QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Merchant, A. T. Search for Related Content PubMed PubMed Citation Articles by Merchant, A. T.

---

Nutritional Epidemiology

Protein Intake Is Inversely Associated with Abdominal Obesity in a Multi-Ethnic Population¹

Anwar T. Merchant^*,,2, Sonia S. Anand^*,,**, Vlad Vuksan, Ruby Jacobs^*,, Bonnie Davis^*,, Koon Teo^*,,**, Salim Yusuf^*,,** for the SHARE and SHARE-AP Investigators

^* Population Health Research Institute, Hamilton ON, Canada; McMaster University, Hamilton ON, Canada; ^** Hamilton Health Sciences, Hamilton ON, Canada; and University of Toronto, Toronto ON, Canada

²To whom correspondence should be addressed. E-mail: anwar.merchant{at}post.harvard.edu.

	ABSTRACT

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Abdominal obesity is related to significant morbidity and mortality and differs by ethnicity; however, the relation between diet and abdominal obesity has not been extensively studied. The aim of this study was to evaluate the dietary and lifestyle determinants of abdominal obesity in a multi-ethnic population. We conducted a cross-sectional study among 617 Canadians of Aboriginal, South Asian, Chinese, and European origins, with diet evaluated using validated, culture specific, interviewer-administered FFQs, and abdominal obesity measured as waist-hip ratio (WHR). The mean proportion of energy intake from protein in the diet was 17.4 vs. 15.8% comparing the lowest and highest tertiles of WHR. Energy-adjusted protein substituted for an equivalent amount of carbohydrate was associated with a reduction in WHR (difference in WHR for every g/d increase in protein intake = –0.0005, P = 0.01) after accounting for age, sex, ethnicity, smoking status, BMI, alcohol intake, height, physical activity, and total energy. Fat or total energy were not related to WHR in the same linear regression model. Smoking was positively and physical activity inversely related to WHR in the multivariate model independent of BMI and other potential confounders. Substituting a modest amount of protein for carbohydrate may reduce abdominal obesity in a diverse multi-ethnic population. Smoking was positively related to abdominal obesity after accounting for BMI.

KEY WORDS: • protein intake • central adiposity • waist-hip ratio

Abdominal obesity is characterized by the deposition of body fat in the upper body (waist and trunk) as opposed to the hips and thighs, and is frequently measured by the waist-hip ratio (WHR) or waist circumference (1). Abdominal obesity reflects the amount of visceral adipose tissue and although it is strongly associated with overweight status (as measured by BMI), it is independently associated with an atherogenic profile (low HDL cholesterol, high triglycerides), increased insulin resistance (2), increased risk of diabetes (3), subclinical atherosclerosis (2), heart disease (4), stroke, and mortality (5). Abdominal obesity increases with age, male sex (6), smoking (7), and decreases with an increase in physical activity or weight loss (6). Identifying the determinants of abdominal obesity may suggest strategies to prevent the development of this risk marker. In the few studies in which the role of diet in relation to abdominal obesity was evaluated, inverse associations were reported between abdominal adiposity and intakes of fiber, polyunsaturated fats (8), fish, and chicken (9), and a positive association existed with trans fatty intake (8). If dietary recommendations are simple, there is a higher likelihood that they will be accepted. Substituting certain types of foods with others (more chicken or fish instead of rice, potatoes, or bread for instance) may be easier to accomplish than making a drastic change in diet. Analytical techniques to describe this type of nutrient exchange were described in the literature (10). We therefore evaluated the relation between abdominal adiposity and substituting carbohydrates in the diet for fats and proteins.

	SUBJECTS AND METHODS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
    Study population. Canadians of Aboriginal, South Asian, Chinese, and European origin took part in 2 concurrent cross-sectional studies of cardiovascular risk factors between 1996 and 2000 in the Study of Health Assessment and Risk in Ethnic groups (SHARE) and the Study of Health Assessment and Risk Evaluation in Aboriginal Peoples (SHARE-AP) (11,12). Participants were between 35 and 75 y of age and had lived in Canada for at least 5 y before the study. We randomly selected Canadians of South Asian, Chinese, and European backgrounds based on unique last names from Toronto, Hamilton, and Alberta, Canada using a previously validated method (13,14), and Aboriginal Peoples from the Six Nations SHARE-AP master list. We excluded participants with active cancer or other serious chronic diseases, such as renal or liver failure. The study was approved by the McMaster University Research Ethics Committee and the Six Nations Band Council.

