WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Blaha, MD, MPH Stephanie E. Chiuve, ScD Mary Cushman, MD, MSc, FAHA Sandeep R. Das, MD, MPH, FAHA Rajat Deo, MD, MTR Sarah D. de Ferranti, MD, MPH James Floyd, MD, MS Myriam Fornage, PhD, FAHA Cathleen Gillespie, MS Carmen R. Isasi, MD, PhD, FAHA Monik C. Jiménez, ScD, SM Lori Chaffin Jordan, MD, PhD Suzanne E. Judd, PhD Daniel Lackland, DrPH, FAHA Judith H. Lichtman, PhD, MPH, FAHA Lynda Lisabeth, PhD, MPH, FAHA Simin Liu, MD, ScD, FAHA Chris T. Longenecker, MD Rachel H. Mackey, PhD, MPH, FAHA Kunihiro Matsushita, MD, PhD, FAHA Dariush Mozaffarian, MD, DrPH, FAHA Michael E. Mussolino, PhD, FAHA Khurram Nasir, MD, MPH, FAHA Robert W. Neumar, MD, PhD, FAHA Latha Palaniappan, MD, MS, FAHA Dilip K. Pandey, MBBS, MS, PhD, FAHA Ravi R. Thiagarajan, MD, MPH Mathew J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Rodriguez, MD, MPH, FAHA Gregory A. Roth, MD, MPH Wayne D. Rosamond, PhD, FAHA Comilla Sasson, MD, PhD, FAHA Amytis Towfighi, MD Connie W. Tsao, MD, MPH Melanie B. Turner, MPH Salim S. Virani, MD, PhD, FAHA Jenifer H. Voeks, PhD Joshua Z. Willey, MD, MS John T. Wilkins, MD Jason HY. Wu, MSc, PhD, FAHA Heather M. Alger, PhD Sally S. Wong, PhD, RD, CDN, FAHA Paul Muntner, PhD, MHSc On behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee Heart Disease and Stroke Statistics—2017 Update

Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

Author(s): Writing Group Members; Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; Das, Sandeep R; de Ferranti, Sarah; Despres, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Isasi, Carmen R; Jimenez, Monik C; Judd, Suzanne E; Kissela, Brett M; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Magid, David J; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Rosamond, Wayne; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee; Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2016 Update: A Report From the American Heart Association

Appendix I: List of Statistical Fact Sheets. URL: http://www.americanheart.org/presenter.jhtml?identifier=2007

We wish to thank Drs Brian Eigel and Michael Wolz for their valuable comments and contributions. We would like to acknowledge Tim Anderson and Tom Schneider for their editorial contributions and Karen Modesitt for her administrative assistance.

Disclosures 



View this table:







View this table:







View this table:





# Summary {#article-title-2}

Each year, the American Heart Association, in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics on heart disease, stroke, other vascular diseases, and their risk factors and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update is a valuable resource for researchers, clinicians, healthcare policy makers, media professionals, the lay public, and many others who seek the best national data available on disease …

Heart Disease and Stroke Statistics—2010 Update A Report From the American Heart Association

March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2019 Update: A Report From the American Heart Association

Author(s): Go, Alan S; Mozaffarian, Dariush; Roger, Veronique L; Benjamin, Emelia J; Berry, Jarett D; Borden, William B; Bravata, Dawn M; Dai, Shifan; Ford, Earl S; Fox, Caroline S; Franco, Sheila; Fullerton, Heather J; Gillespie, Cathleen; Hailpern, Susan M; Heit, John A; Howard, Virginia J; Huffman, Mark D; Kissela, Brett M; Kittner, Steven J; Lackland, Daniel T; Lichtman, Judith H; Lisabeth, Lynda D; Magid, David; Marcus, Gregory M; Marelli, Ariane; Matchar, David B; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Mussolino, Michael E; Nichol, Graham; Paynter, Nina P; Schreiner, Pamela J; Sorlie, Paul D; Stein, Joel; Turan, Tanya N; Virani, Salim S; Wong, Nathan D; Woo, Daniel; Turner, Melanie B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee

Heart Disease and Stroke Statistics—2013 Update A Report From the American Heart Association

Attempts to correlate protein abundance with mRNA expression levels have had variable success. We review the results of these comparisons, focusing on yeast. In the process, we survey experimental techniques for determining protein abundance, principally two-dimensional gel electrophoresis and mass-spectrometry. We also merge many of the available yeast protein-abundance datasets, using the resulting larger 'meta-dataset' to find correlations between protein and mRNA expression, both globally and within smaller categories.

