Mathew M, Amir R, Alford HE, Mohan S and">
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Is Alcohol Use Related to Obesity in Middle Aged Males in the General Population?

Mathew M, Amir R, Alford HE, Mohan S and Hartos JL*

Department of Physician Assistant Studies, University of North Texas Health Science Center, USA

Corresponding Author:
Jessica L Hartos
Department of Physician Assistant Studies
University of North Texas Health Science Center, USA
Tel: 817-735-2454
Fax: 817-735-2529
E-mail: [email protected]

Received Date: June 15, 2018; Accepted Date: July 17, 2018; Published Date: July 25, 2018

Citation: Mathew M, Amir R, Alford HE, Mohan S, Hartos JL (2018) Is Alcohol Use Related to Obesity in Middle Aged Males in the General Population? J Prev Med Vol.2 No.3: 20. doi:10.21767/2572-5483.100041

Copyright: © 2018 Mathew M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

 
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Abstract

Purpose: Alcohol misuse is a serious public health issue with conflicting findings relating it to obesity. The purpose of this study was to assess whether alcohol use differs by obesity status in middle aged males in the general population.

Methods: This cross sectional analysis used 2016 Behavioral Risk Factor Surveillance System (BRFSS) data for males ages 45 to 64 from Maine (N=1,667), North Dakota (N=1,060), South Carolina (N=1,669), and Wisconsin (N=998). Multiple logistic regression analyses conducted by state were utilized to examine the relationship between alcohol use and obesity while controlling for tobacco use, number of health conditions, mental health, physical activity, education level, employment status, income level, and ethnicity.

Results: Across states, about one third of the sample was obese (35%-38%) and about half to two thirds reported alcohol use (55%-69%). Adjusted results indicated that excessive alcohol use was inversely related to weight status. In addition, having multiple health conditions was positively related to weight status while physical activity and tobacco use were inversely related to weight status.

Conclusion: The results indicated that obesity differed by alcohol use in 45 to 64 year old males in that excessive alcohol use was related to non-obesity. In primary care settings, about one third of middle-aged males may be obese and up to two-thirds may consume alcohol; however, these should be assessed and treated separately. For those with excessive alcohol use, practitioners should evaluate adequate nutrition. In addition, number of health conditions, tobacco use, and physical activity were significantly related to obesity. Therefore, clinicians should screen for all if middle aged males present with any, coordinate treatment of multiple conditions, and educate patients on the benefits of exercise and smoking cessation.

Keywords

Obesity; Alcohol use; Weight status; Middle aged males; Health conditions

Introduction

Obesity is caused by the accumulation of a substantial amount of body fat, leading to a BMI level of more than 30 kg/m2, which consequently harms a person’s health [1-4]. Approximately one-third of adults in the United States are obese, which is about 72 million individuals, with a higher incidence among middle aged adults [1,5-7]. As obesity rates increase, the risk for medical conditions such as type II diabetes, heart disease, hypertension, stroke, cancer, and musculoskeletal problems also increase [1,2,5,8]. Healthcare related to obesity results in a large financial burden both nationally and individually with annual costs approximating $147 billion and $1429 in medical bills, respectively [1,2].

Research shows that obesity is related to health behaviors and demographic factors. For example, consuming excess calories, sodas, fast foods, snacks, and increased portions all contribute to weight gain [1,4,9]. In addition, physical inactivity is known to be positively related to obesity [2,9], whereas smoking is inversely related [10,11]. Although obesity is a health risk for all populations, it has been found that men with higher incomes and women with lower incomes are at higher risk for obesity [5]. Obesity also differs by ethnicity/race and education, as Native Americans, Pacific Islanders, Non-Hispanic Blacks, and Mexican Americans as well as individuals with lower levels of education have a higher overweight/obesity prevalence [4].

In addition to obesity, alcohol misuse is a serious public health issue. Increased alcohol intake is the third leading cause of death in the United States [12] with the prevalence of alcohol abuse being approximately 13% [13]. Alcohol consumption has been linked to a higher probability of violence, injury, suicide, and unintentional death [14]. In addition, excess alcohol consumption causes an increased risk for diseases such as cardiovascular disease, hypertension, cirrhosis, cancers, substance abuse disorders, and mental health problems including depression and anxiety [14,15]. Furthermore, higher daily consumption of alcohol can result in increased alcohol dependence, hospitalizations, and alcohol related deaths from motor vehicle accidents or suicides [16,17].

