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Original Research Communication |
1 From the Departments of Nutrition (PK-B, WW, and ER), Epidemiology (DS, GC, WW, and ER), and Biostatistics (DS and BR), Harvard School of Public Health, Boston; the Departments of Public Health and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China (N-FC); and the Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, and Harvard Medical School, Boston (BR, GC, WW, and ER).
2 Supported by research grants CA55075 and HL35464 from the NIH and grant DK46200 from the Boston Obesity and Nutrition Research Center. 3 Address reprint requests to E Rimm, Department of Nutrition, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115. E-mail: erimm{at}hsph.harvard.edu. 4 Address correspondence to P Koh-Banerjee, Department of Preventive Medicine, University of Tennessee Health Science Center, 66 North Pauline, Suite 633, Memphis, TN 38163.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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Objective: The objective of the study was to determine the associations of changes in diet, physical activity, alcohol consumption, and smoking with 9-y waist gain among US men.
Design: A prospective cohort comprised 16 587 US men aged 40–75 y at baseline in 1986. Data on lifestyle factors were provided periodically with the use of self-reported questionnaires, and participants measured and reported their waist circumference in 1987 and 1996.
Results: 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 metabolic equivalent tasks (METs) • h/wk in vigorous physical activity and of 
0.5 h/wk in weight training were associated with 0.38-cm 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. Changes in total fat and alcohol consumption and in walking volume were not significantly related to waist gain.
Conclusions: Waist gain may be modulated by changes in trans fat and fiber consumption, smoking cessation, and physical activity.
Key Words: Dietary fat • trans fats • fiber • physical activity • smoking • waist gain • obesity
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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Whereas the rising prevalence of obesity in the past few years has been attributed to changes in lifestyle associated with increasing modernization, few studies have prospectively examined the relation between lifestyle factors and the accumulation of abdominal fat. The modifiable factors that were associated with changes in android obesity include generalized obesity (9–11), physical activity (12, 13), and cigarette smoking (14). The effect of diet on central fat stores is more controversial, and the effects of macronutrient composition, alcohol consumption, and dietary fiber are not clearly established. In addition, the findings from various observational and intervention studies are difficult to compare because various anthropometric indicators of abdominal obesity were used. Whereas the waist-to-hip ratio is often used as an indicator of abdominal fat mass, this ratio is difficult to interpret biologically because the waist and hip circumference measures are reflective of different anatomical entities (15). The waist circumference measures both visceral and subcutaneous fat, whereas the hip circumference includes fat mass, lean muscle mass, and skeletal frame (15). Furthermore, the waist circumference contributes less error than does the waist-to-hip ratio because the former is a single measurement (8) and has been independently associated with increased serum indexes of cardiovascular disease risk (16, 17) and with an increased risk of diabetes (8), cardiovascular disease (6), and total mortality (18, 19). Therefore, we prospectively examined the association of changes in dietary intake, physical activity, alcohol consumption, and smoking with a 9-y gain in waist circumference among a cohort of 16 587 men.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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We excluded from the analysis 17 584 men who either died (n = 1751) or developed cardiovascular disease, cancer, or diabetes (n = 15 833) before 1996 because the development of those diseases may alter weight and waist measures, dietary intake, and physical activity level. Furthermore, we excluded 17 358 men who failed to report waist circumference measures, body weights, or dietary data. Our analysis is therefore based on 16 587 healthy men for whom we have a complete set of predictor and outcome information for the study period of 1986 to 1996. The Institutional Review Board of the Harvard School of Public Health approved the protocol for this study.
Outcome assessment
We evaluated the reproducibility and validity of the self-reported measures of waist circumference and weight by comparing them with technician-assessed measurements taken 6 mo apart in a subset of the cohort participants (20). The self-reported measurements and the average of 2 technician measurements were highly correlated (weight: r = 0.97; waist circumference: r = 0.95). Furthermore, there were no significant linear trends in accuracy of reported waist circumference across quartiles of either age or BMI (20). The validity of self-reported waist measurements was further examined by using Bland-Altman plots with ANALYSE-IT for Excel software (version 1.67; Analyse-It Software, Leeds, United Kingdom; 21). The differences between the self-reported measurements and the average of the 2 technician measurements were normally distributed, and the degree of bias was 0.14 cm in (95% CI: -0.40, 0.69). Bias did not appear to increase or decrease with the underlying true value. On the basis of these observations and of the observation that 2.3% of the data fell outside the 95% limits of agreement (-6.02 to 6.30 cm), the two methods were deemed interchangeable (Figure 1
). The validity of self-reported height was not evaluated because it was previously reported as highly valid (22).
