eNews June 10, 2014

Longevity biomarkers revealed from inbred mouse strains

Biomarkers of aging

Although gray hair and skin wrinkles increase with age, they are not indicators of future functionality, and cannot be called true biomarkers of aging. A biomarker of aging is defined as a measurable biological feature of an organism that predicts functional capacity at some later age better than chronological age (Baker & Sprott). The identification of such biomarkers is an important goal for aging research, with thus far limited success.

One major limiting factor is the requirement of specific feature measurements in a large set of individuals exhibiting different lifespans, and such data are scarce. The reason is that some feature measurements in animal studies are either destructive (i.e. the animal must be euthanized) or interventional (e.g. taking a sufficient amount of blood may markedly influence the physiology of an animal), and therefore it is not always possible to determine the normal lifespan of the animal from which the measurements were taken (Moeller et al.).

The Mouse Phenome Database and aging-related phenotypes

To overcome this difficulty, one can combine studies with attendant problems of interpretation. If the animals used are genetically closely related groups such as inbred strains, features can then be measured at certain ages, and life expectancy can be estimated independently for each strain, (Moeller et al.).

The Nathan Shock Center of Excellence in the Basic Biology of Aging at The Jackson Laboratory has extensively characterized 32 commonly used inbred strains for aging-related phenotypes and made the primary data publicly available on the Mouse Phenome Database (MPD) (Sundberg et al.; Yuan et al.; and Maddatu et al.). MPD is a collaborative standardized collection of measured data on laboratory mouse strains, which includes baseline phenotype data sets as well as studies of drug, diet, disease and aging effects. MPD also provides free and readily available protocols, projects, publications, as well as SNP, variation and gene expression studies.

These data sets from MPD form the basis for the analyses presented by the Fuellen group in Aging Cell (Moeller et al.) that permit investigation on prognostic biomarkers of aging by combining longitudinal studies of features (e.g. blood count, leucocyte, blood chemistry, and body composition) and a separate study of life expectancy, all done for about 30 strains. The same individual animals were also tracked to allow true longitudinal analysis and avoid the interpretation problems of a cross-sectional study. Extraordinarily, the standardization of environment, assays and strains in the study means that their report represents the most coherent and well-controlled lifelong study yet conducted in mammals.

Clear-cut biomarkers of aging

The authors simultaneously performed regression and correlation, to investigate the longitudinal trends of all data set features, as well as Cox analyses to investigate their prognostic power for lifespan. They were able to validate 7 clear-cut cases where both kinds of evidence are corroborative (Table 1). These were related to immune cells (B lymphocytes and neutrophils), anemia (red blood cell and linked measurements) and inflammation (magnesium and neutrophils), and their classification as pro-longevity or anti-longevity (neutrophils only) was clear (Moeller et al.). These observations might be explained by mechanisms that were reported previously. Specifically, other investigators have found a decreased role of lymphocytes and B cells in adaptive immunity along with increases in neutrophils that might indicate an age-related shift of the immune system from adaptive to innate, driven by pro-inflammatory cytokines and chemokines. The anti-inflammatory effects of magnesium were previously described in Barbagallo et al.

Table 1 Clear-cut feature analysis results

Feature

Evidence

Known effect

Classification

Prognostic

Longitudinal

B Lymphocytes

Long Lifespan (Pronounced in females)

Down

Anti-immunosenescent

Pro-longevity

Red Blood Cells, Hemoglobin, Hematocrit

Long Lifespan (Pronounced in males)

Down

Anti-anemia

Pro-longevity

Magnesium

Long Lifespan (Pronounced at 12 months)

Down (Pronounced in females)

Anti-inflammatory

Pro-longevity

Neutrophils

Sort Lifespace

Up

Pro-inflammatory

Anti-longevity

Their study also revealed biomarkers that are conflicting at first sight. Conflict resolution is possible by postulating a role switch for the feature. For example, high biomarker values could be anti-longevity in early life and pro-longevity in later life. Role switching biomarkers correspond to features that must, for example, be minimized early in life, but maximized later, to achieve optimal longevity. Most role-switching biomarkers relate to blood serum features and whole-body phenotypes (e.g. iron, thyroxin, body mass index, and heart rate).

The biomarker classification approach presented by Moeller et al. may be applicable to any combination of longitudinal studies with life expectancy data, and might provide insights beyond a simplified scheme of biomarkers for long or short lifespan.

Additional research resources available: