So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

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 



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# 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

The metabolic syndrome

OBJECTIVE. The goal was to link hypertension and the metabolic syndrome in adulthood directly to blood pressures measured decades earlier for the same individuals as children and to establish criterion values for blood pressure that predict hypertension and the metabolic syndrome later in life. METHODS. We analyzed serial data for 240 men and 253 women in the Fels Longitudinal Study. We derived age- and gender-specific childhood blood pressures that predict hypertension and the metabolic syndrome in adulthood, and we validated these criterion values in a larger sample. RESULTS. Blood pressure diverged between adults with and without the metabolic syndrome beginning at age 5 for boys and age 8 for girls. The odds ratios for developing hypertension at ≥30 years of age ranged from 1.1 for 14- to 18-year-old boys to 3.8 for 5- to 7-year-old boys and from 2.7 for 8- to 13-year-old girls to 4.5 for 5- to 7-year-old girls, if their blood pressure exceeded criterion values at a single examination in childhood. The corresponding odds ratios for the metabolic syndrome, with or without hypertension, ranged from 1.2 for 14- to 18-year-old boys to 2.6 for 8- to 13-year-old boys and from 1.5 for 14- to 18-year-old girls to 3.1 for 5- to 7-year-old girls. The relative risk of adult hypertension ranged from 1.5 to 3.8 for boys and from 1.5 to 4.7 for girls, and that of the metabolic syndrome ranged from 1.1 to 1.8 for boys and from 1.2 to 5.6 for girls. These relative risks varied directly with the number of examinations at which systolic blood pressure exceeded criterion values. CONCLUSION. Children with systolic blood pressures above the criterion values established in this longitudinal study are at increased risk of hypertension and the metabolic syndrome later in life.

Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life.

BACKGROUND: Childhood overweight develops during ‘critical periods’, but the relationship of body mass index (BMI) patterns during ‘critical periods’ from childhood into adulthood with subsequent overweight and adiposity has not been previously investigated. BMI patterns during early childhood, pubescence and post-pubescence and their independent effects on overweight and body fatness at 35–45 y of age were examined along with birth weight and the effects of adult lifestyle factors. METHODS: BMI parameters describing the timing, velocity minimum (min) and maximum (max) values from 2 to 25 y of age were related to adulthood BMI values and total and percentage body fat (TBF, %BF) at 35–45 y. These data were from 180 males and 158 females in the Fels Longitudinal Study. RESULTS: There was no sex difference in the timing of BMI rebound, but the age of BMI maximum velocity and maximum BMI were both earlier in girls. Children with an earlier BMI rebound had larger BMI values at rebound and at maximum velocity. Children who reached maximum BMI at later age had larger maximum BMI values. Maximum BMI was a strong predictor for adult BMI and in females, a strong predictor of adulthood TBF and %BF. Maximum BMI was closely related to maximum BMI velocity in females and in males, BMI at maximum velocity is a strong predictor of TBF and %BF. CONCLUSIONS: Changes in childhood BMI were related to adult overweight and adiposity more so in females than males. BMI rebound is a significant important period related to overweight at 35–45 y in females but not in males. However BMI patterns during and post-adolescence were more important than the BMI rebound for adulthood TBF and %BF status. There is marked tracking in BMI from approximately 20 y into 35–45 y. The pattern of BMI changes from 2 to 25 y had stronger effects on subsequent adult overweight than birth weight and adult lifestyle variables.

Body mass index during childhood, adolescence and young adulthood in relation to adult overweight and adiposity: the Fels Longitudinal Study.

Aging, body composition, and lifestyle: the Fels Longitudinal Study

The incidence of obesity in the United States and other developed countries is epidemic. Because the prevalence of comorbidities to obesity, such as type 2 diabetes, has also increased, it is clear there is a great need to monitor and treat obesity and its comorbidities. Body composition assessments vary in precision and in the target tissue of interest. The most common assessments are anthropometric and include weight, stature, abdominal circumference, and skinfold measurements. More complex methods include bioelectrical impedance, dual-energy X-ray absorptiometry, body density, and total body water estimates. There is no single universally recommended method for body composition assessment in the obese, but each modality has benefits and drawbacks. We present here the most common methods and provide guidelines by way of examples to assist the clinician/researcher in choosing methods appropriate to their situation.

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Body Composition Methods: Comparisons and Interpretation:

Body composition during puberty is a marker of metabolic changes that occur during this period of growth and maturation, and, thus, holds key information regarding current and future health. During pu

/pdf/puberty-and-body-composition-1uxs74iis8.pdf

Roger M. Siervogel

Papers

Systolic blood pressure in childhood predicts hypertension and metabolic syndrome later in life.

Body mass index during childhood, adolescence and young adulthood in relation to adult overweight and adiposity: the Fels Longitudinal Study.

Aging, body composition, and lifestyle: the Fels Longitudinal Study

Body Composition Methods: Comparisons and Interpretation:

Puberty and body composition.