An evaluation of aging measures: from biomarkers to clocks

Genetic and environmental determinants of bone quality: a cross-sectional analysis of the Hungarian Twin Registry

There is abundant evidence that bone mineral content is highly heritable, while the heritability of bone quality (i.e. trabecular bone score [TBS] and quantitative ultrasound index [QUI]) is rarely investigated. We aimed to disentangle the role of genetic, shared and unique environmental factors on TBS and QUI among Hungarian twins. Our study includes 82 twin (48 monozygotic, 33 same-sex dizygotic) pairs from the Hungarian Twin Registry. TBS was determined by DXA, QUI by calcaneal bone ultrasound. To estimate the genetic and environmental effects, we utilized ACE-variance decomposition. For the unadjusted model of TBS, an AE model provided the best fit with > 80% additive genetic heritability. Adjustment for age, sex, BMI and smoking status improved model fit with 48.0% of total variance explained by independent variables. Furthermore, there was a strong dominant genetic effect (73.7%). In contrast, unadjusted and adjusted models for QUI showed an AE structure. Adjustments improved model fit and 25.7% of the total variance was explained by independent variables. Altogether 70-90% of the variance in QUI was related to additive genetic influences. We found a strong genetic heritability of bone quality in unadjusted models. Half of the variance of TBS was explained by age, sex and BMI. Furthermore, the adjusted model suggested that the genetic component of TBS could be dominant or an epistasis could be present. In contrast, independent variables explained only a quarter of the variance of QUI and the additive heritability explained more than half of all the variance.

Geroscience in the continuum from healthy longevity to frailty

The socioeconomic and technological developments of the past decades have enabled unique progress associated to increased life expectancy and better health for a large part of the world's population; however, multimorbidity, frailty and disability are also on the rise. Geroscience as the new biology of aging is based on the evidence that the main risk factor for noncommunicable chronic diseases (NCD) is the aging process; however, its technology is mostly used for the scientific study of longevity and its interaction with aging medicine and geriatrics is still limited. In this perspective, the need for a tighter exchange between geroscience and geriatrics for longer health span and intrinsic capacity is discussed in the context of existing evidence and knowledge gaps.

Biological Age in Congenital Heart Disease-Exploring the Ticking Clock

Over the past 50 years, there has been a major shift in age distribution of patients with congenital heart disease (CHD) thanks to significant advancements in medical and surgical treatment. Patients with CHD are, however, never cured and face unique challenges throughout their lives. In this review, we discuss the growing data suggesting accelerated aging in this population. Adults with CHD are more often and at a younger age confronted with age-related cardiovascular complications such as heart failure, arrhythmia, and coronary artery disease. These can be related to the original birth defect, complications of correction, or any residual defects. In addition, and less deductively, more systemic age-related complications are seen earlier, such as renal dysfunction, lung disease, dementia, stroke, and cancer. The occurrence of these complications at a younger age makes it imperative to further map out the aging process in patients across the spectrum of CHD. We review potential feasible markers to determine biological age and provide an overview of the current data. We provide evidence for an unmet need to further examine the aging paradigm as this stresses the higher need for care and follow-up in this unique, newly aging population. We end by exploring potential approaches to improve lifespan care.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.