The Danish Health Examination Survey

Data from the Danish Health Examination Survey (DANHES) from 2007 to 2008 was used [19]. DANHES was conducted in 13 of 98 municipalities in Denmark, where all citizens in the participating municipalities aged ≥18 years were invited to complete a self-reported questionnaire comprising items on lifestyle, health and morbidity. In 12 municipalities, an internet-based questionnaire was used, whereas a paper-based questionnaire was used in the remaining municipality. Overall 538,497 people were invited and 76,484 participated (14%). The personal identification number—a unique ten-digit number assigned to each Danish citizen—enabled linkage to Danish nationwide registries from where information on social factors, morbidity and mortality was obtained.

Alcohol drinking patterns

Lifetime abstainers were defined as individuals, who reported never drinking alcohol, and current abstainers were defined as individuals with a history of drinking alcohol, but who had not drunk alcohol within the last year. Respondents who had been drinking alcohol within the last year were asked about their alcohol drinking pattern. Information on frequency of alcohol drinking was reported as: <1 day/week; 1–2 days/week; 3–4 days/week; or 5–7 days/week. Participants were asked about frequency of binge drinking (≥5 beverages/occasion), which was reported as: never; <1 day/week; 1 day/week; or >1 day/week. Furthermore, participants were asked to report, in drinks, their average daily consumption of wine, beer and spirits during the seven weekdays. One Danish standard drink corresponds to 12 g ethanol. Based on average weekly alcohol consumption of specific beverage types, we calculated beverage-specific and the overall average weekly alcohol amount. The beverage-specific alcohol amount was coded as: <1 drink/week; 1–6 drinks/week; ≥7 drinks/week (women); 7–13 drinks/week (men); or ≥14 drinks/week (men). Last, participants were asked whether their consumption of alcohol within the last 5 years had been decreasing, increasing or stable.

Diabetes

Information on diabetes during follow-up was obtained from the Danish National Diabetes Register, which comprises all incident cases of diabetes diagnosed in Denmark from 1991 and onwards [20]. The registry uses five diagnostic criteria: (1) hospitalisation with a diagnosis of diabetes according to the ICD 8th or 10th Revisions (ICD-8 codes 249 or 250; ICD-10 codes E10-14, H36.0 or O24 [excluding O24.4], www.who.int/classifications/icd/en/) obtained from the Danish National Patient Registry; (2) registration of chiropody (coded for diabetes) in the Danish National Health Service Register; (3) registration in the Danish National Health Service Register with measurement of blood glucose five or more times within 365 days; (4) two or more annual measurements of glucose during a 5 year period; or (5) registration in the Danish National Prescription Registry with prescription of insulin or oral glucose-lowering medication on at least two occasions. If one of these criteria is met, an individual is registered as having diabetes. The register does not distinguish between type 1 and type 2 diabetes. Participants were followed up for diabetes in the register to December 2012.

Covariates

Based on the literature, we considered the following as confounders: age, sex, BMI (calculated as kg/m2) as a linear and squared term, education, smoking status (never smoker, former smoker, occasional smoker or daily smoker), diet, leisure-time physical activity, hypertension (current or previous) and family history of diabetes. All covariates except age and sex were based on self-report in DANHES. Level of school education was defined according to the International Standard Classification of Education, combining ongoing or completed school and vocational education and divided into: <10 years; 10–12 years; 13–14 years; or ≥15 years. Information on diet was based on five questions about intake of: (1) fibre-rich bread or grain; (2) boiled, fried or baked vegetables; (3) salad; (4) fruit; and (5) fish at dinner. For each of diet questions 1–4, respondents were coded as infrequent eaters if they ate the respective food never/very infrequently, <1 time/week, 1 time/week, or a couple of times/week. Respondents were coded as frequent eaters if they ate the food almost every day, or every day/more than once a day. For diet question 5, respondents were categorised as infrequent eaters if they ate fish for dinner never/very infrequent or <1 time/week. Otherwise they were reported as frequent eaters. Leisure-time physical activity during the last year was assessed based on a measure developed by Saltin and Grimby [21], with the following response categories: (1) vigorous (strenuous activities usually involving competition or endurance training performed regularly or several times a week); (2) moderate (exercise, endurance training or heavy gardening for at least 4 h a week); (3) light (walking, cycling or other light activities for a minimum of 4 h a week); or (4) inactive (reading, TV watching or other sedentary activities). Respondents were categorised as having a family history of diabetes if they reported having a mother, father and/or full brothers and sisters with diabetes.

