Moderate alcohol consumption is associated with lower chronic disease burden expressed in disability-adjusted life years: a prospective cohort study

The EPIC-NL study consists of the two Dutch contributions to the European Prospective Investigation into Cancer and Nutrition (EPIC), which were set up simultaneously between 1993 and 1997 [18]. In brief, the Prospect-EPIC study includes 17,357 women (49–70 years) who participated in the Dutch breast cancer screening programme. The MORGEN-EPIC cohort consists of 22,654 men and women (20–65 years) from random samples of the population in three Dutch towns (Doetinchem, Amsterdam and Maastricht). At baseline, a general questionnaire and a food-frequency questionnaire (FFQ) were administered and a physical examination was performed. This study was conducted according to the Declaration of Helsinki and approved by the local institutional review boards. Written informed consent was obtained from all subjects.

From the total cohort (n = 40,011), participants without permission for linkage with disease registries were excluded (n = 2879). Participants with any of the studied diseases at baseline [Cancer, Coronary Heart Disease (CHD), Stroke, Diabetes Mellitus, Chronic Obstructive Pulmonary Disease (COPD), Asthma, Parkinson’s disease, Rheumatoid Arthritis, Osteoarthritis, and Inflammatory Bowel Disease (IBD)] (n = 3625) were excluded. We also excluded subjects with missing information from the FFQ on alcohol consumption and dietary intake (n = 142) or with implausible high or low scores for total energy intake (those in the top 0.5% and bottom 0.5% of the ratio of reported energy intake over estimated energy requirement based on basal metabolic rate) (n = 299), leaving 33,066 men and women for analysis.

Alcohol consumption was assessed by the general questionnaire and a food frequency questionnaire (FFQ). The FFQ contained questions on the usual frequency of consumption of 79 main food items during the year preceding enrolment [19, 20]. This questionnaire allowed the estimation of the average daily consumption of 178 foods, including alcoholic beverages. The relative validity of alcohol consumption measured with the FFQ is good as confirmed by a Spearman correlation of 0.87 between the FFQ and the average of 12 monthly 24-h recalls [19, 20]. Subjects were asked whether they formerly or currently used alcohol. If doing so currently, they were asked the number of units of beer, white wine, red wine, port/sherry/vermouth and spirits consumed. Subjects indicated their consumption frequency on a daily/weekly/monthly/yearly scale or as never consumed. Alcohol consumption was determined based on the FFQ by multiplying the amount of each beverage consumed by their alcohol content using the standard ethanol weight content (5% for beer, 18.5% for fortified wine, 12.5% for red wine, 12% for white wine and 40% for liquor; means from the average type of beverages) and was categorized into 5 categories of total alcohol consumption; lifetime abstainers, former drinkers, light drinkers (0–4.9 g/day), moderate drinkers (5–14.9 g/day for women, 5–29.9 g/day for men), and substantial drinkers (≥15 g/day for women and ≥30 g/day for men). Substantial drinkers were further subdivided into heavy drinkers (15–29.9 g/day for women, 30–59.9 g/day for men) and excessive drinkers (≥30 for women, ≥60 for men).

The general questionnaire included questions on presence of risk factors for chronic diseases, like smoking status and intensity (categorized as never; former: quit smoking >20 years ago, quit 10–20 years ago, quit ≤10 years ago; current smoker 1–15 cigarettes/day, 16–25 cigarettes/day, >25 cigarettes/day; pipe or cigar smoker) and level of education [categorized as very low (only primary school), low (lower vocational education), middle (secondary school or intermediate vocational training) or high (higher vocational training or university)]. Usual dietary intake was obtained from the validated FFQ and was used to score intake of eight components of a Mediterranean-style Diet: vegetables; legumes; fruit, nuts and seeds; cereals; fish; the ratio of unsaturated to saturated fatty acids; meat; and dairy products [21]. Physical activity was assessed using the EPIC physical activity questionnaire and categorized according to the validated Cambridge Physical Activity Index (CPAI) [22, 23] in 4 categories (inactive, moderately inactive, moderately active or active). Missing data on physical activity (14%), smoking status (0.4%), educational level (0.8%), and/or BMI (0.1%) was imputed using single linear regression modelling (SPSS MVA procedure).

