Peter M. Engelfriet

Objectives: Older persons generally have weaker antibody responses to vaccines than younger individuals, but heterogeneity is large. We aimed to identify genetic variants associated with primary SARS‐CoV‐2 vaccine‐induced antibody responses in older persons that might contribute to this heterogeneity. Methods: Demographic and genotype data were collected in the Doetinchem Cohort Study prior to the COVID‐19 pandemic. Antibody responses were measured 1 month after the first and second SARS‐CoV‐2 vaccinations, and genome‐wide association analysis was performed in 842 and 890 individuals respectively. Polygenic scores were calculated and tested in an independent sample, and the variance explained by the scores was estimated using a bootstrap procedure. Genes were mapped to genome‐wide suggestive ( P < 1 × 10 ⁻⁵ ) loci, and gene set enrichment was performed using the hypergeometric test. Results: Antibody responses 1 month after the first and second SARS‐CoV‐2 vaccinations were linked to genome‐wide significant ( P < 5 × 10 ⁻⁸ ) loci on Chromosome 5. Polygenic scores related to these antibody responses could explain 9% (95% CI P1: [−4% to 21%], 95% CI P2: [−4% to 24%]) of the variance. Genome‐wide suggestive loci related to the responses after two vaccinations could be mapped to several genes in the human leukocyte antigen (HLA) region on chromosome 6p21. Conclusion: Genetic variation is suggested to play a role in the primary vaccine‐induced IgG antibody responses to SARS‐CoV‐2 in older persons. The most prominent source of variation was found to lie in HLA genes, which are enriched in several immune pathways and immune‐mediated diseases.

Chronic lymphocytic leukemia (CLL) is preceded by monoclonal B-cell lymphocytosis (MBL), a CLL precursor state with a prevalence of up to 12% in aged individuals; however, the duration of MBL and the mechanisms of its evolution to CLL remain largely unknown. In this study, we sequenced the B-cell receptor (BcR) immunoglobulin heavy chain (IGH) gene repertoire of 124 patients with CLL and 118 matched controls in blood samples taken up to 22 years prior to diagnosis. Significant skewing in the BcR IGH gene repertoire was detected in the majority of patients, even before the occurrence of lymphocytosis and irrespective of the clonotypic IGH variable gene somatic hypermutation status. Furthermore, we identified dominant clonotypes belonging to major stereotyped subsets associated with poor prognosis up to 16 years before diagnosis in 14 patients with CLL. In 22 patients with longitudinal samples, the skewing of the BcR IGH gene repertoire increased significantly over time to diagnosis or remained stable at high levels. For 14 of 16 patients with available samples at diagnosis, the CLL clonotype was already present in the prediagnostic samples. Overall, our data indicate that the preclinical phase of CLL could be longer than previously thought, even in adverse-prognostic cases.

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.

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.

Much progress has been made in the past decades in unraveling the mechanisms that are responsible for aging. The discovery that particular gene mutations in experimental species such as yeast, flies, and nematodes are associated with longevity has led to many important insights into pathways that regulate aging processes. However, extrapolating laboratory findings in experimental species to knowledge that is valid for the complexity of human physiology remains a major challenge. Apart from the restricted experimental possibilities, studying aging in humans is further complicated by the development of various age-related diseases. The availability of a set of biomarkers that really reflect underlying aging processes would be of much value in disentangling age-associated pathology from specific aging mechanisms. In this review, we survey the literature to identify promising biochemical markers of aging, with a particular focus on using them in longitudinal studies of aging in humans that entail repeated measurements on easily obtainable material, such as blood samples. Our search strategy was a 2-pronged approach, one focused on general mechanisms of aging and one including studies on clinical biomarkers of age-related diseases.

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.

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).

Aims: Mortality attributed to a disease is an important public health measure of the ‘burden’ of that disease. A discrepancy has been noted between the high mortality rates associated with heart failure (HF) and the share of deaths ascribed to HF in official mortality statistics. It was our main aim to estimate excess mortality associated with HF and use the estimates to better understand the burden of HF. Methods and results: Excess mortality was defined as the difference in mortality rates between individuals with and those without HF. An epidemiological model was formulated that allowed deriving age-specific excess mortality rates in HF patients from HF incidence and prevalence. Incidence and prevalence were estimated from yearly collected cross-sectional data from four nationally representative General Practice registries in the Netherlands. The year 2007 was chosen as a reference. Next, excess mortality rates were used to calculate numbers of deaths among HF patients and compare the figures with national cause-of-death statistics. The latter were found to be more than three times smaller than the former (roughly 6000 vs. 21 000). Further, by applying HF prevalence and mortality rates to a life table of the Dutch population, average numbers of life years lost due to HF were calculated to be 6.9 years. Conclusion: National mortality statistics strongly underestimate the number of deaths associated with HF. Moreover, the high mortality rate in HF patients amounts to a remarkably large number of life years lost given the advanced age of disease onset.

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.