    Data collection. After obtaining informed consent, we recorded each participant’s lifestyle characteristics and medical history using standardized questionnaires. Height, weight, waist, and hip were measured using a standardized protocol (15). We measured diet using validated, culture-specific, semiquantitative FFQs (16). The FFQs were compared with 7- to 14-d food records to assess validity. The energy-adjusted, deattenuated correlation coefficients were 0.45–0.57 (P < 0.001) for protein, from 0.17 (P > 0.05) to 0.62 (P < 0.001) for total fats, 0.31–0.60 (P < 0.01) for carbohydrates, and 0.63–0.70 (P < 0.001) for total fiber (16).

    Nutrient analysis. We excluded participants who reported a history of angina, cancer, diabetes, cardiovascular disease, hypertension, hypercholesterolemia, kidney, or liver disease, or reported implausible dietary intake (<3347 or >18828 kJ/d), or who reported that they had changed their usual diet.

On the FFQs, participants reported how often, on average, they consumed selected foods in the previous year. We calculated nutrient intakes by multiplying the average nutrient content of a particular food portion by the number of times it was consumed. We determined nutrient content by analyzing diet records using the Food Processor nutrient analysis software (version 6.11, 1996, ESHA), which incorporated the 1991 Canadian Nutrient File and USDA databases (16). We log-transformed all nutrients and then adjusted for total energy by linear regression with the nutrient as the outcome and total energy intake as the predictor (10). The residuals from this model were added to the expected value of the nutrient at average energy intake. Energy-adjusted nutrients calculated in this manner can be interpreted as the composition of the particular nutrient in the diet independent of total energy intake (10). Log-transformed nutrients were exponentiated for ease of interpretation.

    Statistical analyses. We used multivariate linear regression models to examine the relation between WHR, the outcome variable, and nutrients (continuous), the independent variables. To examine the relation between macronutrients (protein, total fat, and carbohydrates) we used the residual model (10). In this model, energy-adjusted protein and fat intakes are modeled with total energy, adjusting for potential confounders, including alcohol intake and BMI. The coefficient of protein in this model can be interpreted as a change in WHR for a unit increase in protein intake. Because intakes of fat, alcohol, and total energy and BMI are held constant in this model, the increase in protein intake represents a substitution of carbohydrate in the diet (10). We also examined energy-adjusted protein, type of fat (saturated, trans, and other), and total energy in similar multivariate residual models. We then examined individual energy-adjusted intakes of each nutrient of interest (protein, total fat, saturated fat, trans fat, carbohydrate, sugar, total, insoluble, and soluble fiber) in multivariate models. We used the multivariate nutrient density model to evaluate substitution of 5% of energy from carbohydrate for protein or total fat intakes. In all multivariate models, we adjusted for age (y), total energy (kJ), height (m), physical activity score, BMI (kg/m²) (continuous variables), sex (dichotomous), smoking (never, past, current), alcohol intake (never or < 1 time/mo, 1 time/mo to 5 times/wk, > 5 times/wk), and ethnicity (Aboriginal, South Asian, Chinese, European). We centered all continuous variables in the regression models by subtracting the mean of that variable in the population from each respective value. For example, the mean age in the population (47 y) was subtracted from each person’s age. This was done to allow the intercept of the models to be biologically interpretable. For instance, the intercept in the centered model would be interpreted as the expected value of the WHR at age 47 rather than 0 y. We calculated the physical activity index by summing ordinal categories of intensity of physical exertion estimated from reported type of work, time spent playing sports, and type of leisure time activities. Occupation, sports, and leisure time activities were classified according to exertion as 1 = low, 2 = moderate, and 3 = high based on the published literature. To evaluate whether the relation between protein intake and WHR was confounded by other dietary variables, we evaluated them in multivariate models by further adjustment for total, soluble, and insoluble fiber, total, saturated, trans, and other fats, sugar, and carbohydrate intakes.

