The association between alcohol intake and obesity in a sample of the Irish adult population

Abstract

Background: The prevalence of obesity is increasing worldwide. Alcohol has been studied as a possible risk factor for obesity, but the evidence is discordant. This study examined the association between alcohol consumption and obesity in an Irish population.

Method: A cross-sectional study using secondary data from the Healthy Ireland Survey 2017 was conducted. Descriptive and comparative data were analysed to identify associations of alcohol-related variables with waist circumference (WC) and body mass index (BMI). Regression analysis was performed to examine the associations between harmful alcohol consumption (AUDIT-C score≥ 5) and obesity indicators. Adjustments were made for sociodemographic variables, health-related variables, and other alcohol-related variables.

Result: A total of 7486 participants took part in this survey (response rate=60.4%). Most of the participants (86.5%) were alcohol drinkers, with the majority drinking less than 3 times per week (77.5%); 49.1% were considered harmful drinkers. After controlling for possible confounders, positive associations of harmful alcohol consumption with WC (β=1.98, 95% CI: 1.00, 2.96) and BMI (OR=1.25, 95% CI: 1.06, 1.47) were observed. Further controlling for alcohol consumption frequency and binge drinking made this association nonsignificant. Unlike less frequent binge drinking, frequent binge drinking was positively associated with WC (β=2.03, 95% CI: 0.89, 3.17).

Conclusion: Harmful alcohol consumption was associated with obesity (high BMI, large WC) after controlling for possible confounders; however, this association became nonsignificant after controlling for other alcohol-related variables. Frequent binge drinkers were more likely to have a large WC. Further longitudinal studies to examine the exact association between alcohol consumption and obesity are warranted.

1. Background

 Obesity is considered an endemic problem worldwide. Its prevalence has tripled over the last few decades, with 13% of adults considered obese and 39% considered overweight (1). It is linked to the development of various chronic illnesses, including diabetes mellitus, cardiovascular diseases, and certain types of cancers. The underlying cause of obesity is multifactorial, including genetic and environmental factors, and attempting to understand the direct cause of obesity is difficult due to the interactions of different predisposing factors. The prevalence of obesity varies significantly among countries, with some countries showing an alarming increase in the number of cases (2). According to the World Population Review 2021, the lowest prevalence of obesity worldwide was observed in Vietnam, at 2.1%, while the highest proportion was observed in Nauru, at 61% (2). In Ireland, 25.3% of the population is obese (2). Several factors have contributed to the discrepancy in obesity prevalence rates among countries, including diet, environmental and cultural factors, and the implementation of programs that increase awareness about healthy lifestyles.

Alcohol consumption has been examined in different epidemiological studies as a possible risk factor for the development of obesity. In addition to its association with many behavioural and mental health problems, alcohol consumption is thought to be associated with the development of obesity (3). Both alcohol consumption and obesity are considered public health problems in Ireland. According to the Healthy Ireland Survey 2017, 6 out of 10 people were either obese or overweight (23% were obese and 37% were overweight) (4). Additionally, the proportion of individuals who consumed alcohol was 76%, of whom 39% were considered binge drinkers (≥ 6 standard units of alcohol per occasion) (4). The results of studies on the association between alcohol consumption and obesity are discordant and inconclusive. Some studies have shown an inverse association between alcohol consumption and obesity (5-8). Wannamethee et al. (9), on the other hand, found a positive association between alcohol consumption and obesity. However, this association was not observed in other studies (10). Varied types of alcoholic beverages have been studied about obesity, and the results are conflicting (9, 11-13). The inconsistency in the evidence of the association between alcohol consumption and obesity could be attributed to many factors, including methods used, types of confounders controlled for, different alcohol exposure measurements (quantity, frequency, or both), and outcomes of interest (BMI, WC, waist-to-hip ratio [WHR], waist-to-height ratio [WHtR], or percent body fat [%BF]) studied.

To the best of author’s knowledge, no single study has examined the association between alcohol consumption and obesity in Ireland, despite their high prevalence in the country. This study will explore the association between alcohol consumption (specifically harmful alcohol consumption) and obesity in the Irish population using Healthy Ireland Survey 2017 data.

2. Method

2.1. Study population

The population in this study comprised those aged 15 years and older who had participated in wave three of the Healthy Ireland Survey 2017. This survey was part of the Healthy Ireland framework 2013-2025 that aims to improve overall wellbeing and reduce inequality within the population in Ireland. Access to the anonymised, secondary data was requested from and permitted by the Irish Social Science, Data Archive (ISSDA). 

