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Department of Social and Preventive Medicine, University at Buffalo, Buffalo, NY 14214;
* Prevention Research Center, Berkeley, CA 94704; and
School of Social Work, University at Buffalo, Buffalo, NY 14214
1To whom correspondence should be addressed. E-mail: jdorn{at}acsu.buffalo.edu.
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ABSTRACT |
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 TOP ABSTRACT SUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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KEY WORDS: • alcohol • drinking pattern • central adiposity • sagittal diameter
The consumption of light-to-moderate amounts of alcohol has been associated with a reduced risk of coronary heart disease (CHD) in both men and women (1–6). Much of what is known about the role of alcohol in the etiology of CHD comes from observational epidemiologic studies in which a variety of methods were used to measure alcohol. The majority of studies have focused primarily on the volume of alcohol consumed, often determined as a mean number of drinks for a given time period, such as monthly, weekly or daily. Few studies have considered the complex components of consumption, particularly the specific pattern of drinking (7). In those that have, evidence continues to emerge indicating that the way in which people consume alcohol may have differential effects on disease risk (8). For example, Murray and colleagues (9) recently examined the associations between both volume of usual alcohol consumption and a binge-type drinking pattern and morbidity and mortality due to cardiovascular disease (CVD). The findings corroborated those of others that although the amount of alcohol tends to reduce CVD risk, compared with nonbinge drinking, binging (defined as consuming 
8 drinks in one sitting) is associated with significantly higher risks of CVD morbidity and mortality. Most recently, Mukamal et al. (10) found frequency of drinking to be inversely associated with myocardial infarction risk, regardless of beverage type or amount consumed.
A number of mechanisms to explain the protective effects of alcohol consumption on CHD have been elucidated, with increased HDL cholesterol levels as one of the most well established (11–14). Other explanations include inhibition of thrombogenesis (15–17), antioxidant properties of specific beverage types (18) and favorable effects on insulin concentrations and insulin sensitivity (19–21). The effect of alcohol on body fat distribution, particularly central adiposity, has also received considerable consideration because it is a well-known risk factor for CHD and is associated with both increased insulin resistance and type 2 diabetes. Research in this area remains inconclusive, with some studies showing alcohol to be positively associated (22–24), others showing no association (25) and still others, an inverse association (26). Differential effects have also been reported among various race-gender groups and for different beverage types (22,23,27,28) and little is known about the relationship between other patterns of alcohol consumption such as frequency, or drinking with meals compared with drinking outside meals and body fat distribution.
Given the evolving research showing that differences in the way people drink may differentially affect their risk of CHD, the goal of our study was to examine the relation between pattern of alcohol use and body fat distribution in men and women. The specific objective of this study was to examine the association between selected drinking patterns and abdominal height, measured with the Holtain-Kahn abdominal caliper (29). This measure has been shown to be highly correlated with the volume of visceral fat as determined by multiscan tomography (30–32) and represents a factor that has been associated with a number of CVD risk factors, as well as an increased risk of CVD incidence and mortality.
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SUBJECTS AND METHODS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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All agreeing participants came to the Center for Preventive Medicine at the University at Buffalo for an interview and physical examination that lasted 
2.5 h. Upon arrival, informed consent was obtained and participants proceeded to an interviewer-administered, computer-assisted interview regarding their lifetime alcohol consumption and a number of other lifestyle habits. The interview questionnaire was developed after a number of careful pretests (33).
Complete abdominal height data were available for 2381 (91%) participants. Participants missing data for abdominal height (n = 237), drinking status (n = 19) and various covariates (education n = 4, smoking status n = 2) were excluded from the analyses. An additional three participants were excluded due to erroneous data for body weight and number of drinks consumed. Ten other participants were excluded because they reported that they did not drink alcohol due to health or medical reasons, leaving a total of 2343 participants available for the present study.
Drinking pattern.
Alcohol drinking pattern was assessed in detail. All participants who reported never having had 12 drinks in their lifetime or in any 1-y period in their lifetime were defined as lifetime abstainers. A special section of the interview was devoted specifically to the 30 d before the date of interview, as a measure of participants’ most current alcohol consumption. For the present study, this was used as the period of interest.
