Introduction to the special issue on gene-hormone interplay

Quest for Biomarkers of Positive Health: A Review

The positive health of a person can be defined as the ability to live long in good health, possibly with no activity limitation. No method is yet available for its objective assessment in individuals, and we propose a framework in this communication that can operationalize this concept. Instead of distal factors, such as diet and lifestyle because these are subjective and difficult to measure, we concentrate on the objectively measurable biomarkers such as immunity level, endorphins, and handgrip strength. The focus is on the major parameters that may protect from diseases and infirmity and can be assessed by noninvasive methods. A combination of such parameters may signify positive health. This may be a novel way to measure positive health at the individual level. In this communication, we briefly review the literature and identify a few major biomarkers that provide a protective shield and could determine the status of positive health at the individual level. This exercise demonstrates that the assessment of the positive health of a person is feasible. A scale based on these and other relevant parameters can be developed later that could quantitatively measure the exact level of positive health. As the exact combination of the parameters that protects from ailments is not fully known yet, a framework such as this may help in identifying the data gaps that require attention in this context. The proposed framework may initiate a discussion on indicators of positive health and characterize the parameters for intervention that could increase a healthy life.

Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems

BACKGROUND

Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size.

RESULTS

To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods.

CONCLUSIONS

With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.

Longitudinal effects of family psychopathology and stress on pubertal maturation and hormone coupling in adolescent twins

Adolescents experience profound neuroendocrine changes, including hormone "coupling" between cortisol, testosterone, and dehydroepiandrosterone. Emerging research has only begun to elucidate the role of hormone coupling, its genetic and environmental etiology, and the extent to which coupling is impacted by gender, puberty, and family context. We included measures on parent and child mental health, parenting stress, and family conflict of 444 twin pairs and their parents across two timepoints, when youth were on average 8 and 13 years old, respectively. Structural equation models examined the impact of family context effects on coupling during adolescence. Biometric twin models were then used to probe additive genetic, shared, and non-shared environmental effects on hormone coupling. Hormones were more tightly coupled for females than males, and coupling was sensitive to parental depression and co-twin psychopathology symptoms and stress exposure in females. The association between family context and coupling varied across specific neuroendocrine measures and was largely distinct from pubertal maturation. Biometric models revealed robust shared and non-shared environmental influences on coupling. We found that family antecedents modify the strength of coupling. Environmental influences account for much of the variation on coupling during puberty. Gender differences were found in genetic influences on coupling.