Neuroactive steroids: new biomarkers of cognitive aging.
Biomarkers of aging in women and the rate of longitudinal changes.
Use of mathematical models of survivorship in the study of biomarkers of aging: the role of heterogeneity.
Strategy for identifying biomarkers of aging in long-lived species.
Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population.
Behavioral biomarkers of aging: illustration of a multivariate approach for detecting age-related behavioral changes.
Growth curves and survival characteristics of the animals used in the Biomarkers of Aging Program.
Lesion biomarkers of aging in B6C3F1 hybrid mice.
Immunohistochemical and ELISA assays for biomarkers of oxidative stress in aging and disease.
A strategy for identifying biomarkers of aging: further evaluation of hematology and blood chemistry data from a calorie restriction study in rhesus monkeys.
Biomarkers of age and aging.
Candidate biomarkers of aging: age-sensitive indices of immune and muscle function covary in genetically heterogeneous mice.
Plasma proteins as biomarkers of the aging process.
Biomarkers of aging: correlation of DNA I-compound levels with median lifespan of calorically restricted and ad libitum fed rats and mice.
The reliability and stability of biomarkers of aging.
Biological and functional definition of the older patient: the role of biomarkers of aging.
Neurobehavioral biomarkers of aging: influence of genotype and dietary restriction.
Biomarkers of aging: do we know what to look for?
Biomarkers of aging: tissue markers. Future research needs, strategies, directions and priorities.
Cellular biomarkers of aging.
Key questions in developing biomarkers of aging.

   There are people who reach the age of 85 in a very good physical and mental condition. There are others who have extensive cognitive difficulties and physical disorders already by the age of 60. This is why it is logical to think that a person’s biological age is more indicative of their health than their chronological age. If an anti-aging theory or program is developed, it has to be tested whether it works. In the laboratory, using experimental animals, it is relatively easy to determine whether a certain anti-aging regime extends the life span. Not so with humans, because a lifelong study seems at present far from possible – nobody wants to engage in dedicating their life to studying something for over 50 years without any knowledge if the results would be favorable.
   This is why there is a need to identify the effects this certain program has not only in the body’s systems, but in the general aging process. To determine a person’s biological age and to assess the effects of different anti-aging techniques scientist use the so-called biomarkers of aging. It is generally believed that seven major health areas are affected by aging: cardiovascular health, glucose regulation, brain function, muscle and skeletal health, endocrine function, immune system and oxidative stress.
    Biomarkers of aging are physical properties in the human body which indicate that the body is aging. It is indicators of the normal phenomena of growing old. They are not, however, simply things which change with age. In order to be called a biomarker, a factor has to satisfy a number of criteria. The best markers will be the ones which are not susceptible to influence from the outside environment. For example, in the US cholesterol levels increase with age, but this is due to the nature of the American diet and is not characteristic for other parts of the world. Thus, a true biomarker would satisfy the following criteria:
A. The marker must predict the rate of aging and be a better predictor of life span than chronological age.
B. It must be able to be tested on a regular basis
C. It must work both for humans and other species, such as laboratory animals
D. There is support from human clinical assessment and complementary research studies.
E. The studies are based on a significant representative sample.
F. The result is a clear association with aging.
G. A relatively narrow standard deviation is present.
   So far, around 24 factors have met the criteria and can be considered biomarkers. They may be indicated especially for males or for females, and figures may vary between the sexes. Here is their list:

1. 17-ketosteroid/ 17-hydroxycortiosteroid ratio (male) 13. Handgrip strength
2. Ascorbic acid 14. Hemoglobin A1C
3. Basal Metabolic Rate 15. Lung capacity- FEV1
4. Blood pressure- pulse 16. Lung capacity- FVC
5. Blood pressure- systolic 17. Maximum oxygen update (male)
6. Body Mass Index (female) 18. Near vision
7. Caries index 19. Noradrenaline- plasma (male)
8. Creatinine clearance 20. Peridontal index
9. DHEA-S 21. PSA total (male)
10. Fibrinogen 22. Skin elasticity
11. Hair baldness (male) 23. Testosterone free (male)
12. Hair grayness 24. Zinc- serum

   In addition, there are also a number of other factors which may be considered partially biomarkers of aging. The main problem with these is that their reliability has not been confirmed through a sufficient amount of clinical and experimental data. These include body flexibility, blood urea nitrogen, LDL cholesterol, melatonin levels, static balance, serotonin levels and many others. They are to a certain degree indicative of a person’s biological age, but should not be confused with other general health factors, which do not have a clear association with age.
   Biomarkers of aging could be divided in three major categories. There are the ones which determine the biological age, e.g. skin elasticity and visual accommodation. There are markers which predict the remaining life expectancy; they include DHEA-S, hand grip strength, etc. Finally, there are factors which determine disease susceptibility, such as systolic blood pressure and glucose-tolerance tests. All of the biomarker tests can be classified either as laboratory tests (e.g. blood and urine tests) or as physical tests undertaken in a clinic.

