Study population

From December 1993 to May 1997 the study recruited 56,468 participants without cancer prior to enrollment, between the age of 50–65 years, residing in the greater area of Copenhagen and Aarhus, Denmark. Details of the Danish Diet, Cancer, and Health cohort study, an associated cohort of the European Prospective Investigation into Nutrition and Cancer (EPIC), have been published previously26. All Danish residents are provided with a unique and permanent civil registration number enabling crosslinking between nationwide registers and the Danish Diet, Cancer, and Health cohort on the individual level. The following databases were crosslinked to the cohort: The Civil Registration System which includes data on age, sex, and vital status; The Integrated Database for Labor Market Research which contains information on annual income since 1980; The Danish Register of Causes of death with information on cause of death since 1994 by International Classification of Diseases (ICD) codes; and The Danish National Patient Register (DNPR) which holds information on all hospital admissions in Denmark since 1978. All diagnoses were defined by ICD using the 8th revision (ICD-8) until 1993 and the 10th revision (ICD-10) since 1994.

Participants for whom information was missing or implausible (n = 215) and those with extreme energy intakes [<2 092 kJ/day (<500 kcal/day) and >20 920 kJ/day (>5000 kcal/day)] (n = 205), were excluded from the analysis (Fig. 4).

Fig. 4 Consort flow diagram. CVD cardiovascular disease Full size image

In Denmark, register studies do not require approval from ethics committees. The Danish Data Protection Agency approved this study (Ref no 2012–58–0004 I-Suite nr: 6357, VD-2018-117). Informed consent was obtained from all participants to search for information from medical registries.

Dietary assessment

Dietary data were collected using a validated 192-item food-frequency questionnaire, mailed out to participants prior to their baseline visit to one of the two study centers27. Participants were asked to indicate their usual frequency of intake of different food and beverage items over the past 12 months, using a 12-category frequency scale that ranged from never to 8 times or more per day. Details on the calculation of specific foods and nutrients have been published previously26.

Exposures

The exposures of interest for this study were intakes of total flavonoids and flavonoid subclasses. An estimate of the flavonoid content of each food and beverage in the food-frequency questionnaire was derived from the Phenol-Explorer database28. Correlations between levels of 12 flavonoids in 24-hr urine samples and intake of their main food sources has been examined in 475 EPIC subjects, showing significant correlations with their main food sources29. The effect of food processing on flavonoid content was taken into consideration using retention factors30. In the present study intakes of all flavonoids that were both available in Phenol-Explorer and could be estimated from foods in the FFQ (n = 219) were used (Supplementary Table 5). These were grouped into 10 subclasses according to their chemical structure [flavonols, flavanol monomers, flavanol oligo + polymers, flavanones, flavones, anthocyanins, isoflavones, dihydrochalcones, dihydroflavonols and chalcones] by summing the intakes of all individual flavonoid compounds within that flavonoid subclass (Supplementary Table 5). As the average intakes of isoflavones, dihydrochalcones, dihydroflavonols and chalcones were very low in this cohort (<5 mg/day), they were not included in the individual subclass analyses. Total flavonoid intake was calculated by summing all 219 flavonoid compounds. The content of flavonoids was expressed as aglycones in mg/100 g fresh food weight.

Study outcomes

Vital status and date of death for every participant was obtained from the Civil Registration System. Cause of death data was obtained from the National Death Register. CVD-related mortality was defined as any ICD-10 diagnoses registered as a cause of death related to CVD (I00-I99) and cancer-related mortality was defined as any ICD-10 diagnosis registered as a cause of death related to cancer (C00-C99), dated after participant enrollment.

Covariates

Information on sex, age, and lifestyle factors such as smoking, alcohol consumption, and daily activity were obtained using the self-administered questionnaire at study enrollment. Clinical measurements such as BMI and total cholesterol were taken at the study centers. Annual income was used as a proxy for socio-economic status and was defined as household income after taxation and interest, for the value of the Danish currency in 2015. Income, grouped in quartiles, was estimated as the mean income of 5 years up to and including the year of study inclusion. Self-reported myocardial infarction and self-reported stroke at baseline were combined with ICD codes (Supplementary Table 6) of ischemic heart disease and ischemic stroke, respectively, dated prior to participant enrollment. ICD codes dated prior to participant enrollment were used to identify baseline comorbidities of peripheral artery disease, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), heart failure, atrial fibrillation, and cancers (Supplementary Table 6). For hypertension and diabetes mellitus, only self-reported prevalence was used due to the low validity of ICD codes in the DNPR31. Prevalent CVD was defined by the presence of at least one diagnosis of ischemic heart disease, peripheral artery disease, ischemic stroke, heart failure, or atrial fibrillation prior to recruitment.

