Depressive Symptoms and Plasma Markers of Alzheimer's Disease and Neurodegeneration: A Coordinated Meta-Analysis of 8 Cohort Studies.

BACKGROUND: Depressive symptoms are associated with an increased risk of Alzheimer's disease (AD). There has been a recent emergence in plasma biomarkers for AD pathophysiology, such as amyloid-beta (Aß) and phosphorylated tau (p-tau), as well as for axonal damage (neurofilament light, NfL) and astrocytic activation (glial fibrillary acidic protein, GFAP). Hypothesizing that depressive symptoms may occur along the AD process, we investigated associations between plasma biomarkers of AD with depressive symptoms in individuals without dementia. METHODS: A two-stage meta-analysis was performed on 2 clinic-based and 6 population-based cohorts (N = 7210) as part of the Netherlands Consortium of Dementia Cohorts. Plasma markers (Aß42/40, p-tau181, NfL, and GFAP) were measured using Single Molecular Array (Simoa; Quanterix) assays. Depressive symptoms were measured with validated questionnaires. We estimated the cross-sectional association of each standardized plasma marker (determinants) with standardized depressive symptoms (outcome) using linear regressions, correcting for age, sex, education, and APOE ε4 allele presence, as well as subgrouping by sex and APOE ε4 allele. Effect estimates were entered into a random-effects meta-analysis. RESULTS: Mean age of participants was 71 years. The prevalence of clinically relevant depressive symptoms ranged from 1% to 22%. None of the plasma markers were associated with depressive symptoms in the meta-analyses. However, NfL was associated with depressive symptoms only in APOE ε4 carriers (ß 0.11; 95% CI: 0.05-0.17). CONCLUSIONS: Late-life depressive symptoms did not show an association to plasma biomarkers of AD pathology. However, in APOE ε4 allele carriers, a more profound role of neurodegeneration was suggested with depressive symptoms.

Genomics Research of Lifetime Depression in the Netherlands: The BIObanks Netherlands Internet Collaboration (BIONIC) Project.

In this cohort profile article we describe the lifetime major depressive disorder (MDD) database that has been established as part of the BIObanks Netherlands Internet Collaboration (BIONIC). Across the Netherlands we collected data on Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) lifetime MDD diagnosis in 132,850 Dutch individuals. Currently, N = 66,684 of these also have genomewide single nucleotide polymorphism (SNP) data. We initiated this project because the complex genetic basis of MDD requires large population-wide studies with uniform in-depth phenotyping. For standardized phenotyping we developed the LIDAS (LIfetime Depression Assessment Survey), which then was used to measure MDD in 11 Dutch cohorts. Data from these cohorts were combined with diagnostic interview depression data from 5 clinical cohorts to create a dataset of N = 29,650 lifetime MDD cases (22%) meeting DSM-5 criteria and 94,300 screened controls. In addition, genomewide genotype data from the cohorts were assembled into a genomewide association study (GWAS) dataset of N = 66,684 Dutch individuals (25.3% cases). Phenotype data include DSM-5-based MDD diagnoses, sociodemographic variables, information on lifestyle and BMI, characteristics of depressive symptoms and episodes, and psychiatric diagnosis and treatment history. We describe the establishment and harmonization of the BIONIC phenotype and GWAS datasets and provide an overview of the available information and sample characteristics. Our next step is the GWAS of lifetime MDD in the Netherlands, with future plans including fine-grained genetic analyses of depression characteristics, international collaborations and multi-omics studies.

Modifiable Risk Factors for Accelerated Decline in Processing Speed: Results from Three Dutch Population Cohorts.