A portion of the interview was devoted to alcohol consumption and included questions regarding the type of beverage (e.g., beer, wine, wine coolers, liquor), serving size for each beverage and the number of drinks usually consumed. Participants were shown examples of containers in which alcohol is typically served and lines were drawn indicating the potential level of beverage within each container to assist with ascertaining the size of a typical drink. For the 30 d before the interview, participants were asked if they had at least one alcoholic beverage in that time period. Those who answered yes were defined as "current" drinkers and their frequency of drinking was probed in detail. Those who drank once each week or more were asked how many Fridays, Saturdays, Sundays and weekdays (Monday–Thursday) they drank, and how many drinks they had on those days. Those who drank <1 time/wk, but at least once in the past 30 d were asked how many drinks they had on the days they drank. The interview also probed the percentage of time alcohol drinking took place while eating meals or snacks, or without food.
For the present study, the following alcohol variables were computed from the above questions and used in the analyses:
75% of the time that they drank alcohol in the previous 30 d were classified as drinkers with food. Those who drank 75% of the time during the same period without food or snacks were defined as drinkers without food. Physical examination.
Physical measurements of participants were made wearing light clothing and no shoes. Measurements included abdominal height, blood pressure, heart rate, height and weight. Abdominal height, measured using the Holtain-Kahn caliper, is a dimension that has been shown to be highly correlated with the volume of visceral fat as determined by multiscan tomography (30–32). Three separate measurements were made to the nearest 0.1 cm of the sagittal (e.g., anteroposterior) abdominal diameter. If the three readings were not within 0.5 cm of each other, the three readings were repeated until they were all within 0.5cm of each other.
During the study we examined the intra- and interobserver variability of the abdominal height measurement. The intraobserver variability, considering two observers, four observed volunteers and two observations for each observer, and evaluated by the intraclass correlation (ICC) coefficient was 0.99 (0.96 lower bound). The interobserver variability was also excellent with an ICC of 0.99 (0.97 lower bound).
Height was measured on a permanently mounted vertical board. Weight was measured to the nearest tenth of a pound on a calibrated balance beam scale. BMI was calculated as weight (kg) divided by height squared (m2).
Other lifestyle habits.
In addition to the questions regarding alcohol drinking pattern, participants were asked about their current and lifetime smoking habits, including the use of cigarettes, cigars, pipes, chewing tobacco and marijuana. Current physical activity was ascertained with the Seven-Day Physical Activity Recall questionnaire used in the Stanford Five-City Project (34) and dietary habits were determined using a semiquantitative food-frequency questionnaire (35). A self-reported medical history was obtained to determine the prevalence of physician-diagnosed diseases including hypertension, hypercholesterolemia and diabetes, as well as completed medical procedures and use of prescription and over the counter medications. For women, menopausal status was determined using criteria that consider age, surgical or natural cessation of menses, hormone use and bilateral oopherectomy.
Statistical methods.
Descriptive statistics were examined and differences between men and women for continuous variables were compared using t tests. Categorical variables were tested using 
2 analysis. Among women, tests for interactions between drinking patterns and menopausal status were nonsignificant for abdominal height. Consequently, all analyses including women were conducted without stratification by menopausal status. Among current drinkers, categorical variables were created for total grams of alcohol consumed and for beverage-specific grams. For total grams, the categories included drinkers classified into tertiles of consumption. For beverage-specific volumes (i.e., wine, beer or liquor), participants who did not consume the beverage of interest (i.e., wine, beer or liquor) were considered as the reference and participants who did consume the specific beverage were dichotomized according to their reported beverage-specific grams of consumption. In separate models, analysis of covariance was used to examine the relation between abdominal height and the various categories of drinking amount, frequency and drinks/drinking day. We examined the cross-comparison (using analysis of covariance) between frequency and drinks/drinking day and tested for interaction, which was not present (P = 0.8203 among drinkers only, and P = 0.8443 when all participants were included, i.e., abstainers and noncurrent drinkers). Differences among categories of alcohol variables were examined using the Tukey-Kramer method to adjust for multiple comparisons. Tests for trend were conducted using regression analyses. Models were adjusted for several confounding variables, including age, education, smoking, physical activity and for total grams of alcohol in models examining current drinkers. Models for women were also adjusted for menopausal status. Results were similar with and without adjustment for this factor; consequently, the nonmenopausal adjusted data are presented. A two-tailed P < 0.05 was used for all statistical tests. All analyses were completed using SAS Release 8.1 (36).
