{"paper_id":"0ffd8256-20cc-4a0b-beab-8e37bdceb6d8","body_text":"Socioeconomic Inequalities in the Association Between Diet Quality and Incident Cardiovascular Diseases: A Prospective Study in the Netherlands | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Socioeconomic Inequalities in the Association Between Diet Quality and Incident Cardiovascular Diseases: A Prospective Study in the Netherlands Ming-Jie Duan, Maartje P Poelman, Eva Corpeleijn, Sander Biesbroek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7471887/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Individuals with lower socioeconomic position (SEP) have higher risks of cardiovascular diseases (CVD), with diet quality being a crucial risk factor. However, whether the association between diet quality and CVD differs across SEP (education and income) groups is unclear, which was examined in this study. Methods: This study included participants aged 30-80 years, free of CVD at baseline, from the Dutch Lifelines cohort. The Lifelines Diet Score, a diet quality indicator based on Dutch dietary guidelines, was calculated with data assessed by a 110-item food frequency questionnaire. Cox proportional hazards models were used to assess the association between diet quality and incident CVD (the first non-fatal major cardiovascular event), and whether this association was modified by SEP, adjusted for age, sex, energy intake, alcohol intake, smoking, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress, family history of CVD, and BMI. Results: Of 82,360 participants included, 2827 incident CVD cases were identified (median follow-up 7.4 years, incidence rate 4.7 per 1000 person-years). Education modified the association between diet quality and incident CVD (P-interaction = 0.033). Comparing the poorest to the best diet quality quartiles, hazard ratios (95%CI) were 1.27 (1.06, 1.51) in low, 1.28 (1.06, 1.54) in middle, and 0.98 (0.76, 1.27) in high education group. Conclusions: For low- and middle-education populations, poor diet quality was associated with disproportionately higher risks of incident CVD; improving diet quality may therefore enhance their health. However, to address persistent health inequalities, health policies should tackle broader structural factors of diet and SEP. Epidemiology Cardiac & Cardiovascular Systems Nutrition & Dietetics socioeconomic position diet quality cardiovascular diseases health inequalities prevention Figures Figure 1 Introduction Cardiovascular diseases (CVD) are the leading cause of population-level disability and mortality, substantially contributing to health loss and societal burdens [ 1 , 2 ]. Individuals with lower socioeconomic position (SEP) face higher risks of CVD and cardiovascular mortality [ 3 ]. Poor diet quality, a crucial modifiable risk factor for the development of CVD, is more prevalent in low-SEP populations and is widely considered a major contributor to health inequalities [ 4 ]. However, it remains unclear whether such socioeconomic difference exists in the association between diet quality and the incidence of CVD, and whether this difference may further contribute to health inequalities. Study of the synergistic interaction between diet quality and SEP may improve our understanding of health inequalities and elucidate the health potential of improving diet quality for CVD prevention within different socioeconomic groups [ 5 – 7 ]. Therefore, using a large Dutch population cohort, this study aimed to investigate whether SEP (education and income) modifies the association between diet quality and incident CVD. Methods Cohort design and study population The Lifelines cohort study is a multidisciplinary prospective population-based cohort study that applies a unique three-generation design to study the health and health-related behaviors of 167,729 persons living in the north of The Netherlands. Participants were included in the study between 2006 and 2013. Before study entry, a signed informed consent form was obtained from each participant. After the baseline assessment (T1), six follow-up assessments (T2-T7) were conducted until 2024. The Lifelines study is conducted according to the principles of the Declaration of Helsinki and approved by the Medical Ethics Committee of the University Medical Center Groningen, The Netherlands (UMCG-METC 2007/152). The overall design and rationale of the study have been described in detail elsewhere [ 8 , 9 ]. Participants aged between 30 and 80 years, who were free of CVD at baseline and had valid dietary intake data, were included in this study. Participants who had a history of cancer at baseline, had no follow-up data on CVD status, had no information on education, or had less than 12 months of follow-up, were excluded from the analysis (see flow chart in Supplementary Figure S1 ). Data collection Ascertainment of incident CVD In this study, the primary outcome for incident CVD was defined as the earliest non-fatal major cardiovascular event, including myocardial infarction, heart failure, major cardiothoracic intervention (including coronary artery bypass grafting, percutaneous coronary intervention, and heart transplantation), stroke, and transient ischemic attack. The secondary outcome was defined as a composite of death from any cause and the primary outcome [ 4 , 10 ]. Incident CVD was assessed by self-report questionnaires at each follow-up assessment, except for T5. At T4 and T6, 12-lead resting electrocardiograms were performed by trained research staff following standardized protocols to identify cases of CVD. All electrocardiograms were checked by the Welch Allyn algorithm using CardioPerfect software (version E/F; Welch Allyn, Skaneateles Falls, NY, USA) and reviewed by research nurses. Abnormal electrocardiogram results were further checked by Lifelines physicians and cardiologists, and, when necessary, the participant’s general practitioner was contacted [ 9 ]. Data on prescribed medication, medical records, and causes of deaths were not available during follow-ups. Dietary assessment Dietary intake at baseline was assessed using a semi-quantitative, self-administered food frequency questionnaire (FFQ), which was designed to assess the habitual intake of 110 food items (including alcohol) over the past four weeks [ 11 , 12 ]. For 46 main food items (such as bread and milk), frequency of consumption was assessed as ‘not this month’ or in days per week or month, along with the amount consumed each time (in units or specified portion sizes). The FFQ also included 37 questions on sub-items (such as different types of cheese), for which the frequency of consumption was assessed as never, sometimes, often, or always. The intake of food items and energy intake have been tested and validated against three 24-hour dietary recalls and actual energy intake in controlled feeding trials, respectively [ 11 , 12 ]. Macro- and micro-nutrients intake was calculated based on the FFQ according to the 2011 Dutch Food Composition Table (NEVO) [ 13 ]. Participants’ dietary intake data were considered unreliable, identified using Black’s method based on the original approach developed by Goldberg et al, when the ratio of reported energy intake to basal metabolic rate (calculated using the Schofield equation) was below 0.50 or above 2.75 [ 14 , 15 ]. Alcohol intake was divided into six groups: one for participants with no alcohol intake, and the remaining five as sex-specific quintiles. The Lifelines Diet Score (LLDS) was calculated to assess diet quality [ 16 ]. This score is based on the 2015 Dutch dietary guidelines, which summarize contemporary scientific evidence on diet and its relation to chronic diseases [ 17 ]. The LLDS ranks the relative intake of nine food groups with positive health effects (vegetables, fruit, whole grain products, legumes/nuts, fish, oils/soft margarines, unsweetened dairy, coffee, and tea) and three food groups with negative health effects (red/processed meat, butter/hard margarines, and sugar-sweetened beverages). For each food group, quintiles of consumption (grams per 1000 kcal) were determined and assigned a score from zero to four points, with higher scores awarded for higher consumption of positive food groups and lower consumption of negative food groups. The sum of these components forms the LLDS, which ranges from 0 to 48. For analysis, LLDS scores were further categorized into sex-specific quartiles. More details on the development of the LLDS and the Dutch dietary guidelines are available elsewhere [ 16 , 17 ]. Education and income levels Education and income levels at baseline were assessed by self-report questionnaires ( Supplementary Table S1 ). Highest education level was categorized according to the International Standard Classification of Education (ISCED): (1) low (level 0, 1, or 2); (2) middle (level 3 or 4); and (3) high (level 5 or 6) [ 18 ]. Income level was based on monthly household net income and was categorized as (1) low (< 1000 euro/month); (2) lower-middle (1000–2000 euro/month); (3) upper-middle (2000–3000 euro/month); (3) high (> 3000 euro/month); and (4) do not know/prefer not to answer. Assessment of other covariates at baseline At the baseline visit, fasting blood samples were collected by venipuncture and were subsequently analyzed. Measurements of blood pressure and anthropometry were made by trained research staff following standardized protocols [ 9 ]. These measurements were performed without shoes and heavy clothing. Age, employment status (employed with a paid job including part-time; retired with a pension; unemployed; unfit for work; and other, such as ‘still in education’ or ‘full-time homemaker’), smoking status (never, former, current), sleep duration, participation in social/hobby clubs (yes or no), TV watching time, and family history of cardiovascular diseases of first-degree relatives (yes or no) were assessed using self-administered questionnaires [ 9 , 19 ]. Sleep duration was further categorized as 7–9 hours/day or < 7 or > 9 hours/day. TV watching time was divided into sex-specific quartiles. Physical activity level was assessed by the validated Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH), from which moderate-to-vigorous physical activities (MVPA) were calculated in minutes per week [ 20 ]. MVPA was further divided into five groups: one for participants with no MVPA, and the remaining four as sex-specific quartiles. Chronic stress level was assessed using the self-reported Long-term Difficulties Inventory (LDI), which consists of 12 items evaluating stress levels across various life domains, including housing, work, social relationships, free time, finances, health, school/study and religion over the past year [ 21 ]. Each item is rated on a three-point Likert scale, ranging from 0 (not stressful) to 2 (very stressful), with a total maximum score of 24. Chronic stress scores were further grouped into 0, 1, 2, 3, 4–5, 6–7, and 8–20, considering the highly skewed distribution of the data [ 21 ]. Medication use at baseline was self-reported, with participants asked to bring their medication packages to the study site for verification [ 9 ]. ATC codes were subsequently documented. Hypertension status at baseline was determined by (1) hypertensive medication use (ATC codes C02, C03, C07, C08, C09) [ 22 ]; (2) systolic blood pressure ≥ 140 mmHg; and/or (3) diastolic blood pressure ≥ 90 mmHg [ 23 ]. Participants were further categorized as having treated hypertension, untreated hypertension, and no hypertension. Abnormal blood lipids were defined based on any of the following: (1) use of lipid-modifying agents (ATC code C10) [ 22 ]; (2) self-reported diagnosis via questionnaires; (3) HDL-cholesterol < 1.03 mmol/L for men or < 1.30 mmol/L for women; (4) LDL-cholesterol ≥ 4.1 mmol/L; and/or (5) triglycerides ≥ 1.70 mmol/L [ 24 , 25 ]. Participants were further categorized as having treated abnormal blood lipids, untreated abnormal blood lipids, or no abnormal blood lipids. Diabetes status at baseline was determined by: (1) self-report questionnaires; (2) fasting blood glucose ≥ 7.0 mmol/L; and/or (3) HbA 1c ≥ 6.5% (48 mmol/mol) [ 26 ]; or (4) use of glucose-lowering medication (ATC code A10) [ 22 ]. Participants were further categorized as having treated diabetes, untreated diabetes, or no diabetes. Statistical analysis Associations of diet quality (LLDS), education, and income with incident CVD were estimated using Cox proportional hazards models, with age as the primary timescale. Results are shown as hazard ratios (HRs) with 95% confidence intervals (CIs). Participants contributed person-time until the date of the first CVD event (identified via follow-up assessments), death, the date of the last completed questionnaire, or age 85 (for primary outcome), whichever occurred first. The proportional hazards assumption was assessed using Schoenfeld residuals and by fitting Cox models with time-varying covariates for LLDS. If the assumption was violated, a stratified model was performed [ 27 ]. For separate associations of diet quality, education, and income with incident CVD, models were adjusted in four steps. Model 1 was adjusted for age (timescale), sex, LLDS (as a continuous variable or in sex-specific quartiles), total energy intake, education, and income. Model 2 was further adjusted for alcohol intake, smoking status, TV watching time, MVPA, sleep duration, participation in social/hobby clubs, and chronic stress scores. Model 3 was additionally adjusted for family history of CVD. Model 4 was further adjusted for BMI. In models using LLDS quartiles, quartile 4 (indicating the best diet quality) was used as the reference group. In continuous models, HRs corresponded to the risks associated with lower diet quality – a one standard deviation decrease in the LLDS. Unadjusted Kaplan–Meier survival curves were generated for quartiles of LLDS. To assess whether the associations between LLDS and incident CVD differ by education or income level, multiplicative interaction terms between LLDS and education, and between LLDS and income were separately added to the models. When the interaction term was significant, analyses were stratified by education or income, and joint associations