2.2. Study sample and sampling strategy

The sample of the parent study was selected using a two-stage probability-based methodology. The survey was conducted face-to-face by trained interviewers. Verbal consent was obtained from participants aged 18 years and older, and the parents/guardians of participants under 18 years of age provided written consent before participation in the survey. All interviews were performed at participants’ homes using computer-assisted personal interviewing (CAPI). Physical measurements (weight (kg), height (cm), and waist circumference (cm)) were measured by trained staff. Further details on original survey sampling are discussed elsewhere (4). Out of 12389 eligible addresses preselected to take part in the survey, 7487 households completed the interview, with an overall response rate of 60.4%. A total of 5868 (78%) participants completed the physical measurement examination.

2.3. Study design

This was an observational, analytical, cross-sectional study where both descriptive and analytical data were presented.

2.4. Data collection and study instrument

 Out of 134 variables presented in the original Healthy Ireland Survey 2017 dataset, twenty-three variables were included in this study. These data comprised sociodemographic data, health-related data, alcohol-related data, and physical measurement data.

2.4.1. Independent variables

 Independent variables included alcohol-related variables which were obtained via face-to-face interview. In this study, participants were classified into two categories: “drinkers” and “nondrinkers.” Drinkers were defined as those who responded “Yes” to the question “Have you ever consumed an alcoholic beverage in your lifetime?” while nondrinkers were defined as those who responded, “I have never had a drink” or only “I drank a few sips of an alcoholic beverage in the past.” In terms of the alcohol frequency question “How often did you consume alcohol in the last 12 months?”, alcohol frequency was categorized into two categories: “less than 3 drinks per week” and “three or more drinks per week”. Binge drinking was defined as the consumption of six or more standard units per occasion for both sexes. According to the response to the question “During the last 12 months, how often did you consume the equivalent of six standard drinks on one occasion?”, binge drinking was categorized as “less than one occasion of binge drinking per week” and “one or more occasion of binge drinking per week”.

 The Alcohol Use Disorder Identification Test-Consumption, AUDIT-C, tool was used to screen for hazardous/harmful alcohol consumption. Three questions included in the AUDIT-C questionnaire were as follows: “How often did you have a drink containing alcohol in the past year?”, “How many drinks containing alcohol did you have on a typical day when you were drinking in the past year?”, and “How often did you have six or more drinks on one occasion in the past year?” The results of these three questions were given scores of 0-2, 3-4, or 5 or higher. A score of 5 or higher was considered harmful drinking (14). For the regression analysis, binary variables of harmful alcohol consumption (score < 5 and score ≥ 5) was created.

2.4.2. Dependent variables

The dependent variables of interest in this study were WC and BMI, which were measured by trained staff with standard protocols. WC was considered a continuous variable and was measured in centimetres. BMI is expressed as a weight (kg) divided by height squared (m²). BMI was further categorized into three categories: normal weight (< 25. 0 kg/m²), overweight (25.0-29.9 kg/m²) and obesity (≥ 30.0 kg/m²). For the multivariable logistic regression analysis, a binary variable of BMI (< 25.0 kg/m² and ≥ 25.0 kg/m²) was created. WC was used as a continuous variable in the multivariable linear regression analysis.

2.4.3. Sociodemographic and health-related factors

Sociodemographic data (including age, sex, marital status, level of education, employment status, urban or rural residency, having a full medical card, and a private medical card) and health-related data (including general health condition, long-term illness, smoking status, active transportation, and frequency of alcohol consumption) were examined to analyse their associations with obesity indicators. For regression analysis, binary variables were created for age (< 45 years and ≥ 45 years), marital status (married, including “married” and “civil partnership”, or single, including “widowed,” “divorced,” “separated,” and “single never married”), educational level (low/medium and high) and employment status (employed and unemployed, including “unemployed looking for a job,” “retired,” and “pupil/student”). Answers that were recorded as “don’t know” or data missing from the original dataset were treated as missing variables and thus excluded from the analysis.

3.5. Ethical approval

This study was based on secondary data of wave three of the Healthy Ireland Survey 2017. The parent survey was approved by the Research Ethics Committee of the Royal College of Physicians of Ireland (RCPI) on 18 September 2014 (4). For the current study, a request for exemption from full ethical approval was obtained from the UCD Research Ethics Committee of the School of Public Health, Physiotherapy and Sport Science, at University College Dublin.

3.6. Statistical analysis

For descriptive analysis, the categorical variables are presented as numbers and percentages (n/%). Anthropometric measurements (BMI, WC) are presented as means (standard deviation, SDs) and medians (ranges). BMI is further presented in categories and thus expressed in numbers and percentages.