/pdf/comparing-protein-abundance-and-mrna-expression-levels-on-a-4w43n6tnc1.pdf

Comparing protein abundance and mRNA expression levels on a genomic scale

Background— Markers of systemic inflammation (eg, C-reactive protein [CRP] and interleukin-6 [IL-6]) have been proposed to be “nontraditional” risk factors for cardiovascular disease in patients with type 2 diabetes mellitus. Matrix metalloproteinase-9 (MMP-9) has been implicated in the pathogenesis of atherosclerotic plaque rupture, which raises the possibility of the use of MMP-9 levels as a marker for future myocardial infarction or unstable angina. In vitro and animal studies suggest that thiazolidinediones can reduce the expression of these markers. The purpose of this analysis was to determine whether rosiglitazone alters serum concentrations of CRP, IL-6, MMP-9, and white blood cell count (WBC) and to examine the relationship of these effects with demographic and disease variables. Methods and Results— CRP, IL-6, MMP-9, and WBC were analyzed from stored frozen serum samples obtained from patients with type 2 diabetes who completed a 26-week randomized, double-blind, placebo-controlled study. After ...

Effect of Rosiglitazone Treatment on Nontraditional Markers of Cardiovascular Disease in Patients With Type 2 Diabetes Mellitus

Single-stranded dna binding proteins required for dna replication

OBJECTIVE —Recent evidence suggests that C-reactive protein (CRP) may predict development of diabetes in Caucasian populations. We evaluated CRP as a possible risk factor of the development of diabetes and metabolic syndrome in a 6-year study of 515 men and 729 women from the Mexico City Diabetes Study. RESEARCH DESIGN AND METHODS —Baseline CRP, indexes of adiposity, and insulin resistance (homeostasis model assessment [HOMA-IR]) were used to predict development of the metabolic syndrome, defined as including two or more of the following: 1 ) dyslipidemia (triglyceride ≥2.26 mmol/l or HDL cholesterol ≤0.91 mmol/l in men and ≤1.17 mmol/l in women; 2 ) hypertension (blood pressure >140/90 mmHg or on hypertensive medication); or 3 ) diabetes (1999 World Health Organization criteria). RESULTS —At baseline, CRP correlated significantly ( P CONCLUSIONS —CRP was not a significant predictor of the development of the metabolic syndrome in men. Our data strongly support the notion that inflammation is important in the pathogenesis of diabetes and metabolic disorders in women.

https://care.diabetesjournals.org/content/diacare/25/11/2016.full.pdf

Prospective study of C-reactive protein in relation to the development of diabetes and metabolic syndrome in the Mexico City Diabetes Study.

Motivation: Novel methods, both molecular and statistical, are urgently needed to take advantage of recent advances in biotechnology and the human genome project for disease diagnosis and prognosis. Mass spectrometry (MS) holds great promise for biomarker identification and genome-wide protein profiling. It has been demonstrated in the literature that biomarkers can be identified to distinguish normal individuals from cancer patients using MS data. Such progress is especially exciting for the detection of early-stage ovarian cancer patients. Although various statistical methods have been utilized to identify biomarkers from MS data, there has been no systematic comparison among these approaches in their relative ability to analyze MS data. Results: We compare the performance of several classes of statistical methods for the classification of cancer based on MS spectra. These methods include: linear discriminant analysis, quadratic discriminant analysis, k -nearest neighbor classifier, bagging and boosting classification trees, support vector machine, and random forest (RF). The methods are applied to ovarian cancer and control serum samples from the National Ovarian Cancer Early Detection Program clinic at Northwestern University Hospital. We found that RF outperforms other methods in the analysis of MS data.

/pdf/comparison-of-statistical-methods-for-classification-of-2998m4gy60.pdf

Kenneth R. Williams

Papers

Comparing protein abundance and mRNA expression levels on a genomic scale

Effect of Rosiglitazone Treatment on Nontraditional Markers of Cardiovascular Disease in Patients With Type 2 Diabetes Mellitus

Single-stranded dna binding proteins required for dna replication

Prospective study of C-reactive protein in relation to the development of diabetes and metabolic syndrome in the Mexico City Diabetes Study.

Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data