The analyses were stratified by ethnicity (Aboriginal, South Asian, Chinese, European), age (<45, 45), sex (male, female), BMI (<25, 25 kg/m²), smoking (never, current or past), alcohol intake (never or <1 time/mo, >1 time/mo), and physical activity (less than median of physical activity index, median or more). To assess interaction, multiplicative terms were computed between the stratifying categories and each nutrient (for example, sex x protein intake) and evaluated in analysis of covariance models by the partial F-test, adjusting for confounders. We compared the percentage of energy from protein in the diet (energy from protein divided by total energy) by tertiles of WHR. We evaluated the possibility of a nonlinear association between energy-adjusted protein intake and WHR adjusted for confounders, using restricted cubic splines (17).

	RESULTS

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 
Of the 1286 participants who completed all aspects of the clinic assessment, 560 were excluded because of prevalent health conditions or reports of altered diet, and 109 were excluded because of incomplete dietary data, leaving 617 participants for this analysis. The mean age of participants was 47 y; Aboriginal and Chinese participants were more likely to be female; Aboriginal participants had the highest mean BMI and WHR and Chinese the lowest. (Additional demographic characteristics are found in Table 1.) Alcohol consumption and smoking were least common among South Asians and Chinese participants. Alcohol consumption was highest among participants of European origin and smoking was highest among Aboriginal participants. Europeans were the tallest and most physically active of the 4 ethnic groups. Chinese participants reported consuming more protein and total fat and South Asians consumed the most fiber and carbohydrates and the least total fat (Table 1).

View larger version (18K): [in this window] [in a new window]   FIGURE 1 Difference in WHR for every g/d increase in protein intake after multivariate adjustment in study participants (P for significance of overall spline < 0.001). Adjustments were made for age (y), total energy (kJ/d), height (cm), physical activity score (continuous variables), sex (dichotomous), BMI (kg/m2) (continuous), smoking (never, past, current), alcohol intake (never or <1 time/mo, 1 time/mo to 5 times/wk, >5 times/wk), and ethnicity (Aboriginal, South Asian, Chinese, European).

	DISCUSSION

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

Randomized clinical trials of weight loss comparing low-carbohydrate (high-protein and fat) diets with conventional energy-restricted diets (high-carbohydrate) consistently showed lower triglyceride and higher HDL cholesterol levels (18–20) in the low-carbohydrate groups, even though weight loss was seen in only 2 of the 3 studies (18,20). Increased abdominal obesity is related to higher triglyceride and lower HDL cholesterol levels, but was not evaluated in any of these investigations. Our data suggest that a small increase in protein intake is inversely associated with abdominal obesity independent of BMI, and may explain in part the improvement in the lipid profile observed in the low-carbohydrate intake groups in these studies.

There are a number of plausible mechanisms by which proteins substituted for carbohydrates may reduce abdominal obesity. First, long-term adherence to the low-carbohydrate diet is better than adherence to high-carbohydrate diets, possibly because of increased satiety (21,22). Second, postprandial thermogenesis is higher after consumption of a high-protein compared with a high-carbohydrate diet (21,22). Third, conjugated linoleic acid in the diet, found in beef and dairy products, is associated with increased satiety and fullness and decreased hunger (23). Fourth, high-protein diets, compared with conventional carbohydrate diets, are associated with improved insulin sensitivity (24), which is correlated with a higher resting energy expenditure (25). Compliance was higher in high-protein compared with high-carbohydrate diets in several clinical trials (18–20).

Our results were consistent with earlier reports. Specifically, increased chicken and fish consumption was inversely associated with WHR in an observational study conducted among Bengalee men (9). Higher protein intake (125 vs. 68 g/d) was associated with greater loss of weight and body fat compared with a diet with lower protein content in a trial of weight loss comparing isocaloric diets (26), and high protein intake after weight loss was associated with lower body weight gain and fat mass (27). This was hypothesized to be related to a higher resting energy expenditure (28) and satiety (27,28) associated with protein compared with carbohydrate intake. A positive association between saturated fat (found in animal protein) and abdominal obesity was reported (8). However, the inverse relation between protein intake and WHR in our data persisted after further adjustment for SFA and when animal and vegetable proteins were evaluated separately.

There are concerns that high-protein diets may adversely effect renal function. With a moderate increase in protein intake, adaptive changes in renal volume without altered renal function were reported (29). We observed differences in abdominal obesity even with small changes in diet composition. Protein intake in our sample ranged from 35 to 163 g/d and carbohydrate from 120 to 380 g/d. Substituting 150 g of cooked rice for half a breast of chicken, for example, would represent an 30 g exchange of carbohydrate for protein. The low-carbohydrate diets in contrast recommend a total carbohydrate intake of 20 g/d in the induction phase. Health agencies recommend 70–90 g/d, and diabetes associations recommend 0.7 g dietary protein/kg body weight.