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RESULTS |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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53 y; the BMI were indicative of an overweight sample (28 kg/m2). Women were slightly less educated, more likely to have never smoked cigarettes, less physically active and had a smaller abdominal height than men. All differences were significant (P < 0.01). For drinking status, women were more likely than men to be lifetime abstainers (P < 0.001), and the general frequency of drinking was lower among women than men, with 11.1% of men and 5.0% of women reporting daily drinking (P < 0.001). The prevalence of drinking alcohol with meals was significantly higher in women than in men, and men were more likely to drink without any food (without meals or snacks) compared with women. When the entire study sample, including noncurrent drinkers was examined, women consumed less alcohol overall than men (total, 122 vs. 309 g, P < 0.001) in the 30 d before the interview, and drank at a lower intensity than men (drinks/drinking day 1.2 vs. 2.2, P < 0.001). Men drank more beer and hard liquor (P < 0.001) than women. When only participants who were current drinkers were examined, men again reported more drinks/drinking day than women; gender differences for the remaining alcohol drinking patterns were similar to those noted for all participants (data not shown).
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First, we examined the associations between abdominal height and grams of beverage-specific and total ethanol consumed in the 30 d before the interview (Table 3). Among women, abdominal height tended to decrease with increasing amounts of total alcohol (P trend 0.0003) and wine consumption (P trend 0.0005). In contrast, among liquor drinkers, the greater the consumption, the larger the abdominal height (P trend 0.0107). Women who were current drinkers but did not drink liquor (Category 1) tended to have smaller abdominal heights than drinkers in the highest liquor drinking category (P = 0.0578). Beer consumption was unrelated to abdominal height. Similar findings were observed in men; however, only wine was significantly inversely associated with abdominal height (P trend 0.0141) and the inverse total alcohol-abdominal height association was of borderline significance (P trend 0.0684). Beer and liquor were unrelated to abdominal height; however, as observed in women, current alcohol drinkers who did not consume liquor tended to have the smallest abdominal heights (P = 0.7108). For both genders, the results were nearly identical when consumption of wine coolers was excluded.
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Drinking intensity (drinks/drinking day) was positively related to abdominal height (Fig. 2). In both women and men, the more drinks the participants consumed per drinking day, the larger the abdominal height. The tests for trend were significant, even when the effects of total grams of alcohol were included in the model (P trend women, 0.0010; men, 0.0004).
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4 drinks/drinking day had an abdominal height that was 2.72 cm larger than daily drinkers who drank <1 drink/drinking day. Among weekly and less than weekly drinkers, the abdominal height differences of drinkers of 4+ drinks/drinking day compared with drinkers of <1 drink/drinking day were 2.01 cm and 1.28 cm, respectively. Results were similar when adjusted for age and gender.
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We also recognize that it would be informative to determine whether our findings regarding drinking pattern and abdominal height were independent of overall obesity. Because we measured both height and weight at the clinical exam, we used BMI as a measure of relative weight. However, in our study sample, BMI and abdominal height were highly correlated (men r = 0.86, women r = 0.89), making it difficult to examine one outcome (abdominal height) while controlling for the other (BMI). We repeated all analyses using BMI as the outcome and the results were nearly identical to those for abdominal height (data not shown).
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DISCUSSION |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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We also observed that in addition to the amount of alcohol consumed, the pattern of drinking may affect fat distribution. For example, when we examined frequency of drinking, we found that among both women and men, daily drinkers of alcohol had smaller measures than less frequent drinkers. However, as drinking intensity measured as drinks/drinking day increased, so did the abdominal heights. The effects of frequency and intensity were independent of the effects of the total amount of alcohol consumed. When frequency and intensity of drinking were combined, small amounts of alcohol on a regular basis were associated with the smallest abdominal heights, whereas participants with the most intense drinking (3–4+ drinks/drinking day) but on a sporadic basis (<weekly) had some of the largest abdominal heights.
Body fat distribution, specifically central adiposity, has been implicated as an important risk factor for CVD, including both stroke and CHD mortality (37–43). Central adiposity has also been positively associated with blood pressure, total cholesterol, LDL cholesterol, triglyceride levels, diabetes mellitus and inversely with HDL cholesterol, with the underlying mechanism postulated to be insulin resistance (37–40,42,44–49). Many different methods have been used in epidemiologic studies to characterize fat distribution, with waist circumference, waist-to-hip ratio, subscapular skinfold measurements and abdominal height among the most commonly used to quantify a central distribution. Despite the use of varied measures, the association between central adiposity and CVD and its various risk factors is consistent, it has been observed in men and women and has been shown to be independent of overall BMI (39,41,50,51) in most, but not all studies (52).