Final study population

We excluded participants with a diagnosis of diabetes at baseline (date of participation in DANHES) by linkage to the Danish National Diabetes Register (n = 3079) or if they reported a history of diabetes in DANHES (n = 543). Women who were pregnant at baseline or had given birth within the last 6 months from baseline were excluded (n = 894), based on the assumption that women reduce their alcohol consumption during pregnancy. Furthermore, we excluded participants with no information about whether they had been drinking alcohol within the last year (n = 783), how often they consumed alcohol (n = 460) or how often they binge drank (n = 174). The final study population comprised 70,551 individuals (28,704 men and 41,847 women).

Statistical analyses

Through linkage to the Danish Civil Registration System we obtained information concerning time of death and emigration. Participants were followed from baseline until diagnosis of diabetes (n = 1746), emigration (n = 454), death (n = 1254) or 29 December 2012 (n = 67,097), whichever occurred first. The total observation time comprised 342,349 person-years, and both men and women were followed for a median of 4.9 years (range = 0.0–5.8 years).

To account for missing values for potential confounders, we performed multiple imputation by chained equations with 20 repetitions. The imputation model included variables that were hypothesised to predict missing information (age, sex and diabetes). The numbers of missing values were 1944 (physical activity), 61 (smoking), 152 (vegetables), 182 (salad), 182 (fruit), 192 (fibre-rich bread or grain), 194 (fish), 1953 (beer, wine and spirits), 2460 (BMI), 4110 (school education), 4682 (hypertension) and 5815 (family history of diabetes).

Data were analysed by means of the Cox proportional hazards regression model to estimate HRs and 95% CIs for the risk of developing diabetes. Age (in days) was used as the underlying time axis, which ensured maximal adjustment for confounding by age. All analyses were carried out separately for men and women, as the literature suggests sex differences in the relationship between alcohol consumption and risk of type 2 diabetes [6, 22]. Further, preliminary analyses of data suggested interactions between sex and several of the independent variables and the outcome. The assumption of proportional hazards was tested graphically and in statistical tests and the assumption was found to be fulfilled for all models. Curvilinear associations between alcohol variables and diabetes were examined by numerical recoding of the median value within categories of the examined alcohol variables and adding these as continuous variables to the models. Lifetime and current abstainers were excluded from the analysis testing for trend. In the analyses of frequency of binge drinking and beverage type, abstainers were not included in the tests.

To examine the continuous measure of average weekly alcohol amount, a restricted cubic spline model, using three knots (values were 1, 8 and 22 for men, and 0, 4 and 13 for women), was fitted [23]. A nested log likelihood test was performed to compare a model with three knots with a model with four knots. There was no statistically significant difference between the two in the prediction of the relationship between alcohol and risk for diabetes; hence the model with three knots was applied. The reference level was set at 0 drinks/week (comprising individuals consuming 0 drinks/week, current abstainers and lifetime abstainers). As cubic spline models are sensitive to outliers, we excluded participants with an average weekly alcohol amount above the 99th centile, corresponding to 40 drinks/week in men and 28 drinks/week in women.

To test for interaction between drinking frequency and alcohol amount we used a nested log likelihood test where we compared a model containing the variables as single terms with a model including the interaction terms.

Because BMI is most likely both a confounder and a mediator in the association between alcohol consumption and type 2 diabetes [2], we conducted multivariate analyses where BMI was not included as a covariate in order to examine how this affected the results.

We performed sensitivity analyses where the effects of average weekly alcohol amount and frequency of alcohol consumption on the risk of diabetes were tested. First, we excluded participants who had reduced or increased their alcohol consumption within the last 5 years of baseline. Second, as the Danish National Diabetes Register does not contain information about diabetes type, and incidence of type 2 diabetes rises from middle life, we performed a sensitivity analysis based on participants aged 40 years or above at baseline. Furthermore, as suggested by others [24], a sensitivity analysis was performed where criteria 3 (measurement of blood glucose five or more times within 365 days) and 4 (two or more annual measurements of glucose during a 5 year period) in the Danish National Diabetes Register were not used as indicators of diabetes. This was because of a suspicion that a large proportion of the individuals included in the Danish National Diabetes Register based on these criteria do not have clinically diagnosed diabetes [24].

The significance level was set to a p value of 0.05 (two-sided). Analyses were performed using SAS Software Enterprise Guide version 5.1, SAS Institute, Cary, NC, USA and STATA version 14.2, StataCorp TX, USA. All study participants gave their informed consent to the research, and the publication of results was approved by the Danish Data Protection Agency.