of LLDS with education or income with incident CVD were assessed, adjusting for the covariates described above. Multiple imputation by chained equations was performed (generating 25 imputed datasets) to handle missing data for income level (15.0% non-response/missing), MVPA (7.0%), and chronic stress scores (2.1%) [ 28 ]. All statistical analyses were conducted using Stata (version 18.0; StataCorp). Sensitivity analysis First, the main analysis was repeated with further adjustments for employment status, savory and fast foods (warm savory snacks, chips, fast foods, and pizza), HDL-cholesterol, total cholesterol, triglycerides, hypertension status, abnormal blood lipid status, and diabetes status, and with LLDS, education, income, and chronic stress scores excluded from the adjustments in a stepwise manner. Missing values for HDL-cholesterol, triglycerides, and total cholesterol (1.8% missing for all three biomarkers) were additionally imputed using the same methods described above. Second, potential effect modification by sex, alcohol intake, smoking status, TV watching time, MVPA, sleep duration, participation in social/hobby clubs, chronic stress scores, family history of CVD, and BMI with LLDS was assessed by adding a multiplicative interaction term with LLDS for each factor. Third, analyses were repeated excluding participants with follow-up time less than 24 or 36 months to examine potential reverse causation. Fourth, complete case analyses were performed without imputed values. Finally, the assumption of linearity between LLDS and incident CVD was examined by applying restricted cubic spline functions with four knots using the rms package (version 8.0.0, [ 29 ]) in RStudio (version 2024.12.1 + 563; Posit). Results The present study included 82,360 participants: 47,668 women (57.9%) and 34,692 men (42.1%). Baseline characteristics across sex-specific quartiles of LLDS are shown in Table 1 . With increasing quartiles of LLDS (higher diet quality), participants tended to be older, have higher education and income levels, be more physically active, spend less time watching TV, be never smokers, have lower BMI, and have high prevalence of family history of CVD. Baseline characteristics across education and income levels are shown in Supplementary Table S2 and Supplementary Table S3 , respectively. With higher education or income levels, the proportion of male participants increased, and participants tended to be younger, have higher LLDS, consume more alcohol, have healthier lifestyles, and have lower BMI. Table 1 Baseline characteristics of study participants according to quartiles of Lifelines Diet Score* Lifelines Diet Score quartiles Q1 (Poorest) Q2 Q3 Q4 (Best) Total Number of participants, n 19,130 19,922 20,085 23,223 82,360 Age, years 43.8 (8.7) 45.5 (9.4) 47.3 (10.1) 49.5 (10.3) 46.6 (9.9) Sex, % Women 58.8 57.9 56.2 58.6 57.9 Men 41.2 42.1 43.8 41.4 42.1 Lifelines Diet Score 16.2 (2.6) 21.6 (1.1) 25.4 (1.1) 31.3 (2.9) 24.0 (6.0) Total energy intake, kcal/day 2185 (1813, 2646) 2084 (1741, 2504) 1987 (1648, 2389) 1814 (1507, 2192) 2004 (1656, 2423) Total alcohol intake, grams/day 3.8 (0.7, 11.7) 4.1 (0.9, 11.3) 4.5 (1.0, 10.7) 4.3 (1.2, 9.8) 4.0 (0.9, 10.5) Savory and fast foods intake, grams/day 39.5 (21.3, 61.7) 33.2 (17.2, 53.4) 26.9 (13.2, 46.0) 18.3 (7.7, 35.2) 28.1 (13.6, 49.1) Education level, % Low 32.7 29.4 27.8 25.1 28.6 Middle 43.8 41.4 38.1 34.5 39.2 High 23.4 29.2 34.1 40.4 32.2 Income (euro/month), % <1000 3.6 3.0 2.7 3.0 3.1 1000–2000 19.4 18.0 17.7 17.7 18.2 2000–3000 30.9 31.0 30.3 27.9 29.9 >3000 29.1 32.6 34.9 37.8 33.8 No response or missing 16.9 15.4 14.4 13.5 15.0 Employment status, % Employed, doing a paid job 85.6 84.5 82.5 78.7 82.6 Retired, with a pension 2.8 4.7 6.8 9.5 6.1 Unemployed 4.3 3.8 3.6 3.7 3.9 Unfit for work 1.8 1.5 1.6 2.0 1.7 Other 5.4 5.5 5.5 6.1 5.6 TV watching time, hours/day 2.6 (1.3) 2.5 (1.3) 2.4 (1.3) 2.2 (1.2) 2.4 (1.3) Moderate-to-vigorous physical activity, minutes/week 210 (60, 545) 240 (90, 540) 245 (110, 540) 285 (120, 555) 240 (100, 540) Sleep duration, % 7–9 hours/day 83.4 84.2 84.4 84.4 84.1 <7 or > 9 hours/day 16.3 15.5 15.4 15.4 15.6 Smoking status, % Never 43.8 46.1 45.0 45.9 45.2 Former 29.0 32.7 37.4 41.3 35.4 Current 26.8 20.9 17.1 12.2 18.9 Participation in social or hobby clubs, % 20.4 21.1 20.9 20.5 20.7 BMI, kg/m 2 26.3 (4.4) 26.2 (4.1) 26.1 (4.0) 25.8 (3.9) 26.1 (4.1) Chronic stress scores 2 (1, 4) 2 (1, 4) 2 (1, 3) 2 (0, 3) 2 (1, 3) Family history of cardiovascular diseases, % 15.9 16.1 18.0 19.1 17.4 Hypertension, % Yes, treated 6.3 6.5 7.4 8.4 7.2 Yes, untreated 14.9 15.1 15.3 14.8 15.0 No hypertension 78.9 78.4 77.3 76.8 77.8 Systolic blood pressure, mmHg 124.9 (14.8) 125.2 (15.0) 125.5 (15.0) 125.2 (15.4) 125.2 (15.1) Diastolic blood pressure, mmHg 74.3 (9.4) 74.4 (9.4) 74.3 (9.3) 74.1 (9.3) 74.3 (9.3) Diabetes, % Yes, treated 0.7 1.1 1.3 1.6 1.2 Yes, untreated 1.5 1.7 1.5 1.7 1.6 No diabetes 97.8 97.2 97.2 96.7 97.2 Fasting blood glucose, mmol/L 5.0 (0.7) 5.0 (0.8) 5.0 (0.7) 5.0 (0.8) 5.0 (0.8) HbA 1c , % 5.5 (0.4) 5.6 (0.4) 5.6 (0.4) 5.6 (0.4) 5.6 (0.4) Abnormal blood lipids, % Yes, treated 3.6 4.2 4.9 6.2 4.8 Yes, untreated 40.3 37.6 35.4 31.7 36.0 No abnormal blood lipids 56.2 58.2 59.7 62.2 59.2 Triglycerides, mmol/L 1.2 (0.9) 1.2 (0.8) 1.2 (0.8) 1.1 (0.7) 1.2 (0.8) HDL-cholesterol, mmol/L 1.5 (0.4) 1.5 (0.4) 1.5 (0.4) 1.6 (0.4) 1.5 (0.4) Total cholesterol, mmol/L 5.1 (1.0) 5.2 (1.0) 5.2 (1.0) 5.2 (1.0) 5.2 (1.0) * Baseline characteristics of study participants included in the analysis for the primary outcome (non-fatal major cardiovascular events). Data are expressed as unadjusted mean ± standard deviation for age, Lifelines Diet Score (no unit), TV watching time, systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA 1c , triglycerides, HDL-cholesterol, total cholesterol, and BMI; data are expressed as median (interquartile) for total energy intake, total alcohol intake, savory and fast foods intake, moderate-to-vigorous physical activity, and chronic stress scores; data are expressed as observed percentages for other variables in the table if not specified. The frequency of incident first non-fatal major cardiovascular events (primary outcome) across LLDS quartiles, education levels, and income levels is shown in Table 2 . During a total of 599,791 person-years of follow-up (median follow-up time 7.4 years, interquartile range 3.8–10.8 years), 2827 incident CVD cases were identified (incidence 3.4%, incidence rate 4.7 per 1000 person-years). The frequency for the secondary outcome – a composite of incident first non-fatal major cardiovascular events and all-cause mortality – is shown in Supplementary Table S4 . A total of 2123 deaths (incidence 2.6%) occurred during follow-up. Table 2 Frequency of incident non-fatal major cardiovascular events according to Lifelines Diet Score quartiles, education levels, and income levels Events/population Proportion of cases, %* Incidence, % Incidence rate (per 1000 person-years) Lifelines Diet Score quartiles Q1 (Poorest) 566/19,130 20.0 3.0 4.2 Q2 653/19,922 23.1 3.3 4.5 Q3 711/20,085 25.2 3.5 4.8 Q4 (Best) 897/23,223 31.7 3.9 5.1 Education Low 1118/23,518 39.5 4.8 7.1 Middle 995/32,312 35.2 3.1 4.2 High 714/26,530 25.3 2.7 3.5 Income (euro/month) <1000 88/2537 3.1 3.5 5.2 1000–2000 619/14,983 21.9 4.1 5.9 2000–3000 874/24,637 30.9 3.5 4.8 >3000 841/27,859 29.7 3.0 4.0 No response or missing 405/12,344 14.3 3.3 4.8 Total 2827/82,360 100 3.4 4.7 * Proportion of cases was calculated by dividing number of cases in the group of interests by total number of cases. Associations of LLDS, education, and income with incident first non-fatal major cardiovascular events are shown in Table 3 . Unadjusted Kaplan–Meier survival curves for incident CVD by quartiles of LLDS are shown in Supplementary Figure S2 and Supplementary Figure S3 . Lower LLDS (poorer diet quality), lower education, and lower income were significantly associated with higher risk of incident CVD after adjustment for age (underlying timescale) and total energy intake (Model 1). The associations of LLDS and education with incident CVD remained but were attenuated after additional adjustment for total alcohol intake, smoking status, TV watching time, MVPA, sleep duration, social/hobby clubs participation, and chronic stress scores (Model 2), whereas income was no longer associated with incident CVD. Additional adjustment for family history of CVD (Model 3) and BMI (Model 4) only slightly attenuated the associations. In the fully adjusted model (Model 4), participants in the lowest LLDS quartile (Q1, poorest diet quality) had 21% higher hazards of incident CVD compared with those in the highest LLDS quartile (Q4, best diet quality; HR = 1.21 [95% CI 1.09, 1.36]). A one standard deviation decrease in LLDS was associated with 8% higher hazards of incident CVD (HR 1.08 [95%CI 1.04, 1.12]). Table 3 Associations of Lifelines Diet Score, education, and income with incident non-fatal major cardiovascular events Model 1* Model 2† Model 3‡ Model 4§ Lifelines Diet Score quartiles Q1 (Poorest) 1.35 (1.21, 1.50) 1.23 (1.10, 1.38) 1.24 (1.11, 1.38) 1.21 (1.09, 1.36) Q2 1.22 (1.10, 1.35) 1.17 (1.05, 1.30) 1.17 (1.06, 1.30) 1.15 (1.04, 1.28) Q3 1.09 (0.98, 1.20) 1.06 (0.95, 1.17) 1.06 (0.96, 1.17) 1.04 (0.94, 1.15) Q4 (Best) 1 1 1 1 Continuous|| 1.12 (1.08, 1.17) 1.09 (1.04, 1.13) 1.09 (1.05, 1.13) 1.08 (1.04, 1.12) Education Low 1.38 (1.25, 1.53) 1.34 (1.20, 1.49) 1.35 (1.21, 1.50) 1.30 (1.17, 1.45) Middle 1.27 (1.16, 1.41) 1.25 (1.13, 1.38) 1.25 (1.13, 1.38) 1.22 (1.10, 1.35) High 1 1 1 1 Income (euro/month) <1000 1.26 (1.01, 1.58) 1.05 (0.83, 1.33) 1.06 (0.84, 1.33) 1.06 (0.84, 1.33) 1000–2000 1.14 (1.02, 1.27) 1.05 (0.94, 1.17) 1.05 (0.94, 1.18) 1.05 (0.94, 1.17) 2000–3000 1.02 (0.93, 1.13) 0.99 (0.90, 1.09) 1.00 (0.90, 1.10) 0.99 (0.90, 1.10) >3000 1 1 1 1 Multiplicative interaction ¶ Model 1a Model 2a Model 3a Model 4a Education × Lifelines Diet Score 1.06 (1.01, 1.11) 1.05 (1.00, 1.10) 1.05 (1.00, 1.10) 1.05 (1.00, 1.10) P-value interaction 0.022 0.042 0.050 0.033 Income × Lifelines Diet Score 1.03 (0.98, 1.08) 1.03 (0.98, 1.07) 1.02 (0.98, 1.07) 1.03 (0.98, 1.08) P-value interaction 0.226 0.293 0.309 0.258 * Model 1: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, Lifelines Diet Score, total energy intake, education, and income, n = 82,089. † Model 2: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 1 covariates plus total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, and chronic stress scores, n = 81,349. ‡ Model 3: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 2 covariates plus family history of cardiovascular diseases, n = 81,349. § Model 4: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 3 covariates plus BMI, n = 81,316. || Continuous models indicate HRs (95% CI) for a one standard deviation decrease in the Lifelines Diet Score. ¶ Multiplicative interaction was assessed with a product term between diet quality (measured as decrease in the Lifelines Diet Score) and education, and between diet quality and income, both treated as continuous variables from low to high. Effects were presented as HRs (95% CI) derived from multivariate Cox proportional hazard models. Model 1a adjusted for Model 1 covariates plus interaction term, n = 82,089. Model 2a adjusted for Model 2 covariates plus interaction term, n = 81,349. Model 3a adjusted for Model 3 covariates plus interaction term, n = 81,349. Model 4a adjusted for Model 4 covariates plus interaction term, n = 81,316. Each interaction term was assessed in separate models. A significant interaction between LLDS and education (P-interaction = 0.033) was found but not between LLDS and income (Table 3 ). Analyses were therefore performed stratified by education level but not further analyzed for income. Table 4 presents the associations between LLDS and incident CVD across education levels. The strength of the associations between LLDS and incident CVD was similar among participants with low and middle education, whereas LLDS was not associated with incident CVD among those with high education. In the fully adjusted model, comparing the poorest to the best diet quality quartiles, HRs (95%CI) were 1.27 (1.06, 1.51) in low, 1.28 (1.06, 1.54) in middle, and 0.98 (0.76, 1.27) in high education group. Joint associations using a combined indicator of LLDS and education with incident CVD are shown in Fig. 1 and Supplementary Table S5 . In the fully adjusted model, participants in the lowest LLDS quartile (Q1, poorest diet quality) with low education had the highest risk of incident CVD (HR 1.62 [95%CI 1.36, 1.93]) compared with those in the highest LLDS quartile (Q4, best diet quality) with high education. There was no evidence of violation of the proportional hazards assumption for all analyses performed, except for the analyses for participants with middle education, where the model was stratified by TV watching time due to violation in proportional hazards assumption. Table 4 Associations between Lifelines Diet Score and incident non-fatal major cardiovascular events across education levels Lifelines Diet Score quartiles Q1 (Poorest) Q2 Q3 Q4 (Best) Continuous * Low education Model 1† 1.43 (1.20, 1.70) 1.19 (1.01, 1.41) 1.05 (0.89, 1.24) 1 1.15 (1.08, 1.22) Model 2‡ 1.27 (1.06, 1.51) 1.12 (0.94, 1.32) 1.02 (0.86, 1.20) 1 1.10 (1.03, 1.17) Middle education Model 1† 1.39 (1.16, 1.67) 1.30 (1.09, 1.55) 1.14 (0.96, 1.35) 1 1.14 (1.07, 1.22) Model 2‡§ 1.28 (1.06, 1.54) 1.25 (1.05, 1.49) 1.09 (0.92, 1.30) 1 1.11 (1.03, 1.19) High education Model 1† 1.08 (0.84, 1.39) 1.18 (0.96, 1.44) 1.09 (0.91, 1.31) 1 1.05 (0.97, 1.14) Model 2‡ 0.98 (0.76, 1.27) 1.10 (0.89, 1.35) 1.04 (0.87, 1.26) 1 1.01 (0.93, 1.09) * Continuous models indicate HRs (95% CI) for a one standard deviation decrease in the Lifelines Diet Score. † Model 1: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, Lifelines Diet Score, total energy intake, and income, n = 23,380 (low education), n = 32,224 (middle education), and n = 26,485 (high education). ‡ Model 2: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 1 covariates plus total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress scores, family history of