We used the Holtain-Kahn abdominal caliper to measure the height of the abdomen. This measure has been shown to be highly correlated with the volume of visceral fat as determined by multiscan tomography (30–32). It is this visceral fat that is considered to be the most important component of a central fat distribution for CVD risk. Compared with more peripheral fat stores, abdominal visceral fat tends to consist of lipolytically active fat cells. Free fatty acids from this fat source may contribute to insulin resistance by supplying surplus free fatty acids directly to the portal system (53,54), inhibiting hepatic insulin uptake and resulting in peripheral hyperinsulinemia (55). Indirect mechanisms whereby a central fat distribution may influence insulin resistance include increased levels of free testosterone and reduced sex hormone–binding globulin, which may encourage deposition of fat in the abdomen and reduce fractional hepatic insulin extraction (56). Using abdominal height as a surrogate marker for insulin resistance, our results indicate that improved insulin sensitivity might be one mechanism whereby light-to-moderate daily drinking protects against various forms of CVD and its risk factors. However, they also indicate that the pattern of drinking, particularly more sporadic and intense consumption, offers no further protection and may actually be associated with increased levels of visceral fat.
Unfortunately, we were unable to differentiate the effects of overall obesity on the observed associations between drinking patterns and abdominal height due to multicollinearity between these measures. As proposed by Seidell and Bouchard (57), it may not be possible to separate the independent effects of fat distribution from overall obesity. However, because few published data exist regarding the effects of drinking pattern on either fat distribution or overall obesity, our findings contribute to the literature by providing evidence that the way people consume alcohol, independent of the amount consumed, has a notable effect on these important health outcomes.
The association between alcohol and body fat distribution has been explored by others, with somewhat inconsistent results. Slattery and colleagues (22) demonstrated a positive association between total amount of alcohol consumed and the waist-hip circumference ratio in young African-American and Caucasian men, but not in women of either race in the Coronary Artery Risk Development in Young Adults Study. Beverage-specific analyses revealed that beer consumption was positively associated with the ratio in African-American and Caucasian men and women. With the exception of African-American women, liquor drinking was associated with a higher waist-hip circumference ratio. Wine was positively associated with the ratio in men, and significantly so in Caucasian men, whereas for women, the association was inverse, and significant only among African-Americans, despite the fact that Caucasian women were the highest consumers of wine.
Positive associations between alcohol intake and central adiposity have been reported in middle-aged Japanese men (23), 35- to 64-y-old men and women in France (27), 31- to 90-y-old women from the Kaiser-Permanente Women Twins Study in the United States (24) and in 50- to 79-y-old men and women in Rancho Bernardo, CA (58); however, among studies that examined beverage type, a number of differences were noted. For example, the study by Sakurai et al. (23) in Japanese men reported that only shochu ethanol, and no other beverage type was associated with abdominal adiposity measured by the waist-to-hip ratio. In the French Study, wine and beer consumption was positively and significantly associated with the waist-to-hip ratio in women, but not in men, even though wine was the main source of alcohol. Spirit consumption was unrelated in both genders (27). In a Swedish study conducted in women, liquor was associated with the waist-hip circumference ratio, but no association was found between consumption of beer or wine (28).
In contrast, Kaye and colleagues (26) reported an inverse association between alcohol consumption and waist-to-hip ratio in postmenopausal women. No consistent relation between alcohol and central adiposity was observed in Mexican American or non-Hispanic Caucasian men and women who were part of the San Antonio Heart Study (25).
Epidemiologic studies examining alcohol intake and more direct measures of fasting insulin and/or insulin resistance seem to provide more consistent results, with low-to-moderate amounts of alcohol associated with more favorable levels of these factors (19–21,59). Our findings concerning the amount of alcohol tend to agree with those in which these direct measures of insulin status were used. Perhaps the use of a more specific marker of visceral fat, i.e., the abdominal height, compared with circumference measures or ratios of these measures accounts for the similarities.
