Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter‐individual heterogeneity in the multisystem ageing process. Using machine‐learning regression models on the UK Biobank data set (N = 32,593, age range 44.6–82.3, mean age 64.1 years), we first estimated tissue‐specific brain age for white and gray matter based on diffusion and T1‐weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict ‘body age’. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion‐based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle‐fat infiltration were related to older‐appearing body age compared to brain age. Conversely, higher hand‐grip strength and muscle volume were related to a younger‐appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals

Evolutionary Views on Entrepreneurial Processes: Managerial and Policy Implications

In this paper we outline an evolutionary framework of entrepreneurial processes where by firms are started, grow, and exit from the market. We explain the important of such a framework in explaining both what contextual factor affects entrepreneurial processes and in explaining the distinction and interaction between self-employment and high-potential entrepreneurship. We highlight the implications from prior empirical work using this evolutionary framework for management and policy making: Three broad implications relevant for managers and entrepreneurs interested in understanding how they can leverage their chances to position their firms as ripe for growth, and six detailed implications relevant for policy makers interested in understanding and affecting the structural conditions where by entrepreneurship can lead to enhanced growth and job creation

The use of public transport and contraction of SARS-CoV-2 in a large prospective cohort in Norway

Background For many people public transport is the only mode of travel, and it can be challenging to keep the necessary distances in such a restricted space. The exact role of public transportation and risk of SARS-CoV-2 transmission is not known. Methods Participants (n = 121,374) were untested adult Norwegian residents recruited through social media who in the spring of 2020 completed a baseline questionnaire on demographics and the use of public transport. Incident cases (n = 1069) had a positive SARS-CoV-2 polymerase chain reaction test registered at the Norwegian Messaging System for Infectious Diseases by January 27, 2021. We investigated the association between the use of public transport and SARS-CoV-2 using logistic regression. Odds ratios (ORs) with 95% confidence intervals (CIs) adjusted for age, calendar time, gender, municipality, smoking, income level, fitness and underlying medical conditions were estimated. Frequency of the use of public transport was reported for 2 week-periods. Results Before lockdown, those who tested positive on SARS-CoV-2 were more likely to have used public transport 1–3 times (OR = 1.28, CI 1.09–1.51), 4–10 times (OR = 1.49, CI 1.26–1.77) and ≥ 11 times (OR = 1.50, CI 1.27–1.78, p for trend < 0.0001) than those who had not tested positive. Conclusion The use of public transport was positively associated with contracting SARS-CoV-2 both before and after lockdown