We used WHR as the outcome because it is the measure of adiposity with the strongest association with CVD risk factors (1). We did not categorize WHR because of suggestions that lower cutoff values may be appropriate for Asian populations (30,31). Waist was not related to nutrient intake in our study. Similarly, Suk et al. (32) reported that WHR, but not waist, predicted stroke in a multi-ethnic population. The use of energy-adjusted nutrients and additional adjustment for height accounted for body size; adjustment for BMI separated the effect of abdominal obesity from body structure. The relation between protein intake and WHR persisted after adjustment for a number of personal characteristics, including ethnicity, diet, and lifestyle factors, and within different subgroups.

Current smoking was positively associated with WHR after adjustment for BMI in our data. Smoking was negatively associated with BMI in a number of studies (33). It was shown that the diets of smokers contain more saturated and trans fats and less fiber (34), but the relation between protein intake and WHR persisted after adjustment for these factors in our data. In the Health Professionals’ Study, smoking was inversely associated with waist but further multivariate adjustment attenuated this association (8). In another study, persons in the highest category of WHR but lowest category of BMI were twice as likely to be current smokers compared with nonsmokers after multivariate adjustment (35). Past and current smoking were also positively associated with WHR among Dutch men after adjustment for BMI in addition to other covariates (7). Smoking increases interleukin-6 and tumor necrosis factor- levels (36), and insulin resistance (37). Higher cytokine levels are associated with decreased muscle mass in middle- to older-aged persons (38). Weight loss associated with smoking may be due to loss of muscle mass. It is plausible, therefore, that even though smoking may be negatively associated with body weight, it is positively related to abdominal obesity.

The study had some limitations. Physical activity was assessed on an ordinal scale, which grouped people according to the type of work, sport, and leisure activity they performed. This was a source of measurement error (most likely random). Diet was assessed by previously validated FFQs (16). Nutrient intake was estimated by multiplying the average reported intake of foods by their mean nutrient content. The precision of nutrient estimation (particularly for fats) varied among the different ethnic groups. These factors would result in some unavoidable misclassification, which would most likely be random. The measurement errors in assessment of diet and physical activity would result in attenuation of any relation.

Because this was a cross-sectional study, there was a possibility that participants with higher WHR altered their diets. To minimize this bias, we excluded participants who reported they tried to lose weight. We further excluded participants who reported morbidity or medication use for comorbid conditions because they may have altered their diets as a consequence of those conditions. Because many of these comorbid conditions are related to central adiposity, the altered diet might not reflect the diet that was related to central adiposity. When we included these people in the analyses, we did not observe any relation between protein intake and WHR (data not shown). A greater proportion of Aboriginal People were excluded because they were more likely to have comorbid conditions. A consequence of the exclusions, however, would be that the results are generalizable only to people without diagnosed comorbidities. Another limitation of this study was the relatively small sample size. The statistically nonsignificant relation between fiber and WHR, for example, may be a type II error due to inadequate power. Therefore the nonsignificant associations should be interpreted with caution. Similarly, we did not have sufficient power to evaluate interactions, but nevertheless presented the stratified results to generate hypotheses for future research.

Compared with Europeans, Aboriginal People had significantly higher WHR, but South Asians and Chinese did not. The possible reasons for ethnic differences in abdominal obesity, apart from diet and physical activity and fitness, are differences in genetics, rates of low birth weight (39) and breast-feeding practices (40), which we did not measure. The differences in WHR in South Asians and Chinese were attenuated after multivariate adjustment, suggesting that lifestyle factors may explain differences in those ethnic groups. Finally, our results could have been due to chance.

In conclusion, abdominal obesity has been related to considerable morbidity and mortality and is increasing worldwide. Substituting a modest amount of protein for carbohydrate is likely to lead to lower abdominal obesity.

	ACKNOWLEDGMENTS

 
We are grateful to Linda Kelemen for her contributions in developing and validating the food frequency questionnaires, and Qi Long Yi and Sui-Lim Chin for assistance with the data.