Research links obesity and alcohol misuse, but findings are conflicting [7,12,13,16,18]. While there is not a significant relationship between light drinking and obesity in adults, the relationship between heavy drinking and obesity in adults is significant [10]. Individuals who drink excessive amounts of alcohol (greater than 60 g/day for men and greater than 30 g/day for women) have a higher likelihood of being obese [10]. Binge drinking has also been related to increased obesity rates [19]. However, other studies have shown no relationship and even inverse associations between obesity and alcohol consumption among adult males [16]. Since research indicates increased alcohol consumption among males compared to females, higher incidence of obesity among middle-aged adults, and conflicting findings for relations with obesity, the purpose of our study is to assess the association between obesity and alcohol consumption among middle aged males in the general population.

Methods

Design

This cross-sectional analysis uses data from the 2016 Behavioral Risk Factor Surveillance System (BRFSS) conducted by the Centers for Disease Control and Prevention (CDC) [20]. BRFSS obtains information about health risk behaviors, medical conditions, and the use of preventative measures against disease through the use of annual telephone surveys using random-digit dial techniques in all fifty states, the District of Columbia, and three U.S. territories. The CDC compiles all BRFSS data and makes de-identified data available to researchers for secondary data analysis. This study was given exempt status by Institutional Review Board of The University of North Texas Health Science Center.

Sample

The samples included males ages 45 to 64 in Maine (N=1,667), North Dakota (N=1,060), South Carolina (N=1,669), and Wisconsin (N=988). These states were selected for their higher proportions of obesity and alcohol use in males across the United States [21].

Data

The outcome, obesity, was originally measured in BRFSS as “underweight”, “normal”, “overweight” or “obese.” We recoded this variable into two categories; “obese” versus “not obese” to better fit our outcome of choice. The factor of interest, alcohol consumption, was originally measured in BRFSS as average number of drinks per day. We then categorized amounts as “none”; “light” (defined as less than one drink per day); “moderate” (defined as 1-4 drinks per day for males); and “excessive” (defined as five or more drinks per day for males) [22].

The control variables included tobacco use, health conditions, mental health status, physical activity, education level, employment status, income, and ethnicity/race. Tobacco use was measured in BRFSS as yes/no to being a current smoker. Health conditions was determined as number of diagnoses for the following health conditions: heart attack, coronary heart disease (CHD), stroke, skin cancer, cancer, chronic obstructive pulmonary disease (COPD), arthritis, depression, kidney disease, diabetes, or asthma. This number was then categorized as “0 health conditions”, “1 health condition”, “2 health conditions”, or “3 or more health conditions.”

Mental health status was measured as yes/no for 30 days of good mental health in the last 30 days. Physical activity was dichotomized as yes/no for “performed physical activity or exercise” within the past 30 days. Education level was also dichotomized as yes/no for “graduated college/technical school.” Employment status was categorized as “employed” or “not employed.” Income was categorized as “$0 to less than $25,000,” “$25,000 to less than $50,000,” and “$50,000 or more.” Ethnicity had multiple categories but because of small numbers of non-white participants in our samples, race/ ethnicity was categorized as “White, non-Hispanic” versus “other.”

Analysis

Frequency distributions were assessed by state to determine sample characteristics and possible issues with variable distributions. Multiple logistic regression analysis conducted by state was used to assess the relationship between alcohol use and obesity after controlling for tobacco use, number of health conditions, mental health status, physical activity, education level, employment status, income, and ethnicity/race.

Adjusted analyses were conducted separately by state to identify patterns in variable relations across similar samples. Similar results in three or four of four states were considered reliable evidence for variable relations. Any observations with missing data for any variables were excluded from the adjusted analysis. Table 1 shows sample characteristics while Table 2 reports multivariable analysis results. All analysis was conducted in STATA 15 (version 15.1, Copyright 1985-2017 StataCorp LLC).

