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Tasnim F Imran,1–3,* Daniel Posner,1,4,* Jacqueline Honerlaw,1 Jason L Vassy,1,2 Rebecca J Song,1 Yuk-Lam Ho,1 Steven J Kittner,5 Katherine P Liao,1,2 Tianxi Cai,1,6 Christopher J O’Donnell,1,2 Luc Djousse,1,2 David R Gagnon,1,4 J Michael Gaziano,1,2 Peter WF Wilson,7,8 Kelly Cho1,2 On behalf of the VA Million Veteran Program 1Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA; 2Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3Department of Medicine, Cardiology Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA; 4Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 5Department of Neurology, Baltimore VA Medical Center and University of Maryland School of Medicine, Baltimore, MD, USA; 6Harvard T. H. Chan School of Public Health, Boston, MA, USA; 7Atlanta VA Medical Center, Decatur, GA, USA; 8Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, USA *These authors contributed equally to this work Background: Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database.Methods: Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm.Results: We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458).Conclusion: Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS. Keywords: acute ischemic stroke, algorithm, large databases, big data, administrative health data, cerebrovascular accident

The adhesion of breast adenocarcinoma cells (MDA-MB-231) to human umbilical vein endothelial cells (HUVEC) was studied in whole blood and under varying flow conditions. This study was done on HUVEC either kept under static conditions or pre-conditioned in flow for 2 hours at a shear stress of 5 or 13 dyn/cm[SUP2]. Coverslips coated by HUVEC were placed in a parallel plate perfusion chamber and perfused at a shear rate of 300 or 1500 sec[SUP-1] with heparin-anticoagulated blood containing [SUP111]In labelled MDA-MB-231 cells. We report here the optimal conditions for studying the adhesion of MDA-MB-231 to endothelial cells under shear constraints corresponding to those observed into small and medium sized arteries. [ABSTRACT FROM AUTHOR]

The article summarizes research published within the issue, titled "The Impact of Whole-Genome Sequencing on the Primary Care and Outcomes of Healthy Adult Patients," by J. L. Vassy, K. D. Christensen, E. F. Schonman and colleagues. Topics discussed include whole-genome sequencing (WGS) in primary care, research methodology, and limitations of the study.

Abstract Background Hemoglobin A1c (HbA1c) levels diagnose diabetes, predict mortality and are associated with ten single nucleotide polymorphisms (SNPs) in white individuals. Genetic associations in other race groups are not known. We tested the hypotheses that there is race-ethnic variation in 1) HbA1c-associated risk allele frequencies (RAFs) for SNPs near SPTA1, HFE, ANK1, HK1, ATP11A, FN3K, TMPRSS6, G6PC2, GCK, MTNR1B; 2) association of SNPs with HbA1c and 3) association of SNPs with mortality. Methods We studied 3,041 non-diabetic individuals in the NHANES (National Health and Nutrition Examination Survey) III. We stratified the analysis by race/ethnicity (NHW: non-Hispanic white; NHB: non-Hispanic black; MA: Mexican American) to calculate RAF, calculated a genotype score by adding risk SNPs, and tested associations with SNPs and the genotype score using an additive genetic model, with type 1 error = 0.05. Results RAFs varied widely and at six loci race-ethnic differences in RAF were significant (p ATP11A, the SNP RAF was 54% in NHB, 18% in MA and 14% in NHW (p 1c in NHW (β = 0.012 HbA1c increase per risk allele, p = 0.04) and MA (β = 0.021, p = 0.005) but not NHB (β = 0.007, p = 0.39). The genotype score was not associated with mortality in any group (NHW: OR (per risk allele increase in mortality) = 1.07, p = 0.09; NHB: OR = 1.04, p = 0.39; MA: OR = 1.03, p = 0.71). Conclusion At many HbA1c loci in NHANES III there is substantial RAF race-ethnic heterogeneity. The combined impact of common HbA1c-associated variants on HbA1c levels varied by race-ethnicity, but did not influence mortality.

Background: This study aims to assess the impact of healthy lifestyle on prostate cancer (PCa) risk in a diverse population., Methods: Data for 281,923 men from the Million Veteran Program (MVP), a nationwide, health system-based cohort study, were analyzed. Self-reported information at enrollment included smoking status, exercise, diet, family history of PCa, and race/ethnicity. Body mass index (BMI) was obtained from clinical records. Genetic risk was assessed via a validated polygenic score. Cox proportional hazards models were used to assess associations with PCa outcomes., Results: After accounting for ancestry, family history, and genetic risk, smoking was associated with an increased risk of metastatic PCa (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.64-2.02; p < 10 -16 ) and fatal PCa (HR, 2.73; 95% CI, 2.36-3.25; p < 10 -16 ). Exercise was associated with a reduced risk of fatal PCa (HR, 0.86; 95% CI, 0.76-0.98; p = .03). Higher BMI was associated with a slightly reduced risk of fatal PCa, and diet score was not independently associated with any end point. Association with exercise was strongest among those who had nonmetastatic PCa at MVP enrollment. Absolute reductions in the risk of fatal PCa via lifestyle factors were greatest among men of African ancestry (1.7% for nonsmokers vs. 6.1% for smokers) or high genetic risk (1.4% for nonsmokers vs. 4.3% for smokers)., Conclusions: Healthy lifestyle is minimally related to the overall risk of developing PCa but is associated with a substantially reduced risk of dying from PCa. In multivariable analyses, both exercise and not smoking remain independently associated with reduced metastatic and fatal PCa., (© 2024 American Cancer Society.)

Aims: To see whether the diabetic individuals identified by the Indian Diabetes Risk Score (IDRS) also have a higher prevalence of diabetes related complications., Methods: Type 2 diabetic subjects were selected from the Chennai Urban Rural Epidemiology Study in south India. Four field stereo retinal colour photography was done and diabetic retinopathy [DR] was classified according to Early Treatment Diabetic Retinopathy Study grading system. Coronary artery disease was diagnosed using Minnesota coding of 12-lead electrocardiograms. Diabetic peripheral neuropathy (DPN) was diagnosed if vibratory perception threshold [VPT] of the right great toe measured by biothesiometry was > or =20. The criterion for diagnosis of peripheral vascular disease (PVD) was an ankle-brachial index < 0.9. Macroalbuminuria was diagnosed if urinary albumin excretion was > or =300 microg/mg creatinine. A total of 1476 individuals who had information on all test parameters were included for analysis., Results: Subjects with IDRS score > or =60 had significantly higher prevalence of coronary artery disease (CAD) [9.2% vs. 5.4%, p = 0.043], DPN [29.2% vs. 8.8%, p < 0.001] and PVD [4.8% vs. 1.9%, p = 0.038] compared to subjects with IDRS score <60. However, the prevalence of DR and macroalbuminuria did not differ between the two IDRS subgroups. Age explained much of the observed differences in prevalence of CAD, PVD and DPN between the two IDRS subgroups., Conclusions: This study further extends the clinical usefulness of IDRS to predicting diabetic complications like CAD, PVD and DPN as well.