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Abstract 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 (R T1 = -0.095, P T1 = 0.05; R T2 = -0.11, P T2 = 0.02) and women (R T1 = -0.24, P T1

Abstract Background Socioeconomic status has a profound effect on the risk of having a first acute myocardial infarction (AMI). Information on socioeconomic inequalities in AMI incidence across age- gender-groups is lacking. Our objective was to examine socioeconomic inequalities in the incidence of AMI considering both relative and absolute measures of risk differences, with a particular focus on age and gender. Methods We identified all patients with a first AMI from 1997 to 2007 through linked hospital discharge and death records covering the Dutch population. Relative risks (RR) of AMI incidence were estimated by mean equivalent household income at neighbourhood-level for strata of age and gender using Poisson regression models. Socioeconomic inequalities were also shown within the stratified age-gender groups by calculating the total number of events attributable to socioeconomic disadvantage. Results Between 1997 and 2007, 317,564 people had a first AMI. When comparing the most deprived socioeconomic quintile with the most affluent quintile, the overall RR for AMI was 1.34 (95 % confidence interval (CI): 1.32 – 1.36) in men and 1.44 (95 % CI: 1.42 – 1.47) in women. The socioeconomic gradient decreased with age. Relative socioeconomic inequalities were most apparent in men under 35 years and in women under 65 years. The largest number of events attributable to socioeconomic inequalities was found in men aged 45–74 years and in women aged 65–84 years. The total proportion of AMIs that was attributable to socioeconomic inequalities in the Dutch population of 1997 to 2007 was 14 % in men and 18 % in women. Conclusions Neighbourhood socioeconomic inequalities were observed in AMI incidence in the Netherlands, but the magnitude across age-gender groups depended on whether inequality was expressed in relative or absolute terms. Relative socioeconomic inequalities were high in young persons and women, where the absolute burden of AMI was low. Absolute socioeconomic inequalities in AMI were highest in the age-gender groups of middle-aged men and elderly women, where the number of cases was largest.

Abstract Background The high prevalence of chronic diseases in Western countries implies that the presence of multiple chronic diseases within one person is common. Especially at older ages, when the likelihood of having a chronic disease increases, the co-occurrence of distinct diseases will be encountered more frequently. The aim of this study was to estimate the age-specific prevalence of multimorbidity in the general population. In particular, we investigate to what extent specific pairs of diseases cluster within people and how this deviates from what is to be expected under the assumption of the independent occurrence of diseases (i.e., sheer coincidence). Methods We used data from a Dutch health survey to estimate the prevalence of pairs of chronic diseases specified by age. Diseases we focused on were diabetes, myocardial infarction, stroke, and cancer. Multinomial P-splines were fitted to the data to model the relation between age and disease status (single versus two diseases). To assess to what extent co-occurrence cannot be explained by independent occurrence, we estimated observed/expected co-occurrence ratios using predictions of the fitted regression models. Results Prevalence increased with age for all disease pairs. For all disease pairs, prevalence at most ages was much higher than is to be expected on the basis of coincidence. Observed/expected ratios of disease combinations decreased with age. Conclusion Common chronic diseases co-occur in one individual more frequently than is due to chance. In monitoring the occurrence of diseases among the population at large, such multimorbidity is insufficiently taken into account.

Abstract Background Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. Methods Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. Results Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. Conclusion Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

Abstract Background Elderly often show reduced immune functioning and can develop chronic low-grade inflammation. Why some elderly are more prone to become frail is unknown. We investigated whether frailty is associated with altered cytokine signaling through the JAK-STAT pathway in leukocytes of 34 individuals aged 65–74 years. In addition, we investigated how this relation is affected by chronic low-grade inflammation during the previous 20 years. Cytokine signaling was quantified by measuring intracellular STAT1, STAT3, and STAT5 phosphorylation in monocytes, B cells, CD4+ T cells and CD8+ T cells upon stimulation with IL-2, IL-6, IL-10, IFNα and IFNγ, using phospho-flow cytometry. Presence of chronic low-grade inflammation was investigated by evaluating 18 different plasma inflammatory markers that had been measured repeatedly in the same individuals over the previous 20 years. Frailty was assessed as a score on a frailty index. Results We found that lower cytokine-induced pSTAT responsiveness in the various cell subsets was seen with higher frailty scores in both men and women, indicative of dysfunctional pSTAT responses in frailer individuals. Associations differed between men and women, with frailer women showing lower pSTAT1 responses in monocytes and frailer men showing lower pSTAT5 responses in CD4+ and CD8+ T cells. Notably, lower IL-10-induced pSTAT3 responses in men were related to both higher frailty scores and higher CRP levels over the past 20 years. This might indicate poor resolution of low-grade inflammation due to defective regulatory pSTAT signaling in older men. Conclusions Our results emphasize the importance of preserved JAK-STAT pathway signaling in healthy aging and reveal cellular pSTAT levels as a candidate biomarker of frailty.

