Changes in Diet and Lifestyle and Long-Term Weight Gain in Women and Men
22 Jun 2011-The New England Journal of Medicine (Massachusetts Medical Society)-Vol. 364, Iss: 25, pp 2392-2404
TL;DR: Specific dietary and lifestyle factors are independently associated with long-term weight gain, with a substantial aggregate effect and implications for strategies to prevent obesity.
Abstract: BackgroundSpecific dietary and other lifestyle behaviors may affect the success of the straightforward-sounding strategy “eat less and exercise more” for preventing long-term weight gain. MethodsWe performed prospective investigations involving three separate cohorts that included 120,877 U.S. women and men who were free of chronic diseases and not obese at baseline, with follow-up periods from 1986 to 2006, 1991 to 2003, and 1986 to 2006. The relationships between changes in lifestyle factors and weight change were evaluated at 4-year intervals, with multivariable adjustments made for age, baseline body-mass index for each period, and all lifestyle factors simultaneously. Cohort-specific and sex-specific results were similar and were pooled with the use of an inverse-variance–weighted meta-analysis. ResultsWithin each 4-year period, participants gained an average of 3.35 lb (5th to 95th percentile, −4.1 to 12.4). On the basis of increased daily servings of individual dietary components, 4-year weight cha...
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TL;DR: WRITING GROUP MEMBERS Emelia J. Benjamin, MD, SCM, FAHA Michael J. Reeves, PhD Matthew Ritchey, PT, DPT, OCS, MPH Carlos J. Jiménez, ScD, SM Lori Chaffin Jordan,MD, PhD Suzanne E. Judd, PhD
Abstract: 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
7,190 citations
TL;DR: 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
Abstract: 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
6,181 citations
Mohammad H. Forouzanfar1, Lily Alexander, H. Ross Anderson, Victoria F Bachman1 +733 more•Institutions (289)
TL;DR: The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) as discussed by the authors provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution.
5,668 citations
TL;DR: 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; Huff
Abstract: 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
5,449 citations
Additional excerpts
...Reprinted from Mozaffarian et al11 with permission....
[...]
TL;DR: The American Heart Association's 2020 Impact Goals for Cardiovascular Diseases and Disorders are revealed, with a focus on preventing, treating, and preventing heart disease and stroke.
Abstract: Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .e3
1. About These Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .e7
2. American Heart Association's 2020 Impact Goals. . . . . . . . . . . . . . . . .e10
3. Cardiovascular Diseases . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .e21
4. Subclinical Atherosclerosis . . . . . . . . . . . . . . . . . . . . .e45
5. Coronary Heart Disease, Acute Coronary Syndrome, and Angina Pectoris . . . . . . . . .e54
6. Stroke (Cerebrovascular Disease) . . . . . . . . . . . . . . . . . . . . . . . . . . . .e68
7. High Blood Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . .e88
8. Congenital Cardiovascular Defects . . . . . . . . . . . . . . . . . . . . . . . . . . . .e97
9. Cardiomyopathy and Heart Failure . . . . . . . . . . . . . . . . . . . . . . . . . . . .e102
10. Disorders …
5,260 citations
References
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TL;DR: The Statistical Update 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 each year.
Abstract: 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.
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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 …
6,176 citations
TL;DR: The relations between change in adult weight and the risk for noninsulin-dependent diabetes mellitus among women during 14 years of follow-up were quantified.
Abstract: Objective: To examine the relation between adult weight change and the risk for clinical diabetes mellitus among middle-aged women. Design: Prospective cohort study with follow-up from 1976 to 1990...
2,049 citations
TL;DR: In this article, the effects of reducing television, videotape, and video game use on changes in adiposity, physical activity, and dietary intake were evaluated in a randomized controlled school-based trial.
Abstract: ContextSome observational studies have found an association between television
viewing and child and adolescent adiposity.ObjectiveTo assess the effects of reducing television, videotape, and video game
use on changes in adiposity, physical activity, and dietary intake.DesignRandomized controlled school-based trial conducted from September 1996
to April 1997.SettingTwo sociodemographically and scholastically matched public elementary
schools in San Jose, Calif.ParticipantsOf 198 third- and fourth-grade students, who were given parental consent
to participate, 192 students (mean age, 8.9 years) completed the study.InterventionChildren in 1 elementary school received an 18-lesson, 6-month classroom
curriculum to reduce television, videotape, and video game use.Main Outcome MeasuresChanges in measures of height, weight, triceps skinfold thickness, waist
and hip circumferences, and cardiorespiratory fitness; self-reported media
use, physical activity, and dietary behaviors; and parental report of child
and family behaviors. The primary outcome measure was body mass index, calculated
as weight in kilograms divided by the square of height in meters.ResultsCompared with controls, children in the intervention group had statistically
significant relative decreases in body mass index (intervention vs control
change: 18.38 to 18.67 kg/m2 vs 18.10 to 18.81 kg/m2,
respectively; adjusted difference −0.45 kg/m2 [95% confidence
interval {CI}, −0.73 to −0.17]; P=.002),
triceps skinfold thickness (intervention vs control change: 14.55 to 15.47
mm vs 13.97 to 16.46 mm, respectively; adjusted difference, −1.47 mm
[95% CI, −2.41 to −0.54]; P=.002), waist
circumference (intervention vs control change: 60.48 to 63.57 cm vs 59.51
to 64.73 cm, respectively; adjusted difference, −2.30 cm [95% CI, −3.27
to −1.33]; P<.001), and waist-to-hip ratio
(intervention vs control change: 0.83 to 0.83 vs 0.82 to 0.84, respectively;
adjusted difference, −0.02 [95% CI, −0.03 to −0.01]; P<.001). Relative to controls, intervention group changes
were accompanied by statistically significant decreases in children's reported
television viewing and meals eaten in front of the television. There were
no statistically significant differences between groups for changes in high-fat
food intake, moderate-to-vigorous physical activity, and cardiorespiratory
fitness.ConclusionsReducing television, videotape, and video game use may be a promising,
population-based approach to prevent childhood obesity.
1,980 citations
TL;DR: Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.
Abstract: OBJECTIVE To investigate the relation between obesity, fat distribution, and weight gain through adulthood and the risk of non-insulin-dependent diabetes melli-tus (NIDDM). RESEARCH DESIGN AND METHODS We analyzed data from a cohort of 51,529 U.S. male health professionals, 40-75 years of age in 1986, who completed biennial questionnaires sent out in 1986, 1988, 1990, and 1992. During 5 years of follow-up (1987-1992), 272 cases of NIDDM were diagnosed among men without a history of diabetes, heart disease, and cancer in 1986 and who provided complete health information. Relative risks (RRs) associated with different anthropometrie measures were calculated controlling for age, and multivariate RRs were calculated controlling for smoking, family history of diabetes, and age. RESULTS We found a strong positive association between overall obesity as measured by body mass index (BMI) and risk of diabetes. Men with a BMI of ≥35 kg/m 2 had a multivariate RR of 42.1 (95% confidence interval [CI] 22.0-80.6) compared with men with a BMI 2 . BMI at age 21 and absolute weight gain throughout adulthood were also significant independent risk factors for diabetes. Fat distribution, measured by waist-to-hip ratio (WHR), was a good predictor of diabetes only among the top 5%, while waist circumference was positively associated with the risk of diabetes among the top 20% of the cohort. CONCLUSIONS These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.
1,838 citations