Meeting Summary
Agenda and Abstracts
Speaker Roster

Predictors of obesity and weight gain in the Health Professionals Follow-up Study and the Nursesí Health Studies

Eric Rimm, ScD
Associate Professor of Epidemiology and Nutrition
Harvard School of Public Health

The etiology of obesity is made up of a complex set of behavioral and lifestyle factors which interact to create a environment that leads to insidious weight gain. To determine the root causes of obesity and weight gain is challenging from an epidemiologic perspective because the development of the outcome (weight gain and obesity) is almost always associated with changes in the exposures under study. For example, in some cross-sectional epidemiologic studies, obesity is associated with more diet soda consumption. This irretractable confounding by indication or reverse causality, can make research on obesity complicated and make the interpretation of results particularly challenging.

In the Health Professionals Follow-up Study, the Nurses Health Study and the Nursesí Health Study 2, we have been following over 280,000 men and women with data collected biennially to study prospectively changes in risk factors as they relate to changes in weight gain. The main areas of research can be broken down into three domains: 1) diet, 2) physical activity and inactivity, and 3) biological markers or mediators of these exposures.

In the Health Professionals Follow-up Study, a prospective study of 51,529 male dentists, veterinarians, and pharmacists, we have conducted several sets of analyses to examine new onset obesity or to look at changes in weight or waist girth over time. In our initial work (Coakley, 1998), we looked at predictors of 4-year weight change. Over this follow-up period, middle-aged men who increased their exercise, decreased TV viewing and stopped eating between meals, lost an average weight of -1.4 kg (95% confidence interval (CI) -1.6 - -1.1 kg), compared to a weight gain of 1.4 kg among the overall population. The prevalence of obesity among middle-aged men was lowest among those who maintained a relatively high level of vigorous physical activity, compared to those who were relatively sedentary. With further follow-up, we found that whole grain intake and specifically bran intake were important predictors of a slower rate of weight gain (Koh-Banerjee, 2004).

In an interesting and novel twist we also looked at waist gain (assessed in 1987 and 1996) as a potential better marker of atherogenic weight gain. In multivariate analyses, a 2% increment in energy intake from trans fats that were isocalorically substituted for either polyunsaturated fats or carbohydrates was significantly associated with a 0.77-cm waist gain over 9 y (P < 0.001 for each comparison). An increase of 12 g total fiber/d was associated with a 0.63-cm decrease in waist circumference (P < 0.001), whereas smoking cessation and a 20-h/wk increase in television watching were associated with a 1.98-cm and 0.59-cm waist gain, respectively (P < 0.001). Increases of 25 (METs) ∑ h/wk in vigorous physical activity and of >=0.5 h/wk in weight training were associated with 0.3cm and 0.91-cm decreases in waist circumference, respectively (P < 0.001 for each comparison). These associations remained significant after further adjustment for concurrent change in body mass index. (Koh-Banerjee, 2003). The findings for trans fat intake, television watching, and physical activity on waist gain are further supported by our recent work (Mozaffarian, 2003; Fung, 2000) which suggests associations between these exposures and inflammatory markers - potential precursors of insulin resistance and obesity.

In the Nurses Health Study (n=121,600) and the Nursesí Health Study 2 (n=116,000) covering a 45-year age range, we also found strong associations between lifestyle factors and risk of obesity. Among the younger women in the NHS2 study, two of the more novel factors studied were related to beverage consumption. For alcohol consumption we found a U-shaped association between alcohol and weight gain (Wannamathee, 2004). Compared to non-drinkers the adjusted relative odds (95% CI) of 8-year weight gain >=5kg according to grams/day were 0.93 (0.89,0.97) for those consuming 0.1-4.9 g/day, 0.91 (0.86,0.96) for 5-14.9g/day, 0.85 (0.76,0.95) for 15-29.9g/day and 1.06 (0.89,1.26) for those consuming 30+g/day (p<0.0001 for quadratic trend). For soft drinks we studied weight change over 2 separate 4-year periods (Schulze, 2004). Those with stable consumption patterns had no difference in weight gain, but weight gain over a 4-year period was highest among women who increased their sugar-sweetened soft drink consumption from <=1/week to >=1/day (multivariate adjusted means: 4.69 kg for 1991-95, 4.20 kg for 1995-99), and smallest among women who decreased their intake (1.34 and 0.15 kg for the two time periods), after adjusting for lifestyle and dietary confounders. An increased consumption of fruit punch was also associated with greater weight gain compared to those who decreased their consumption.

In the NHS, we also have documented the separate domains of television/sedentary activity and vigorous activity on risk of obesity. In the multivariate analyses adjusting for age, smoking, exercise levels, dietary factors, and other covariates, each 2-h/d increment in TV watching and sitting at work was associated with a 23% (95% CI, 17%-30%) and 5% (95% CI, 0%-10%) increase in obesity respectively. In contrast, standing or walking around at home (2 h/d) was associated with a 9% (95% CI, 6%-12%) reduction in obesity. Each 1 hour per day of brisk walking was associated with a 24% (95% CI, 19%-29%) reduction in obesity. Even small differences in energy expenditure can have important long term implications for obesity if patterns of behavior are sustained.

References

  1. Chu N-F, Stampfer MJ, Spiegelman D, Rifai N, Hotamisligil GA, Rimm EB. Dietary and lifestyle factors in relation to plasma leptin concentrations among normal weight and overweight men. Int J Obesity 2001;25:106-114.
  2. Chu N-F, Spiegelman D, Yu J, Rifai N, Hotamisligil GA, Rimm EB. Plasma leptin concentrations and 4-year weight gain among US men. Int J Obesity 2001;25:346-353.
  3. Coakley EH, Rimm EB, Colditz GA, Kawachi I, Willett WC. Predictors of weight change in men: Results from the Health Professionals Follow-up Study. Int J Obesity 1998;22:89-96.
  4. Fung TT, Hu FB, Yu J, Chu N-F, Spiegelman D, Tofler GH, Willett WC, Rimm EB. Leisure-time physical activity, television watching, and plasma biomarkers of obesity and cardiovascular disease risk. Am J Epidemiol 2000;152:1171-1178.
  5. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE. Television Watching and Other Sedentary Behaviors in Relation to Risk of Obesity and Type 2 Diabetes Mellitus in Women. JAMA. 289(14):1785-1791, April 9, 2003.
  6. Koh-Banerjee P, Franz M, Sampson L, Liu S, Jacobs D, Spiegelman D, Willett WC, Rimm EB. Changes in whole grain, bran, and cereal fiber consumption in relation to 8-year weight gain among men. Am J Clin Nutr (In Press)
  7. Koh-Banerjee P, Chu N-F, Spiegelman D, Rosner B, Colditz G, Willett WC, Rimm EB. Prospective study of the association of changes in dietary intake, physical activity, alcohol consumption and smoking with 9-y gain in waist circumference among 16,587 US men. Am J Clin Nutr 2003;78:719-27.
  8. Mozaffarian D, Pischon T, Hankinson SE, Rifai N, Joshipura K, Willett WC, Rimm EB. Dietary intake of trans fatty acids and systemic inflammation in women. Am J Clin Nutr. 2004 Apr;79(4):606-12.
  9. Schulze MB, Manson JE, Ludwig DS, Colditz GA, Stampfer MJ, Willett WC, Hu FB. Sugar-sweetened beverages, weight gain, and incidence of type 2 diabetes in young and middle-aged women. JAMA (in press)
  10. Wannamethee SG , Field AE, Colditz GA, Rimm EB. Alcohol intake and 8 year weight gain in women: a prospective study. Obesity Research (in press)

 

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