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6 times/d." Alcohol consumption per day was calculated as the sum of the amount of alcohol in any wine, beer, and liquor consumed, multiplied by the average number of servings per day. The amount of alcohol contained within each beverage is 12.8 g/355-mL (12-oz) can or bottle of beer, 11 g/118-mL (4-oz) glass of wine, and 14 g/44-mL (1.5-oz) shot of liquor (25). The FFQ was validated among a subset of the study participants (23). In the validation study, 2 FFQs were administered 1 y apart, with two 1-wk diet records administered 6 mo apart during this 1-y period. The Pearson correlation coefficients between the FFQs and the average of the two 1-wk dietary records, after correction for variation in the 1-wk dietary records, ranged from 0.44 for protein to 0.92 for vitamin C (23). The correlation for total alcohol was 0.86 and that for alcohol-containing beverages ranged from 0.70 for wine to 0.86 for liquor (26).
The level of physical activity was ascertained in 1986 and biennially thereafter. Participants were queried regarding the average time spent per week over the past year in specific activities including walking or hiking outdoors, jogging [< 9.6 km/h (< 6 mph)], running [ 9.6 km/h ( 6 mph)], bicycling, swimming, tennis, squash, racquetball, rowing, and calisthenics. After 1986, questions were added regarding the average time spent per week in heavy outdoor work, weight training, and television watching. Walking pace, categorized as casual [
3.2 km/h ( 2 mph)], normal [3.2–4.7 km/h (2–2.9 mph)], brisk [4.8–6.3 km/h (3–3.9 mph)], or striding ( 6.4 km/h ( 4 mph)], was also recorded. The time spent at each activity in hours per week was multiplied by its typical energy expenditure, expressed in metabolic equivalent tasks (METs), then summed over all activities, to yield a MET • hour score (27). One MET, the energy expended by sitting quietly, is equivalent to 3.5 mL of oxygen uptake per kilogram of body weight per minute. Vigorous activities were defined as those requiring 6 METs or more: jogging, running, bicycling, swimming, tennis, squash, racquetball, and rowing.
The validity and reproducibility of the physical activity questionnaire were assessed in 1991 when 280 participants in the HPFS completed a 1-wk activity diary at 4 time periods corresponding to different seasons of the year (28). The correlations between scores of physical activity from the diaries and from the questionnaires were 0.65 for total physical activity, 0.28 for nonvigorous activity, and 0.58 for vigorous activity. The correlation between questionnaire-derived vigorous activity and resting pulse was -0.45; after a self-administered step test, the correlation of the same measurements was -0.41 (28).
Statistical analysis
The mean baseline characteristics for the different age groups were first compared by using analysis of variance with the generalized linear model procedure. We further performed post hoc multiple comparison tests by using the Tukey method to examine all pairwise comparisons at the overall experiment rate of P < 0.05.
Using multivariate linear regression, we examined how changes in lifestyle factors (1986–1994 for dietary exposures, and 1986–1996 for all other exposures) were associated with the nonrepeated dependent variable, namely the change in waist circumference (in cm) in the same period (1987–1996). We used the robust variance estimate (29) to avoid needing assumptions of normality for the linear regression to obtain valid inference. In all analyses, there was one observation per participant. The exposures were modeled as differences between the baseline and most recent follow-up measure when tests for nonlinearity using spline regression were not statistically significant; otherwise, the exposures were categorized.
We calculated the age-adjusted regression coefficients for each of the lifestyle factors and waist gain, and associations were estimated from the regression models as the change in waist circumference (in cm) over the 9-y period per unit of change in the lifestyle factor. To control for potential confounding, we adjusted the models for baseline age (continuous variable), baseline waist circumference (quartiles), baseline BMI (quartiles), baseline and changes in total calories (continuous variables), baseline and changes in alcohol consumption (continuous variables), baseline (continuous variable) and changes in (quintiles) total physical activity, and changes in smoking.