cardiovascular diseases, and BMI, n = 23,123 (low education), n = 31,947 (middle education), and n = 26,241 (high education). § Multivariate Cox proportional hazard models stratified by TV watching time. * HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, total energy intake, income, total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress scores, family history of cardiovascular diseases, and BMI, using high education and the best Lifelines Diet Score quartile (Q4) as reference group (HR = 1), n = 81,316. Results for the secondary outcome, defined as the primary outcome plus deaths from any cause, are shown in Supplementary Table S6 (separate associations), Supplementary Table S7 (associations stratified by education level), and Supplementary Table S8 (joint associations of LLDS and education). Results were comparable to those for the primary outcome, while the interaction term between LLDS and education was not significant. Nevertheless, similar gradients of associations of LLDS with incident CVD across education levels were observed, with stronger associations in low and middle education groups. For sensitivity analyses, further adjustment for employment status, savory and fast foods, HDL-cholesterol, total cholesterol, triglycerides, hypertension, abnormal blood lipids, and diabetes did not change the results; similarly, omitting adjustment for LLDS, education, income, or chronic stress scores also did not change the findings ( Supplementary Table S9 ). Smoking status and total alcohol intake were found to modify the associations between LLDS and incident CVD, with stronger associations observed in current smokers and in those who consumed no alcohol ( Supplementary Table S10 ). Excluding participants with follow-up shorter than 24 months ( Supplementary Table S11 ) or 36 months ( Supplementary Table S12 ) did not materially change the results. Complete case analysis yielded similar results ( Supplementary Table S13 ). Restricted cubic splines indicated no violation of the linearity assumption for the association between LLDS and incident CVD ( Supplementary Figure S4 ). Discussion While previous studies have mainly investigated how the prevalence of poor diet quality in low-SEP populations explains CVD inequalities, our findings extend this evidence by showing that the association between diet quality and incident CVD was modified by education (but not income), after adjustment for age, sex, energy intake, lifestyle factors, chronic stress, family history of CVD, BMI, and income. This disproportionate harm from poor diet quality among those with low and middle education may further contribute to CVD inequalities. Among participants with low and middle education, those with the poorest diet quality (Q1) had 27% and 28% higher hazards of incident CVD, respectively, compared with those with the best diet quality (Q4), while no such association was observed among participants with high education. Improving diet quality therefore has the potential to enhance health for those with low and middle education by lowering CVD incidence. Only two studies so far have investigated whether socioeconomic inequalities exist in the association between diet quality and CVD. In the Italian Moli-sani study, higher adherence to the Mediterranean diet was associated with lower risk of incident coronary heart disease, but this association was only observed in participants with high education or high income [ 30 ]. In another U.S. study (the Southern Community Cohort), higher adherence to the Dietary Approaches to Stop Hypertension (DASH) diet was associated with lower risk of heart failure in high-income participants, whereas no such association was found in those with low or middle income [ 31 ]. These findings are not consistent with ours, which may be due to differences in study populations. Participants in the U.S. cohort included individuals with a history of major cardiovascular events (e.g., coronary artery bypass graft), and approximately 62% had hypertension at baseline [ 31 ]; in the Italian cohort, the prevalence of hypertension was approximately 50% [ 30 ] – both markedly higher than the 22% in our study population. These pre-existing medical conditions – which could already have led to irreversible cardiovascular structural changes – may substantially outweigh the influence of diet on CVD risk [ 32 ]. Consequently, in populations with low SEP and a high burden of comorbidities, primary prevention for CVD through dietary improvements may be more difficult. It should be noted that our findings show that income did not modify the association between diet quality and incident CVD – unlike education, suggesting that these two SEP dimensions may contribute to health inequalities through different mechanisms in the study population [ 4 ]. Nevertheless, all studies consistently show that socioeconomic inequalities are common in the association between diet quality and CVD and should be considered in research and policies addressing health inequalities. Our findings support the vulnerability hypothesis whereby socioeconomically disadvantaged populations are more vulnerable to the harm of risk factor exposure than their more advantaged counterparts [ 6 , 7 ], although the underpinning mechanisms remain unclear. One possible explanation may lie in a combination of disadvantaged structural conditions – closely tied to low education but often unmeasured – such as high stress levels [ 33 ], limited use of health and preventive care [ 34 ], weaker social networks [ 35 ], and adverse neighborhood environments [ 36 ]. These conditions may have synergistic interactions with poor diet quality, leading both directly (e.g., through elevated inflammation) and indirectly (e.g., via mood disorders) to disproportionate cardiovascular pathology [ 37 ]. Our results showed no indication that chronic stress levels and social/hobby clubs participation affected the associations investigated. In contrast, the absence of an association between diet quality and CVD among highly educated participants might be explained by better secondary prevention – earlier detection and more consistent treatment of established CVD risk factors, such as hypertension and abnormal blood lipids [ 34 , 38 ]. Future research is needed to clarify how these factors interact with SEP and diet quality in shaping CVD risk and pathology. From a health policy perspective, improving diet quality, such as adhering to dietary guidelines as reflected by higher LLDS, may improve health for those with low and middle education. This improvement would not only be due to a higher prevalence of poor diet quality among those with lower education, but also to their increased vulnerability to the harmful effects from poor diet quality. In fact, in our study population, LLDS distributions showed no major differences across education and income levels, with somewhat more participants with lower education in the lowest quartile. Also, within each LLDS quartile, there were only minor differences in the consumption of included food groups across education levels ( Supplementary Table S14 ). However, improving diet quality alone is unlikely to address persistent health inequalities. As our results showed, participants with low education – even those with the highest diet quality – still had disproportionately higher risk of incident CVD compared with those with high education, after accounting for differences in lifestyle factors, chronic stress, family history of CVD, BMI, and income. This synergistic detrimental interaction between low education and poor diet quality clearly underscores that public health policies should not focus solely on modifying individual health behaviors. More importantly, they should aim to increase health resources and support for low-SEP populations and address broader structural factors of diet and SEP – factors clearly beyond individual responsibility, such as food environment [ 39 ] and preventive health care use [ 34 ] – to improve health of low-SEP populations and avoid further widening inequalities [ 40 , 41 ]. A major strength of this study is the large sample size, which enabled the analysis of the interaction between diet quality and SEP indicators. Our analysis was also adjusted for a wide range of confounders – including less commonly considered ones, such as chronic stress levels, social/hobby clubs participation, sleep duration, and TV watching time – thereby reducing potential residual confounding, although residual confounding cannot be ruled out. Nevertheless, this study has several limitations. First, while SEP may change during follow-up, this information was unavailable. Education is generally stable throughout adult life. For income, because of the structure of the Dutch labor market (e.g., widespread collective wage bargaining and generous unemployment insurance), an individual’s relative income position in the population is not expected to change substantially over time [ 42 ]. Second, misclassification could occur in the ascertainment of CVD cases. Incident CVD cases were primarily self-reported, and data on medication use, medical records, and causes of death were unavailable during follow-up, although electrocardiograms at two follow-up visits (T4 and T6) provided additional objective clinical information. Underreporting of CVD cases is possible, especially among participants with low SEP [ 43 ]. Third, the Lifelines cohort consists primarily of White individuals residing in the northern Netherlands, which limits the generalizability of the findings to other populations [ 8 ]. Finally, approximately 10% of participants were excluded due to loss of follow-up. However, we do not expect this attrition to substantially influence the results. Those lost to follow-up were generally similar in sociodemographic, lifestyle, and clinical characteristics to those included ( Supplementary Table S15 ). However, among those who lost to follow-up, there were more participants with low education and low income, as well as more current smokers. A simulation study has shown that loss to follow-up (< 50%) tends to result in minor underestimation of socioeconomic inequalities in cohort studies [ 44 ]. Conclusions This study shows that socioeconomic inequalities exist in the association between diet quality and incident CVD. Participants with low and middle education had disproportionately higher risks of incident CVD associated with poor diet quality, after accounting for a wide range of confounders. Improving diet quality therefore has the potential to enhance the health of low- and middle-education populations by reducing their CVD incidence. However, to address persistent health inequalities, dietary improvement alone is insufficient; public health policies and programs should tackle broader structural factors and provide more support and health resources for low-SEP populations. Declarations Conflicts of interests The authors declare that they have no known competing interests. Funding This project has received funding from the Dutch Science Agenda (NWA) program ‘Transition Towards a Sustainable Food System’ funded by the Dutch Research Council (NWO): NWA.1235.18.201. The Lifelines Biobank initiative has been made possible by subsidies from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), University of Groningen, and the Provinces in the north of The Netherlands (Drenthe, Friesland, and Groningen). The funders had no role in any part of this research. CRediT author statement Ming-Jie Duan : Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Project administration. Maartje P Poelman : Writing - Review & Editing, Funding acquisition. Eva Corpeleijn : Resources, Writing - Review & Editing. Sander Biesbroek : Writing - Review & Editing, Funding acquisition. Acknowledgements The authors wish to acknowledge the services of the Lifelines cohort study, the contributing research centers delivering data to Lifelines and all the study participants. Data availability The manuscript is based on the data from the Lifelines cohort study. Lifelines adheres to standards for data availability. The data catalogue of the Lifelines cohort study is publicly accessible at https://www.lifelines.nl . All researchers can obtain data at the Lifelines research office, for which a fee is required. References Chong B, Jayabaskaran J, Jauhari SM et al (2024) Global burden of cardiovascular diseases: projections from 2025 to 2050. 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12:57:01\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":true,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":true,\"humanSubjectConsent\":true,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-7471887/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7471887/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90319691,\"identity\":\"85110dd6-05f6-4d2b-b987-b540da2bdefe\",\"added_by\":\"auto\",\"created_at\":\"2025-09-01 10:40:22\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":43056,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eJoint associations of Lifelines Diet Score quartiles and education with incident non-fatal major cardiovascular events*\\u003c/p\\u003e\\n\\u003cp\\u003e* HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, total energy intake, income, total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress scores, family history of cardiovascular diseases, and BMI, using high education and the best Lifelines Diet Score quartile (Q4) as reference group (HR = 1), n = 81,316.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7471887/v1/e64649c5b0bb1fcf9ec0b524.png\"},{\"id\":90320571,\"identity\":\"f1f7602f-d5f4-4e95-96f3-41bdc625aa34\",\"added_by\":\"auto\",\"created_at\":\"2025-09-01 10:48:24\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1505479,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7471887/v1/54a44b99-7818-4194-99cd-c5493be93ff4.pdf\"},{\"id\":90318290,\"identity\":\"a57c8689-063b-4297-b2b5-97f0e5b22a03\",\"added_by\":\"auto\",\"created_at\":\"2025-09-01 10:32:23\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":813912,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"PowerHeartSupplementary.