Metabolic syndrome incidence in an aging workforce:Occupational differences and the role of health behaviors

This study investigates whether the incidence of metabolic syndrome (MetS), and its components, differs by occupational group among older workers (45–65 years) and whether health behaviors (smoking, leisure-time physical activity, diet quality, and alcohol consumption) can explain these differences. A sample of older workers (N = 34,834) from the North of the Netherlands was investigated. We analyzed data from two comprehensive measurement waves of the Lifelines Cohort Study and Biobank. MetS components were determined by physical measurements, blood markers, medication use, and self-reports. Occupational group and health behaviors were assessed by questionnaires. The association between occupational groups and MetS incidence was examined using logistic regression analysis. Health behaviors were subsequently added to the model to examine whether they can explain differences in MetS incidence between occupational groups. Low skilled white-collar (OR: 1.24; 95 % CI: 1.12, 1.37) and low skilled blue-collar (OR: 1.37; 95 % CI: 1.18, 1.59) workers had a significantly higher MetS incidence risk than high skilled white-collar workers. Similar occupational differences were observed on MetS component level. Combinations of unhealthy behaviors were more prevalent among blue-collar workers. MetS incidence in older workers differs between occupational groups and health behaviors explain a substantial part of these differences. Health promotion tailored to occupational groups may be beneficial specifically among older low skilled blue-collar workers. Research into other factors that contribute to occupational differences is needed as well as studies spanning the entire working life course

The association between fast-food outlet proximity and density and Body Mass Index:Findings from 147,027 Lifelines cohort study participants

Unhealthy food environments may contribute to an elevated Body Mass Index (BMI), which is a chronic disease risk factor. We examined the association between residential fast-food outlet exposure, in terms of proximity and density, and BMI in the Dutch adult general population. Additionally, we investigated to what extent this association was modified by urbanisation level. In this cross-sectional study, we linked residential addresses of baseline adult Lifelines cohort participants (N = 147,027) to fast-food outlet locations using geo-coding. We computed residential fast-food outlet proximity, and density within 500 m(m), 1, 3, and 5 km(km). We used stratified (urban versus rural areas) multilevel linear regression models, adjusting for age, sex, partner status, education, employment, neighbourhood deprivation, and address density. The mean BMI of participants was 26.1 (SD 4.3) kg/m2. Participants had a mean (SD) age of 44.9 (13.0), 57.3% was female, and 67.0% lived in a rural area. Having two or more (urban areas) or five or more (rural areas) fast-food outlets within 1 km was associated with a higher BMI (B = 0.32, 95% confidence interval (CI):0.03,0.62; B = 0.23, 95% CI:0.10,0.36, respectively). Participants in urban and rural areas with a fast-food outlet within <250 m had a higher BMI (B = 0.30, 95% CI:0.03,0.57; B = 0.20, 95% CI:0.09,0.31, respectively). In rural areas, participants also had a higher BMI when having at least one fast-food outlet within 500 m (B = 0.10, 95% CI:0.02,0.18). In conclusion, fast-food outlet exposure within 1 km from the residential address was associated with BMI in urban and rural areas. Also, fast-food outlet exposure within 500 m was associated with BMI in rural areas, but not in urban areas. In the future, natural experiments should investigate changes in the fast-food environment over time

The interaction of socioeconomic position and type 2 diabetes mellitus family history:A cross-sectional analysis of the Lifelines Cohort and Biobank Study