Smoking was included as a categorical variable, and men were classified according to their change in smoking status between 1986 and 1996. Men who were nonsmokers at both time periods were classified as nonsmokers, whereas men who were smokers at both time periods were categorized as habitual smokers. Men who reported a change in their smoking status from smoking in 1986 to nonsmoking in 1996 were classified as quitters, and men who reported never smoking in 1986 and smoking in 1996 were classified as new smokers.
All nutrient values (except those for alcohol) were energy-adjusted by using the residual method to examine the nutrient composition of the diet rather than the effect of absolute intakes (24). To determine the relative effects of the dietary fat subtypes, the multivariate regression model simultaneously adjusted for intakes of energy, total fat, saturated fat, monounsaturated fat, and trans fatty acid (as percentages of energy). To examine the effect of isoenergetically replacing carbohydrates with each fat subtype, the percentages of energy derived from protein and all fat subtypes were simultaneously included. To identify the lifestyle factors that predicted increases in waist circumference independent of weight gain, we further adjusted for changes in BMI in this period. All statistical analyses were conducted with the use of SAS software (version 8.2; SAS Institute Inc, Cary, NC).
To evaluate the influence of measurement error on the association between changes in exposures and waist gain, we used a subset of participants from a separate but similar study (30) for whom repeated dietary records and FFQs were available in both 1980 and 1986. Using the regression calibration approach (31) and results from our validation study together with data from our main study, we estimated regression coefficients adjusted for measurement error (see Appendix A).
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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). In contrast, the mean waist gain among men aged 50–59 y was 3.2 ± 5.9 cm, and that among men aged 60 y was 2.3 ± 6.6 cm. At baseline, men aged 50–59 y had the highest BMI (25.2 ± 2.8). However, the mean BMI change was greatest among men aged 40–40 y (1.1 ± 1.7); the change among men aged 50–59 y was 0.7 ± 1.5, and that among men aged 60 y was 0.1 ± 1.5.
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30.0 ± 6.8% of total caloric intake, increased their carbohydrate consumption to 50.8 ± 8.8% of total calories, and increased their consumption of dietary fiber to an overall average of 22.4 ± 7.4 g/d. The consumption of trans fats increased slightly over the follow-up period and was 1.3 ± 0.6% of total calories. Alcohol consumption remained fairly stable over time, at an overall average of 11.5 ± 14.9 g/d. Men aged 40–49 y reported the least time spent watching television both at baseline and at follow-up (9.1 ± 7.7 h/wk in 1996). The younger men were more physically active at baseline than were the older men, but the older men increased their levels of vigorous physical activity over time. At follow-up in 1996, men aged 40–49 y reported 16.8 ± 28.2 MET • h/wk of total vigorous activity, whereas those aged 50–59 y reported 14.0 ± 24.7 MET • h/wk, and men aged 60 y reported 11.8 ± 21.6 MET • h/wk.
Multivariate models
In age-adjusted analyses, a higher total fat intake was significantly related to waist gain (Table 2). This association was only modestly attenuated after adjustment for confounding by changes in other lifestyle factors including smoking, alcohol consumption, and physical activity. Because waist gain was only modestly correlated with changes in BMI (r = 0.43), we examined the effects of changes in behaviors on waist gain independent of changes in total obesity. The association between total fat and waist gain virtually disappeared after further adjustment for concurrent change in BMI, which suggested that increases in dietary fat did not alter abdominal adipose tissue independent of overall obesity. In contrast, greater intakes of trans fats were consistently related to waist gain. A 2% increment in energy intake from trans fats, isocalorically substituted for either polyunsaturated fats or carbohydrates, was associated with a 0.77-cm waist gain in multivariate analyses (P < 0.001 for each comparison). After adjustment for changes in BMI, the associations were only modestly reduced (0.52- or 0.53-cm waist gain, respectively; P < 0.01 for each comparison). Because common sources of trans fats such as commercial baked goods are also high in sugars, we simultaneously controlled for changes in glycemic load, a measure of both the quality and quantity of carbohydrates consumed, and the results did not differ appreciably.
