Fight Aging!

As is discussed in today's open access paper, determining the risk of age-related disease is far from a solved problem. This is true even for cardiovascular disease, caused by degenerative processes that occur in every individual over the course of later life, and which would kill everyone in a world absent other fatal age-related conditions. Assessment of cardiovascular disease risk has received decades of sizable funding, large studies, and considerable attention from the research and medical communities. And yet it is still possible to write a lengthy paper on the very real shortcomings of present assessment approaches.

I believe that challenges in assessment of risk are a consequence of trying to assess risk based on factors that are only indirectly connected to causative processes. Aging is caused by forms of underlying molecular damage, and this damage creates a spreading network of downstream consequences, and further damage and problems caused by those consequences. A great deal of variability is present from individual to individual in this network, and so picking out parts of it may not be a good reflection of the actual burden of underlying, root cause damage. It would be better to assess that damage.

To take one example, senescent cell accumulation is an important contributing cause of aging. It isn't the only important contributing cause of aging, but it does appear to cause widespread dysfunction in tissues and systems throughout the body. Measuring senescent cell burden, once good non-invasive approaches are available to achieve this goal, should in principle be a better marker of disease risk than constructs based on lifestyle, diet, weight, and so forth. Once assays for cellular senescence exist, such as the blood sample microRNA approach under development by TAmiRNA, I'd imagine that we'll find out whether or not that is the case within a few years.

Cardiovascular risk and aging: the need for a more comprehensive understanding

The first half of the 20th century was marked by a shift in morbidity and mortality patterns in industrialized countries all over the world, moving from the leading role of infectious diseases to the increasing role of chronic, non-communicable diseases. In particular, cardiovascular disease (CVD) has been an important cause of morbidity and mortality, with coronary heart disease (CHD) being the leading cause of death. The primary reason for this transition was the discovery of antibiotics and vaccines, the widespread use of which has led to a decline in infectious diseases and increase in life expectancy and population aging.

CVD is a leading cause of morbidity and mortality worldwide, with the highest incidence and prevalence in an older population (> 60 years). Ever since the traditional CV risk factors were identified in the Framingham Heart Study, at the end of the 20th century, they have been used as the basis of risk-based strategies for predicting CVD and initiating drug therapy in primary CVD prevention. A number of predictive functions and score systems have been developed for CV risk assessment. Although there are some variations between the systems, most of them use the same limited set of variables, including age, sex, smoking, blood pressure, and cholesterol, to predict the ten-year absolute risk for developing CVD, or CVD-related death.

The current CV risk assessment systems have several limitations, which limit their implementation in practice and their efficacy in reducing the burden of CVD. Most systems perform well in the population from which they were derived but not in other populations. It is necessary to recalibrate the prediction equation for application in other populations, to allow for different CVD mortality rates and risk factor distributions. Another limitation is these systems' inability to represent the inter-individual variations in the CV risk accurately, so that a substantial proportion of individuals are wrongly classified, which can lead to either insufficient treatment or overtreatment. The variable age is the strongest CV predictor, and the current prediction models cannot distinguish between age and other risk factors.

Recent studies indicate that the effects of traditional CV risk factors attenuate among older individuals, and that other age-related factors, including comorbid conditions, become important for predicting CVD in older age. Based on the current knowledge, CVD develops concurrently with many comorbidities and other geriatric conditions, such as frailty, malnutrition, and sarcopenia, which share the common mechanisms and pathophysiology pathways as CVD. This is the reason why older people are very heterogeneous with respect to differences in their health status and functional performances, which makes CV risk prediction in older individuals complicated.