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Biomarkers of Aging and Mortality Risk Markers

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Biomarkers of Aging and Mortality Risk Markers - For populations, the life span is expressed statistically, often as the life expectancy. However, no measurement has been found that accurately predicts the individual life span from the genotype, or from any ‘biomarker of aging.’ Clearly, the traditional aging changes of gray hair and menopause do not assess an individual’s current health or future longevity. Jeanne Calment lived 70 years after menopause to achieve her longevity record of 122 years.

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The N.I.A. has supported an extensive search for biomarkers that predict remaining life span (Biomarkers of Aging Program, begun in 1982) (Reff and Schneider, 1982). Biomarkers have been evaluated in biochemical, cellular, genetics, molecular, and physiological characteristics, and in behavior and cognition. Emphasis has been given to changes of ‘non-pathological aging’ that are distinct from disease. Two decades later, no single biomarker or combination has been found to predict longevity better than the individual age in fly, worm, rodent, or human (Finch, 1990, pp. 558–564; Warner, 2004).

Consider the limits of biomarkers in aging worms, which seem an optimal model for actuarial questions by their minimal genetic, environmental, and social heterogeneity from dominance hierarchies and social interactions (worms are self-fertilizing). Pharyngeal pumping, by which food is ingested, declines progressively during the first week and then nearly ceases a few days before death. Body movements closely parallel the pumping rates, not surprisingly because pumping provides the food needed for energy. The duration of fast pharyngeal pumping and body movements shows strong correlation with individual life spans (Huang and Kaley, 2004). When fast pumping is maintained one day longer, the odds ratio for death by, or later than, a specified date is 1.7- fold greater. The Spearman rank correlation coefficient for the duration of fast pumping and remaining life span was highly significant (r 0.49, P< 0.0001). Despite this statistic, the fraction of life span variance explained by pumping or movement was only 24%. Thus, other variables besides pumping account for about 75% of the individual differences in life span. Mutants with greater longevity have longer phases of active body movement and pharyngeal pumping.


Even at hatching, worms differ hugely in movements, which Kirkwood and I attributed to chance variations in cell organization and gene expression during development (Finch and Kirkwood, 2000, pp. 58–65). A concrete example is Tom Johnson’s elegant study of worm-to-worm variations in expression of a stress-protective gene (hsp-16.2) in young worms, which correlated strongly with individual life spans (Rea et al, 2005). Again, the Spearman coefficient of 0.48 accounts for only 25% of the variance in life span. These sobering examples from rigorous studies suggest limits in the predictability of life spans despite strictly controlled genetics and environment. In worms, as in flies, mice, and humans, the heritability of the life span is also about 25% (Finch and Tanzi, 1997; Finch and Ruvkun, 2000).

As noted above, partial deficits of MnSOD did not alter mouse life span (Van Remmen et al, 2003). Several biomarkers of aging that respond to diet restriction were the same in Sod2+/− as in control aging mice (ad libitum fed in this study): both genotypes had identical age-related decreases of spleenocyte proliferation and increases in skin collagen of pentosidine and carboxymethyllysine. The lack of effects of MnSOD deficiency on these AGEs indicates that blood glucose was not altered. However, Sod2+/− mice had greater accumulations of oxidized nuclear DNA (8oxodG) in brain, heart, and liver, and 2-fold more lymphomas (83% vs. 41%). This study with its careful analytical chemistry and histopathology shows the uncertainty of connections between robust biomarkers of aging, tumor prevalence, and the life span.

There is reason to consider the individual disease load as more informative than tissue-level aging changes in predicting mortality risk (Karasik et al, 2005). During these same decades of the Biomarkers Program, vast clinical research has developed risk indicators of mortality for the major diseases. From the clinical perspective, disease, not aging, is the cause of mortality, as shown in the exponential increase of tissue lesions in rodent models and heart attack and stroke in human populations. Systolic blood pressure may be the most robust overall indicator of human mortality risk, with exponential age-related increases of future heart attack and stroke at all levels of systolic pressure.

The next phase of the biomarker debate may redefine the often vaguely used term ‘disease.’ For example, the clinical threshold of hypertension as a target for intervention is being expanded to include the ‘high normal’ range. A few decades ago, an informal guideline of the expected systolic pressure was “add 100 to the person’s age”! Combinations of risk indicators and disease load are also being intensively studied of the inflammatory marker, C-reactive protein (CRP). In the Women’s Health Study, future heart attacks occurred most frequently in those with both elevated CRP and LDL (Ridker, 2002). New models are needed to resolve the links between the diverse subtle subclinical aging changes that interact to cause circulatory failure on the background of declining organ reserves. It is shocking that 30% of diet-restricted old rats had no gross lesions at necropsy and cause of death was unknown (Shimokawa et al, 1993). Declining homeostasis of glucose and electrolytes, for example, might allow transient disturbances that would arrest a fibrotic heart, even in the absence of thrombotic blockade. The combinatorics of various mild dysfunctions gives rise to a huge number of individual pathophenotypes that may be each estimated as relative risk of mortality, but may never account for more of the variance in life span than in the worm model

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