 Univariate analyses were performed to examine the associations between sociodemographic data, health-related data, alcohol-related data, and obesity indicators (BMI, WC). The means of continuous variables were compared using the independent Student’s t test and one-way analysis of variance (ANOVA). The difference in percentages between groups was compared using the Pearson chi-square (χ²) test. Variables with a p value less than 0.05 in the univariate analysis were retained and included in the multivariable analysis.

 Multivariable linear regression analysis was performed to analyse the association between harmful alcohol consumption (AUDIT-C score ≥ 5) and WC while controlling for possible confounders. Four different models were constructed in this regression analysis, and confounding variables (sociodemographic and health-related variables) with significant p values (p < 0.05) were retained and included in the subsequent model. Model 1 included all variables, while model 2 was adjusted for sociodemographic data (age, sex, marital status, education level, employment status, full medical card, and private medical card). Model 3 was adjusted for the statistically significant variables in model 2, along with health-related data (general health, long-term medical illness, smoking status, active travel, and fruit consumption). The fully adjusted model (model 4) included the statistically significant variables in model 3, along with alcohol-related variables (alcohol frequency and binge drinking). The results are presented as linear regression coefficients (β) and 95% confidence intervals (95% CIs). Similarly, a multivariable logistic regression analysis was performed to assess the association between harmful alcohol consumption and BMI using the different models as described earlier. The results are presented as adjusted odds ratios (ORs) and 95% CIs. The Windows-based SPSS statistical package (version 24) was used to perform the analysis. Two-tailed tests were used, and a p value of <0. 05 was considered statistically significant. 

3. Results

3.1. Descriptive statistics

Table 1 presents descriptive data of the sociodemographic factors. A total of 7486 participants took part in wave three of the Healthy Ireland Survey, 2017 where female participants accounted for more than half of the study population (55.3%). Middle-aged and old individuals accounted for over half of the study population (31.9% for those aged 45-64 years and 26.1% for those aged 65 years and older). Just over half of the participants (51.8%) were married or in a civil partnership, and nearly two-thirds of them (60.6%) were living in urban areas. Almost three-quarters of the participants had either a moderate (36.0%) or high (35.6%) education level. More than half of the participants (56.0%) were employed.

Table 2 presents the descriptive characteristics of the health-related factors, including alcohol consumption. Majority of the participants (81.6%) reported that their health was good or very good. Around one-third of participants (32.2%) reported having a long-term medical illness. More than three-quarters of the study population (79.4%) were non-smokers, and a minority of participants reported using active transportation (15.4%). Alcohol consumption was highly prevalent in this study population, with 8 out of 10 participants consuming alcohol (86.5%). However, most of them (77.5%) reported drinking less than three times per week, and approximately one-third of them (31.5%) engaged in binge drinking at least once per week. Of the 5601 participants who completed the AUDIT-C questionnaire, nearly half (49.1%) were classified as harmful drinkers (AUDIT-C score ≥ 5).

Table 3 presents the anthropometric measurements of the study population. The mean WC of the participants was 92.2 ± 14.3 cm, while the mean BMI was 27.3 ± 5.78 kg/m². More than two-thirds of the participants were either overweight (31.0%) or obese (40.6%).

3.2. Univariate analysis

3.2.1. Sociodemographic data and obesity indicators

Table 4 presents the univariate associations between sociodemographic factors and obesity indicators (WC and BMI). In the univariate analysis, all sociodemographic variables analysed showed significant associations with both WC and BMI (p < 0.05). Mean WC measurements were highest in those aged 65 years and older (97.0 ± 14.68 cm, p < 0.001), males (96.5 ± 13.16 cm, p < 0.001), and those who were married or in a civil partnership (93.3 ± 13.37 cm, p < 0.001). Additionally, participants with low educational attainment and those living in rural areas had high mean WC measurements (95.5 ± 15.39 cm, p < 0.001 and 92.9 ± 14.35 cm, p = 0.004, respectively). Participants who were retired and unemployed were found to have significantly higher mean WCs than their counterparts (97.0 ± 14.46 cm; 95.4 ± 15.74 cm, p < 0.001, respectively).

 Regarding BMI, the rates of overweight (BMI 25-29.9 kg/m²) and obesity (BMI ≥ 30 kg/m²) were highest among those aged 45-64 years (35.8% and 34.0%, respectively, p < 0.001), Table 4. Additionally, the rate of obesity was significantly higher among female participants (56.4%, p < 0.001). When addressing marital status, both overweight and obesity were significantly associated with married and civil partnership statuses, at 58.7% and 50.8%, respectively, p < 0.001). Furthermore, obesity was significantly higher among urban (58.7%, p = 0.012) and employed (50.6%, p < 0.001) participants than among rural and unemployed participants. Over two-thirds of the obese population had a low or moderate educational level (34.2% and 35.7%, respectively, p < 0.001). 

