The Gompertz Law emerges naturally from the inter-dependencies between sub-components in complex organisms.

Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.

Danish Pathology Life Course (PATHOLIFE) cohort: a register based cohort extending upon a national tissue biobank.

PURPOSE: The Danish Pathology Life Course (PATHOLIFE) cohort was established to facilitate epidemiological research relating histological and cytological features extracted from patient tissue specimens to the rich life course histories, including both prior and future register data, of the entire Danish population. Research results may increase quality of diagnosis, prognosis and stratification of patient subtypes, possibly identifying novel routes of treatment. PARTICIPANTS: All Danish residents from 1 January 1986 to 31 December 2019, totalling 8 593 421 individuals. FINDINGS TO DATE: We provide an overview of the subpopulation of Danish residents who have had a tissue specimen investigated within the Danish healthcare system, including both the primary sector and hospitals. We demonstrate heterogeneity in sociodemographic and prognostic factors between the general Danish population and the above mentioned subpopulation, and also between the general Danish population and subpopulations of patients with tissue specimens from selected anatomical sites. Results demonstrate the potential of the PATHOLIFE cohort for integrating many different factors into identification and selection of the most valuable tissue blocks for studies of specific diseases and their progression. Broadly, we find that living with a partner, having higher education and income associates with having a biopsy overall. However, this association varies across different tissue and patient types, which also display differences in time-to-death and causes of death. FUTURE PLANS: The PATHOLIFE cohort may be used to study specified patient groups and link health related events from several national health registries, and to sample patient groups, for which stored tissue specimens are available for further research investigations. The PATHOLIFE cohort thereby provides a unique opportunity to prospectively follow people that were characterised and sampled in the past.

Dynamical indicators in time series of healthcare expenditures predict mortality risk of older adults following spousal bereavement.

BACKGROUND: The process of aging renders older people susceptible for adverse outcomes upon stress. Various indicators derived from complex systems theory have been proposed for quantifying resilience in living organisms, including humans. We investigated the ability of system-based indicators in capturing the dynamics of resilience in humans who suffer the adversity of spousal bereavement and tested their predictive power in mortality as a finite health transition. METHODS: Using longitudinal register data on weekly healthcare consumption of all Danish citizens over the age of 65 from January 1st, 2011, throughout December 31st, 2016, we performed statistical comparisons of the indicators 'average', 'slope', 'mean squared error', and 'lag-1 autocorrelation' one year before and after spousal bereavement, stratified for age and sex. The relation between levels of these indicators before bereavement and mortality hazards thereafter was determined by time to event analysis. We assessed the added value for mortality prediction via the time dependent area (AUC) under the receiver operating characteristic curve. RESULTS: The study included 934,003 citizens of whom 51,890 experienced spousal bereavement and 2862 died in the first year thereafter. Healthcare consumption is increased, more volatile and accelerating with aging and in men compared to women (all p-values < 0.001). All dynamic indicators before bereavement were positively related with mortality hazards thereafter (all p-values < 0.001). The average discriminative performance for the 1-year mortality risk of the model with only age as a predictor (AUC: 68.9% and 70.2%) was significantly increased with the addition of dynamical indicators (78.5% and 82.4%) for males and females, respectively. CONCLUSIONS: Dynamic indicators in time series of health care expenditures are strong predictors of mortality risk and could be part of predictive models for prognosis after life stressors, such as bereavement.