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Habitual smokers experienced a loss in waist circumference of 0.68 cm (P = 0.01), whereas those who quit smoking gained waist circumference (1.98 cm; P < 0.001) (Table 3). However, after we controlled for changes in BMI, only smoking cessation remained significantly related to waist circumference (0.77 cm; P < 0.01). No significant associations were observed between changes in total alcohol consumption and 9-y waist gain.
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). Men who added 0.5 h/wk of weight training experienced a loss in waist circumference of 0.91 cm over 9 y (P < 0.001); when BMI was held constant, waist circumference was reduced by 0.74 cm (P < 0.001). Because walking was the most common type of physical activity reported, we examined changes in this activity in relation to 9-y waist gain. To separate the effects of total walking volume from those of walking pace, we simultaneously controlled for both factors in multivariate analyses. Whereas no significant association between changes in walking volume and waist gain was observed, an increase in walking pace of 1.6 km/h ( 1 mph) was related to a loss in waist circumference of 0.50 cm (P = 0.002). When BMI was held constant, waist circumference was reduced by 0.27 cm (P = 0.05).
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Because the 1987 questionnaire was not part of the usual biennial mailings, we did not use extensive follow-up procedures to increase our follow-up rate (32). For this reason, 12 000 men were excluded from the analysis for failure to report their baseline waist circumference. However, this group of men did not substantially differ in BMI, physical activity, or diet in 1986 (mean characteristics not shown) from the cohort used in the analysis.
Additional analyses
Also excluded from the population for analysis were 15 833 men who developed cardiovascular disease, cancer, or diabetes before 1996. We chose to exclude these men to avoid biases related to changes in diet or physical activity patterns before those diagnoses. However, these men had greater waist circumferences at baseline, and it is possible that dietary fat or vigorous physical activity may differentially affect this population. To explore this possibility, we included these men and reanalyzed the data after we controlled for the development of disease. The results did not differ significantly from those presented above.
Using the results of our validation study, we further adjusted the estimated ß coefficients for measurement error in the significant predictors. Although measures of trans fatty acids were not available from the diet records used in the validation study, we assumed that the validity of the FFQ in measuring trans fats was comparable to its validity in assessing other types of fats (total fat: r = 0.67; saturated fats, r = 0.75). After error correction, the substitution of trans fats as 2% of energy for polyunsaturated fats was associated with a 2.7-cm increase in waist circumference over 9 y (P < 0.001) (as compared with a 0.77-cm waist gain, uncorrected). An increase of 12 g fiber/d (r = 0.68 between FFQ and diet records) was associated with a 2.21-cm reduction in waist circumference after error correction (P < 0.001) (0.63-cm waist gain, uncorrected). Although the change in physical activity was not assessed in our validation study, we assumed that the level of error in the assessment of the change in vigorous physical activity was comparable to that in the assessment of the change in dietary exposures. This seems reasonable, because, in our previous validation studies, the correlation for vigorous physical activity between the activity diaries and our questionnaire was 0.58 (28), which was of similar magnitude to correlations between the dietary records and the FFQs for total fat and saturated fat (r = 0.67 and 0.75, respectively) (23). After correction for measurement error, an increase of 25 MET • h/wk of vigorous activity was associated with a 1.33-cm decrease in waist circumference (P < 0.001) (as compared with a 0.38-cm waist gain, uncorrected).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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In the few studies that examined the relation between dietary fat and central obesity, no association was observed (33, 34), and data from long-term intervention trials suggested that fat consumption within the range of 18–40% of energy intake has little effect on body fatness (35). However, individual fatty acids may differentially affect abdominal adiposity through their effects on insulin action (36). In the Normative Aging Study (2), saturated fat intake was positively correlated with the abdomen-to-hip ratio (r = 0.13), and in a recent clinical trial, the replacement of dietary saturated fats with polyunsaturated fats resulted in improvements in insulin sensitivity and abdominal fat distribution (37). To our knowledge, the current study is the first prospective study to report the association between changes in trans fatty acid intake and increases in abdominal adiposity.
