Alcohol and body weight in United States adults
01 Jan 1987-Vol. 77, Iss: 10, pp 1324-1330
TL;DR: For example, this article found that women who consumed alcohol 7-13 times per week had the greatest reduction in weight, while men had only a slight effect on weight in either survey.
Abstract: Alcohol contributes more than 10 per cent of the total caloric intake of adult drinkers in the United States. However, the effect of alcohol on body weight has not been adequately studied in the general population. The association between weight and frequency of alcohol consumption was examined in two national cross-sectional surveys: the Second National Health and Nutrition Examination Survey (HANESII; n = 10,929) and the Behavioral Risk Factor Surveys (BRFS; n = 18,388). Linear multiple regression was used to estimate the independent effect of alcohol on weight, adjusting for smoking, age, diet practices, physical activity, race, education, and height. Among men, alcohol had only a slight effect on weight in either survey. However, among women, alcohol was associated with a substantial reduction in weight, which was as large as the effect of smoking. Compared with nondrinkers, women who consumed alcohol 7-13 times per week had the greatest reduction in weight: -3.6 kg (95% confidence limits [CL] = -5.6,...
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28 Aug 2010
TL;DR: In this paper, the Third National Health and Nutritional Examination Survey III (NHNIS III) was used to examine the association between macronutrient dietary patterns and alcohol consumption using the Pearson correlation coefficients.
Abstract: AIM: To examine the association between macronutrient dietary patterns and alcohol consumption using the Third National Health and Nutritional Examination Survey III.
METHODS: A total of 9877 subjects (5144 males) constituted the study cohort. Dietary interviews were conducted with all examinees by a trained dietary interviewer in a mobile examination center (MEC). Subjects reported all foods and beverages consumed except plain drinking water for the previous 24-h time period. Physical examination and history of alcohol consumption were obtained. Pearson correlation coefficients were used to evaluate the association of the levels of alcohol consumption and the percentage of energy derived from macronutrients. Univariate and multivariate regression analyses were performed accounting for the study sampling weight to further explore the relationships between alcohol consumption and calories derived from each macronutrient.
RESULTS: Subjects who drank were younger than non-drinker controls in both genders (P < 0.01). Alcohol intake was inversely associated with body mass index and body weight in women. Of all macronutrients, carbohydrate intake was the first to decrease with increasing alcohol consumption. In the multivariate analyses, the level of alcohol consumption was found to be an independent predictor associated with lower intake of other macronutrients.
CONCLUSION: Our results show that there is an alteration in the daily dietary pattern with increasing alcohol consumption and that energy derived from alcoholic beverages substitutes that from other macronutrients such as carbohydrate, protein, and fat.
31 citations
Dissertation•
01 Jan 2014
TL;DR: If a difference exists in income for different categories of drinkers in Ireland using the 2007 Slan data set is examined and the possible impact of alcohol consumption on health status and health care utilisation is examined.
Abstract: he aim of this thesis is to examine if a difference exists in
income
for different
categories of
drinkers in Ireland using the 2007 Slan data set. The possible impact of
alcohol
consumption
on health status and health care utilisation is also examined.
Potential endogeneity and selection bias is accounted for throughout.
Endogeneity is where an i
ndependent variable included in the model is determined
within the context of the model (Chenhall and Moers, 2007). An endogenous
relationship between
income and
alcohol and between health
and alcohol
is
accounted for by the use of
separate income equation
s
and separate health status
equations
for each category of drinker similar to what was done in previous studies
into the effects of alcohol on earnings
(
Hamilton and Hamilton, 1997; Barrett, 2002).
8 citations
01 Jan 2011
TL;DR: Whether genetic factors are associated with anthropometric changes during lifestyle intervention and whether lifestyle factors are mediators within the gene-BMI association in a population-based study are investigated.
Abstract: Genome-wide association studies provided evidence for an association of single nucleotide polymorphisms (SNPs) with body mass index (BMI), and gene expression analyses indicated a hypothalamic role for some of the associated genes. Thus, it was hypothesized that the BMI associations might be due to a modulation of nutritional intake and energy expenditure. The present study investigated whether genetic factors are associated with anthropometric changes during lifestyle intervention. Furthermore, this work addressed whether genetic factors are associated with lifestyle parameters and whether lifestyle factors are mediators within the gene-BMI association in a population-based study.
7 citations
DOI•
27 Jun 2014
TL;DR: Ukrainian population in 2000 did not bear patterns of obesity epidemics found in the West: no associations with either socioeconomic status or foods usually associated with overweight were found.