BACKGROUND: Several lifestyle, cardiovascular and psychosocial factors are associated with risk of cognitive decline and dementia. We studied the independent associations of a broad set of modifiable risk factors with decline in processing speed in three large population-based cohorts with up to 23 years of follow-up. METHODS: We used data of 9,666 participants from the Doetinchem Cohort Study, the Longitudinal Aging Study Amsterdam, and the Maastricht Aging Study. Decline in processing speed was measured with the letter digit substitution task or the alphabet coding task and modeled using quadratic latent growth curves. Associations of modifiable risk factors with level and rate of decline in processing speed were investigated by estimating associations with level of processing speed at different centering ages. RESULTS: Latent growth curves showed that decline in processing speed accelerated with age. Smoking, not drinking alcohol and depressive symptoms were associated with a lower level of processing speed in all cohorts. In two of the cohorts, more physical activity, drinking more than two glasses of alcohol per day, higher BMI and diabetes were associated with a lower level of processing speed. Depressive symptoms and diabetes were also associated with faster decline in processing speed. CONCLUSION: Several modifiable risk factors are associated with the level of processing speed in older age, while few are also related to the rate of decline.

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta-analysis.

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries.

PURPOSE: Previously reported associations of protein-rich foods with stroke subtypes have prompted interest in the assessment of individual amino acids. We examined the associations of dietary amino acids with risks of ischaemic and haemorrhagic stroke in the EPIC study. METHODS: We analysed data from 356,142 participants from seven European countries. Dietary intakes of 19 individual amino acids were assessed using validated country-specific dietary questionnaires, calibrated using additional 24-h dietary recalls. Multivariable-adjusted Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of ischaemic and haemorrhagic stroke in relation to the intake of each amino acid. The role of blood pressure as a potential mechanism was assessed in 267,642 (75%) participants. RESULTS: After a median follow-up of 12.9 years, 4295 participants had an ischaemic stroke and 1375 participants had a haemorrhagic stroke. After correction for multiple testing, a higher intake of proline (as a percent of total protein) was associated with a 12% lower risk of ischaemic stroke (HR per 1 SD higher intake 0.88; 95% CI 0.82, 0.94). The association persisted after mutual adjustment for all other amino acids, systolic and diastolic blood pressure. The inverse associations of isoleucine, leucine, valine, phenylalanine, threonine, tryptophan, glutamic acid, serine and tyrosine with ischaemic stroke were each attenuated with adjustment for proline intake. For haemorrhagic stroke, no statistically significant associations were observed in the continuous analyses after correcting for multiple testing. CONCLUSION: Higher proline intake may be associated with a lower risk of ischaemic stroke, independent of other dietary amino acids and blood pressure.

Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study.

People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.

Consumption of ultra-processed foods and risk of multimorbidity of cancer and cardiometabolic diseases: a multinational cohort study.

Background: It is currently unknown whether ultra-processed foods (UPFs) consumption is associated with a higher incidence of multimorbidity. We examined the relationship of total and subgroup consumption of UPFs with the risk of multimorbidity defined as the co-occurrence of at least two chronic diseases in an individual among first cancer at any site, cardiovascular disease, and type 2 diabetes. Methods: This was a prospective cohort study including 266,666 participants (60% women) free of cancer, cardiovascular disease, and type 2 diabetes at recruitment from seven European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Foods and drinks consumed over the previous 12 months were assessed at baseline by food-frequency questionnaires and classified according to their degree of processing using Nova classification. We used multistate modelling based on Cox regression to estimate cause-specific hazard ratios (HR) and their 95% confidence intervals (CI) for associations of total and subgroups of UPFs with the risk of multimorbidity of cancer and cardiometabolic diseases. Findings: After a median of 11.2 years of follow-up, 4461 participants (39% women) developed multimorbidity of cancer and cardiometabolic diseases. Higher UPF consumption (per 1 standard deviation increment, ∼260 g/day without alcoholic drinks) was associated with an increased risk of multimorbidity of cancer and cardiometabolic diseases (HR: 1.09, 95% CI: 1.05, 1.12). Among UPF subgroups, associations were most notable for animal-based products (HR: 1.09, 95% CI: 1.05, 1.12), and artificially and sugar-sweetened beverages (HR: 1.09, 95% CI: 1.06, 1.12). Other subgroups such as ultra-processed breads and cereals (HR: 0.97, 95% CI: 0.94, 1.00) or plant-based alternatives (HR: 0.97, 95% CI: 0.91, 1.02) were not associated with risk. Interpretation: Our findings suggest that higher consumption of UPFs increases the risk of cancer and cardiometabolic multimorbidity. Funding: Austrian Academy of Sciences, Fondation de France, Cancer Research UK, World Cancer Research Fund International, and the Institut National du Cancer.