3.2.2. Health-related data and obesity indicators

The univariate analysis results of the associations between health-related factors and obesity indicators (BMI, WC) are presented in Table 5. The results clearly demonstrate significant associations between most of the health-related factors and obesity indicators. The mean WC was highest among participants with a fair/poor general health condition (96.8 ± 16.73 cm, p < 0.001), participants with long-term illness (95.4 ± 15.5 cm, p < 0.001) and nonsmokers (92.4 ± 14.37 cm, p < 0.041). On the other hand, the lowest mean WC was found in individuals who ate fruit at least once per day. No significant association between active travel and mean WC was found.

Regarding BMI, low rates of overweight and obesity were reported among participants who described their health as fair/poor (15.2% and 25.5%, respectively, p < 0.001), Table 5. Among those with long-term illness, overweight was reported in nearly one-third of the participants (31.7%), while obesity was reported in less than half of the participants (40.5%, p < 0.001). Participants who used active travel were less likely to be obese or overweight (15.0% and 13.1%, respectively, p < 0.001). Similarly, smokers were less likely to be overweight or obese (18.4% and 21.1%, respectively, p = 0.005).

3.2.3. Alcohol-related data and obesity indicators

Univariate analyses were also conducted to examine the associations between alcohol-related variables and obesity indicators, Table 5. The results demonstrate no significant difference in mean WC between drinkers and nondrinkers. However, those who drank more frequently (≥ 3 times per week) had a higher mean WC (94.0 ± 14.35 cm, P < 0.001) than those who drank less frequently. Binge drinkers who engaged in binge drinking one or more times per week were more likely to have a higher mean WC (95.7 ± 14.16 cm, p < 0.001) than those who engaged in binge drinking less frequently. Participants with harmful drinking patterns (AUDIT-C score ≥ 5) had the highest mean WC (93.4 ± 13.86 cm, p < 0.001) compared to participants with nonharmful drinking patterns.

Regarding BMI, a significant proportion of those who were overweight or obese were drinkers (84.2% and 78.5%, respectively, p < 0.001). No significant relationship between the frequency of alcohol intake and BMI was found. However, obesity was reported in slightly more than one-third of those who reported binge drinking one or more times per week (35.9%, p < 0.001). Finally, overweight and obesity were more prevalent among those who scored 5 or higher on the AUDIT-C questionnaire (53.2% and 48.2%, respectively, p < 0.001).

3.3. Multivariable analysis

3.3.1. Multivariable linear regression analysis

Table 6 shows the multivariable linear regression analysis results, highlighting the association between harmful alcohol consumption (AUDIT-C score ≥ 5) and WC after controlling for sociodemographic and health-related factors. Four models were constructed, of which model 4 is the fully adjusted model. After adjustment for sociodemographic factors and health-related factors in model 3, harmful alcohol consumption (AUDIT-C score ≥ 5) was positively associated with WC (β=1.98, 95% CI: 1.00, 2.96, p <0.001). In the fully adjusted model (4) and after controlling for other alcohol-related variables (alcohol consumption frequency and binge drinking), harmful drinking was no longer associated with WC (β=1.34, 95% CI: -0.79, 2.75, p = 0.064). Binge drinking was significantly associated with WC, with those who engaged in binge drinking once or more per week having a WC 2.03 times higher than that in those who engaged in binge drinking less than once per week, (β= 2.03, 95% CI 0.09, 3.17, p <0.001). There was an inverse association between the frequency of alcohol consumption and mean WC, as participants with frequent alcohol consumption had a lower mean WC than those with less frequent alcohol consumption; however, the difference was not statistically significant. In the fully adjusted model, majority of the sociodemographic (age, gender, marital status, level of education, a full medical card) and health-related (smoking status, frequency of eating fruits) factors were significantly and independently associated with mean WC in the fully adjusted model, Table 6. 

3.3.2. Multivariable logistic regression analysis

Multivariable logistic regression analysis was performed to examine the association between harmful alcohol intake and BMI, controlling for sociodemographic and health-related data Table 7. Out of the four models presented in Table 7, only models 2 and 3 had good model fit, as the Hosmer–Lemeshow test showed nonsignificant p values of 0.150 and 0.106, respectively, and the Nagelkerke R square values were 0.093 and 0.120, respectively.