	FOOTNOTES

 
¹ Supported by Heart and Stroke Foundation of Canada and Canadian Institutes of Health Research grants. A.T.M. is a recipient of the Hirsch Research Career Award, Hamilton Health Sciences. S.S.A. is a recipient of a Canadian Institutes of Health Research Clinician-Scientist Phase 2 Award. S.Y. holds a Heart and Stroke Foundation of Ontario Chair in Cardiovascular Research.

Manuscript received 18 November 2004. Initial review completed 15 December 2004. Revision accepted 16 February 2005.

	LITERATURE CITED

TOP ABSTRACT SUBJECTS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 

1. Dalton, M., Cameron, A. J., Zimmet, P. Z., Shaw, J. E., Jolley, D., Dunstan, D. W. & Welborn, T. A. (2003) Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J. Intern. Med. 254:555-563.[Medline]

2. De Michele, M., Panico, S., Iannuzzi, A., Celentano, E., Ciardullo, A. V., Galasso, R., Sacchetti, L., Zarrilli, F., Bond, M. G. & Rubba, P. (2002) Association of obesity and central fat distribution with carotid artery wall thickening in middle-aged women. Stroke 33:2923-2928.[Abstract/Free Full Text]

3. Carey, V. J., Walters, E. E., Colditz, G. A., Solomon, C. G., Willett, W. C., Rosner, B. A., Speizer, F. E. & Manson, J. E. (1997) Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. The Nurses’ Health Study. Am. J. Epidemiol. 145:614-619.[Abstract/Free Full Text]

4. Rexrode, K. M., Buring, J. E. & Manson, J. E. (2001) Abdominal and total adiposity and risk of coronary heart disease in men. Int. J. Obes. Relat. Metab. Disord. 25:1047-1056.[Medline]

5. Megnien, J. L., Denarie, N., Cocaul, M., Simon, A. & Levenson, J. (1999) Predictive value of waist-to-hip ratio on cardiovascular risk events. Int. J. Obes. Relat. Metab Disord. 23:90-97.[Medline]

6. Lahmann, P. H., Lissner, L., Gullberg, B. & Berglund, G. (2000) Sociodemographic factors associated with long-term weight gain, current body fatness and central adiposity in Swedish women. Int. J. Obes. Relat. Metab. Disord. 24:685-694.[Medline]

7. Visser, M., Launer, L. J., Deurenberg, P. & Deeg, D. J. (1999) Past and current smoking in relation to body fat distribution in older men and women. J. Gerontol. A Biol. Sci. Med. Sci. 54:M293-M298.[Abstract]

8. Koh-Banerjee, P., Chu, N. F., Spiegelman, D., Rosner, B., Colditz, G., Willett, W. & Rimm, E. (2003) 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. 78:719-727.[Abstract/Free Full Text]

9. Ghosh, A., Bose, K. & Das Chaudhuri, A. B. (2003) Association of food patterns, central obesity measures and metabolic risk factors for coronary heart disease (CHD) in middle aged Bengalee Hindu men, Calcutta, India. Asia Pac. J. Clin. Nutr. 12:166-171.[Medline]

10. Willett, W. C., Howe, G. R. & Kushi, L. H. (1997) Adjustment for total energy intake in epidemiologic studies. Am. J. Clin. Nutr. 65:1220S-1228S.[Abstract/Free Full Text]

11. Anand, S. S., Yusuf, S., Vuksan, V., Devanesen, S., Teo, K. K., Montague, P. A., Kelemen, L., Yi, C. & Lonn, E., et al (2000) Differences in risk factors, atherosclerosis and cardiovascular disease between ethnic groups in Canada: the study of health assessment and risk in ethnic groups (SHARE). Indian Heart J 52:S35-S43.[Medline]

12. Anand, S. S., Yusuf, S., Jacobs, R., Davis, A. D., Yi, Q., Gerstein, H., Montague, P. A. & Lonn, E. (2001) Risk factors, atherosclerosis, and cardiovascular disease among Aboriginal people in Canada: the Study of Health Assessment and Risk Evaluation in Aboriginal Peoples (SHARE-AP). Lancet 358:1147-1153.[Medline]

13. Choi, B. C., Hanley, A. J., Holowaty, E. J. & Dale, D. (1993) Use of surnames to identify individuals of Chinese ancestry. Am. J. Epidemiol. 138:723-734.[Abstract/Free Full Text]