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7471887/v1/bf4427ea98170adab589f510.docx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eSocioeconomic Inequalities in the Association Between Diet Quality and Incident Cardiovascular Diseases: A Prospective Study in the Netherlands\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCardiovascular diseases (CVD) are the leading cause of population-level disability and mortality, substantially contributing to health loss and societal burdens [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Individuals with lower socioeconomic position (SEP) face higher risks of CVD and cardiovascular mortality [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Poor diet quality, a crucial modifiable risk factor for the development of CVD, is more prevalent in low-SEP populations and is widely considered a major contributor to health inequalities [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. However, it remains unclear whether such socioeconomic difference exists in the association between diet quality and the incidence of CVD, and whether this difference may further contribute to health inequalities. Study of the synergistic interaction between diet quality and SEP may improve our understanding of health inequalities and elucidate the health potential of improving diet quality for CVD prevention within different socioeconomic groups [\\u003cspan additionalcitationids=\\\"CR6\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTherefore, using a large Dutch population cohort, this study aimed to investigate whether SEP (education and income) modifies the association between diet quality and incident CVD.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eCohort design and study population\\u003c/h2\\u003e\\u003cp\\u003eThe Lifelines cohort study is a multidisciplinary prospective population-based cohort study that applies a unique three-generation design to study the health and health-related behaviors of 167,729 persons living in the north of The Netherlands. Participants were included in the study between 2006 and 2013. Before study entry, a signed informed consent form was obtained from each participant. After the baseline assessment (T1), six follow-up assessments (T2-T7) were conducted until 2024. The Lifelines study is conducted according to the principles of the Declaration of Helsinki and approved by the Medical Ethics Committee of the University Medical Center Groningen, The Netherlands (UMCG-METC 2007/152). The overall design and rationale of the study have been described in detail elsewhere [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eParticipants aged between 30 and 80 years, who were free of CVD at baseline and had valid dietary intake data, were included in this study. Participants who had a history of cancer at baseline, had no follow-up data on CVD status, had no information on education, or had less than 12 months of follow-up, were excluded from the analysis (see flow chart in \\u003cb\\u003eSupplementary Figure S1\\u003c/b\\u003e).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eData collection\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eAscertainment of incident CVD\\u003c/h2\\u003e\\u003cp\\u003eIn this study, the primary outcome for incident CVD was defined as the earliest non-fatal major cardiovascular event, including myocardial infarction, heart failure, major cardiothoracic intervention (including coronary artery bypass grafting, percutaneous coronary intervention, and heart transplantation), stroke, and transient ischemic attack. The secondary outcome was defined as a composite of death from any cause and the primary outcome [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Incident CVD was assessed by self-report questionnaires at each follow-up assessment, except for T5. At T4 and T6, 12-lead resting electrocardiograms were performed by trained research staff following standardized protocols to identify cases of CVD. All electrocardiograms were checked by the Welch Allyn algorithm using CardioPerfect software (version E/F; Welch Allyn, Skaneateles Falls, NY, USA) and reviewed by research nurses. Abnormal electrocardiogram results were further checked by Lifelines physicians and cardiologists, and, when necessary, the participant\\u0026rsquo;s general practitioner was contacted [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Data on prescribed medication, medical records, and causes of deaths were not available during follow-ups.\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eDietary assessment\\u003c/h3\\u003e\\n\\u003cp\\u003eDietary intake at baseline was assessed using a semi-quantitative, self-administered food frequency questionnaire (FFQ), which was designed to assess the habitual intake of 110 food items (including alcohol) over the past four weeks [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. For 46 main food items (such as bread and milk), frequency of consumption was assessed as \\u0026lsquo;not this month\\u0026rsquo; or in days per week or month, along with the amount consumed each time (in units or specified portion sizes). The FFQ also included 37 questions on sub-items (such as different types of cheese), for which the frequency of consumption was assessed as never, sometimes, often, or always. The intake of food items and energy intake have been tested and validated against three 24-hour dietary recalls and actual energy intake in controlled feeding trials, respectively [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Macro- and micro-nutrients intake was calculated based on the FFQ according to the 2011 Dutch Food Composition Table (NEVO) [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Participants\\u0026rsquo; dietary intake data were considered unreliable, identified using Black\\u0026rsquo;s method based on the original approach developed by Goldberg et al, when the ratio of reported energy intake to basal metabolic rate (calculated using the Schofield equation) was below 0.50 or above 2.75 [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Alcohol intake was divided into six groups: one for participants with no alcohol intake, and the remaining five as sex-specific quintiles.\\u003c/p\\u003e\\u003cp\\u003eThe Lifelines Diet Score (LLDS) was calculated to assess diet quality [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. This score is based on the 2015 Dutch dietary guidelines, which summarize contemporary scientific evidence on diet and its relation to chronic diseases [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The LLDS ranks the relative intake of nine food groups with positive health effects (vegetables, fruit, whole grain products, legumes/nuts, fish, oils/soft margarines, unsweetened dairy, coffee, and tea) and three food groups with negative health effects (red/processed meat, butter/hard margarines, and sugar-sweetened beverages). For each food group, quintiles of consumption (grams per 1000 kcal) were determined and assigned a score from zero to four points, with higher scores awarded for higher consumption of positive food groups and lower consumption of negative food groups. The sum of these components forms the LLDS, which ranges from 0 to 48. For analysis, LLDS scores were further categorized into sex-specific quartiles. More details on the development of the LLDS and the Dutch dietary guidelines are available elsewhere [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003ch3\\u003eEducation and income levels\\u003c/h3\\u003e\\n\\u003cp\\u003eEducation and income levels at baseline were assessed by self-report questionnaires (\\u003cb\\u003eSupplementary Table S1\\u003c/b\\u003e). Highest education level was categorized according to the International Standard Classification of Education (ISCED): (1) low (level 0, 1, or 2); (2) middle (level 3 or 4); and (3) high (level 5 or 6) [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Income level was based on monthly household net income and was categorized as (1) low (\\u0026lt;\\u0026thinsp;1000 euro/month); (2) lower-middle (1000\\u0026ndash;2000 euro/month); (3) upper-middle (2000\\u0026ndash;3000 euro/month); (3) high (\\u0026gt;\\u0026thinsp;3000 euro/month); and (4) do not know/prefer not to answer.\\u003c/p\\u003e\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eAssessment of other covariates at baseline\\u003c/h2\\u003e\\u003cp\\u003eAt the baseline visit, fasting blood samples were collected by venipuncture and were subsequently analyzed. Measurements of blood pressure and anthropometry were made by trained research staff following standardized protocols [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. These measurements were performed without shoes and heavy clothing.\\u003c/p\\u003e\\u003cp\\u003eAge, employment status (employed with a paid job including part-time; retired with a pension; unemployed; unfit for work; and other, such as \\u0026lsquo;still in education\\u0026rsquo; or \\u0026lsquo;full-time homemaker\\u0026rsquo;), smoking status (never, former, current), sleep duration, participation in social/hobby clubs (yes or no), TV watching time, and family history of cardiovascular diseases of first-degree relatives (yes or no) were assessed using self-administered questionnaires [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Sleep duration was further categorized as 7\\u0026ndash;9 hours/day or \\u0026lt;\\u0026thinsp;7 or \\u0026gt;\\u0026thinsp;9 hours/day. TV watching time was divided into sex-specific quartiles. Physical activity level was assessed by the validated Short QUestionnaire to ASsess Health-enhancing physical activity (SQUASH), from which moderate-to-vigorous physical activities (MVPA) were calculated in minutes per week [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. MVPA was further divided into five groups: one for participants with no MVPA, and the remaining four as sex-specific quartiles. Chronic stress level was assessed using the self-reported Long-term Difficulties Inventory (LDI), which consists of 12 items evaluating stress levels across various life domains, including housing, work, social relationships, free time, finances, health, school/study and religion over the past year [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Each item is rated on a three-point Likert scale, ranging from 0 (not stressful) to 2 (very stressful), with a total maximum score of 24. Chronic stress scores were further grouped into 0, 1, 2, 3, 4\\u0026ndash;5, 6\\u0026ndash;7, and 8\\u0026ndash;20, considering the highly skewed distribution of the data [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eMedication use at baseline was self-reported, with participants asked to bring their medication packages to the study site for verification [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. ATC codes were subsequently documented. Hypertension status at baseline was determined by (1) hypertensive medication use (ATC codes C02, C03, C07, C08, C09) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]; (2) systolic blood pressure\\u0026thinsp;\\u0026ge;\\u0026thinsp;140 mmHg; and/or (3) diastolic blood pressure\\u0026thinsp;\\u0026ge;\\u0026thinsp;90 mmHg [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Participants were further categorized as having treated hypertension, untreated hypertension, and no hypertension. Abnormal blood lipids were defined based on any of the following: (1) use of lipid-modifying agents (ATC code C10) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]; (2) self-reported diagnosis via questionnaires; (3) HDL-cholesterol\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.03 mmol/L for men or \\u0026lt;\\u0026thinsp;1.30 mmol/L for women; (4) LDL-cholesterol\\u0026thinsp;\\u0026ge;\\u0026thinsp;4.1 mmol/L; and/or (5) triglycerides\\u0026thinsp;\\u0026ge;\\u0026thinsp;1.70 mmol/L [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Participants were further categorized as having treated abnormal blood lipids, untreated abnormal blood lipids, or no abnormal blood lipids. Diabetes status at baseline was determined by: (1) self-report questionnaires; (2) fasting blood glucose\\u0026thinsp;\\u0026ge;\\u0026thinsp;7.0 mmol/L; and/or (3) HbA\\u003csub\\u003e1c\\u003c/sub\\u003e\\u0026thinsp;\\u0026ge;\\u0026thinsp;6.5% (48 mmol/mol) [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]; or (4) use of glucose-lowering medication (ATC code A10) [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Participants were further categorized as having treated diabetes, untreated diabetes, or no diabetes.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e\\u003cp\\u003eAssociations of diet quality (LLDS), education, and income with incident CVD were estimated using Cox proportional hazards models, with age as the primary timescale. Results are shown as hazard ratios (HRs) with 95% confidence intervals (CIs). Participants contributed person-time until the date of the first CVD event (identified via follow-up assessments), death, the date of the last completed questionnaire, or age 85 (for primary outcome), whichever occurred first. The proportional hazards assumption was assessed using Schoenfeld residuals and by fitting Cox models with time-varying covariates for LLDS. If the assumption was violated, a stratified model was performed [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. For separate associations of diet quality, education, and income with incident CVD, models were adjusted in four steps. Model 1 was adjusted for age (timescale), sex, LLDS (as a continuous variable or in sex-specific quartiles), total energy intake, education, and income. Model 2 was further adjusted for alcohol intake, smoking status, TV watching time, MVPA, sleep duration, participation in social/hobby clubs, and chronic stress scores. Model 3 was additionally adjusted for family history of CVD. Model 4 was further adjusted for BMI. In models using LLDS quartiles, quartile 4 (indicating the best diet quality) was used as the reference group. In continuous models, HRs corresponded to the risks associated with lower diet quality \\u0026ndash; a one standard deviation decrease in the LLDS. Unadjusted Kaplan\\u0026ndash;Meier survival curves were generated for quartiles of LLDS.