Abstract: BACKGROUND: Overweight and obesity are among the greatest health challenges nowadays. This study aimed to investigate correlates of overweight among the population ofUkraine. METHODS: Data from the survey “Health and Well-Being in Transition Societies” (2000) were analyzed. Body mass index (BMI) based on self-reported height and weight was used to estimate the extent of overweight both in continuous and dichotomous form. Binary dependent variable was computed by setting BMI of 25 as the borderline between normal weight and overweight. Education, demographic characteristics, field of activity, level of physical activity, behavioral and eating habits were considered as independent variables. RESULTS: In 2000, 44% of Ukrainian population (39% of men and 49% of women) were overweight. Risk of overweight increased with age: among those below 40 years old, 16% of men and 12% of women were overweight, about 47% of men and 60% of women were overweight in 40+ groups. Among young men (under 40), those consuming more fruit (OR=0,31) and more physically active (OR=0,43) were less likely to be overweight. In women over 60, those overweight were more physically active (OR=2,32). In men over 40 and women below 40, prevalence of overweight was lower in smokers than in non-smokers. Eating potatoes, meat and milk was associated with increased BMI in some groups. No consistent associations were found for consumption of fish, butter, oil, sugar, and vegetables, as well as for education. CONCLUSION: In 2000, Ukrainian population did not bear patterns of obesity epidemics found in the West: no associations with either socioeconomic status or foods usually associated with overweight were found. Overweight was more likely related to increased use of traditional foods like dairy products, meat and potatoes. Smoking was related to lower BMI in populations with high prevalence of smoking.
2 citations
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References
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TL;DR: Available evidence suggests that minimum mortality occurs at relative weights at least 10% below the US average, which would lead to a systematic underestimate of the impact of obesity on premature mortality.
Abstract: Conflicting results have been reported concerning the association between body weight and longevity. The shape of the curve relating weight to all-cause mortality has been variously described as linear, J-shaped, and even U-shaped. To assess the validity of the evidence for optimal weight recommendations, we examined the 25 major prospective studies on the subject. Each study had at least one of three major biases: failure to control for cigarette smoking, inappropriate control of biologic effects of obesity, such as hypertension and hyperglycemia, and failure to control for weight loss due to subclinical disease. The presence of these biases leads to a systematic underestimate of the impact of obesity on premature mortality. Although these biases preclude a valid assessment of optimal weight from existing data, available evidence suggests that minimum mortality occurs at relative weights at least 10% below the US average.
624 citations
TL;DR: Screening data from the Hypertension Detection and Follow-up Program in Minneapolis, MN, 1973-1974 provided an opportunity to evaluate the accuracy of self-report of height and weight, and it was found that both were reported, on the average, with small but systematic errors.
Abstract: Screening data from the Hypertension Detection and Follow-up Program in Minneapolis, MN, 1973-1974, provided an opportunity to evaluate the accuracy of self-report of height and weight. It was found that both were reported, on the average, with small but systematic errors. Large errors were found in certain population subgroups. Also, men and women differed somewhat in their pattern of misreporting. Weight was understated by 1.6% by men and 3.1% by women, whereas height was overstated by 1.3% by men and 0.6% by women. As in previous studies, it was found that the most important correlates of the amount of error were the actual measurements of height and weight. An interesting finding was that misreporting of both height and weight in men was correlated with both aspects of body size, whereas for women, it was related mainly to the characteristic in question. Certain other demographic variables, such as age and educational level, were also found to have some importance as factors influencing misreporting.
620 citations
TL;DR: Data from five study populations participating in the Cooperative Lipoprotein Phenotyping Study indicate strong relations between reported alcohol consumption and blood-lipids and Plasma-triglycerides showed a modest positive correlation with alcohol.
595 citations
TL;DR: The box plot is applied to tabular data from two recently published articles to show how readers can use box plots to improve the interpretation of data in complex tables and recommend that the box plot be used more frequently.
Abstract: Exploratory data analysis involves the use of statistical techniques to identify patterns that may be hidden in a group of numbers. One of these techniques is the "box plot," which is used to visually summarize and compare groups of data. The box plot uses the median, the approximate quartiles, and the lowest and highest data points to convey the level, spread, and symmetry of a distribution of data values. It can also be easily refined to identify outlier data values and can be easily constructed by hand. We apply box plots to tabular data from two recently published articles to show how readers can use box plots to improve the interpretation of data in complex tables. The box plot, like other visual methods, is more than a substitute for a table: It is a tool that can improve our reasoning about quantitative information. We recommend that the box plot be used more frequently.
531 citations
TL;DR: Some guidelines are given for when to use the sample clustering and sample weights in the analysis of complex survey data and how to use them depend on certain features of the design.
Abstract: BACKGROUND. Since large-scale health surveys usually have complicated sampling schemes, there is often a question as to whether the sampling design must be considered in the analysis of the data. A recent disagreement concerning the analysis of a body iron stores-cancer association found in the first National Health and Nutrition Examination Survey and its follow-up is used to highlight the issues. METHODS. We explain and illustrate the importance of two aspects of the sampling design: clustering and weighting of observations. The body iron stores-cancer data are reanalyzed by utilizing or ignoring various aspects of the sampling design. Simple formulas are given to describe how using the sampling design of a survey in the analysis will affect the conclusions of that analysis. RESULTS. The different analyses of the body iron stores-cancer data lead to very different conclusions. Application of the simple formulas suggests that utilization of the sample clustering in the analysis is appropriate, but that a...
455 citations