Potential determinants of antibody responses after vaccination against SARS-CoV-2 in older persons: the Doetinchem Cohort Study.

BACKGROUND: Immune responses to vaccination vary widely between individuals. The aim of this study was to identify health-related variables potentially underlying the antibody responses to SARS-CoV-2 vaccination in older persons. We recruited participants in the long-running Doetinchem Cohort Study (DCS) who underwent vaccination as part of the national COVID-19 program, and measured antibody concentrations to SARS-CoV-2 Spike protein (S1) and Nucleoprotein (N) at baseline (T0), and a month after both the first vaccination (T1), and the second vaccination (T2). Associations between the antibody concentrations and demographic variables, including age, sex, socio-economic status (SES), comorbidities (cardiovascular diseases and immune mediated diseases), various health parameters (cardiometabolic markers, inflammation markers, kidney- and lung function) and a composite measure of frailty ('frailty index', ranging from 0 to 1) were tested using multivariate models. RESULTS: We included 1457 persons aged 50 to 92 years old. Of these persons 1257 were infection naïve after their primary vaccination series. The majority (N = 954) of these individuals were vaccinated with two doses of BNT162b2 (Pfizer) and their data were used for further analysis. A higher frailty index was associated with lower anti-S1 antibody responses at T1 and T2 for both men (RT1 = -0.095, PT1 = 0.05; RT2 = -0.11, PT2 = 0.02) and women (RT1 = -0.24, PT1 < 0.01; RT2 = -0.15, PT2 < 0.01). After correcting for age and sex the frailty index was also associated with the relative increase in anti-S1 IgG concentrations between the two vaccinations (ß = 1.6, P < 0.01). Within the construct of frailty, history of a cardiac catheterization, diabetes, gastrointestinal disease, a cognitive speed in the lowest decile of the population distribution, and impaired lung function were associated with lower antibody responses after both vaccinations. CONCLUSIONS: Components of frailty play a key role in the primary vaccination response to the BNT162b2 vaccine within an ageing population. Older persons with various comorbidities have a lowered immune response after their first vaccination, and while frail and sick older persons see a stronger increase after their second vaccination compared to healthy people, they still have a lower antibody response after their second vaccination.

Depression, anxiety, and the risk of cancer: An individual participant data meta-analysis.

BACKGROUND: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. METHODS: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). RESULTS: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06-1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04-1.23). CONCLUSIONS: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. PREREGISTRATION NUMBER: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.

Corrigendum: Characterization of the degree of food processing in the European Prospective Investigation into Cancer and Nutrition: application of the Nova classification and validation using selected biomarkers of food processing.

[This corrects the article DOI: 10.3389/fnut.2022.1035580.].

Associations between dietary inflammatory scores and biomarkers of inflammation in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