\\u003c/p\\u003e\\u003cp\\u003eTo assess whether the associations between LLDS and incident CVD differ by education or income level, multiplicative interaction terms between LLDS and education, and between LLDS and income were separately added to the models. When the interaction term was significant, analyses were stratified by education or income, and joint associations of LLDS with education or income with incident CVD were assessed, adjusting for the covariates described above.\\u003c/p\\u003e\\u003cp\\u003eMultiple imputation by chained equations was performed (generating 25 imputed datasets) to handle missing data for income level (15.0% non-response/missing), MVPA (7.0%), and chronic stress scores (2.1%) [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. All statistical analyses were conducted using Stata (version 18.0; StataCorp).\\u003c/p\\u003e\\u003c/div\\u003e\\n\\u003ch3\\u003eSensitivity analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eFirst, the main analysis was repeated with further adjustments for employment status, savory and fast foods (warm savory snacks, chips, fast foods, and pizza), HDL-cholesterol, total cholesterol, triglycerides, hypertension status, abnormal blood lipid status, and diabetes status, and with LLDS, education, income, and chronic stress scores excluded from the adjustments in a stepwise manner. Missing values for HDL-cholesterol, triglycerides, and total cholesterol (1.8% missing for all three biomarkers) were additionally imputed using the same methods described above. Second, potential effect modification by sex, alcohol intake, smoking status, TV watching time, MVPA, sleep duration, participation in social/hobby clubs, chronic stress scores, family history of CVD, and BMI with LLDS was assessed by adding a multiplicative interaction term with LLDS for each factor. Third, analyses were repeated excluding participants with follow-up time less than 24 or 36 months to examine potential reverse causation. Fourth, complete case analyses were performed without imputed values. Finally, the assumption of linearity between LLDS and incident CVD was examined by applying restricted cubic spline functions with four knots using the rms package (version 8.0.0, [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]) in RStudio (version 2024.12.1\\u0026thinsp;+\\u0026thinsp;563; Posit).\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe present study included 82,360 participants: 47,668 women (57.9%) and 34,692 men (42.1%). Baseline characteristics across sex-specific quartiles of LLDS are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. With increasing quartiles of LLDS (higher diet quality), participants tended to be older, have higher education and income levels, be more physically active, spend less time watching TV, be never smokers, have lower BMI, and have high prevalence of family history of CVD. Baseline characteristics across education and income levels are shown in \\u003cb\\u003eSupplementary Table S2\\u003c/b\\u003e and \\u003cb\\u003eSupplementary Table S3\\u003c/b\\u003e, respectively. With higher education or income levels, the proportion of male participants increased, and participants tended to be younger, have higher LLDS, consume more alcohol, have healthier lifestyles, and have lower BMI.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBaseline characteristics of study participants according to quartiles of Lifelines Diet Score*\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eLifelines Diet Score quartiles\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eQ1 (Poorest)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eQ2\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eQ3\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eQ4 (Best)\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eTotal\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNumber of participants, n\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19,130\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e19,922\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e20,085\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e23,223\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e82,360\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAge, years\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43.8 (8.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e45.5 (9.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e47.3 (10.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e49.5 (10.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e46.6 (9.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSex, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eWomen\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e58.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e57.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e56.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e58.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e57.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMen\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e41.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e42.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e43.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e41.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e42.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLifelines Diet Score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16.2 (2.6)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e21.6 (1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e25.4 (1.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31.3 (2.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e24.0 (6.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal energy intake, kcal/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2185 (1813, 2646)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2084 (1741, 2504)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1987 (1648, 2389)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1814 (1507, 2192)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2004 (1656, 2423)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal alcohol intake, grams/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.8 (0.7, 11.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.1 (0.9, 11.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.5 (1.0, 10.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.3 (1.2, 9.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4.0 (0.9, 10.5)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSavory and fast foods intake, grams/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e39.5 (21.3, 61.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e33.2 (17.2, 53.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e26.9 (13.2, 46.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e18.3 (7.7, 35.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28.1 (13.6, 49.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation level, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e32.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e27.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e25.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e28.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e41.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e38.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e34.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e39.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e23.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e40.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e32.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIncome (euro/month), %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1000\\u0026ndash;2000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e19.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e18.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e17.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e18.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2000\\u0026ndash;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e30.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e30.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e27.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e29.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e29.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e34.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e37.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e33.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo response or missing\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e14.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e13.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e15.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEmployment status, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEmployed, doing a paid job\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e85.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e84.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e82.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e78.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e82.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eRetired, with a pension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e6.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e9.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e6.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnemployed\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e4.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e3.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eUnfit for work\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eOther\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTV watching time, hours/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2.6 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2.5 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.4 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2.2 (1.2)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2.4 (1.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModerate-to-vigorous physical activity, minutes/week\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e210 (60, 545)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e240 (90, 540)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e245 (110, 540)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e285 (120, 555)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e240 (100, 540)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSleep duration, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e7\\u0026ndash;9 hours/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e83.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e84.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e84.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e84.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e84.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;7 or \\u0026gt;\\u0026thinsp;9 hours/day\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e16.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e15.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e15.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSmoking status, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNever\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e43.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e46.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e45.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e45.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e45.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFormer\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e29.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e32.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e37.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e41.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e35.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eCurrent\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e26.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e17.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e12.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e18.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eParticipation in social or hobby clubs, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e20.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e21.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e20.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e20.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e20.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eBMI, kg/m\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e26.3 (4.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e26.2 (4.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e26.1 (4.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e25.8 (3.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e26.1 (4.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eChronic stress scores\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2 (1, 4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e2 (1, 4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2 (1, 3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e2 (0, 3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e2 (1, 3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFamily history of cardiovascular diseases, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e15.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e16.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e18.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e19.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e17.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHypertension, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, treated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e6.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e6.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e7.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e8.