Our study is not without limitations. First, the cross-sectional design did not enable us to examine the temporality of the observed associations. It is possible that some individuals who previously drank alcohol, but no longer do so, may have changed their drinking habits, i.e., stopped drinking for health-related reasons, which could include undesirable body weight, or even weight gain around the waist. This "sick-quitter" phenomenon could have biased our results in favor of current drinkers. However, at least for women, this does not seem to be the case because current drinkers had significantly less central adiposity than even lifetime abstainers. Among men, current drinkers and lifetime abstainers did not differ in fat distribution. However, with only 28 reported lifetime abstainers among men, we were unable to confirm whether drinking provided benefits over lifetime abstention for the distribution of fat.
It is also possible that our findings are due to some unknown confounding variables that we were unable to control for in our analyses rather than drinking pattern per se. We controlled for a number of known confounders including age, education, physical activity, smoking status and in analyses within drinkers, total amount of alcohol consumed, and our results are independent of these known confounders. However, the possibility of residual confounding cannot be excluded with absolute certainty. In the absence of a known physiologic mechanism, residual confounding may have contributed to our findings concerning the different beverage types. In fact, in a similar population sample, McCann and colleagues (60) noted that a particular beverage was associated with differing dietary habits. For example, wine drinkers tended to consume more fruits, vegetables and grains, but less fat than drinkers of predominantly beer, liquor or a more mixed pattern. These dietary differences likely contribute to the observed differential effects of the specific beverages we examined in our sample of current drinkers.
Finally, self-reported recall of alcohol consumption, a frequently used method in epidemiologic research, may be somewhat limited. As for most dietary factors, no gold standard exists by which to measure the validity of reported alcohol consumption. Therefore, reliability has often been used as an indicator of relative validity. Our drinking pattern questionnaire had good-to-excellent reliability at least for volume estimation (r 0.74), both in test-retest analyses (33) and compared with widely used, previously validated food-frequency questionnaires (61). In addition, participants were asked to recall their drinking experience only from the previous 30 d, a relatively short time period, and one that should provide more accurate measures of exposure than studies requiring recall from the more distant past.
In conclusion, our study provides more evidence that alcohol consumption and a central distribution of body fat are related. In addition, it provides new information that indicates that the way in which people consume alcohol has important implications for central adiposity. The observed associations are similar in men and women, and the findings regarding drinking pattern are independent of the total amount of alcohol consumed. Given the existing evidence implicating increased abdominal fat as a risk factor for CVD and other chronic diseases, our study has important public health implications.
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FOOTNOTES |
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Manuscript received 17 January 2003. Initial review completed 17 February 2003. Revision accepted 19 May 2003.
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LITERATURE CITED |
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TOPABSTRACTSUBJECTS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
|---|
1. Yano, K., Rhoad, G. & Kagan, A. (1977) Coffee, alcohol and risk of coronary heart disease among Japanese men living in Hawaii. N. Engl. J. Med. 297:405-409.[Abstract]
2. Marmot, M. G., Rose, G., Shipley, M. J. & Thomas, B. J. (1981) Alcohol and mortality, a U-shaped curve. Lancet 1:580-583.[Medline]
3. Klatsky, A. L., Friedman, G. D. & Siegelaub, A. B. (1982) Alcohol use and cardiovascular disease: the Kaiser-Permanente experience. Circulation 64(III):32-40.
4. Kozarevic, D., Demirovic, J., Gordon, T., Kaelber, C. T., McGee, D. & Zukel, W. J. (1982) Drinking habits and coronary heart disease. The Yugoslavia Cardiovascular Disease Study. Am. J. Epidemiol. 116:748-758.
5. Thun, M. J., Peto, R., Lopez, A. D., Monaco, J. H., Henley, S. J., Heath, C. W., Jr. & Doll, R. (1997) Alcohol consumption and mortality among middle-aged and elderly US adults. N. Engl. J. Med. 337:1705-1714.
6. Stampfer, M. J., Colditz, G. A., Willett, W. C., Speizer, F. E. & Hennekens, C. H. (1988) A prospective study of moderate alcohol consumption and the risk of coronary heart disease and stroke in women. N. Engl. J. Med. 319:267-273.[Abstract]
7. Kuller, L. H. (1991) Epidemiologic data. In: Alcohol and Atherosclerosis (Steinberg, D., moderator). Ann. Int. Med. 114:967-976.