BACKGROUND: Since the first version of the dietary inflammatory index (DII®) developed in the past decade, several other versions have been developed. However, to date no study has attempted to compare these versions with respect to their associations with biomarkers of inflammation. OBJECTIVE: We aimed to investigate the relationship between four dietary inflammatory scores [DII, two energy-adjusted derivatives (E-DII and E-DIIr), and the Inflammatory Score of the Diet (ISD)], and circulating levels of several inflammatory markers and adipokines. METHODS: This study included 17 637 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with at least one marker of inflammation measured in blood. Associations between the four scores and C-reactive protein (CRP), interleukin (IL)6, IL10, IL1RA, tumor necrosis factor-α (TNFα), soluble tumor necrosis factor receptor-1 (sTNFR1), sTNFR2, leptin, soluble leptin receptor (sLeptin R), adiponectin, and High Molecular Weight (HMW) adiponectin were evaluated using multivariable linear regressions adjusted for potential confounders. RESULTS: Positive associations were observed between the four dietary inflammatory scores and levels of CRP, IL6, sTNFR1, sTNFR2 and leptin. However, only the DII and the ISD were positively associated with IL1RA levels and only the DII and the E-DIIr were positively associated with TNFα levels. The proportion of variance of each biomarker explained by the scores was lower than 2%, which was equivalent to the variance explained by smoking status but much lower than that explained by body mass index. CONCLUSIONS: Our results suggest that the four dietary inflammatory scores were associated with some biomarkers of inflammation and could be used to assess the inflammatory potential of diet in European adults but are not sufficient to capture the inflammatory status of an individual. These findings can help to better understand the inflammatory potential of diet, but they need to be replicated in studies with repeated dietary measurements.

Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2.

AIMS: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics. METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)]. CONCLUSION: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.

Heart disease is a major health concern worldwide, and predicting an individual's risk for developing heart disease is an important tool for prevention. Current risk prediction models often use factors such as age, gender, smoking, and blood pressure, but other factors like education level, albuminuria (protein in the urine), and coronary artery calcium (CAC) may also affect an individual's risk. The aim of this study was to develop a new method for using these additional risk factors for predicting risk even more accurately. The researchers used data from several large studies that included over 400 000 apparently healthy individuals who were followed for 10 years. They examined the effect of various risk factors on cardiovascular disease (CVD) risk using a statistical model. They found that adding coronary scan ('CAC score'); NT-proBNP, a biomarker of heart strain; and hs-Troponin-T, a marker of heart damage, to the existing risk prediction model (SCORE2) improved the accuracy of predicted CVD risk. The key findings are: The methods presented in the current study can help to add additional risk factors to predictions of existing models, such as SCORE2. This flexible method may help identify individuals who are at higher risk for CVD and guide prevention strategies.

Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk.

Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.

Predicting self-perceived general health status using machine learning: an external exposome study.

BACKGROUND: Self-perceived general health (SPGH) is a general health indicator commonly used in epidemiological research and is associated with a wide range of exposures from different domains. However, most studies on SPGH only investigated a limited set of exposures and did not take the entire external exposome into account. We aimed to develop predictive models for SPGH based on exposome datasets using machine learning techniques and identify the most important predictors of poor SPGH status. METHODS: Random forest (RF) was used on two datasets based on personal characteristics from the 2012 and 2016 editions of the Dutch national health survey, enriched with environmental and neighborhood characteristics. Model performance was determined using the area under the curve (AUC) score. The most important predictors were identified using a variable importance procedure and individual effects of exposures using partial dependence and accumulated local effect plots. The final 2012 dataset contained information on 199,840 individuals and 81 variables, whereas the final 2016 dataset had 244,557 individuals with 91 variables. RESULTS: Our RF models had overall good predictive performance (2012: AUC = 0.864 (CI: 0.852-0.876); 2016: AUC = 0.890 (CI: 0.883-0.896)) and the most important predictors were "Control of own life", "Physical activity", "Loneliness" and "Making ends meet". Subjects who felt insufficiently in control of their own life, scored high on the De Jong-Gierveld loneliness scale or had difficulty in making ends meet were more likely to have poor SPGH status, whereas increased physical activity per week reduced the probability of poor SPGH. We observed associations between some neighborhood and environmental characteristics, but these variables did not contribute to the overall predictive strength of the models. CONCLUSIONS: This study identified that within an external exposome dataset, the most important predictors for SPGH status are related to mental wellbeing, physical exercise, loneliness, and financial status.

Different Levels of Ultraprocessed Food and Beverage Consumption and Associations with Environmental Sustainability and All-cause Mortality in EPIC-NL.