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e7.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, untreated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e14.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e15.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e15.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e14.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e15.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo hypertension\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e78.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e78.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e77.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e76.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e77.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSystolic blood pressure, mmHg\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e124.9 (14.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e125.2 (15.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e125.5 (15.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e125.2 (15.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e125.2 (15.1)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiastolic blood pressure, mmHg\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e74.3 (9.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e74.4 (9.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e74.3 (9.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e74.1 (9.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e74.3 (9.3)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eDiabetes, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, treated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, untreated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo diabetes\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e97.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e97.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e97.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e96.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e97.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eFasting blood glucose, mmol/L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.0 (0.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.0 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.0 (0.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.0 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5.0 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHbA\\u003csub\\u003e1c\\u003c/sub\\u003e, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.5 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.6 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.6 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.6 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5.6 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eAbnormal blood lipids, %\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, treated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e3.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e4.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e6.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e4.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eYes, untreated\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e40.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e37.6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e35.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e31.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e36.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo abnormal blood lipids\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e56.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e58.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e59.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e62.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e59.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTriglycerides, mmol/L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.2 (0.9)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.2 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.2 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.1 (0.7)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.2 (0.8)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHDL-cholesterol, mmol/L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.5 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.5 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.5 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.6 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.5 (0.4)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eTotal cholesterol, mmol/L\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5.1 (1.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e5.2 (1.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e5.2 (1.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.2 (1.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e5.2 (1.0)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e* Baseline characteristics of study participants included in the analysis for the primary outcome (non-fatal major cardiovascular events). Data are expressed as unadjusted mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation for age, Lifelines Diet Score (no unit), TV watching time, systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA\\u003csub\\u003e1c\\u003c/sub\\u003e, triglycerides, HDL-cholesterol, total cholesterol, and BMI; data are expressed as median (interquartile) for total energy intake, total alcohol intake, savory and fast foods intake, moderate-to-vigorous physical activity, and chronic stress scores; data are expressed as observed percentages for other variables in the table if not specified.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eThe frequency of incident first non-fatal major cardiovascular events (primary outcome) across LLDS quartiles, education levels, and income levels is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. During a total of 599,791 person-years of follow-up (median follow-up time 7.4 years, interquartile range 3.8\\u0026ndash;10.8 years), 2827 incident CVD cases were identified (incidence 3.4%, incidence rate 4.7 per 1000 person-years). The frequency for the secondary outcome \\u0026ndash; a composite of incident first non-fatal major cardiovascular events and all-cause mortality \\u0026ndash; is shown in \\u003cb\\u003eSupplementary Table S4\\u003c/b\\u003e. A total of 2123 deaths (incidence 2.6%) occurred during follow-up.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eFrequency of incident non-fatal major cardiovascular events according to Lifelines Diet Score quartiles, education levels, and income levels\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEvents/population\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eProportion of cases, %*\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eIncidence, %\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eIncidence rate (per 1000 person-years)\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLifelines Diet Score quartiles\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ1 (Poorest)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e566/19,130\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e20.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e653/19,922\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e23.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e711/20,085\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ4 (Best)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e897/23,223\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e31.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1118/23,518\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e39.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e7.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e995/32,312\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e35.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e714/26,530\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e25.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e2.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIncome (euro/month)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e88/2537\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e3.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.2\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1000\\u0026ndash;2000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e619/14,983\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e21.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e4.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e5.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2000\\u0026ndash;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e874/24,637\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e30.9\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e841/27,859\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e29.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.0\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eNo response or missing\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e405/12,344\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e14.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.8\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2827/82,360\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e3.4\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4.7\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e* Proportion of cases was calculated by dividing number of cases in the group of interests by total number of cases.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eAssociations of LLDS, education, and income with incident first non-fatal major cardiovascular events are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Unadjusted Kaplan\\u0026ndash;Meier survival curves for incident CVD by quartiles of LLDS are shown in \\u003cb\\u003eSupplementary Figure S2\\u003c/b\\u003e and \\u003cb\\u003eSupplementary Figure S3\\u003c/b\\u003e. Lower LLDS (poorer diet quality), lower education, and lower income were significantly associated with higher risk of incident CVD after adjustment for age (underlying timescale) and total energy intake (Model 1). The associations of LLDS and education with incident CVD remained but were attenuated after additional adjustment for total alcohol intake, smoking status, TV watching time, MVPA, sleep duration, social/hobby clubs participation, and chronic stress scores (Model 2), whereas income was no longer associated with incident CVD. Additional adjustment for family history of CVD (Model 3) and BMI (Model 4) only slightly attenuated the associations. In the fully adjusted model (Model 4), participants in the lowest LLDS quartile (Q1, poorest diet quality) had 21% higher hazards of incident CVD compared with those in the highest LLDS quartile (Q4, best diet quality; HR\\u0026thinsp;=\\u0026thinsp;1.21 [95% CI 1.09, 1.36]). A one standard deviation decrease in LLDS was associated with 8% higher hazards of incident CVD (HR 1.08 [95%CI 1.04, 1.12]).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAssociations of Lifelines Diet Score, education, and income with incident non-fatal major cardiovascular events\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eModel 1*\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eModel 2\\u0026dagger;\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eModel 3\\u0026Dagger;\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eModel 4\\u0026sect;\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eLifelines Diet Score quartiles\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ1 (Poorest)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.35 (1.21, 1.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.23 (1.10, 1.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.24 (1.11, 1.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.21 (1.09, 1.36)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.22 (1.10, 1.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.17 (1.05, 1.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.17 (1.06, 1.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.15 (1.04, 1.28)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ3\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.09 (0.98, 1.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.06 (0.95, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.06 (0.96, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.04 (0.94, 1.15)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQ4 (Best)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eContinuous||\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.12 (1.08, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.09 (1.04, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.09 (1.05, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.08 (1.04, 1.12)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eEducation\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eLow\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.38 (1.25, 1.53)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.34 (1.20, 1.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.35 (1.21, 1.50)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.30 (1.17, 1.45)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eMiddle\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.27 (1.16, 1.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.25 (1.13, 1.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.25 (1.13, 1.38)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.22 (1.10, 1.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eHigh\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eIncome (euro/month)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026lt;1000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.26 (1.01, 1.58)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.05 (0.83, 1.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.06 (0.84, 1.