8. Puddey, I. B., Rakic, V., Dimmitt, S. B. & Beilin, L. J. (1999) Influence of pattern of drinking on cardiovascular disease and cardiovascular risk factors—a review. Addiction 94:649-663.[Medline]
9. Murray, R. P., Connett, J. E., Tyas, S. L., Bond, R., Ekuma, O., Silversides, C. K. & Barnes, G. E. (2002) Alcohol volume, drinking pattern and cardiovascular disease morbidity and mortality: is there a U-shaped function?. Am. J. Epidemiol. 155:242-248.
10. Mukamal, K. J., Conigrave, K. M., Mittleman, M. A., Camargo, C. A., Jr., Stampfer, M. J., Willett, W. C. & Rimm, E. B. (2003) Roles of drinking pattern and type of alcohol consumed in coronary heart disease in men. N. Engl. J. Med. 348:109-119.
11. Castelli, W. P., Gordon, T., Hjortland, M. C., Kagan, A., Doyle, J. T., Hames, C. G., Hully, S. B. & Zukel, W. J. (1977) Alcohol and blood lipids. Lancet 2:153-155.[Medline]
12. Ernst, N., Fisher, M., Smith, W., Gordon, T., Rifkind, B. M., Littla, A., Mishkel, M. A. & Williams, O. D. (1980) The association of plasma high density lipoprotein cholesterol with dietary intake and alcohol consumption: The Lipid Research Clinics Program Prevalence Study. Circulation 62:41-52.
13. Kuller, L. H., Hulley, S. B., LaPorte, R. E., Neaton, J. & Dai, W. S. (1983) Environmental determinants, liver function and high density lipoprotein cholesterol levels. Am. J. Epidemiol. 117:406-418.
14. Langer, R. D., Criqui, M. H. & Reed, D. M. (1992) Lipoproteins and blood pressure as biological pathways for effect of moderate alcohol consumption on coronary heart disease. Circulation 85:910-915.
15. Meade, T. W., Chakrabarti, R., Haines, A. P., North, W.R.S. & Stirling, Y. (1979) Characteristics affecting fibinolytic activity and plasma fibrinogen concentrations. Br. Med. J. [Clinical Research Edition] 1:153-156.
16. Mikhailidus, D. P., Jeremy, J. Y., Barradas, M. A., Green, N. & Dandona, P. (1983) Effect of ethanol on vascular prostacyclin (prostaglandin I-2) synthesis, platelet aggregation and platelet thromboxane release. Br. Med. J. 287:1495-1498.
17. Moore, R. D. & Pierson, T. A. (1986) Moderate alcohol consumption and coronary artery disease. Medicine 65:242-267.[Medline]
18. Frankel, E. N., Kanner, J., German, J. R., Parks, E. & Kinsella, J. E. (1993) Inhibition of oxidation of human low-density lipoprotein by phenolic substances in red wine. Lancet 341:454-457.[Medline]
19. Razay, G., Heaton, K. W., Bolton, C. H. & Hughes, A. O. (1992) Alcohol consumption and its relation to cardiovascular risk factors in British women. Br. Med. J. 304:80-83.
20. Lazarus, R., Sparrow, D. & Weiss, S. T. (1997) Alcohol intake and insulin levels. The Normative Aging Study. Am. J. Epidemiol. 145:909-916.
21. Kiechel, S., Willeit, J., Poewe, W., Egger, G., Oberhollenzer, F., Muggeo, M. & Bonora, E. (1996) Insulin sensitivity and regular alcohol consumption: large, prospective, cross sectional population study (Bruneck study). Br. Med. J. 313:1040-1044.
22. Slattery, M. L., McDonald, A., Bild, D. E., Caan, B. J., Hilner, J. E., Jacobs, D. R., Jr. & Liu, K. (1992) Association of body fat and its distribution with dietary intake, physical activity, alcohol, and smoking in blacks and whites. Am. J. Clin. Nutr. 55:943-949.