J Steroid Biochem Mol Biol. 2003 Jun;85(2-5):329-35.
Neuroactive steroids: new biomarkers of cognitive aging.
Vallee M, Purdy RH, Mayo W, Koob GF, Le Moal M.
INSERM U588, Institut F. Magendie, 1 rue Camille Saint-Saens, 33077 Bordeaux Cedex, France.

Intensive studies in animals established that neuroactive steroids display neuronal actions and influence behavioral functions. We describe here investigations on the role of neuroactive steroids in learning and memory processes during aging and suggest their role as biomarkers of cognitive aging. Our work demonstrated the role of the steroid pregnenolone (PREG) sulfate as a factor underlying an individual's age-related cognitive decline in animals. As new perspectives of research we argue that knowing whether neuroactive steroids exist as endogenous neuromodulators and modulate physiologically behavioral functions is essential. To this end, a new approach using the sensitive, specific, and accurate quantitative determination of neuroactive steroids by mass spectrometry seems to have potential for examining the role of each steroid in discrete brain areas in learning and memory alterations, as observed during aging.


J Physiol Anthropol Appl Human Sci. 2003 Jan;22(1):37-46p.
Biomarkers of aging in women and the rate of longitudinal changes.

Ueno LM, Yamashita Y, Moritani T, Nakamura E.
Laboratory of Applied Physiology, Graduate School of Human and Environmental Studies, Kyoto University.

The purposes of this study were (1) to estimate biological age score (BAS) in Japanese healthy women based on the 4-7 years longitudinal data for physiological, hematological and biochemical examinations and (2) to examine the rate of aging changes in adult women based on the estimated BAS. The samples consisted of cross-sectional (n=981) and longitudinal (n=110) groups. Out of 31 variables examined, five variables (forced expiratory volume in 1.0 s, systolic blood pressure, mean corpuscular hemoglobin, glucose, albumin/globulin ratio) that met the following criteria: 1) significant cross-sectional correlation with age; 2) significant longitudinal change in the same direction as the cross-sectional correlation; and (3) assessment of redundancy, were selected as candidate biomarkers of aging. This variable set was then submitted into a principal component analysis, and the first principal component obtained from this analysis was used as an equation for assessing one's BAS. Individual BAS showed a high longitudinal stability of age-related changes, suggesting high predictive validity of our newly developed aging measurement equation. However, changes in the aging rate based on the estimated BAS were not constant. The mean slopes of the regression lines of BAS for the three age groups (age<45, 45</=age<65 yrs, 65</=age) were 0.095, 0.065, 0.138, respectively. One-way analysis of variance detected a significant difference (F=5.14, p<0.01) among the three age groups. These results suggest that the rate of aging in adult women is relatively slower until 65 years of age, but after 65, the rate of aging shows a rapid increase. We concluded that the longitudinal method used for selection of variables to compute the BAS was useful and theoretically valid compared to those obtained from cross-sectional data analysis.

Mech Ageing Dev. 2001 Sep 15;122(13):1461-75.
Use of mathematical models of survivorship in the study of biomarkers of aging: the role of heterogeneity.
Piantanelli L, Rossolini G, Basso A, Piantanelli A, Malavolta M, Zaia A.
Gerontologic Research Department--INRCA, Center of Biochemistry, Via Birarelli 8, I-60123, Ancona, Italy.