The methyltransferase-like protein 13 (METTL13) methylates the eukaryotic elongation factor 1 alpha (eEF1A) on two locations: the N-terminal amino group and lysine 55. The absence of this methylation leads to reduced protein synthesis and cell proliferation in human cancer cells. Previous studies showed that METTL13 is dispensable in non-transformed cells, making it potentially interesting for cancer therapy. However, METTL13 has not been examined yet in whole animals. Here, we used the nematode Caenorhabditis elegans as a simple model to assess the functions of METTL13. Using methyltransferase assays and mass spectrometry, we show that the C. elegans METTL13 (METL-13) methylates eEF1A (EEF-1A) in the same way as the human protein. Crucially, the cancer-promoting role of METL-13 is also conserved and depends on the methylation of EEF-1A, like in human cells. At the same time, METL-13 appears dispensable for animal growth, development, and stress responses. This makes C. elegans a convenient whole-animal model for studying METL13-dependent carcinogenesis without the complications of interfering with essential wild-type functions.

For five health-related lifestyle factors (physical activity, weight, smoking, sleep, and alcohol consumption) we describe both population trends and individual changes over a period of 30 years in the same adult population. Dichotomous indicators (healthy/unhealthy) of lifestyle were analyzed for 3,139 participants measured every 5 years in the Doetinchem Cohort Study (1987–2017). Population trends over 30 years in physical inactivity and “unhealthy” alcohol consumption were flat (i.e., stable); overweight and unhealthy sleep prevalence increased; smoking prevalence decreased. The proportion of the population being healthy on all five lifestyle factors declined from 17% in the round 1 to 10.8% in round 6. Underlying these trends a dynamic pattern of changes at the individual level was seen: sleep duration and physical activity level changed in almost half of the individuals; Body Mass Index (BMI) and alcohol consumption in one-third; smoking in one-fourth. Population trends don't give insight into change at the individual level. In order to be able to gauge the potential for change of health-related lifestyle, it is important to take changes at the individual level into account.

Abstract Objective The aim of this exploratory study was to investigate the development of low‐grade inflammation during ageing and its relationship with frailty. Methods The trajectories of 18 inflammatory markers measured in blood samples, collected at 5‐year intervals over a period of 20 years from 144 individuals aged 65–75 years at the study endpoint, were related to the degree of frailty later in life. Results IFN‐γ‐related markers and platelet activation markers were found to change in synchrony. Chronically elevated levels of IL‐6 pathway markers, such as CRP and sIL‐6R, were associated with more frailty, poorer lung function and reduced physical strength. Being overweight was a possible driver of these associations. More and stronger associations were detected in women, such as a relation between increasing sCD14 levels and frailty, indicating a possible role for monocyte overactivation. Multivariate prediction of frailty confirmed the main results, but predictive accuracy was low. Conclusion In summary, we documented temporal changes in and between inflammatory markers in an ageing population over a period of 20 years, and related these to clinically relevant health outcomes.

We introduce MSO graph storage types, and call a storage type MSO-expressible if it is isomorphic to some MSO graph storage type. An MSO graph storage type has MSO-definable sets of graphs as storage configurations and as storage transformations. We consider sequential automata with MSO graph storage and associate with each such automaton a string language (in the usual way) and a graph language; a graph is accepted by the automaton if it represents a correct sequence of storage configurations for a given input string. For each MSO graph storage type, we define an MSO logic which is a subset of the usual MSO logic on graphs. We prove a B\"uchi-Elgot-Trakhtenbrot theorem, both for the string case and the graph case. Moreover, we prove that (i) each MSO graph transduction can be used as storage transformation in an MSO graph storage type, (ii) every automatic storage type is MSO-expressible, and (iii) the pushdown operator on storage types preserves the property of MSO-expressibility. Thus, the iterated pushdown storage types are MSO-expressible.

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

The objective of this study was to explore trajectories of lung function decline with age in the general population, and to study the effect of sociodemographic and life style related risk factors, in particular smoking and BMI. For this purpose, we used data from the Doetinchem Cohort Study (DCS) of men and women, selected randomly from the general population and aged 20-59 years at inclusion in 1987-1991, and followed until the present. Participants in the DCS are assessed every five years. Spirometry has been performed as part of this assessment from 1994 onwards. Participants were included in this study if spirometric measurement of FEV1, which in this study was the main parameter of interest, was acceptable and reproducible on at least one measurement round, leading to the inclusion of 5727 individuals (3008 females). Statistical analysis revealed three typical trajectories. The majority of participants followed a trajectory that closely adhered to the Global Lung Initiative Reference values (94.9% of men and 96.4% of women). Two other trajectories showed a more pronounced decline. Smoking and the presence of respiratory complaints were the best predictors of a trajectory with stronger decline. A greater BMI over the follow-up period was associated with a more unfavorable FEV1 course both in men (β = -0.027 (SD = 0.002); P < 0.001) and in women (β = -0.008 (SD = 0.001); P < 0.001). Smokers at baseline who quit the habit during follow-up, showed smaller decline in FEV1 in comparison to persistent smokers, independent of BMI change (In men β = -0.074 (SD = 0.020); P < 0.001. In women β = -0.277 (SD = 0.068); P < 0.001). In conclusion, three typical trajectories of age-related FEV1 decline could be distinguished. Change in the lifestyle related risk factors, BMI and smoking, significantly impact aging-related decline of lung function. Identifying deviant trajectories may help in early recognition of those at risk of a diagnosis of lung disease later in life.

BackgroundObesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention.Methods and findingsWith a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and "healthy-living" persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions.ConclusionsAlthough effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.