23. Sakurai, Y., Umeda, T., Shinchi, K., Honjo, S., Wakabayashi, K., Todoroki, I., Nishikawa, H., Ogawa, S. & Katsurada, M. (1997) Relation of total and beverage-specific alcohol intake to body mass index and waist-to-hip ratio: a study of self-defense officials in Japan. Eur. J. Epidemiol. 13:893-898.[Medline]
24. Rose, K. M., Newman, B., Mayer-Davis, E. J. & Selby, J. V. (1998) Genetic and behavioral determinants of waist-hip ratio and waist circumference in women twins. Obes. Res. 6:383-392.[Medline]
25. Haffner, S. M., Stern, M. P., Hazuda, H. P., Pugh, J., Patterson, J. K. & Malina, R. (1986) Upper body and centralized adiposity in Mexican Americans and non-Hispanic whites: relationships to body mass index and other behavioral and demographic variables. Int. J. Obes. 10:493-502.[Medline]
26. Kaye, S. A., Folsom, A. R., Prineas, R. J., Potter, J. D. & Gapstur, S. M. (1990) The association of body fat distribution with lifestyle and reproductive factors in a population study of postmenopausal women. Int. J. Obes. 14:583-591.[Medline]
27. Dallongeville, J., Marecaux, N., Ducimetiere, P., Ferrieres, J., Arveiler, D., Bingham, A., Ruidavets, J. B., Simon, C. & Amouyel, P. (1998) Influence of alcohol consumption and various beverages on waist girth and waist-to-hip ratio in a sample of French men and women. Int. J. Obes. Relat. Metab. Disord. 22:1178-1183.[Medline]
28. Lapidus, L., Bengtsson, C., Hallstrom, T. & Bjorntorp, P. (1989) Obesity, adipose tissue distribution and health in women—results from a population study in Gothenburg, Sweden. Appetite 13:25-35.[Medline]
29. Kahn, H. S. (1993) Choosing an index for abdominal obesity: an opportunity for epidemiologic clarification. J. Clin. Epidemiol. 46:491-494.[Medline]
30. Pouliot, M. C., Despres, J. P., Lemieux, S., Moorjani, S., Bouchard, C., Tremblay, A., Nadeau, A. & Lupien, P. J. (1994) Waist circumference and abdominal sagittal diameter: best simple anthropometric index of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am. J. Cardiol. 73:460-468.[Medline]
31. van der Kooy, K., Leenen, R., Seidell, J. C., Deurenberg, P. & Visser, M. (1993) Abdominal diameters as indicators of visceral fat: comparison between magnetic resonance imaging and anthropometry. Br. J. Nutr. 70:47-58.[Medline]
32. Kvist, H., Chowdhury, B., Grangard, U., Tylen, U. & Sjostrom, L. (1988) Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am. J. Clin. Nutr. 48:1351-1361.
33. Russell, M., Marshall, J. R., Trevisan, M., Freudenheim, J. L., Chan, A. W. K., Markovic, N., Vana, J. E. & Priore, R. L. (1997) Test-retest reliability of the cognitive lifetime drinking history. Am. J. Epidemiol. 146:975-981.
34. Sallis, J. F., Haskell, W. L., Wood, P. D., Fortmann, S. P., Rogers, T., Blair, S. N. & Paffenbarger, R. S., Jr. (1985) Physical activity assessment methodology in the Five-City Project. Am. J. Epidemiol. 121:91-106.
35. Block, G., Hartman, A. M., Dresser, C. M., Carroll, M. D., Gannon, J. & Gardner, L. (1986) A data-based approach to diet questionnaire design and testing. Am. J. Epidemiol. 124:453-469.
36. SAS Institute Inc. (1999–2001) The SAS System for Windows, Release 8.2(TS2M0) 1999–2001 Cary, NC.
37. Vague, J. (1956) The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout and uric calculus disease. Am. J. Clin. Nutr. 4:20-34.[Abstract]
38. Lapidus, L., Bengtsson, C., Larsson, B., Pennert, K., Rybo, E. & Sjostrom, L. (1984) Distribution of adipose tissue and risk of cardiovascular disease and death: a 12-year follow-up of participants in the population study of women in Gothenberf Sweden. Br. Med. J. 289:1257-1261.
39. Donahue, R. P., Abbott, R. D., Blood, E., Reed, D. M. & Yano, K. (1987) Central obesity and coronary heart disease in men. Lancet i:821-824.
40. Despres, J. P., Moorjani, S., Lupien, P. J., Tremblay, A., Nadeau, A. & Bouchard, C. (1990) Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 10:497-511.
41. Folsom, A. R., Kaye, S. A., Sellers, T. A., Hang, C.-P., Cerhan, J. R., Potter, J. D. & Prineas, R. J. (1993) Body fat distribution and 5-year risk of death in older women. J. Am. Med. Assoc. 269:483-487.
42. Larsson, B. (1988) Fat distribution and risk for death, myocardial infarction and stroke. Bouchard, C. Johnston, F. E. eds. Fat Distribution During Growth and Later Health Outcomes 1988:193-201 Alan R. Liss New York, NY. .