BACKGROUND: The adverse health effects of high ultraprocessed food and drink (UPFD) consumption are well documented. However, the environmental impact remains unclear, and the separate effects of ultraprocessed foods (UPFs) and drinks (UPDs) on all-cause mortality have not been studied previously. OBJECTIVES: To assess the association between levels of UPFD, UPF, and UPD consumption and diet-related environmental impacts and all-cause mortality in Dutch adults. METHODS: Habitual diets were assessed by a Food Frequency Questionnaire (FFQ) from 1993-1997 in 38,261 participants of the Dutch European Prospective Investigation into Cancer and Nutrition cohort. The mean follow-up time was 18.2 y (SD = 4.1); 4,697 deaths occurred. FFQ items were categorized according to the NOVA classification. Associations between quartiles of UPFD, UPF, and UPD consumption and environmental impact indicators were analyzed using general linear models and all-cause mortality by Cox proportional hazard models. The lowest UPFD, UPF, and UPD consumption quartiles were used as comparator. RESULTS: The average UPFD consumption was 181 (SD = 88) g/1000 kcal. High UPF consumption was statistically significantly inversely associated with all environmental impact indicators (Q4vsQ1: -13.6% to -3.0%), whereas high UPD consumption was, except for land use, statistically significant positively associated with all environmental impact indicators (Q4vsQ1: 1.2% to 5.9%). High UPFD consumption was heterogeneously associated with environmental impacts (Q4vsQ1: -4.0% to 2.6%). After multivariable adjustment, the highest quartiles of UPFD and UPD consumption were significantly associated with all-cause mortality (HRQ4vsQ1: 1.17, 95%CI: 1.08, 1.28 and HRQ4vsQ1: 1.16, 95%CI: 1.07, 1.26, respectively). UPF consumption of Q2 and Q3 were associated with a borderline significant lower risk of all-cause mortality (HRQ2vsQ1: 0.93, 95% CI: 0.85, 1.00; HRQ3vsQ1: 0.91, 95% CI: 0.84, 0.99) whereas Q4 was not statistically significant (HRQ4vsQ1: 1.06, 95% CI: 0.97, 1.15). CONCLUSIONS: Reducing UPD consumption may lower environmental impact and all-cause mortality risk; however, this is not shown for UPFs. When categorizing food consumption by their degree of processing, trade-offs are observed for human and planetary health aspects.

Masculine gender affects sex differences in the prevalence of chronic health problems - The Doetinchem Cohort Study.

Both (biological) sex and (socio-cultural) gender are relevant for health but in large-scale studies specific gender measures are lacking. Using a masculine gender-score based on 'traditional masculine-connotated aspects of everyday life', we explored how masculinity may affect sex differences in the prevalence of chronic health problems. We used cross-sectional data (2008-2012) from the Doetinchem Cohort Study to calculate a masculine gender-score (range 0-19) using information on work, informal care, lifestyle and emotions. The sample consisted of 1900 men and 2117 women (age: 40-80). Multivariable logistic regressions including age and SES were used to examine the role of masculine gender on sex differences in the prevalence of diabetes, coronary heart disease, CVA, arthritis, chronic pain and migraine. Men had higher masculine gender-scores than women (12.2 vs 9.1). For both sexes, a higher masculine gender-score was associated with lower prevalence of chronic health problems. Diabetes, CHD, and CVA were more prevalent in men, and gender-adjustment resulted in greater sex differences: e.g. for diabetes the ORsex changed from 1.21 (95 %CI 0.93-1.58) to 1.60 (95 %CI 1.18-2.17). Arthritis, chronic pain, and migraine were more prevalent in women, and gender-adjustment resulted in smaller sex differences: e.g. for chronic pain the ORsex changed from 0.53 (95 %CI 0.45-0.60) to 0.73 (95 %CI 0.63-0.86). Gender measured as 'everyday masculinity' is associated with lower prevalence of chronic health problems in both men and women. Our findings also suggest that the commonly found sex differences in the prevalence of chronic health problems have a large gender component.

