Socio-economic disadvantage at both individual and neighbourhood levels has been found to be associated with single lifestyle risk factors. However, it is unknown to what extent their combined effects contribute to a broad lifestyle profile. We aimed to (i) investigate the associations of individual socio-economic disadvantage (ISED) and neighbourhood socio-economic disadvantage (NSED) in relation to an extended score of health-related lifestyle risk factors (lifestyle risk index); and to (ii) investigate whether NSED modified the association between ISED and the lifestyle risk index. Of 77 244 participants [median age (IQR): 46 (40–53) years] from the Lifelines cohort study in the northern Netherlands, we calculated a lifestyle risk index by scoring the lifestyle risk factors including smoking status, alcohol consumption, diet quality, physical activity, TV-watching time and sleep time. A higher lifestyle risk index was indicative of an unhealthier lifestyle. Composite scores of ISED and NSED based on a variety of socio-economic indicators were calculated separately. Linear mixed-effect models were used to examine the association of ISED and NSED with the lifestyle risk index and to investigate whether NSED modified the association between ISED and the lifestyle risk index by including an interaction term between ISED and NSED. Both ISED and NSED were associated with an unhealthier lifestyle, because ISED and NSED were both positively associated with the lifestyle risk index {highest quartile [Q4] ISED beta-coefficient [95% confidence interval (CI)]: 0.64 [0.62–0.66], P < 0.001; highest quintile [Q5] NSED beta-coefficient [95% CI]: 0.17 [0.14–0.21], P < 0.001} after adjustment for age, sex and body mass index. In addition, a positive interaction was found between NSED and ISED on the lifestyle risk index (beta-coefficient 0.016, 95% CI: 0.011–0.021, P_interaction < 0.001), which indicated that NSED modified the association between ISED and the lifestyle risk index; i.e. the gradient of the associations across all ISED quartiles (Q4 vs Q1) was steeper among participants residing in the most disadvantaged neighbourhoods compared with those who resided in the less disadvantaged neighbourhoods. Our findings suggest that public health initiatives addressing lifestyle-related socio-economic health differences should not only target individuals, but also consider neighbourhood factors.

Background: Education and income, as two primary socioeconomic indicators, are often used interchangeably in health research. However, there is a lack of clear distinction between these two indicators concerning their associations with health. Objective: This study aimed to investigate the separate and combined effects of education and income in relation to incident type 2 diabetes and cardiovascular diseases in the general population. Design and Participants: Participants aged between 30 and 65 years from the prospective Dutch Lifelines cohort study were included. Two sub-cohorts were subsequently created, including 83,759 and 91,083 participants for a type 2 diabetes cohort and a cardiovascular diseases cohort, respectively. Main Measures: Education and income level were assessed by self-report questionnaires. The outcomes were incident type 2 diabetes and cardiovascular diseases (defined as the earliest non-fatal cardiovascular event). Key Results: A total of 1228 new cases of type 2 diabetes (incidence 1.5%) and 3286 (incidence 3.6%) new cases of cardiovascular diseases were identified, after a median follow-up of 43 and 44 months, respectively. Low education and low income (<1000 euro/month) were both positively associated with a higher risk of incident type 2 diabetes (OR 1.24 [95%CI 1.04–1.48] and OR 1.71 [95%CI 1.30–2.26], respectively); and with a higher risk of incident cardiovascular diseases (OR 1.15 [95%CI 1.04–1.28] and OR 1.24 [95%CI 1.02–1.52], respectively); independent of age, sex, lifestyle factors, BMI, clinical biomarkers, comorbid conditions at baseline, and each other. Results from the combined associations of education and income showed that within each education group, a higher income was associated with better health; and similarly, a higher education was associated with better health within each income group, except for the low-income group. Conclusions: Education and income were both independently associated with incident type 2 diabetes and cardiovascular diseases. The combined associations of these two socioeconomic indicators revealed that within each education or income level, substantial health disparities existed across strata of the other socioeconomic indicator. Education and income are two equally important socioeconomic indicators in health, and should be considered simultaneously in health research and policymaking.

Socioeconomic health inequalities are an important global public health problem. However, it is not well known to what extent socioeconomic inequalities culminate in impaired vitamin status and whether this is mediated by diet. We, therefore, aimed to assess vitamin status in a population already at increased risk of micronutrient deficiency, i.e., elderly with high and low socioeconomic status (SES), and to investigate whether potential differences therein were mediated by diet quality. Vitamin status in 1605 individuals (60–75 years) from the Lifelines- Micronutrients and Health inequalities in Elderly (MINUTHE) Study was assessed by measuring folic acid and the vitamins B6, B12, D, A, E, and K. Multinomial logistic and linear regression analyses were applied to test the associations between SES and vitamin status. Mediation analysis was used to explore the interrelationship between SES, diet quality, and vitamin status. Low SES was associated with poorer status of vitamin B6, vitamin B12, and, notably, folic acid. Moreover, multivitamin deficiencies were more prevalent in the low SES group. Diet quality was found to mediate the associations of SES with folic acid (for 39.1%), vitamin B6 (for 37.1%), and vitamin B12 (for 37.2%). We conclude that low SES is a risk factor for a spectrum of vitamin deficiencies. Diet quality can partially explain the socioeconomic differences in vitamin status, suggesting that policymakers can mitigate socioeconomic inequality in nutritional status through improving diet quality.