After adjustment for possible sociodemographic confounders in model 2, harmful alcohol consumption (AUDIT-C score ≥ 5) was associated with a 19% increased risk of obesity/overweight compared to nonharmful alcohol consumption (OR=1.19, 95% CI: 1.04, 1.35, p =0.010). This association remained statistically significant after controlling for health-related data in model 3, with participants who reported harmful alcohol consumption being 25% more likely to be overweight or obese (OR= 1.25, 95% CI: 1.06, 1.471, p = 0.009). Further adjustment for alcohol-related variables (model 4) showed poor model fit and thus are not discussed here. Age, sex, marital status, employment status and health status were positively and independently associated with overweight and obesity in model 3.

Discussion

4.1. Summary of the results

This analytical cross-sectional study highlights the association between harmful alcohol consumption (AUDIT-C score ≥5) and obesity (BMI, WC). The prevalence of overweight and obesity among the study population was more than 60%. Alcohol consumption was prevalent in this study sample, with nearly 9 out of 10 participants (86.5%) reporting alcohol consumption. Less than one-quarter of alcohol drinkers drank three or more times a week, and almost one-third of drinkers engaged in binge drinking at least one time per week. Nearly half of the participants (49.1%) who completed the AUDIT-C questionnaire had harmful alcohol consumption patterns.

 In the univariate analysis, all the sociodemographic variables and most of the health-related variables showed significant associations with WC and BMI. Drinking status was significantly associated with BMI but not with WC. Obesity and overweight were significantly associated with alcohol consumption (78.5% and 84.2%, respectively, p<0.001). Those who drank frequently (≥ 3 times a week) were less likely to be obese and overweight (23.9% and 22.9%, respectively) than those who drank less frequently. However, this difference was not statistically significant. In contrast with participants who engaged in binge drinking less than once per week, over one-third of those who indulged in more frequent binge drinking were obese (35.9%, p < 0.001). Harmful drinkers had a larger WC (93.4±13.86 cm, p<0.001) and were significantly more obese (48.2%, p<0.001) than nonharmful drinkers.

 The results of the multivariable linear regression analysis revealed a positive association between harmful alcohol consumption and WC after adjustment for sociodemographic and health-related factors. However, after controlling for other alcohol-related variables (frequency of alcohol intake and binge drinking), this association became nonsignificant. On the other hand, those who frequently binge drank (≥ 1 time per week) had a WC 2.03 times bigger than those who binge drank less frequently (β = 2.03, 95% CI: 0.89, 3.17). Additionally, an inverse association between alcohol consumption frequency and mean WC was observed; however, this association was not statistically significant. In the multivariable logistic regression analysis, participants who scored 5 or higher on the AUDIT-C questionnaire had higher odds of overweight/obesity than nonharmful drinkers. Age, sex, marital status, education level, employment status, general health condition, and frequency of fruit consumption were all significantly and independently associated with BMI.

4.2. Data interpretation

4.2.1. Harmful alcohol consumption and obesity

 The current study showed positive associations between harmful alcohol consumption (assessed with the AUDIT-C questionnaire) and obesity indicators (both BMI and WC) after controlling for sociodemographic and health-related factors. After further controlling for binge drinking and alcohol consumption frequency, the association between harmful alcohol consumption and obesity (WC) was no longer significant. Most studies on obesity looked at either the frequency or quantity of alcohol consumed, or both, but did not use the AUDIT-C questionnaire to investigate this association. To the best of author knowledge, this is the first study to examine the association between harmful alcohol consumption, using the AUDIT-C, and obesity in a sample of the Irish population. Since the AUDIT-C is a standardized screening instrument that identifies harmful alcohol consumption in primary health care settings, it can be utilized as a tool in future studies on the association between alcohol consumption and obesity and help further validate the controversial evidence of this association.

4.2.2. Frequency of alcohol consumption and obesity

 Although the current study showed an inverse association between alcohol consumption frequency and mean WC, this association was not statistically significant (β = -0.19 95% CI: -1.44, 1.06). This nonsignificant association could be attributed to the small proportion of the study population who consumed alcohol at least three times per week (n=886, 22.5%); thus, it was difficult to obtain a significant result. Examining the association between frequency of alcohol consumption and BMI was not possible, as the fully adjusted model showed poor model fit (Hosmer–Lemeshow p = 0.002 and Nagelkerke R²= 0.126). Previous studies examined the association between alcohol frequency and obesity, and the results were discordant. Some studies found inverse associations between the frequency of alcohol consumption and obesity indicators (BMI and WC) in both sexes (5-7). Dumesnil et al. (5), found that the inverse association between the frequency of alcohol intake and obesity existed regardless of total alcohol intake (6). O'Donovan et al. (5), on the other hand, discovered a bell-shaped association between the frequency of alcohol consumption and obesity, with no difference in risk observed between those who never drank and those who drank the most frequently. Several factors could explain such an inverse relationship between alcohol frequency and obesity. First, alcohol intake might affect macronutrient absorption, leading to reduced energy intake. Furthermore, alcohol consumption may stimulate thermogenesis by activating the ethanol oxidizing system, which can result in weight loss (16). Park et al. (10), on the other hand, found no association between the frequency of alcohol consumption and abdominal obesity in individuals with normal weight. However, this lack of an association could be related to the lower prevalence of abdominal obesity in the study population, < 8% in both sex; thus, an exact association might be difficult to obtain. Overall, based on the current evidence, the promotion of the consumption of alcohol to reduce the risk of obesity is not advisable, as the exact mechanism is not well explored.

