A Model of Individual BMI Trajectories.

A risk factor model of body mass index (BMI) is an important building block of health simulations aimed at estimating government policy effects with regard to overweight and obesity. We created a model that generates representative population level distributions and that also mimics realistic BMI trajectories at an individual level so that policies aimed at individuals can be simulated. The model is constructed by combining several datasets. First, the population level distribution is extracted from a large, cross-sectional dataset. The trend in this distribution is estimated from historical data. In addition, longitudinal data are used to model how individuals move along typical trajectories over time. The model faithfully describes the population level distribution of BMI, stratified by sex, level of education and age. It is able to generate life course trajectories for individuals which seem plausible, but it does not capture extreme fluctuations, such as rapid weight loss.

Dynamics of non-household contacts during the COVID-19 pandemic in 2020 and 2021 in the Netherlands.

The COVID-19 pandemic was in 2020 and 2021 for a large part mitigated by reducing contacts in the general population. To monitor how these contacts changed over the course of the pandemic in the Netherlands, a longitudinal survey was conducted where participants reported on their at-risk contacts every two weeks, as part of the European CoMix survey. The survey included 1659 participants from April to August 2020 and 2514 participants from December 2020 to September 2021. We categorized the number of unique contacted persons excluding household members, reported per participant per day into six activity levels, defined as 0, 1, 2, 3-4, 5-9 and 10 or more reported contacts. After correcting for age, vaccination status, risk status for severe outcome of infection, and frequency of participation, activity levels increased over time, coinciding with relaxation of COVID-19 control measures.

A randomized crossover trial assessing time of day snack consumption and resulting postprandial glycemic response in a real-life setting among healthy adults.

The postprandial glycemic response is an important metabolic health factor, which, from laboratory studies, is known to change from low to high over the course of the day, and from which negative health outcomes have been linked to nightly eating. We applied interstitial continuous glucose monitoring to examine the glycemic response to a standardized carbohydrate-rich snack (198 kcal) across the day in a real-life setting. Twenty-four healthy participants (12 men, 12 women, 27-61 y old) consumed the snack nine times during 6 d in a crossover design, altering the time of consumption between morning, afternoon and evening. The snack was consumed in the participant's own environment with a preceding fast of at least 2.5 h between their customary main meals and practices. Linear mixed models were used with fixed effect of timing, and participant as random effect, to assess incremental area under the curve, peak value and time-to-peak of the glycemic response. Overall, the highest glycemic excursions were observed in the morning, while a more dampened but prolonged response was observed in the evening. These findings do not concur with previously published laboratory studies. This implies that results obtained under controlled experimental conditions in laboratories cannot be generalized directly to predict chrononutritional effects on the glycemic response in healthy individuals and their daily routines.

SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries.

BACKGROUND: SARS-CoV-2 dynamics are driven by human behaviour. Social contact data are of utmost importance in the context of transmission models of close-contact infections. METHODS: Using online representative panels of adults reporting on their own behaviour as well as parents reporting on the behaviour of one of their children, we collect contact mixing (CoMix) behaviour in various phases of the COVID-19 pandemic in over 20 European countries. We provide these timely, repeated observations using an online platform: SOCRATES-CoMix. In addition to providing cleaned datasets to researchers, the platform allows users to extract contact matrices that can be stratified by age, type of day, intensity of the contact and gender. These observations provide insights on the relative impact of recommended or imposed social distance measures on contacts and can inform mathematical models on epidemic spread. CONCLUSION: These data provide essential information for policymakers to balance non-pharmaceutical interventions, economic activity, mental health and wellbeing, during vaccine rollout.