Transitioning to more plant-based diets is crucial for both planetary and human health, and ensuring an equitable transition across all socio-economic groups is also important. However, empirical evidence on the prevalence and socio-economic disparities in vegetarianism and flexitarianism over time in the same population is scarce. This study investigated this in a general Dutch adult population over 15 years. From three general assessments of the Dutch Lifelines study, 143 359 participants from assessment 1 (2006–2013), 100 859 participants from assessment 2 (2013–2017), and 55 282 participants from assessment 3 (2019–2024) were included in this study. The dietary identity was self-reported, collected at each assessment, and categorized into following a vegetarian, flexitarian, other, and no special diet. Socio-economic status was indicated by education attainment. The association between socio-economic status and different dietary identities was estimated using multinomial logistic regression. The prevalence of individuals following vegetarian or flexitarian diet doubled over the three assessment periods, with the proportion of vegetarians and flexitarians increasing from 2.02% to 4.11% and from 3.50% to 7.16%, respectively. Across three assessments, lower education attainment was consistently associated with a lower likelihood of following a vegetarian or flexitarian diet. For example, in assessment 1 individuals with low education attainment were 77% (relative risk ratio [95% CI]: 0.23 [0.20–0.25]) less likely to follow a vegetarian diet compared to those with high education attainment. In a Dutch population cohort, we observed an increasing trend of vegetarian and flexitarian diets over 15 years, along with persistent socio-economic inequalities in these diets.

Objectives Studies in clinical settings showed a potential relationship between socioeconomic status (SES) and lifestyle factors with COVID-19, but it is still unknown whether this holds in the general population. In this study, we investigated the associations of SES with self-reported, tested and diagnosed COVID-19 status in the general population. Design, setting, participants and outcome measures Participants were 49 474 men and women (46±12 years) residing in the Northern Netherlands from the Lifelines cohort study. SES indicators and lifestyle factors (i.e., smoking status, physical activity, alcohol intake, diet quality, sleep time and TV watching time) were assessed by questionnaire from the Lifelines Biobank. Self-reported, tested and diagnosed COVID-19 status was obtained from the Lifelines COVID-19 questionnaire. Results There were 4711 participants who self-reported having had a COVID-19 infection, 2883 participants tested for COVID-19, and 123 positive cases were diagnosed in this study population. After adjustment for age, sex, lifestyle factors, body mass index and ethnicity, we found that participants with low education or low income were less likely to self-report a COVID-19 infection (OR [95% CI]: low education 0.78 [0.71 to 0.86]; low income 0.86 [0.79 to 0.93]) and be tested for COVID-19 (OR [95% CI]: low education 0.58 [0.52 to 0.66]; low income 0.86 [0.78 to 0.95]) compared with high education or high income groups, respectively. Conclusion Our findings suggest that the low SES group was the most vulnerable population to self-reported and tested COVID-19 status in the general population.

Diagnosed and undiagnosed Type 2 Diabetes (T2D) remains a challenge in high-income countries. In addition, the presence of T2D can cause further disease burden because of its high susceptibility to complications. Nevertheless, there is limited evidence of socio-economic gradients in undiagnosed T2D and its complications in a large population cohort. We investigated this using the Dutch Lifelines Cohort Study (Lifelines). Within Lifelines, baseline data of 102 163 adults aged 30 and above were collected from 2007 to 2013. The associations of Socio-Economic Status (SES), indicated by monthly household income, with the prevalence of T2D status and the number of T2D complications were assessed using multinomial Poisson and linear regressions with adjustments for age and sex. The prevalence of diagnosed and undiagnosed T2D was, respectively, 3.0% and 3.0% in the low SES group compared to 1.1% and 1.8% in the high SES group. Individuals with lower SES were at higher risk of having undiagnosed T2D (relative risk ratio (rrr) [95% CI]: 1.63 [1.47-1.81] for low SES and 1.16 [1.05-1.29] for middle SES) and diagnosed T2D, compared with those with high SES. Lower SES was positively associated with the number of T2D complications (low SES vs. high SES (ref); B [95% CI]: 0.15 [0.13-0.16]). Complementing the known socio-economic gradients in diagnosed T2D, we document socio-economic gradients in undiagnosed T2D and T2D complications in a single, large general representative population. Furthermore, individuals with low SES with diagnosed or undiagnosed T2D were more susceptible to T2D complications.