14. Sheth, T., Nargundkar, M., Chagani, K., Anand, S., Nair, C. & Yusuf, S. (1997) Classifying ethnicity utilizing the Canadian Mortality Data Base. Ethn. Health 2:287-295.[Medline]

15. Anand, S. S., Yusuf, S., Vuksan, V., Devanesen, S., Montague, P., Kelemen, L., Bosch, J., Sigouin, C. & Teo, K. K., et al (1998) The Study of Health Assessment and Risk in Ethnic groups (SHARE): rationale and design. The SHARE Investigators. Can. J. Cardiol. 14:1349-1357.[Medline]

16. Kelemen, L. E., Anand, S. S., Vuksan, V., Yi, Q., Teo, K. K., Devanesen, S. & Yusuf, S. (2003) Development and evaluation of cultural food frequency questionnaires for South Asians, Chinese, and Europeans in North America. J. Am. Diet. Assoc. 103:1178-1184.[Medline]

17. Stone, C. J. & Koo, C. Y. (1985) Additive splines in statistics. Proceedings of the Statistical Computing Section 1985:45-48 American Statistical Association Washington, DC .

18. Foster, G. D., Wyatt, H. R., Hill, J. O., McGuckin, B. G., Brill, C., Mohammed, B. S., Szapary, P. O., Rader, D. J., Edman, J. S. & Klein, S. (2003) A randomized trial of a low-carbohydrate diet for obesity. N. Engl. J. Med. 348:2082-2090.[Abstract/Free Full Text]

19. Stern, L., Iqbal, N., Seshadri, P., Chicano, K. L., Daily, D. A., McGrory, J., Williams, M., Gracely, E. J. & Samaha, F. F. (2004) The effects of low-carbohydrate versus conventional weight loss diets in severely obese adults: one-year follow-up of a randomized trial. Ann. Intern. Med. 140:778-785.[Abstract/Free Full Text]

20. Yancy, W. S., Jr, Olsen, M. K., Guyton, J. R., Bakst, R. P. & Westman, E. C. (2004) A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: a randomized, controlled trial. Ann. Intern. Med. 140:769-777.[Abstract/Free Full Text]

21. Halton, T. L. & Hu, F. B. (2004) The effects of high protein diets on thermogenesis, satiety and weight loss: a critical review. J. Am. Coll. Nutr. 23:373-385.[Abstract/Free Full Text]

22. Johnston, C. S., Day, C. S. & Swan, P. D. (2002) Postprandial thermogenesis is increased 100% on a high-protein, low-fat diet versus a high-carbohydrate, low-fat diet in healthy, young women. J. Am. Coll. Nutr. 21:55-61.[Abstract/Free Full Text]

23. Kamphuis, M. M., Lejeune, M. P., Saris, W. H. & Westerterp-Plantenga, M. S. (2003) Effect of conjugated linoleic acid supplementation after weight loss on appetite and food intake in overweight subjects. Eur. J. Clin. Nutr. 57:1268-1274.[Medline]

24. McAuley, K. A., Hopkins, C. M., Smith, K. J., McLay, R. T., Williams, S. M., Taylor, R. W. & Mann, J. I. (2004) Comparison of high-fat and high-protein diets with a high-carbohydrate diet in insulin-resistant obese women. Diabetologia .

25. Pereira, M. A., Swain, J., Goldfine, A. B., Rifai, N. & Ludwig, D. S. (2004) Effects of a low-glycemic load diet on resting energy expenditure and heart disease risk factors during weight loss. J. Am. Med. Assoc. 292:2482-2490.[Abstract/Free Full Text]

26. Layman, D. K., Boileau, R. A., Erickson, D. J., Painter, J. E., Shiue, H., Sather, C. & Christou, D. D. (2003) A reduced ratio of dietary carbohydrate to protein improves body composition and blood lipid profiles during weight loss in adult women. J. Nutr. 133:411-417.[Abstract/Free Full Text]

27. Westerterp-Plantenga, M. S., Lejeune, M. P., Nijs, I., van Ooijen, M. & Kovacs, E. M. (2004) High protein intake sustains weight maintenance after body weight loss in humans. Int. J. Obes. Relat. Metab. Disord. 28:57-64.[Medline]