4.2.3. Binge drinking and obesity

 Binge drinking was also examined in this study, and the results showed that those who frequently engaged in binge drinking (≥ 1 time per week) had a larger mean WC than those who engaged in binge drinking less frequently. Most of the studies conducted on the association between binge drinking and obesity have shown positive associations. A Korean study conducted in normal weight; middle-aged adults of both sexes showed that those who engaged in daily binge drinking had a significantly higher rate of abdominal obesity (WC) than those who engaged in binge drinking less frequently (10). Similarly, a dose–response relationship between binge drinking and obesity was found by Arif and Rohrer, (17). Tolestrup et al. (5) found that binge drinkers were 77% more likely to be obese (OR= 1.77, 95%: 1.18, 2.65) than nonbinge drinkers. Rohrer et al. (8), on the other hand, used BMI to examine the relationship between the frequency of binge drinking and obesity in nonsmoking participants in the United States, and they found no correlation. However, Rohrer et al. used a purposive sample for their study, which might not have allowed the calculation of an exact value for the association. The reason for the positive association between binge drinking and obesity could be the presence of other impulsive behaviours, e.g., binge eating or abnormal eating patterns, which could confound the association between binge drinking and obesity (18).

4.3. Causes of the conflicting results of the association between alcohol intake and obesity

The aetiology of obesity is considered multifactorial, with several factors contributing to the development of obesity. Not all these factors have been studied or adjusted for when examining the association between alcohol consumption and obesity, and this in turn might bias the estimated relationship between them. The current study controlled for a considerable number of sociodemographic and health-related factors that might confound the relationship between alcohol intake and obesity, with the highest estimated effect sizes for both mean WC and BMI found in males. Type of employment, which was not explored in this study, might function as a potential confounder. Most of the studies on alcohol consumption did not examine individual preferences or cultural influences behind alcohol intake neither examine the type of legislation that might regulate alcohol intake within a country.

Epidemiological studies have examined the association between alcohol consumption and obesity using different instruments. This might also contribute to the conflicting results obtained from different studies. Furthermore, it makes comparisons among studies exceedingly difficult, as no single well-established method was used to measure alcohol consumption. Moreover, the baseline prevalence rates of both alcohol consumption and obesity in a study population can also contribute to inconsistencies in the results obtained when analysing this association. The window period used to recall alcohol intake varied between studies, ranging from a 24-hour recall period (10,19), a 7-day recall period (5, 20), to a 12-month recall period (5). However, a short-term recall period does not account for the usual drinking patterns of individuals, the context under which individuals drink, or the influence of different seasons on alcohol consumption. The current study used a 12-month recall period to examine the association between alcohol consumption and obesity, which is considered more accurate in measurements of the usual consumption trends in individuals.

Types of alcoholic beverages might also affect the life habits of individuals, including eating patterns and physical activity, subsequently affecting body weight. Those who consume beer tend to have worse dietary habits than those who consume other beverages. A US study showed that individuals who drank beer tended to eat fewer fruits, vegetables, and grains than those who consumed wine (21) and were more likely to eat ready-made food (22). Beer consumption was found to be positively associated with smoking, and an interaction of these factors might alter the effect of smoking on body weight. A study showed that current smokers who were heavy drinkers (> 60 grams/day) had a lower BMI than never smokers/former smokers. On the other hand, WC was largest among smokers with heavy lifetime alcohol consumption (23). The current study found that smoking was negatively associated with WC but not BMI after adjustment for sociodemographic and health-related factors. Physical activity is another lifestyle factor that might confound the relationship between alcohol intake and obesity; however, it is difficult to measure.

Most of the studies used a self-report questionnaire to collect data; however, self-report questionnaires are prone to several types of bias (nonresponse bias, recall bias, social desirability bias), and the results may be affected by intentional and unintentional misinterpretations of the questions being asked. Bellis et al. (24) found that self-reported alcohol intake accounted for only 40-60% of total sales. Specifically, heavy drinkers tend to underreport their alcohol intake. The period for recall can also affect the accuracy of self-reported data. However, the data used in the current study were collected during face-to-face interviews conducted by trained staff with the aid of CAPI to minimize errors that might occur during the interview. However, a 12-month recall period can lead to recall bias, which cannot be avoided in studies with such a design.