Statistical analysis

Participants were followed for a maximum of 23 years, from the date of enrollment until the date of death, emigration, or end of follow-up (August, 2017), whichever came first. The exposure variables (total flavonoid and flavonoid subclass intakes) were categorized by quintiles of intake (20% of participants from the total study population in each). Correlations between flavonoid subclasses were examined using Pearsons’ correlation coefficients.

Potential nonlinear relationships were examined using restricted cubic splines, with hazard ratios (HRs) based on Cox proportional hazards models. HRs with 95% confidence intervals (CIs) were plotted for each unit of the exposure against the median intake in quintile 1. In all spline analyses, the exposure variables were treated as continuous and individuals with intakes more than four standard deviations above the mean were excluded. The test of nonlinearity used analysis of variance to compare the model with only the linear term to the model that included both the linear and the cubic spline terms. HRs and 95% CIs for the median intakes in each quintile of the exposure variables were obtained from the splines. Cox proportional hazards assumptions were tested using log-log plots of the survival function vs. time and assessed for parallel appearance. As our aim was to obtain relative estimates for risk factors, deaths from causes other than the outcome of interest were censored rather than treated as a competing risk32. Three models of adjustment were used: (1) age and sex; (2) age, sex, BMI, smoking status, physical activity (total daily metabolic equivalent), pure alcohol intake (g/d), hypertension (yes/no), hypercholesterolemia (yes/no), social economic status (income), and prevalent disease (diabetes, CVD, COPD, CKD and cancer, entered into the model separately); (3) model 2 plus intakes (g/d) of fish, red meat, processed meat, dietary fiber, polyunsaturated fatty acids, monounsaturated fatty acids and saturated fatty acids. Covariates were chosen a priori to the best of our knowledge of potential confounders of flavonoid intake and mortality. As some covariates in model 2 (hypertension, hypercholesterolemia and prevalent diseases) are potentially on the causal pathway and therefore introduce collider stratification bias, we removed them in a sensitivity analysis. When investigating CVD-related mortality we excluded participants with CVD at baseline. Using ICD-8 codes, we identified an additional 247 participants with a diagnosis of cancer prior to enrollment; when investigating cancer-related mortality we excluded these participants (Fig. 4).

We stratified our analyses by sex, BMI, smoking history, alcohol intake, physical activity, and prevalent diabetes to test for potential effect modification. When stratifying by alcohol intake and BMI, we excluded all participants with an alcohol intake of zero (n = 1 298) and a BMI < 18.5 (n = 453) respectively, as these were not our subgroups of interest. We chose stratification cut-off points of 20 g pure alcohol per day, corresponding to 2 standard drinks, and a BMI of 30 kg/m2 as the risk of mortality is highest beyond these levels18,33. A further subgroup ‘dose–response’ analysis was performed by pack-years of smoking, alcohol intake and BMI categories.

To account for the possibility of differing dietary habits in those at a high risk of death, we repeated our primary analysis after excluding participants with prevalent diabetes, CVD, COPD, CKD, and cancer. As we believe crude values of flavonoid intake to be more relevant than energy-adjusted values, we did not include total energy intake as a covariate in any model. However, energy intake was added to model 2 in a sensitivity analysis to assess its impact on the association between flavonoid intake and all-cause mortality. To determine whether the associations between flavonoid intake and all-cause mortality were independent of fruit and vegetable consumption, we stratified the analysis by tertiles of total fruit and vegetable intake. In order to assess the likelihood of confounding we used a falsification endpoint which we considered unlikely to be causally affected by flavonoid intake; any emergency, inpatient, or outpatient visit for a burn or foreign object (Supplementary Table 6). Analyses were undertaken using STATA/IC 14.2 (StataCorp LLC) and R statistics (R Core Team34). Statistical significance was set at p ≤ 0.05 (two-tailed) for all tests.

Role of funding source

The funding source had no role in study design, preparation of this manuscript, or decision to submit the paper for publication.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.