Participants were followed for mortality and morbidity through linkage with several registries. The selection of the diseases was based on their prevalence and disease burden in the Netherlands, but also on the availability of data sources. Information on vital status and the date of death was obtained through linkage with municipal registries. Causes of death were obtained from Statistics Netherlands. Information on disease occurrence (Cancer, CHD, Stroke, Diabetes Mellitus, COPD, Asthma, Parkinson’s disease, Rheumatoid Arthritis, Osteoarthritis, and IBD) was obtained from the National Cancer Registry and the national hospital discharge diagnosis database from the Dutch National Medical Registry. The National Cancer Registry provided information on the type of cancer and the date of histological diagnosis. The national hospital discharge diagnosis database provided the date of diagnosis for CHD, stroke, Diabetes Mellitus, COPD, Asthma, Parkinson’s disease, Rheumatoid Arthritis, Osteoarthritis, and IBD. The national hospital discharge diagnosis database was linked to the cohort with a validated probabilistic method [24]. Self-report and urinary glucose strip tests provided additional information on diabetes. Diabetes cases were verified against information of the general practitioner or pharmacist [25]. Follow-up was complete until 31 December 2007.

DALYs, i.e. the sum of the Years Lost due to Disability (YLD) and the Years of Life Lost due to premature mortality (YLL) [26], were calculated for each individual in the cohort as previously described [17]. The YLL were computed as the number of years that death occurred earlier than expected. The expected number of life years (life expectancy) are the remaining years that a person of a certain age is expected to live on average defined at the time of death, loss of follow-up or end of follow-up [17]. The expected life expectancy was obtained from Statistics Netherlands that provides age, sex and calendar year specific life expectancies based on mortality rates [27]. The YLD are calculated by the number of years a person lives with a disability multiplied by a disability weight reflecting the severity of that disability. The disability weights were derived from the Dutch Disability Weight study [28]. The disability weights can range between zero (no burden, i.e. full health) and one (death) (Supplemental Table 1). The years lived with a chronic disease are calculated from the disease onset until death or until the end of life expectancy. One DALY represents the loss of one year in full health. For example, a person who lived five years with diabetes (disability weight 0.20) and died 30 years before his life expectancy obtains one YLD and 30 YLL which equals 31 healthy years of life lost (31 DALYs) (Fig. 1). Total DALYs were computed, as well as DALYs for specific diseases: CHD, CVD (includes CHD and stroke), and cancer. DALYs from cancer were further separated into alcohol-related (the upper aero-digestive tract, breast, liver, and colorectal cancer) and non-alcohol related cancer. DALYs for participants that suffered from both alcohol- and non-alcohol-related cancers were included in the alcohol-related cancer DALYs. Potentially alcoholism-related disorders were not included, since several of these disorders were not chronic and occurred in less than 1% of our cohort. However, to explore the influence of these disorders on the association between alcohol consumption and total disease burden, we conducted a sensitivity analysis as a worst-case scenario. For this analysis, we assumed that all of these disorders were chronic. Thus, in this analysis, disorders with a transient nature such as delirium and psychosis were assumed to be present from the date of first diagnosis onwards. The following potentially alcoholism-related disorders were included in these analyses and obtained from the hospital discharge diagnosis database: alcohol dependence, delirium, liver cirrhosis, psychosis and mental disorders.

Due to the distribution of the DALYs, i.e. a peak at 0 and a normal distribution in participants with DALYs > 0, we used a two-part model to estimate the association of alcohol intake with DALYs as dependent variable [29]. This two-part model combines the estimation of the probability of having DALYs using logistic regression with the estimation of the number of DALYs among participants with DALYs > 0 using linear regression. Alcohol intake was analyzed categorically, using light drinkers (0–5 g/day) as the reference. Confidence intervals were constructed with bootstrapping (500 samples). A quadratic P for trend was investigated by applying a regression with quadratic contrast coefficients to DALY estimates of each of the categories, excluding lifetime abstainers and former drinkers, in all bootstrapping samples. The P value was the percentage of positive (or negative, depending on the direction of the association) regression coefficients of the bootstrap times two because of two sided testing.

The analyses were adjusted for age, sex, physical activity level, education level, smoking status and intensity and energy intake (kcal/day). In a second model, BMI (continuous) and diet (continuous mMDS score) were included as covariates. We investigated whether associations differed across strata of age and sex by including an interaction term in the logistic and linear model and by stratifying the analyses by sex and age (>50/≤50 years). To exclude possible residual confounding by smoking, the analyses were repeated among never-smokers only. To explore the influence of alcoholism-related disorders, the analyses were repeated while including these disorders in the DALY calculation. Statistical analyses were conducted using SAS 9.2 (SAS Institute, Cary, US) and SPSS 14.0 (Chicago, IL, USA).