 A limited number of studies have examined the effect of the interaction between genetic factors and environmental factors on obesity. Lio et al., 2016, examined male twins and concluded that alcohol consumption was positively associated with BMI after controlling for sociodemographic and lifestyle factors. However, this association was not observed in the within-pair analysis (25). Edward and colleagues found that in drinkers, alcohol consumption interacted with the presence of a certain allele, (PPARGC1A) rs4619879 (26). Yokoyama et al. found that a specific genotype (alcohol dehydrogenase-1 B) functioned as a strong determinant of body weight among drinkers (27). However, the exact mechanism by which different genes can affect body weight is still not well defined. More research is needed to elucidate the influence of genetic effects on the association between alcohol consumption and obesity.

4.4. Strengths and limitations

  This is the first study to examine the association between alcohol consumption and obesity in a nationally representative sample in Ireland. Data collection, including anthropometric measurements, was done by trained personnel, so the chances of question misinterpretation or having missing data are extremely low. The overall response rate for the 2017 Healthy Ireland Survey was significantly high (above 60%), making the data more generalizable to the entire Irish population. Moreover, this study used the AUDIT-C score in the analysis of the association between obesity and alcohol consumption, making the results more generalizable, as the AUDIT-C is a standardized tool to measure harmful alcohol consumption. Last, this study controlled for a variety of sociodemographic and health-related factors that could confound the association between alcohol consumption and obesity.

Nonetheless, there are some limitations to this study. The major limitation is the cross-sectional design, which precludes the inference of a causal relationship between harmful alcohol consumption and obesity. Additionally, this study was prone to recall bias and social desirability bias, which tends to be more prevalent in face-to-face interviews. The study did not look at the type or the quantity of alcohol consumed, nor did it account for other potential confounders like sleep patterns, mental health, or type of occupation as these variables were not collected in the original survey. Moreover, confounders, such as physical activity and diet, were poorly presented in the parent data, which made it difficult to control for them. Furthermore, overweight and obesity were prevalent in nearly two-thirds of the study population, increasing the likelihood of finding an association between alcohol consumption and obesity. The current study did not stratify participants by sex to identify variations in the relationship between harmful alcohol use and obesity between the strata. Furthermore, it did not look at total energy intake; thus, it is not clear if the association is a true association or confounded by unmeasured risk factors. Last underreporting of the amount of alcohol intake is problematic in both self-reporting and face-to-face interviews, especially among heavy drinkers, which might cause significant bias.

4.5. Implications of the study

The results of this study have many implications owing to the high prevalence of both alcohol consumption and obesity in Ireland. Current national and international guidelines for obesity management have not emphasized the possible association between alcohol intake and the risk of obesity development; thus, no recommendation regarding this matter has been made (28, 29). This is partly because of the conflicting evidence of the association between alcohol consumption (both quantity and frequency) and obesity. Nevertheless, these guidelines should highlight the current evidence available and recommendations around the relationship.

 Obesity is a public health problem in Ireland, where more than 60% of the population is overweight or obese. Furthermore, the Health Service Executive reported in 2021 that the prevalence of obesity was higher (one in every four people) in the most deprived areas than in the least deprived areas (one in every six people), necessitating more investment in tackling obesity in these disadvantaged communities (28). The management of obesity and overweight in primary healthcare is challenging due to several issues, including a shortage of health services, access to health services, the availability of nutritious food, and the built environment conducive to physical exercise. Additionally, treatment of this condition is highly stigmatized, even among health care workers, and obese patients are commonly subjected to negative judgement. Furthermore, individuals may lack the necessary skills and knowledge to address obesity, rendering them unable to manage their problems. As many determinants, including alcohol consumption, play a role in the development of obesity, treating obesity is considerably difficult and requires a holistic, multilevel, cross-sectoral approach. Ireland established the "Healthy Weight for Ireland: Obesity Policy and Action Plan 2016-2025," which involves various sectors and divides responsibility for obesity prevention among the health and other sectors (transportation, industrial, educational sectors, etc.) (28). However, alcohol intake recommendations were not included in the action plan strategies to combat obesity, due to a lack of definitive evidence of the association between alcohol consumption and obesity. However, it is critical to raise public awareness of the current evidence linking alcohol consumption to obesity, as this may help individuals control their drinking habits. Integrating alcohol consumption recommendations into obesity management action plans is also important at this stage, as it may help standardize the care provided to patients. 