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.06 (0.84, 1.33)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e1000\\u0026ndash;2000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.14 (1.02, 1.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.05 (0.94, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.05 (0.94, 1.18)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.05 (0.94, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e2000\\u0026ndash;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.02 (0.93, 1.13)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.99 (0.90, 1.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.00 (0.90, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.99 (0.90, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u0026gt;3000\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMultiplicative interaction\\u003c/b\\u003e\\u0026para;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eModel 1a\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eModel 2a\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eModel 3a\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eModel 4a\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eEducation \\u0026times; Lifelines Diet Score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.06 (1.01, 1.11)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.05 (1.00, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.05 (1.00, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.05 (1.00, 1.10)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eP-value interaction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.022\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.042\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.050\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.033\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eIncome \\u0026times; Lifelines Diet Score\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.03 (0.98, 1.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.03 (0.98, 1.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.02 (0.98, 1.07)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1.03 (0.98, 1.08)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eP-value interaction\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.226\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e0.293\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e0.309\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e0.258\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e* Model 1: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, Lifelines Diet Score, total energy intake, education, and income, n\\u0026thinsp;=\\u0026thinsp;82,089.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u0026dagger; Model 2: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 1 covariates plus total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, and chronic stress scores, n\\u0026thinsp;=\\u0026thinsp;81,349.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u0026Dagger; Model 3: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 2 covariates plus family history of cardiovascular diseases, n\\u0026thinsp;=\\u0026thinsp;81,349.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u0026sect; Model 4: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 3 covariates plus BMI, n\\u0026thinsp;=\\u0026thinsp;81,316.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e|| Continuous models indicate HRs (95% CI) for a one standard deviation decrease in the Lifelines Diet Score.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u0026para; Multiplicative interaction was assessed with a product term between diet quality (measured as decrease in the Lifelines Diet Score) and education, and between diet quality and income, both treated as continuous variables from low to high. Effects were presented as HRs (95% CI) derived from multivariate Cox proportional hazard models. Model 1a adjusted for Model 1 covariates plus interaction term, n\\u0026thinsp;=\\u0026thinsp;82,089. Model 2a adjusted for Model 2 covariates plus interaction term, n\\u0026thinsp;=\\u0026thinsp;81,349. Model 3a adjusted for Model 3 covariates plus interaction term, n\\u0026thinsp;=\\u0026thinsp;81,349. Model 4a adjusted for Model 4 covariates plus interaction term, n\\u0026thinsp;=\\u0026thinsp;81,316. Each interaction term was assessed in separate models.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eA significant interaction between LLDS and education (P-interaction\\u0026thinsp;=\\u0026thinsp;0.033) was found but not between LLDS and income (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Analyses were therefore performed stratified by education level but not further analyzed for income. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e presents the associations between LLDS and incident CVD across education levels. The strength of the associations between LLDS and incident CVD was similar among participants with low and middle education, whereas LLDS was not associated with incident CVD among those with high education. In the fully adjusted model, comparing the poorest to the best diet quality quartiles, HRs (95%CI) were 1.27 (1.06, 1.51) in low, 1.28 (1.06, 1.54) in middle, and 0.98 (0.76, 1.27) in high education group. Joint associations using a combined indicator of LLDS and education with incident CVD are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e and \\u003cb\\u003eSupplementary Table S5\\u003c/b\\u003e. In the fully adjusted model, participants in the lowest LLDS quartile (Q1, poorest diet quality) with low education had the highest risk of incident CVD (HR 1.62 [95%CI 1.36, 1.93]) compared with those in the highest LLDS quartile (Q4, best diet quality) with high education. There was no evidence of violation of the proportional hazards assumption for all analyses performed, except for the analyses for participants with middle education, where the model was stratified by TV watching time due to violation in proportional hazards assumption.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eAssociations between Lifelines Diet Score and incident non-fatal major cardiovascular events across education levels\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e\\u003cp\\u003eLifelines Diet Score quartiles\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eQ1 (Poorest)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eQ2\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eQ3\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eQ4 (Best)\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eContinuous\\u003c/b\\u003e*\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eLow education\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 1\\u0026dagger;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.43 (1.20, 1.70)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.19 (1.01, 1.41)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.05 (0.89, 1.24)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.15 (1.08, 1.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 2\\u0026Dagger;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.27 (1.06, 1.51)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.12 (0.94, 1.32)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.02 (0.86, 1.20)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.10 (1.03, 1.17)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eMiddle education\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 1\\u0026dagger;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.39 (1.16, 1.67)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.30 (1.09, 1.55)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.14 (0.96, 1.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.14 (1.07, 1.22)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 2\\u0026Dagger;\\u0026sect;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.28 (1.06, 1.54)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.25 (1.05, 1.49)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.09 (0.92, 1.30)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.11 (1.03, 1.19)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e\\u003cb\\u003eHigh education\\u003c/b\\u003e\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 1\\u0026dagger;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e1.08 (0.84, 1.39)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.18 (0.96, 1.44)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.09 (0.91, 1.31)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.05 (0.97, 1.14)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eModel 2\\u0026Dagger;\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e0.98 (0.76, 1.27)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e1.10 (0.89, 1.35)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e1.04 (0.87, 1.26)\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e1\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e1.01 (0.93, 1.09)\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e* Continuous models indicate HRs (95% CI) for a one standard deviation decrease in the Lifelines Diet Score.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u0026dagger; Model 1: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, Lifelines Diet Score, total energy intake, and income, n\\u0026thinsp;=\\u0026thinsp;23,380 (low education), n\\u0026thinsp;=\\u0026thinsp;32,224 (middle education), and n\\u0026thinsp;=\\u0026thinsp;26,485 (high education).\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u0026Dagger; Model 2: HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for Model 1 covariates plus total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress scores, family history of cardiovascular diseases, and BMI, n\\u0026thinsp;=\\u0026thinsp;23,123 (low education), n\\u0026thinsp;=\\u0026thinsp;31,947 (middle education), and n\\u0026thinsp;=\\u0026thinsp;26,241 (high education).\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u0026sect; Multivariate Cox proportional hazard models stratified by TV watching time.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e* HRs (95% CI) derived from multivariate Cox proportional hazard models adjusted for age (timescale), sex, total energy intake, income, total alcohol intake, smoking status, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress scores, family history of cardiovascular diseases, and BMI, using high education and the best Lifelines Diet Score quartile (Q4) as reference group (HR\\u0026thinsp;=\\u0026thinsp;1), n\\u0026thinsp;=\\u0026thinsp;81,316.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eResults for the secondary outcome, defined as the primary outcome plus deaths from any cause, are shown in \\u003cb\\u003eSupplementary Table S6\\u003c/b\\u003e (separate associations), \\u003cb\\u003eSupplementary Table S7\\u003c/b\\u003e (associations stratified by education level), and \\u003cb\\u003eSupplementary Table S8\\u003c/b\\u003e (joint associations of LLDS and education). Results were comparable to those for the primary outcome, while the interaction term between LLDS and education was not significant. Nevertheless, similar gradients of associations of LLDS with incident CVD across education levels were observed, with stronger associations in low and middle education groups.\\u003c/p\\u003e\\u003cp\\u003eFor sensitivity analyses, further adjustment for employment status, savory and fast foods, HDL-cholesterol, total cholesterol, triglycerides, hypertension, abnormal blood lipids, and diabetes did not change the results; similarly, omitting adjustment for LLDS, education, income, or chronic stress scores also did not change the findings (\\u003cb\\u003eSupplementary Table S9\\u003c/b\\u003e). Smoking status and total alcohol intake were found to modify the associations between LLDS and incident CVD, with stronger associations observed in current smokers and in those who consumed no alcohol (\\u003cb\\u003eSupplementary Table S10\\u003c/b\\u003e). Excluding participants with follow-up shorter than 24 months (\\u003cb\\u003eSupplementary Table S11\\u003c/b\\u003e) or 36 months (\\u003cb\\u003eSupplementary Table S12\\u003c/b\\u003e) did not materially change the results. Complete case analysis yielded similar results (\\u003cb\\u003eSupplementary Table S13\\u003c/b\\u003e). Restricted cubic splines indicated no violation of the linearity assumption for the association between LLDS and incident CVD (\\u003cb\\u003eSupplementary Figure S4\\u003c/b\\u003e).\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eWhile previous studies have mainly investigated how the prevalence of poor diet quality in low-SEP populations explains CVD inequalities, our findings extend this evidence by showing that the association between diet quality and incident CVD was modified by education (but not income), after adjustment for age, sex, energy intake, lifestyle factors, chronic stress, family history of CVD, BMI, and income. This disproportionate harm from poor diet quality among those with low and middle education may further contribute to CVD inequalities. Among participants with low and middle education, those with the poorest diet quality (Q1) had 27% and 28% higher hazards of incident CVD, respectively, compared with those with the best diet quality (Q4), while no such association was observed among participants with high education. Improving diet quality therefore has the potential to enhance health for those with low and middle education by lowering CVD incidence.