Adherence to the EAT-Lancet Healthy Reference Diet in Relation to Risk of Cardiovascular Events and Environmental Impact: Results From the EPIC-NL Cohort.

Background The Healthy Reference Diet (HRD) was created to formulate dietary guidelines that would be healthy and sustainable. We aimed to construct a diet score measuring adherence to the HRD and to explore its association with cardiovascular events and environmental impact. Methods and Results We included 35 496 participants from the population-based EPIC-NL (European Prospective Investigation into Cancer and Nutrition-Netherlands) study. HRD scores were calculated using data from food frequency questionnaires (0-140). Data on morbidity and mortality were retrieved through linkage with national and death registries. Data on environmental impact indicators were obtained from life cycle assessments. Associations between adherence to the HRD and cardiovascular events were estimated with Cox proportional hazard models. Linear regression analysis was conducted for the adherence to the HRD and each environmental indicator. High adherence to the HRD was associated with 14%, 12%, and 11% lower risks of cardiovascular disease (hazard ratio [HR]Q4vsQ1, 0.86 [95% CI, 0.78-0.94]), coronary heart disease (HRQ4vsQ1, 0.88 [95% CI, 0.78-1.00]), and total stroke (HRQ4vsQ1, 0.89 [95% CI, 0.72-1.10]), respectively. High HRD adherence was associated with 2.4% (95% CI, -5.0 to 0.2) lower greenhouse gas emissions, 3.9% (95% CI, -5.2 to -2.6) less land use, 0.5% (95% CI, -2.6 to 1.6), less freshwater eutrophication, 3.3% (95% CI, -5.8 to -0.8), less marine eutrophication, 7.7% (95% CI, -10.8 to -4.6), less terrestrial acidification, and 32.1 % (95% CI, 28.5-35.7) higher blue water use. Conclusions High adherence to the HRD was associated with lower risk of cardiovascular disease, coronary heart disease, and modestly lower levels of most environmental indicators but a higher level of blue water use.

Perspectives of older women in the Netherlands: identifying motivators and barriers for healthy lifestyles and determinants of healthy aging.

BACKGROUND: Women have a higher life expectancy than men but experience more years with physical disabilities in daily life at older ages, especially women with a migration background. This pinpoints older women as an important target group for strategies that stimulate healthy lifestyle, which benefits healthy aging. Our study investigates motivators and barriers for healthy lifestyles and perspectives on determinants of healthy aging of older women. This provides essential information for developing targeted strategies. METHODS: Data was collected by semi-structured digital interviews from February till June 2021. Women aged 55 years and older living in the Netherlands (n = 34) with a native Dutch (n = 24), Turkish (n = 6) or Moroccan (n = 4) migration background were included. Two main subjects were investigated: (1) motivators and barriers on their current lifestyles regarding smoking, alcohol consumption, physical activity, diet and sleep and (2) perspectives on determinants of healthy aging. Interviews were analyzed using Krueger's framework. RESULTS: Personal health was the most common motivator for a healthy lifestyle. In addition, peer pressure and being outdoors were specific motivators for physical activity. Bad weather conditions and personal dislike to be active were specific barriers. The social environment, personal preferences and personal belief to compensate with other healthy lifestyle behaviors were barriers for low alcohol consumption. Personal preferences (liking unhealthy food and not making time) were the main barriers for a healthy diet. Sleep was not perceived as a form of lifestyle behavior, but rather as a personal trait. Since there were no smokers, specific barriers were not mentioned. For Turkish-Dutch and Moroccan-Dutch women, additional barriers and motivators were culture and religion. These were strong motivators to abstain from alcohol consumption and smoking, but a barrier for a healthy diet. With regard to perspectives on determinants of healthy aging, positive views on aging and being physically active were perceived as most important. Women often wanted to increase their physical activity or healthy diet to stimulate healthy aging. Among Turkish-Dutch and Moroccan-Dutch women, healthy aging was also perceived as something in the hands of God. CONCLUSIONS: Although motivators and barriers for a healthy lifestyle and perspectives on healthy aging vary for distinct lifestyles, personal health is a common motivator across all lifestyles. Having a migration background added culture and religion as distinct barriers and motivations. Strategies to improve lifestyle among older women should therefore have a tailored, culture sensitive approach (if applicable) for distinct lifestyle factors.