Background Low socioeconomic position (SEP) and family history of type 2 diabetes mellitus (T2DM) contribute to increased T2DM risk, but it is unclear whether they exacerbate each other's effect. This study examined whether SEP reinforces the association of T2DM family history with T2DM, and whether behavioural and clinical risk factors can explain this reinforcement. Methods We used cross-sectional data on 51 725 participants from Lifelines. SEP was measured as educational level and was self-reported, just as family history of T2DM. T2DM was diagnosed based on measured fasting plasma glucose and glycated haemoglobin, combined with self-reported disease and recorded medication use. We assessed interaction on the additive scale by calculating the relative excess risk due to interaction (RERI). Results ORs of T2DM were highest for males (4.37; 95% CI 3.47 to 5.51) and females (7.77; 5.71 to 10.56) with the combination of low SEP and a family history of T2DM. The RERIs of low SEP and a family history of T2DM were 0.64 (-0.33 to 1.62) for males and 3.07 (1.53 to 4.60) for females. Adjustment for behavioural and clinical risk factors attenuated associations and interactions, but risks remained increased. Conclusion Low SEP and family history of T2DM are associated with T2DM, but they also exacerbate each other's impact in females but not in males. Behavioural and clinical risk factors partly explain these gender differences, as well as the associations underlying the interaction in females. The exacerbation by low SEP of T2DM risks in T2DM families deserves attention in prevention and community care

The association between the presence of fast-food outlets and BMI:the role of neighbourhood socio-economic status, healthy food outlets, and dietary factors

BACKGROUND: Evidence on the association between the presence of fast-food outlets and Body Mass Index (BMI) is inconsistent. Furthermore, mechanisms underlying the fast-food outlet presence-BMI association are understudied. We investigated the association between the number of fast-food outlets being present and objectively measured BMI. Moreover, we investigated to what extent this association was moderated by neighbourhood socio-economic status (NSES) and healthy food outlets. Additionally, we investigated mediation by frequency of fast-food consumption and amount of fat intake. METHODS: In this cross-sectional study, we used baseline data of adults in Lifelines (N = 149,617). Geo-coded residential addresses were linked to fast-food and healthy food outlet locations. We computed the number of fast-food and healthy food outlets within 1 kilometre (km) of participants' residential addresses (each categorised into null, one, or at least two). Participants underwent objective BMI measurements. We linked data to Statistics Netherlands to compute NSES. Frequency of fast-food consumption and amount of fat intake were measured through questionnaires in Lifelines. Multivariable multilevel linear regression analyses were performed to investigate associations between fast-food outlet presence and BMI, adjusting for individual and environmental potential confounders. When exposure-moderator interactions had p-value < 0.10 or improved model fit (∆AIC ≥ 2), we conducted stratified analyses. We used causal mediation methods to assess mediation. RESULTS: Participants with one fast-food outlet within 1 km had a higher BMI than participants with no fast-food outlet within 1 km (B = 0.11, 95% CI: 0.01, 0.21). Effect sizes for at least two fast-food outlets were larger in low NSES areas (B = 0.29, 95% CI: 0.01, 0.57), and especially in low NSES areas where at least two healthy food outlets within 1 km were available (B = 0.75, 95% CI: 0.19, 1.31). Amount of fat intake, but not frequency of fast-food consumption, explained this association for 3.1%. CONCLUSIONS: Participants living in low SES neighbourhoods with at least two fast-food outlets within 1 km of their residential address had a higher BMI than their peers with no fast-food outlets within 1 km. Among these participants, healthy food outlets did not buffer the potentially unhealthy impact of fast-food outlets. Amount of fat intake partly explained this association. This study highlights neighbourhood socio-economic inequalities regarding fast-food outlets and BMI

Multimorbidity and exit from paid employment:the effect of specific combinations of chronic health conditions