It may be that trans fats, which have been associated with an elevated risk of type 2 diabetes (38), impair insulin sensitivity by eliciting alterations in cell membrane structure (39) and by increasing concentrations of interleukin 6, tumor necrosis factor, and prostaglandins, which may reduce insulin sensitivity (40). The resulting hyperinsulinemia may promote lipid accumulation by expressing lipoprotein lipase activity (41), and insulin’s effects may be more highly expressed in abdominal visceral adipocytes than in subcutaneous adipocytes because of the potentially greater cellularity, innervation, and blood flow (41).
Fiber may affect abdominal adipose tissue through its effects on insulin sensitivity; in particular, soluble fiber may blunt postprandial glycemic and insulinemic responses in the small intestine (42) that are linked to reductions in the rate of return of hunger and subsequent energy intake (43). In cross-sectional studies, fiber generally has been inversely associated with body weight (44) and body fat (45), and, in a longitudinal investigation, fiber was inversely associated with BMI at all levels of fat intake (46). In the Coronary Artery Risk Development in Young Adults Study, fiber consumption further predicted insulin concentrations and 10-y weight gain (46).
Prior studies linked increased central adiposity with smoking cessation. Kahn et al (12) reported that men who quit smoking were twice as likely to report waist gain at 10-y follow-up as were those who remained habitual smokers (12). Similarly, men in the Normative Aging Study who quit smoking experienced increases in central adiposity that were independent of age or initial BMI that were greater than the increases in both never or former smokers and current smokers during 15 y of follow-up (14).
Sedentary behavior, represented in this study by television watching, was significantly related to increases in abdominal adiposity independent of physical activity. Fung et al (47) reported that television watching was significantly related to plasma biomarkers of obesity among men. The effects of physical inactivity may have been underestimated in our study because time spent watching television may not have represented the total amount of time spent inactively (48).
Vigorous physical activity and weight training were significantly inversely associated with waist gain. Whereas an increase in total walking volume was not significantly related to reduced waist circumference, an increase in walking pace was inversely associated with waist gain, which supported the importance of exercise intensity in protection against abdominal obesity. Similarly, in the Cancer Prevention II Study (12), men who engaged in vigorous activities such as running 1–3 h/wk were significantly less likely to experience waist gain than were men who did not engage in any vigorous activities (odds ratio: 0.75; 95% CI: 0.64, 0.88). Walking afforded no significant benefit when performed < 4 h/wk (odds ratio: 1.08; 95% CI: 0.99, 1.17), and nonvigorous activities including gardening provided no significant benefit (12).
Physical activity may reduce abdominal obesity through the utilization of more fat from the intraabdominal region than from the gluteal region, which results in the redistribution of adipose tissue (49). Chronic exercise training may also enhance insulin-stimulated glucose uptake through increased activity or expression of key proteins involved in skeletal muscle glucose metabolism (50). To induce the physiologic changes necessary to influence central fat stores, weight training and more vigorous types of activity may be needed.
Because our study obtained only 2 waist measurements, we were not able to assess changes in exposures that preceded waist gain, and this inability limited the drawing of causal inferences. However, the consistency of our findings with those of prior studies and the likely effects induced by the exposures on waist gain lend credence to important causal relations. Furthermore, whereas the educational backgrounds, socioeconomic status, and behavioral practices of our participants may not be characteristic of the entire US population, the physiologic effects induced by these lifestyle exposures should not vary substantially by income or education. The prospective collection of data in our study further reduces the potential for bias that is attributable to differential recall by the amount of waist gain.
Implemented together, a 2% reduction in energy from trans fats, a 12-g/d increase in fiber, a 3-h/d reduction in television watching, and a 0.5-h/wk increase in weight training may decrease waist circumference by 2.9 cm over 9 y. Whereas the individual contributions of these lifestyle modifications may appear trivial, the clinical implications of even minor reductions in waist circumference are substantial. In our previous report, a 2.5-cm difference in waist circumference was associated with an 20% greater risk for the development of diabetes (8). Furthermore, this risk reduction does not take into account the weight loss that would likely ensue because of the implementation of these behaviors or the favorable changes in insulin sensitivity, serum cholesterol, and cardiorespiratory fitness that may result.
