA, remain to be determined. Socio-economic disparities such as lack of access to healthy and affordable food may promote obesity and exacerbate hypertension and other CVD risk factors in AA. In turn, the resulting pro-inflammatory milieu contributes to the higher burden of CVD and cancer in AA. Additionally, biological factors that regulate systemic inflammation may be contributory. Mutations in atypical chemokine receptor 1 (ACKR1), otherwise known as the Duffy antigen receptor for chemokines (DARC), confer protection against malaria. Many AAs carry a mutation in the gene encoding this receptor, resulting in loss of its expression. ACKR1 functions as a decoy chemokine receptor, thus dampening chemokine receptor activation and inflammation. Published and preliminary data in humans and mice genetically deficient in ACKR1 suggest that this common gene mutation may contribute to ethnic susceptibility to obesity-related disease, CVD, and cancer. In this narrative review, we present the evidence regarding obesity-related disparities in the bidirectional risk of CVD and cancer and also discuss the potential association of gene polymorphisms in AAs with emphasis on ACKR1.

Are We Ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: Students and Faculty Survey.

BACKGROUND: The effects of Artificial Intelligence (AI) technology applications are already felt in healthcare in general and in the practice of medicine in the disciplines of radiology, pathology, ophthalmology, and oncology. The expanding interface between digital data science, emerging AI technologies and healthcare is creating a demand for AI technology literacy in health professions. OBJECTIVE: To assess medical student and faculty attitudes toward AI, in preparation for teaching AI foundations and data science applications in clinical practice in an integrated medical education curriculum. METHODS: An online 15-question semi-structured survey was distributed among medical students and faculty. The questionnaire consisted of 3 parts: participant's background, AI awareness, and attitudes toward AI applications in medicine. RESULTS: A total of 121 medical students and 52 clinical faculty completed the survey. Only 30% of students and 50% of faculty responded that they were aware of AI topics in medicine. The majority of students (72%) and faculty (59%) learned about AI from the media. Faculty were more likely to report that they did not have a basic understanding of AI technologies (χ2, P = .031). Students were more interested in AI in patient care training, while faculty were more interested in AI in teaching training (χ2, P = .001). Additionally, students and faculty reported comparable attitudes toward AI, limited AI literacy and time constraints in the curriculum. There is interest in broad and deep AI topics. Our findings in medical learners and teaching faculty parallel other published professional groups' AI survey results. CONCLUSIONS: The survey conclusively proved interest among medical students and faculty in AI technology in general, and in its applications in healthcare and medicine. The study was conducted at a single institution. This survey serves as a foundation for other medical schools interested in developing a collaborative programming approach to address AI literacy in medical education.

Artificial Intelligence in Medical Practice: The Question to the Answer?

Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.

Impact of diabetes on the risk stratification using stress single-photon emission computed tomography myocardial perfusion imaging in patients with symptoms suggestive of coronary artery disease.

BACKGROUND: Coronary artery disease can develop prematurely and is the leading cause of death among diabetics, making noninvasive risk stratification desirable. METHODS AND RESULTS: Patients with symptoms of coronary artery disease who were undergoing stress myocardial perfusion imaging (MPI) from 5 centers were prospectively followed (2.5+/-1.5 years) for the subsequent occurrence of cardiac death, myocardial infarction (MI), and revascularization. Stress MPI results were categorized as normal or abnormal (fixed or ischemic defects and 1, 2, or 3 vessel distribution). Of 4755 patients, 929 (19.5%) were diabetic. Patients with diabetes, despite an increased revascularization rate, had 80 cardiac events (8.6%; 39 deaths and 41 MIs) compared with 172 cardiac events (4.5%; 69 deaths and 103 MIs) in the nondiabetic cohort (P<0.0001). Abnormal stress MPI was an independent predictor of cardiac death and MI in both populations. Diabetics with ischemic defects had an increased number of cardiac events (P<0.001), with the highest MI rates (17.1%) observed with 3-vessel ischemia. Similarly, a multivessel fixed defect was associated with the highest rate of cardiac death (13.6%) among diabetics. The unadjusted cardiac survival rate was lower for diabetic patients (91% versus 97%, P<0.001), but it became comparable once adjusted for the pretest clinical risk and stress MPI results. In multivariable Cox analysis, both ischemic and fixed MPI defects independently predicted cardiac death alone or cardiac death/MI. Diabetic women had the worst outcome for any given extent of myocardial ischemia. CONCLUSIONS: In this large cohort of diabetics undergoing stress MPI, the presence and the extent of abnormal stress MPI independently predicted subsequent cardiac events. Using stress MPI in conjunction with clinical information can provide risk stratification of diabetic patients.