28. Agus, M. S., Swain, J. F., Larson, C. L., Eckert, E. A. & Ludwig, D. S. (2000) Dietary composition and physiologic adaptations to energy restriction. Am. J. Clin. Nutr. 71:901-907.[Abstract/Free Full Text]

29. Skov, A. R., Toubro, S., Bulow, J., Krabbe, K., Parving, H. H. & Astrup, A. (1999) Changes in renal function during weight loss induced by high vs low-protein low-fat diets in overweight subjects. Int. J. Obes. Relat. Metab. Disord. 23:1170-1177.[Medline]

30. WHO Expert Consultation (2004) Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363:157-163.[Medline]

31. Deurenberg, P., Deurenberg-Yap, M. & Guricci, S. (2002) Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes. Rev. 3:141-146.[Medline]

32. Suk, S. H., Sacco, R. L., Boden-Albala, B., Cheun, J. F., Pittman, J. G., Elkind, M. S. & Paik, M. C. (2003) Abdominal obesity and risk of ischemic stroke: the Northern Manhattan Stroke Study. Stroke 34:1586-1592.[Abstract/Free Full Text]

33. Fogelholm, M., Kujala, U., Kaprio, J. & Sarna, S. (2000) Predictors of weight change in middle-aged and old men. Obes. Res. 8:367-373.[Medline]

34. Dyer, A. R., Elliott, P., Stamler, J., Chan, Q., Ueshima, H. & Zhou, B. F. (2003) Dietary intake in male and female smokers, ex-smokers, and never smokers: the INTERMAP study. J. Hum. Hypertens. 17:641-654.[Medline]

35. Jee, S. H., Lee, S. Y., Nam, C. M., Kim, S. Y. & Kim, M. T. (2002) Effect of smoking on the paradox of high waist-to-hip ratio and low body mass index. Obes. Res. 10:891-895.[Medline]

36. Zhang, J., Liu, Y., Shi, J., Larson, D. F. & Watson, R. R. (2002) Side-stream cigarette smoke induces dose-response in systemic inflammatory cytokine production and oxidative stress. Exp. Biol. Med. 227:823-829.[Abstract/Free Full Text]

37. Dzien, A., Dzien-Bischinger, C., Hoppichler, F. & Lechleitner, M. (2004) The metabolic syndrome as a link between smoking and cardiovascular disease. Diabetes Obes. Metab. 6:127-132.[Medline]

38. Visser, M., Pahor, M., Taaffe, D. R., Goodpaster, B. H., Simonsick, E. M., Newman, A. B., Nevitt, M. & Harris, T. B. (2002) Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study. J. Gerontol. A Biol. Sci. Med. Sci. 57:M326-M332.[Abstract/Free Full Text]

39. Bhargava, S. K., Sachdev, H. S., Fall, C. H., Osmond, C., Lakshmy, R., Barker, D. J., Biswas, S. K., Ramji, S., Prabhakaran, D. & Reddy, K. S. (2004) Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. N. Engl. J. Med. 350:865-875.[Abstract/Free Full Text]

40. Ravelli, A. C., van der Meulen, J. H., Osmond, C., Barker, D. J. & Bleker, O. P. (2000) Infant feeding and adult glucose tolerance, lipid profile, blood pressure, and obesity. Arch. Dis. Child. 82:248-252.[Abstract/Free Full Text]

This article has been cited by other articles:

				A. T Merchant, L. E Kelemen, L. de Koning, E. Lonn, V. Vuksan, R. Jacobs, B. Davis, K. K Teo, S. Yusuf, S. S Anand, et al. Interrelation of saturated fat, trans fat, alcohol intake, and subclinical atherosclerosis Am. J. Clinical Nutrition, January 1, 2008; 87(1): 168 - 174. [Abstract] [Full Text] [PDF]

				A. T Merchant, S. S Anand, L. E Kelemen, V. Vuksan, R. Jacobs, B. Davis, K. Teo, S. Yusuf, and for the SHARE and SHARE-AP Investigators Carbohydrate intake and HDL in a multiethnic population Am. J. Clinical Nutrition, January 1, 2007; 85(1): 225 - 230. [Abstract] [Full Text] [PDF]

				A Merchant, S Yusuf, and A M Sharma A cardiologist's guide to waist management. Heart, July 1, 2006; 92(7): 865 - 866. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Merchant, A. T. Search for Related Content PubMed PubMed Citation Articles by Merchant, A. T.