Differences in the Mediating Role of HL in Socioeconomic Inequalities in Health Across Age Groups: Results from the Dutch Doetinchem Cohort Study.

BACKGROUND: Although it is known that health literacy (HL) plays an explanatory role in educational inequalities in health, it is unknown whether this role varies across age groups. OBJECTIVE: The purpose of this study was to investigate whether the mediating role of HL in educational inequalities in four health outcomes varies across age groups: age 46 to 58 years, age 59 to 71 years, and age 72 to 84 years. METHODS: We used data from the Dutch Doetinchem Cohort Study, which included 3,448 participants. We included years of education as predictor, chronic illness prevalence and incidence, mental and self-perceived health as outcomes, and HL, based on self-report, as mediator. We used multiple-group mediation models to compare indirect effects across age groups. KEY RESULTS: In the complete sample without age stratification, HL partly mediated the effect of education on all health outcomes except for incidence of chronic diseases. These indirect effect estimates were larger for subjective (self-perceived health, proportion mediated [PM] = 37%, and mental health, PM = 37%) than for objective health outcomes (prevalence of chronic disease, PM = 17%). For the prevalence of chronic disease, the indirect effect estimate was significantly larger among individuals age 46 to 58 years compared to individuals age 59 to 71 years and for incidence of chronic disease also compared to individuals age 72 to 84 years. All other indirect effect estimates did not differ significantly between age groups. Using an alternative cut-off point for HL or adjusting for cognitive functioning did not meaningfully change the results. CONCLUSIONS: Overall, we found that the explanatory role of HL in educational inequalities in mental and subjective health was stable but that it varied across age groups for chronic diseases, where it was largest among individuals age 46 to 58 years. Future studies may investigate the benefits of starting to intervene on HL from a younger age but means to improve HL may also benefit the subjective health of older adults with lower education. [HLRP: HL Research and Practice. 2023;7(1):e26-e38.] Plain Language Summary: This study examined age-group differences in the mediating role of HL in the relationship between education and health. Overall, we found that the explanatory role of HL in educational inequalities in mental and subjective health was stable but that it varied across age groups for chronic diseases, where it was largest among individuals age 46 to 58 years compared to individuals age 59 to 71 years and individuals age 72 to 84 years.

Predictive Value of SLCO1B1 c.521T>C Polymorphism on Observed Changes in the Treatment of 1136 Statin-Users.

Pharmacogenomic testing is a method to prevent adverse drug reactions. Pharmacogenomics could be relevant to optimize statin treatment, by identifying patients at high risk for adverse drug reactions. We aim to investigate the clinical validity and utility of pre-emptive pharmacogenomics screening in primary care, with SLCO1B1 c.521T>C as a risk factor for statin-induced adverse drug reactions. The focus was on changes in therapy as a proxy for adverse drug reactions observed in statin-users in a population-based Dutch cohort. In total, 1136 statin users were retrospectively genotyped for the SLCO1B1 c.521T>C polymorphism (rs4149056) and information on their statin dispensing was evaluated as cross-sectional research. Approximately half of the included participants discontinued or switched their statin treatment within three years. In our analyses, we could not confirm an association between the SLCO1B1 c.521T>C genotype and any change in statin therapy or arriving at a stable dose sooner in primary care. To be able to evaluate the predictive values of SLCO1B1 c.521T>C genotype on adverse drug reactions from statins, prospective data collection of actual adverse drug reactions and reasons to change statin treatment should be facilitated.