\\u003c/p\\u003e\\u003cp\\u003eOnly two studies so far have investigated whether socioeconomic inequalities exist in the association between diet quality and CVD. In the Italian Moli-sani study, higher adherence to the Mediterranean diet was associated with lower risk of incident coronary heart disease, but this association was only observed in participants with high education or high income [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. In another U.S. study (the Southern Community Cohort), higher adherence to the Dietary Approaches to Stop Hypertension (DASH) diet was associated with lower risk of heart failure in high-income participants, whereas no such association was found in those with low or middle income [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. These findings are not consistent with ours, which may be due to differences in study populations. Participants in the U.S. cohort included individuals with a history of major cardiovascular events (e.g., coronary artery bypass graft), and approximately 62% had hypertension at baseline [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]; in the Italian cohort, the prevalence of hypertension was approximately 50% [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e] \\u0026ndash; both markedly higher than the 22% in our study population. These pre-existing medical conditions \\u0026ndash; which could already have led to irreversible cardiovascular structural changes \\u0026ndash; may substantially outweigh the influence of diet on CVD risk [\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Consequently, in populations with low SEP and a high burden of comorbidities, primary prevention for CVD through dietary improvements may be more difficult. It should be noted that our findings show that income did not modify the association between diet quality and incident CVD \\u0026ndash; unlike education, suggesting that these two SEP dimensions may contribute to health inequalities through different mechanisms in the study population [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Nevertheless, all studies consistently show that socioeconomic inequalities are common in the association between diet quality and CVD and should be considered in research and policies addressing health inequalities.\\u003c/p\\u003e\\u003cp\\u003eOur findings support the vulnerability hypothesis whereby socioeconomically disadvantaged populations are more vulnerable to the harm of risk factor exposure than their more advantaged counterparts [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], although the underpinning mechanisms remain unclear. One possible explanation may lie in a combination of disadvantaged structural conditions \\u0026ndash; closely tied to low education but often unmeasured \\u0026ndash; such as high stress levels [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e], limited use of health and preventive care [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e], weaker social networks [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e], and adverse neighborhood environments [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]. These conditions may have synergistic interactions with poor diet quality, leading both directly (e.g., through elevated inflammation) and indirectly (e.g., via mood disorders) to disproportionate cardiovascular pathology [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. Our results showed no indication that chronic stress levels and social/hobby clubs participation affected the associations investigated. In contrast, the absence of an association between diet quality and CVD among highly educated participants might be explained by better secondary prevention \\u0026ndash; earlier detection and more consistent treatment of established CVD risk factors, such as hypertension and abnormal blood lipids [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Future research is needed to clarify how these factors interact with SEP and diet quality in shaping CVD risk and pathology.\\u003c/p\\u003e\\u003cp\\u003e From a health policy perspective, improving diet quality, such as adhering to dietary guidelines as reflected by higher LLDS, may improve health for those with low and middle education. This improvement would not only be due to a higher prevalence of poor diet quality among those with lower education, but also to their increased vulnerability to the harmful effects from poor diet quality. In fact, in our study population, LLDS distributions showed no major differences across education and income levels, with somewhat more participants with lower education in the lowest quartile. Also, within each LLDS quartile, there were only minor differences in the consumption of included food groups across education levels (\\u003cb\\u003eSupplementary Table S14\\u003c/b\\u003e). However, improving diet quality alone is unlikely to address persistent health inequalities. As our results showed, participants with low education \\u0026ndash; even those with the highest diet quality \\u0026ndash; still had disproportionately higher risk of incident CVD compared with those with high education, after accounting for differences in lifestyle factors, chronic stress, family history of CVD, BMI, and income. This synergistic detrimental interaction between low education and poor diet quality clearly underscores that public health policies should not focus solely on modifying individual health behaviors. More importantly, they should aim to increase health resources and support for low-SEP populations and address broader structural factors of diet and SEP \\u0026ndash; factors clearly beyond individual responsibility, such as food environment [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e] and preventive health care use [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e] \\u0026ndash; to improve health of low-SEP populations and avoid further widening inequalities [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eA major strength of this study is the large sample size, which enabled the analysis of the interaction between diet quality and SEP indicators. Our analysis was also adjusted for a wide range of confounders \\u0026ndash; including less commonly considered ones, such as chronic stress levels, social/hobby clubs participation, sleep duration, and TV watching time \\u0026ndash; thereby reducing potential residual confounding, although residual confounding cannot be ruled out. Nevertheless, this study has several limitations. First, while SEP may change during follow-up, this information was unavailable. Education is generally stable throughout adult life. For income, because of the structure of the Dutch labor market (e.g., widespread collective wage bargaining and generous unemployment insurance), an individual\\u0026rsquo;s relative income position in the population is not expected to change substantially over time [\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Second, misclassification could occur in the ascertainment of CVD cases. Incident CVD cases were primarily self-reported, and data on medication use, medical records, and causes of death were unavailable during follow-up, although electrocardiograms at two follow-up visits (T4 and T6) provided additional objective clinical information. Underreporting of CVD cases is possible, especially among participants with low SEP [\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Third, the Lifelines cohort consists primarily of White individuals residing in the northern Netherlands, which limits the generalizability of the findings to other populations [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Finally, approximately 10% of participants were excluded due to loss of follow-up. However, we do not expect this attrition to substantially influence the results. Those lost to follow-up were generally similar in sociodemographic, lifestyle, and clinical characteristics to those included (\\u003cb\\u003eSupplementary Table S15\\u003c/b\\u003e). However, among those who lost to follow-up, there were more participants with low education and low income, as well as more current smokers. A simulation study has shown that loss to follow-up (\\u0026lt;\\u0026thinsp;50%) tends to result in minor underestimation of socioeconomic inequalities in cohort studies [\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e].\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis study shows that socioeconomic inequalities exist in the association between diet quality and incident CVD. Participants with low and middle education had disproportionately higher risks of incident CVD associated with poor diet quality, after accounting for a wide range of confounders. Improving diet quality therefore has the potential to enhance the health of low- and middle-education populations by reducing their CVD incidence. However, to address persistent health inequalities, dietary improvement alone is insufficient; public health policies and programs should tackle broader structural factors and provide more support and health resources for low-SEP populations.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003ch2\\u003eConflicts of interests\\u003c/h2\\u003e\\u003cp\\u003eThe authors declare that they have no known competing interests.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis project has received funding from the Dutch Science Agenda (NWA) program \\u0026lsquo;Transition Towards a Sustainable Food System\\u0026rsquo; funded by the Dutch Research Council (NWO): NWA.1235.18.201. The Lifelines Biobank initiative has been made possible by subsidies from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), University of Groningen, and the Provinces in the north of The Netherlands (Drenthe, Friesland, and Groningen). The funders had no role in any part of this research.\\u003c/p\\u003e\\u003ch2\\u003eCRediT author statement\\u003c/h2\\u003e\\u003cp\\u003e\\u003cb\\u003eMing-Jie Duan\\u003c/b\\u003e: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review \\u0026amp; Editing, Project administration. \\u003cb\\u003eMaartje P Poelman\\u003c/b\\u003e: Writing - Review \\u0026amp; Editing, Funding acquisition. \\u003cb\\u003eEva Corpeleijn\\u003c/b\\u003e: Resources, Writing - Review \\u0026amp; Editing. \\u003cb\\u003eSander Biesbroek\\u003c/b\\u003e: Writing - Review \\u0026amp; Editing, Funding acquisition.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e\\u003cp\\u003eThe authors wish to acknowledge the services of the Lifelines cohort study, the contributing research centers delivering data to Lifelines and all the study participants.\\u003c/p\\u003e\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\u003cp\\u003eThe manuscript is based on the data from the Lifelines cohort study. 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PLOS MED 18(12):e1003845. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1371/journal.pmed.1003845\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pmed.1003845\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eHowe LD, Tilling K, Galobardes B, Lawlor DA (2013) Loss to follow-up in cohort studies: bias in estimates of socioeconomic inequalities. Epidemiology 24(1):1\\u0026ndash;9. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1097/EDE.0b013e31827623b1\\u003c/span\\u003e\\u003cspan address=\\\"10.1097/EDE.0b013e31827623b1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[{\"identity\":\"f75da61d-d53e-4e1d-b4ac-c2cd8c6f096f\",\"identifier\":\"10.13039/501100003246\",\"name\":\"Nederlandse Organisatie voor Wetenschappelijk Onderzoek\",\"awardNumber\":\"NWA.1235.18.201\",\"order_by\":0}],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Wageningen University \\u0026 Research\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"socioeconomic position, diet quality, cardiovascular diseases, health inequalities, prevention\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7471887/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7471887/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003eIndividuals with lower socioeconomic position (SEP) have higher risks of cardiovascular diseases (CVD), with diet quality being a crucial risk factor. However, whether the association between diet quality and CVD differs across SEP (education and income) groups is unclear, which was examined in this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003eThis study included participants aged 30-80 years, free of CVD at baseline, from the Dutch Lifelines cohort. The Lifelines Diet Score, a diet quality indicator based on Dutch dietary guidelines, was calculated with data assessed by a 110-item food frequency questionnaire. Cox proportional hazards models were used to assess the association between diet quality and incident CVD (the first non-fatal major cardiovascular event), and whether this association was modified by SEP, adjusted for age, sex, energy intake, alcohol intake, smoking, TV watching time, moderate-to-vigorous physical activity, sleep duration, social/hobby clubs participation, chronic stress, family history of CVD, and BMI.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003eOf 82,360 participants included, 2827 incident CVD cases were identified (median follow-up 7.4 years, incidence rate 4.7 per 1000 person-years). Education modified the association between diet quality and incident CVD (P-interaction = 0.033). Comparing the poorest to the best diet quality quartiles, hazard ratios (95%CI) were 1.27 (1.06, 1.51) in low, 1.28 (1.06, 1.54) in middle, and 0.98 (0.76, 1.27) in high education group.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003eFor low- and middle-education populations, poor diet quality was associated with disproportionately higher risks of incident CVD; improving diet quality may therefore enhance their health. However, to address persistent health inequalities, health policies should tackle broader structural factors of diet and SEP.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Socioeconomic Inequalities in the Association Between Diet Quality and Incident Cardiovascular Diseases: A Prospective Study in the Netherlands\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-01 10:32:18\",\"doi\":\"10.21203/rs.3.rs-7471887/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"681ed8ef-b00c-4ba1-8334-64dfb6f8d077\",\"owner\":[],\"postedDate\":\"September 1st, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":53787563,\"name\":\"Epidemiology\"},{\"id\":53787564,\"name\":\"Cardiac \\u0026 Cardiovascular Systems\"},{\"id\":53787565,\"name\":\"Nutrition \\u0026 Dietetics\"}],\"tags\":[],\"updatedAt\":\"2025-09-01T10:32:18+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-01 10:32:18\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7471887\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7471887\",\"identity\":\"rs-7471887\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}