Variable Maine (N=1,667) North Dakota (N=1,060) South Carolina (N=1,669) Wisconsin (N=988)
n % n % n % n %
Weight Status 1,635 98 1,020 96 1,627 97 945 96
   Obese 566 35 389 38 608 37 343 36
   Not Obese 1,069 65 631 62 1,019 63 602 64
Alcohol Use 1,618 97 1,025 97 1,626 97 955 97
  None 553 34 314 31 730 45 291 30
  Light 214 13 164 16 176 11 137 14
  Moderate 353 22 283 28 295 18 247 26
  Excessive 498 31 264 26 425 26 280 29
Tobacco Use 1,635 98 1,022 96 1,609 96 940 95
  Current Smoker 310 19 218 21 354 22 175 19
  Nonsmoker 1,325 81 804 79 1,255 78 765 81
Health Conditions 1,630 98 1,039 98 1,601 96 961 97
0 675 41 499 48 628 39 451 47
1 492 30 294 28 475 30 255 27
2 255 16 149 14 250 16 124 13
  3 or more 208 13 97 9 248 16 131 14
Mental Health Status 1,667 100 1,060 100 1,669 100 988 100
  Good in the past 30 days 1,194 72 819 77 1,198 72 716 72
  Not good in the past 30 days 473 28 241 23 471 28 272 28
Physical Activity 1,665 100 1,059 100 1,667 100 987 100
  Performed in the past 30 days 1,352 81 780 74 1,226 74 790 80
  Did not perform in the past 30 days 313 19 279 26 441 26 197 20
Education Level 1,665 100 1,060 100 1,666 100 987 100
  Graduated college/technical school 578 35 321 30 521 31 314 32
  Did not 1,087 65 739 70 1,145 69 673 68
Employment Status 1,656 99 1,057 100 1,665 100 969 98
  Employed 1,716 71 856 81 1,103 66 706 73
  Not Employed 480 29 201 19 562 34 263 27
Income Level 1,567 94 974 92 1,479 89 866 88
  $0 to less than $25,000 359 23 119 12 361 24 156 18
  $25,000 to less than $50,000 337 22 197 20 351 24 198 23
  $50,000 or more 871 56 658 68 767 52 515 59
Ethnicity/Race 1,632 98 1,040 98 1,608 96 968 98
  White, non-Hispanic 1,554 95 969 93 1,076 67 844 87
  Other 78 5 71 7 532 33 124 13

Table 1: Participant characteristics by state.

Predicting Weight Status (obese vs not obese) Maine North Dakota South Carolina Wisconsin
AOR 95% CI AOR 95% CI AOR 95% CI AOR 95% CI
Low High   Low High Low High Low High
Alcohol Use
  None ref - - ref - - ref - - ref - -
  Light 0.9 0.63 1.28 1.62 1.06 2.48 1.24 0.86 1.79 1.43 0.9 2.3
  Moderate 0.68 0.5 0.93 1.68 1.16 2.44 0.57 0.41 0.8 1.02 0.68 1.52
  Excessive 0.46 0.34 0.63 0.86 0.57 1.29 0.55 0.4 0.75 0.57 0.37 0.88
Tobacco Use                        
  Current Smoker 0.52 0.37 0.72 0.53 0.37 0.77 0.55 0.4 0.75 0.45 0.29 0.71
Health Conditions
0 ref - - ref - - ref - - ref - -
1 1.61 1.22 2.13 1.32 0.94 1.85 1.57 1.17 2.1 1.41 0.96 2.06
2 1.72 1.21 2.44 2.09 1.38 3.17 2.05 1.43 2.93 2.67 1.65 4.32
  3 or more 2.88 1.9 4.35 2.08 1.25 3.46 2.13 1.46 3.12 2.45 1.44 4.18
Mental Health Status
  Good mental health in the past 30 days 1.07 0.82 1.41 1.28 0.91 1.81 0.81 0.62 1.07 1.34 0.92 1.95
Physical Activity
  Performed in the past 30 days 0.66 0.49 0.89 0.65 0.47 0.91 0.84 0.63 1.11 0.64 0.43 0.95
Education Level
  Graduated college/technical school 0.53 0.41 0.69 0.57 0.41 0.79 0.65 0.49 0.87 0.71 0.5 1
Employment Status
  Employed 1.12 0.82 1.54 0.66 0.44 0.97 1.36 1 1.86 0.68 0.45 1.02
Income Level
  $0 to less than $25,000 ref - - ref - - ref - - ref - -
  $25,000 to less than $50,000 0.91 0.63 1.32 1.3 0.76 2.22 1.27 0.88 1.85 0.85 0.5 1.43
  $50,000 or more 1.12 0.78 1.59 1.35 0.81 2.24 1.11 0.76 1.64 1.54 0.92 2.56
Ethnicity/Race
  White, non-Hispanic 2.28 1.23 4.21 1.02 0.57 1.82 0.81 0.62 1.05 1.37 0.82 2.3

Table 2: Adjusted results by state.

Results

Descriptive statistics for participant characteristics

Table 1 shows sample characteristics for middle aged male’s ages 45 to 64 years in Maine, North Dakota, South Carolina, and Wisconsin. Across the four states, about one-third of the samples were obese (35%-38%) and about half to two-thirds reported light (11%-16%), moderate (18%-28%), or excessive (26%-31%) alcohol use. For health behaviors, the majority reported not smoking (78%-81%), while approximately onefourth reported no physical activity or exercise in the past 30 days (19%-26%). In addition, about half reported having one (27%-30%), two (13%-16%), or three or more (9%-16%) health conditions, and approximately three fourths (72%-77%) reported good mental health in the past 30 days. For socioeconomic status, about one-third graduated from college or technical school (30%-35%), and the majority of participants reported current employment (66%-81%) and making $50,000 or more (52%-68%). Most participants were White, non-Hispanic (67%-95%)

Adjusted Statistics for relations among variables

As shown in Table 2, the results of multiple logistic regression analysis for middle aged males in Maine, North Dakota, South Carolina, and Wisconsin indicated that after controlling for all other variables in the model, weight status was consistently related to alcohol use. Across three of four states, participants who reported excessive alcohol use were about 2 times less likely to be obese compared to those who did not consume alcohol. In addition, across all four states, those who were current smokers were about 2 times less likely to be obese than those who were not current smokers.