Because trans fatty acid intake, low fiber consumption, and sedentary behavior tend to be more prevalent in the general population, the magnitude of waist reduction that could be achieved would be even greater than that observed in our population of 16 587 healthy men. Because of measurement errors that are included at both the beginning and end of follow-up, our regression coefficients are also underestimations of the true effect estimates. After accounting for errors in assessing changes in diet and physical activity, these behaviors would predict a difference in waist gain of 10.2 cm (Figure 2).
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APPENDIX A. |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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X1 represent true dietary intake (diet record) in 1980;
X2 represent true dietary intake (diet record) in 1986;
Z1 represent dietary intake measured by a surrogate (food-frequency questionnaire) in 1980; and
Z2 represent dietary intake measured by a surrogate (food-frequency questionnaire) in 1986.
Using the regression calibration approach, the change in true dietary intakes (X2 - X1) is estimated as a function of the change in surrogate intakes (Z2 - Z1) derived from the validation study data:
 | (A1) |
In the case of univariate linear regression for which the exposure is a difference in 2 linear terms measured with error, the corrected point estimate for the exposure measure is:
 | (A2) |
is the estimated regression slope of X on Z from the validation study.
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ACKNOWLEDGMENTS |
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PK-B was responsible for the design of the study, analysis of data, and writing of the manuscript. N-FC was responsible for analysis of data and writing of the manuscript. DS contributed to the design of the study, analysis of data, and writing of the manuscript. BR provided statistical advice and contributed to the writing of the manuscript. GC contributed to the collection of data and the writing of the manuscript. WW contributed to the securing of funding, collection of data, design of the study, analysis of data, and writing of the manuscript. ER contributed to the securing of funding, collection of data, design of the study, analysis of data, and writing of the manuscript. None of the authors had advisory board affiliations or financial interests in organizations sponsoring the research.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONAPPENDIX A.REFERENCES |
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E. Lopez-Garcia, R. M van Dam, S. Rajpathak, W. C Willett, J. E Manson, and F. B Hu Changes in caffeine intake and long-term weight change in men and women Am. J. Clinical Nutrition, March 1, 2006; 83(3): 674 - 680. [Abstract] [Full Text] [PDF]
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A. T. Merchant, S. S. Anand, V. Vuksan, R. Jacobs, B. Davis, K. Teo, S. Yusuf, and for the SHARE and SHARE-AP Investigators Protein Intake Is Inversely Associated with Abdominal Obesity in a Multi-Ethnic Population J. Nutr., May 1, 2005; 135(5): 1196 - 1201. [Abstract] [Full Text] [PDF]
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M. S. Freiberg, H. J. Cabral, T. C. Heeren, R. S. Vasan, and R. Curtis Ellison Alcohol Consumption and the Prevalence of the Metabolic Syndrome in the U.S.: A cross-sectional analysis of data from the Third National Health and Nutrition Examination Survey Diabetes Care, December 1, 2004; 27(12): 2954 - 2959. [Abstract] [Full Text] [PDF]
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B. Sternfeld, H. Wang, C. P. Quesenberry Jr., B. Abrams, S. A. Everson-Rose, G. A. Greendale, K. A. Matthews, J. I. Torrens, and M. Sowers Physical Activity and Changes in Weight and Waist Circumference in Midlife Women: Findings from the Study of Women's Health Across the Nation Am. J. Epidemiol., November 1, 2004; 160(9): 912 - 922. [Abstract] [Full Text] [PDF]
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P. Koh-Banerjee, Y. Wang, F. B. Hu, D. Spiegelman, W. C. Willett, and E. B. Rimm Changes in Body Weight and Body Fat Distribution as Risk Factors for Clinical Diabetes in US Men Am. J. Epidemiol., June 15, 2004; 159(12): 1150 - 1159. [Abstract] [Full Text] [PDF]
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, not controlled for weight change; 
, with weight change held constant. The potential effect on 9-y waist circumference (in cm) was calculated for an increase in dietary fiber of 12 g/d, an increase in weight training by