Alcohol consumption is a major health issue in Ireland, where more than 80% of the population consumes alcohol. The current study showed that almost half of the participants who completed the AUDIT-C questionnaire were harmful drinkers (scored 5 or more), and over one-third of drinkers engaged in at least one binge drinking occasion per week. Despite current legislation to control alcohol intake, including minimum price units and a ban on advertising alcohol in public areas, the prevalence of harmful alcohol consumption is still high in Ireland. This necessitates a multisectoral approach to tackle the problem and reduce the associated burden. Increasing awareness of psychological as well as physical adverse effects of harmful alcohol consumption is recommended at the population level. It is also important to strengthen the capacity of screening for and treatment of harmful alcohol patterns. Ireland has started a brief advice service through the Making Every Contact Count (MECC) programme that tackles different health-related problems, including obesity and alcohol intake; this service increases awareness of the adverse effects of alcohol dependence on different health factors and provides support for the treatment of harmful alcohol consumption. However, data on the influence of alcohol consumption on obesity are scarce in this programme and thus need to be updated regularly as the evidence evolve. Increasing public awareness about the possible contribution of alcohol consumption, especially harmful alcohol consumption and binge drinking, to obesity is important at this stage. Finally, the information that healthcare professionals convey to patients on this matter needs to be consistent and up to date.

When addressing the association between alcohol consumption and obesity in epidemiological studies, it is important to consider the baseline prevalence of obesity and alcohol consumption in the study population, as this might affect the overall results. The measurement of exposure should be standardized to improve the generalizability and applicability of findings to other population settings. The current study was unique in that it examined the relationship between alcohol consumption and obesity using the AUDIT-C questionnaire, which can reduce inconsistencies in the results obtained. Personal interviews are also preferable to self-report questionnaires because they provide more accurate, complete, and high-quality data. The alcohol intake recall period should be long enough to accurately predict the pattern of alcohol consumption and account for periods where alcohol intake is high. A prospective study design is better for examining temporal sequences and reverse causation than a cross-sectional design, where such relationships cannot be revealed.

Increased spending on research that addresses alcohol consumption and obesity is recommended, especially in Ireland, where both are considered public health issues. This will help deepen the understanding of the exact relationship between alcohol consumption and obesity and further refine the future recommendations regarding alcohol consumption in a population with a high prevalence of obesity. Tailoring advice about alcohol intake according to an individual’s medical background is also recommended. The current recommendations promote moderation in the consumption of alcohol, but further elaboration of an individual’s risk factors is needed.

4.6. Conclusion

 This is the first analytical, cross-sectional study to examine the association between alcohol consumption and obesity in an Irish population using Healthy Ireland Survey 2017 data. Harmful alcohol consumption was found to be associated with overweight and obesity (BMI ≥ 25.0 kg/m²) and a larger mean WC after adjustment for sociodemographic and health-related factors. Further controlling for the frequency of alcohol consumption and binge drinking, harmful alcohol consumption was no longer associated with mean WC. Frequent binge drinking was positively and significantly associated with mean WC. Alcohol consumption frequency was not associated with mean WC in this study. Further longitudinal studies are required to explore the causal relationship between alcohol consumption and obesity.

Declarations

5.1. Ethics approval and consent to participate:                                                                                       

This study was based on secondary data from the Healthy Ireland survey 2017 which was approved by Research Ethics Committee of the Royal College of Physicians of Ireland (RCPI) on 18 September 2014. The request for exemption from full ethical approval was obtained from the UCD Research Ethics Committee of the School of Public Health, Physiotherapy and Sport Science, at University College Dublin. Access to anonymised data was obtained from the Irish Social Science, Data Archive (ISSDA). Consent was obtained from participants prior to their participation in the parent study. All methods were performed in accordance with the relevant guidelines and regulations".

5.2. Consent for publication.  Not applicable

5.3. Availability of Data and Material (ADM)    

The data that support the findings of this study are available from [ The Irish Social Science, Data Archive (ISSDA)] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [ The Irish Social Science, Data Archive (ISSDA)].

5.4. Competing interests     No financial or non-financial competing interest exist for this research.

5.5. Funding                                                                                                                                                                              No funding was obtained for the current research.

5.6. Authors' contributions 

The author conducted the research and performed the statistical analysis and wrote the paper with supervision of Asst Professor Celine Murrin, Lecturer in Public Health Nutrition, from University College Dublin.

5.7. Acknowledgment

This study was carried out as part of the University College Dublin Master of Public Health program. Following my gratitude to God, I'd like to thank and appreciate my supervisor, Assistant Professor Dr. Celine Murrin, for her consistent guidance and support while conducting this study.

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