Furthermore, when compared to those who reported no health problems, those who reported 2 health conditions were about 2 to 2.5 times more likely to report obese status, whereas those who reported 3 or more health conditions were about 2 to 3 times more likely to report obese status. In contrast, participants who performed physical activity in three of four states in the past 30 days were about 1.5 times less likely to be obese than those who did not perform physical activity in the past 30 days.

Discussion

The purpose of this study was to assess whether alcohol use differed by obesity status in middle-aged males in the general population. The results of adjusted analyses indicated that weight status was significantly but inversely related to alcohol use across states. Males ages 45 to 64 who reported excessive drinking were about 2 times less likely to be obese, findings similar to prior research that found inverse relations [11,16], but dissimilar to others that found positive relations [10,13,16,18] between obesity and alcohol use. Differing results across studies may be due to varying methods used in measuring alcohol use such as the number of alcoholic drinks, percentage of energy intake per drink, and the concentration of alcohol in the beverage. There were also discrepancies in target populations across studies including general and medical populations from Europe, Asia, or North America as well as including both genders and all ages, whereas this study focused on middle-aged males from the general population.

This study also found that those who use tobacco are less likely to be obese, similar to prior research findings [1,2,8,10,18,23]. The findings for alcohol and tobacco use as related to reduced weight status in this study are concerning. Not only is substance use related to poorer health outcomes, especially over time, but the results of this study may suggest that substance users may not have healthy eating habits [3] given that alcohol intake should increase calorie counts and amount consumed [18,19]. Poor nutrition in addition to substance use would also make poorer health outcomes more likely. In addition, food and substance use may trigger similar pleasure receptors in the brain [7] so over indulgence may only happen in one area.

In addition, obesity in middle-aged males was consistently related to reported number of health conditions and physical activity [1,2,8,10,18,23]. Those reporting two or more health conditions were about 2 to 3 times more likely to be obese, whereas those who reported physical activity in were about 1.5 times less likely to be obese. These results may suggest that the management of health and health conditions are important for weight status in middle-aged males.

Limitations

BRFSS 2016 data includes a large number of respondents, allowing us to assess variable relations within a specific gender and age group. However, participants did not specify what type of alcoholic drinks were consumed, which may impact results because alcohol beverages come in varying amounts of ethanol and caloric content. In addition, there was no information on whether the amount of alcohol use reported represented a long term habit or simply recent use. Moreover, type of diet, nutrition, and calorie intake are major factors that play a role in weight status; however, data was unavailable for these. Future studies may want to focus on eating habits as related to weight status and alcohol use in middle-aged males. In addition, although number of health conditions was related to obesity in this study, we had no information for health condition management or medication use. This may impact results as varying interactions and effects on drug metabolism in combination with alcohol can affect weight status.

Conclusion

Because this was a population-based study, results may be generalizable to 45- to 64-year-old males in primary care. Providers may expect that up to one-third of middle-aged males may be obese or excessive alcohol users. Because excessive alcohol use was inversely related to obesity, practitioners should continue to screen middle-aged males for both obesity and alcohol as symptoms present, but treat separately. For obesity, providers should educate patients about the benefits of proper exercise and diet and provide referrals to specialists for obesity issues. For excessive alcohol use, providers should further screen patients for adequate nutrition and make referrals to addiction specialists. In addition, providers may expect that up to onethird of their male patients ages 45 to 64 have two or more health conditions (23%-32%) and this may be moderately related to obesity. Thus, practitioners should coordinate treatment for comorbid health conditions, assess patient management and compliance, and educate patients about the importance of drug compliance, co-management of multiple health conditions, and having regular follow up appointments to assess progress. Finally, up to one-fourth of middle-aged male patients may use tobacco and report physical inactivity and these are moderately related to obesity. Thus, providers should educate males ages 45 to 64 on the importance of smoking cessation and physical activity and discuss options that would fit into patients’ lifestyles.

Disclaimers

No author has any conflict of interest.

Source(s) of Support

There was no financial support for the conduct or findings of this study.

References

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