Association between metabolomics-based biomarker scores and 10-year cognitive decline in men and women. The Doetinchem Cohort Study.

Background: Metabolomic scores based on age (MetaboAge) and mortality (MetaboHealth) are considered indicators of overall health, but their association with cognition in the general population is unknown. Therefore, the association between MetaboAge/MetaboHealth and level and decline in cognition was studied, as were differences between men and women.

Methods: Data of 2821 participants (50% women, age range 45-75) from the Doetinchem Cohort Study was used.

Evaluating changes in the well-being of older adults during the COVID-19 pandemic: a longitudinal cohort study.

The COVID-19 pandemic affected the lives of older adults. Yet, little is known about changes in well-being among older adults during the pandemic, especially when COVID-19 measures were relaxed. Therefore, we aimed to assess changes in the well-being of older adults during multiple turning points of the pandemic.

Physical Activity, Sleeping Problems, Weight, Feelings of Social Isolation, and Quality of Life of Older Adults After Coronavirus Infection: A Longitudinal Cohort Study.

Background: There is debate as to whether a coronavirus infection (SARS-CoV-2) affects older adults' physical activity, sleeping problems, weight, feelings of social isolation, and quality of life (QoL). We investigated differences in these outcomes between older adults with and without coronavirus infection over 180 days following infection.

Methods: We included 6789 older adults (65+) from the Lifelines COVID-19 cohort study who provided data between April 2020 and June 2021.

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.

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.

Predictors of healthy physiological aging across generations in a 30-year population-based cohort study: the Doetinchem Cohort Study.

Background: Predicting healthy physiological aging is of major interest within public health research. However, longitudinal studies into predictors of healthy physiological aging that include numerous exposures from different domains (i.e.

Using random forest to identify longitudinal predictors of health in a 30-year cohort study.

Due to the wealth of exposome data from longitudinal cohort studies that is currently available, the need for methods to adequately analyze these data is growing. We propose an approach in which machine learning is used to identify longitudinal exposome-related predictors of health, and illustrate its potential through an application. Our application involves studying the relation between exposome and self-perceived health based on the 30-year running Doetinchem Cohort Study.

Socioeconomic differences in healthcare expenditure and utilization in The Netherlands.

Background: Worldwide, socioeconomic differences in health and use of healthcare resources have been reported, even in countries providing universal healthcare coverage. However, it is unclear how large these socioeconomic differences are for different types of care and to what extent health status plays a role. Therefore, our aim was to examine to what extent healthcare expenditure and utilization differ according to educational level and income, and whether these differences can be explained by health inequalities.