43. Larsson, B., Svardsudd, K., Welin, L., Wilhelmsen, L., Bjorntorp, P. & Tibblin, G. (1984) Abdominal adipose tissue distribution, obesity and risk of cardiovascular disease and death: a 13-year follow-up of participants in the Study of Men Born in 1913. Br. Med. J. 288:1401-1404.
44. Kaplan, N. M. (1989) The deadly quartet: upper-body obesity, glucose intolerance, hypertriglyceridemia, and hypertension. Arch. Intern. Med. 149:1514-1520.
45. Kissebah, A. H., Vydelingum, N., Murray, R., Evans, D. J., Hartz, A. J., Kalkhoff, R. K. & Adams, P. W. (1982) Relation of body fat distribution to metabolic complications of obesity. J. Clin. Endocrinol. Metab. 54:254-260.
46. Krotkiewski, M., Bjorntorp, P., Sjostrom, L. & Smith, U. (1983) Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J. Clin. Investig. 72:1150-1162.
47. Bouchard, C., Bray, G. A. & Hubbard, V. S. (1990) Basic and clinical aspects of regional fat distribution. Am. J. Clin. Nutr. 52:946-950.
48. Bjorntorp, P. (1988) The associations between obesity, adipose tissue distribution and disease. Acta Med. Scand. Suppl. 723:121-134.[Medline]
49. Okosun, I. S., Cooper, R. S., Prewitt, E. & Rotimi, C. N. (1999) The relation of central adiposity to components of the insulin resistance syndrome in a biracial US population sample. Ethn. Dis. 9:218-229.[Medline]
50. Lapidus, L. & Bengtsson, C. (1988) Regional obesity as a health hazard in women-a prospective study. Acta Med. Scand. Suppl. 723:53-59.[Medline]
51. Higgins, M., Kannel, W., Garrison, R., Pinsky, J. & Stokes, J., III (1988) Hazards of obesity—The Framingham Experience. Acta Med. Scand. Suppl 723:23-36.[Medline]
52. 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]
53. Salans, L. B., Knittle, J. L. & Hirsch, J. (1968) The role of adipose cell size and adipose tissue insulin sensitivity in the carbohydrate intolerance of human obesity. J. Clin. Investig. 47:153-165.[Medline]
54. Bjorntorp, P. (1994) Fatty acids, hyperinsulinemia and insulin resistance: which comes first?. Curr. Opin. Lipidol. 5:166-174.[Medline]
55. Bjorntorp, P. (1990) ’Portal’ adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis 10:493-496.
56. Evans, D. J., Murray, R. & Kissebah, A. H. (1984) Relationship between skeletal muscle insulin resistance, insulin-mediated glucose disposal, and insulin binding: effects of obesity and body fat topography. J. Clin. Investig. 74:1515-1525.
57. Seidell, J. C. & Bouchard, C. (1997) Visceral fat in relation to health: is it a major culprit or simply an innocent bystander?. Int. J. Obes. 21:626-631.
58. Laws, A., Terry, R. B. & Barrett-Connor, E. (1990) Behavioral covariates of waist-to-hip ratio in Rancho Bernardo. Am. J. Public Health 80:1358-1362.
59. Flanagan, D. E. H., Moore, V. M., Godsland, I. F., Cockington, R. A., Robinson, J. S. & Phillips, D. I. (2000) Alcohol consumption and insulin resistance in young adults. Eur. J. Clin. Investig. 30:297-301.[Medline]
60. McCann, S. E., Sempos, C., Freudenheim, J. L., Muti, P., Russell, M., Nochajski, T. H., Ram, M., Hovey, K. & Trevisan, M. (2003) Alcoholic beverage preference and characteristics of drinkers and nondrinkers in western New York (United States). Nutr. Metab. Cardiovasc. Dis. 13:2-11.[Medline]
61. McCann, S. E., Marshall, J. R., Trevisan, M., Russell, M., Muti, P., Markovic, N., Chan, A.W.K. & Freudenheim, J. L. (1999) Recent alcohol intake as estimated by the Health Habits and History Food Frequency Questionnaire; The Harvard Semiquantitative Food Frequency Questionnaire and a more detailed alcohol intake questionnaire. Am. J. Epidemiol. 150:334-340.
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