Background This study aimed to assess the association between multimorbidity and exit from paid employment, and which combinations of chronic health conditions (CHCs) have the strongest association with exit from paid employment. Methods Data from 111 208 workers aged 18-64 years from Lifelines were enriched with monthly employment data from Statistics Netherlands. Exit from paid employment during follow-up was defined as a change from paid employment to unemployment, disability benefits, economic inactivity or early retirement. CHCs included cardiovascular diseases (CVD), chronic obstructive pulmonary disease (COPD), rheumatoid arthritis (RA), type 2 diabetes (T2DM) and depression. Cox-proportional hazards models were used to examine the impact of multimorbidity and combinations of CHCs on exit from paid employment. Results Multimorbidity increased the risk of exiting paid employment compared with workers without CHCs (hazard ratio (HR): 1.52; 95% confidence interval (CI): 1.35-1.71) or one CHC (HR: 1.14; 95% CI: 1.01-1.28). The risk for exit from paid employment increased among workers with COPD if they additionally had CVD (HR: 1.39; 95% CI: 1.03-1.88), depression (HR: 1.46; 95% CI: 1.10-1.93) or RA (HR: 1.44; 95% CI: 1.08-1.91), for workers with T2DM if they additionally had CVD (HR: 1.43; 95% CI: 1.07-1.91) or depression (HR: 2.09; 95% CI: 1.51-2.91) and for workers with depression who also had T2DM (HR: 1.68; 95% CI: 1.21-2.32). Conclusion This study showed that workers with multimorbidity, especially having a combination of COPD and depression or T2DM and depression, have a higher risk for early exit from paid employment and, therefore, may need tailored support at the workplace

Does social support at home moderate the association between social support at work and work functioning among cancer patients?

PURPOSE: The aims of this study were to examine (1) the longitudinal associations of supervisor and colleague social support with work functioning in cancer patients who have returned to work and (2) the moderating role of social support at home.METHODS: Data from the longitudinal Work Life after Cancer study were used (n = 384). Work functioning (low versus moderate to high work functioning) was measured with the validated Work Role Functioning Questionnaire 2.0. Social support at work was measured from both supervisor and colleagues with the Copenhagen Psychosocial Questionnaire. Social support at home was measured with the Social Support List-Discrepancies. Logistic generalized estimating equations were used to analyse associations between supervisor and colleague social support and work functioning, and to examine the possible moderating effect of social support at home.RESULTS: More supervisor (OR: 1.21; 95% CI: 1.10, 1.32) and colleague (1.13; 1.03, 1.24) social support were significantly associated with moderate to high work functioning. The association between colleague social support and work functioning was attenuated for those who did not experience enough social support at home but remained almost significant for supervisor social support (1.17; 1.00, 1.37).CONCLUSIONS: Supervisor social support is associated with better work functioning regardless of social support at home, while colleague social support is only associated with better work functioning when cancer patients experience enough social support at home.IMPLICATIONS FOR CANCER SURVIVORS: Occupational physicians may play a key role in creating awareness that social support at work and at home are beneficial for cancer patients' work functioning.</p

Mediators of the association between educational attainment and type 2 diabetes mellitus:a two-step multivariable Mendelian randomisation study

Aims/hypothesis: Type 2 diabetes mellitus is a major health burden disproportionately affecting those with lower educational attainment (EA). We aimed to obtain causal estimates of the association between EA and type 2 diabetes and to quantify mediating effects of known modifiable risk factors. Methods: We applied two-step, two-sample multivariable Mendelian randomisation (MR) techniques using SNPs as genetic instruments for exposure and mediators, thereby minimising bias due to confounding and reverse causation. We leveraged summary data on genome-wide association studies for EA, proposed mediators (i.e. BMI, blood pressure, smoking, television watching) and type 2 diabetes. The total effect of EA on type 2 diabetes was decomposed into a direct effect and indirect effects through multiple mediators. Additionally, traditional mediation analysis was performed in a subset of the National Health and Nutrition Examination Survey 2013–2014. Results: EA was inversely associated with type 2 diabetes (OR 0.53 for each 4.2 years of schooling; 95% CI 0.49, 0.56). Individually, the largest contributors were BMI (51.18% mediation; 95% CI 46.39%, 55.98%) and television watching (50.79% mediation; 95% CI 19.42%, 82.15%). Combined, the mediators explained 83.93% (95% CI 70.51%, 96.78%) of the EA–type 2 diabetes association. Traditional analysis yielded smaller effects but showed consistent direction and priority ranking of mediators. Conclusions/interpretation: These results support a potentially causal protective effect of EA against type 2 diabetes, with considerable mediation by a number of modifiable risk factors. Interventions on these factors thus have the potential of substantially reducing the burden of type 2 diabetes attributable to low EA