An ever increasing number of people have been engaging in aging research using various interventions aimed to modify aging processes, and/or life span, of experimental animals. Since this type of studies needs outcome parameters for assessing the efficacy of such interventions, research on biomarkers of aging (ABs) has received new stimuli. In the present paper, the problem of the occurrence of a vicious circle any time we study ABs and determinants of aging is addressed. In fact, while ABs would represent the standard reference to be used in the study of the main causes of processes of aging, these very determinants should already be known in order to get reliable ABs. A feasible way to overcome this impasse is proposed, using mathematical models of survivorship or mortality based on biological hypotheses and accounting for inter-individual heterogeneity, a necessary ingredient for a correct interpretation of survival results. Specific kinetics of experimental parameters that are candidates as ABs can be compared to the kinetics hypothesized for general biological functions entering the model. We have built a model of this type that can also be used to perform a reliable overall gross estimate of the rate of aging, R(a), in the population, a parameter useful when judging the success of interventions aimed to act on determinants of aging. The perspective that theory of complex systems can be of help in the search for ABs is also discussed.

Exp Gerontol. 2001 Jul;36(7):1025-34.
Strategy for identifying biomarkers of aging in long-lived species.
Ingram DK, Nakamura E, Smucny D, Roth GS, Lane MA.
Laboratory of Neurosciences, Gerontology Research Center, National Institute on Aging, National Institutes of Health, 5600 Nathan Shock Drive, Baltimore, MD 21224, USA.

If effective anti-aging interventions are to be identified for human application, then the development of reliable and valid biomarkers of aging are essential for this progress. Despite the apparent demand for such gerotechnology, biomarker research has become a controversial pursuit. Much of the controversy has emerged from a lack of consensus on terminology and standards for evaluating the reliability and validity of candidate biomarkers. The initiation of longitudinal studies of aging in long-lived non-human primates has provided an opportunity for establishing the reliability and validity of biomarkers of aging potentially suitable for human studies. From the primate study initiated in 1987 at the National Institute on Aging (NIA), the following criteria for defining a biomarker of aging have been offered: (1) significant cross-sectional correlation with age; (2) significant longitudinal change in the same direction as the cross-sectional correlation; (3) significant stability of individual differences over time. These criteria relate to both reliability and validity. However, the process of validating a candidate biomarker requires a greater standard of proof. Ideally, the rate of change in a biomarker of aging should be predictive of lifespan. In short-lived species, such as rodents, populations differing in lifespan can be identified, such as different strains of rodents or groups on different diets, such as those subjected to calorie restriction (CR), which live markedly longer. However, in the NIA primate study, the objective is to demonstrate that CR retards the rate of aging and increases lifespan. In the absence of lifespan data associated with CR in primates, validation of biomarkers of aging must rely on other strategies of proof. With this challenge, we have offered the following strategy: If a candidate biomarker is a valid measure of the rate of aging, then the rate of age-related change in the biomarker should be proportional to differences in lifespan among related species. Thus, for example, the rate of change in a candidate biomarker of aging in chimpanzees should be twice that of humans (60 vs 120 years maximum lifespan); in rhesus monkeys three times that of humans (40 vs 120 years maximum lifespan). The realization of this strategy will be aided by developing a primate aging database, a project that was recently launched in cooperation with the NIA, the National Center for Research Resources, and the University of Wisconsin Regional Primate Research Center.

J Gerontol A Biol Sci Med Sci. 2001 Apr;56(4):B180-6.
Biomarkers of aging: prediction of longevity by using age-sensitive T-cell subset determinations in a middle-aged, genetically heterogeneous mouse population.
Miller RA.
Department of Pathology and Geriatrics Center, University of Michigan School of Medicine, Ann Arbor 48109-0940, USA.

Seven T-cell subset values were measured in each of 559 mice at 8 months of age, and then again in the 494 animals that reached 18 months of age. The group included virgin males, virgin females, and mated females, and it was produced by using a four-way cross-breeding system that generates genetic heterogeneity equivalent to a very large sibship. An analysis of covariance showed that four T-cell subsets-CD4, CD4 memory, CD4 naive, and CD4 cells expressing P:-glycoprotein-were significant predictors (p <.003) of longevity when measured at 18 months of age after adjustment for the possible effects of gender and mating. The subset marked by CD4 and P:-glycoprotein expression showed a significant interaction effect: this subset predicted longevity only in males. Among subsets measured when the mice were 8 months of age, only the levels of CD8 memory cells predicted longevity (p =.016); the prognostic value of this subset was largely limited to mated females. A cluster analysis that separated mice into two groups based upon similarity of T-cell subset patterns measured at 18 months showed that these two groups differed in life expectancy. Specifically, mice characterized by relatively low levels of CD4 and CD8 memory cells, high levels of CD4 naive cells, and low levels of CD4 cells with P:-glycoprotein (64% of the total) lived significantly longer (50 days = 6%; p <.0007) than mice in the other cluster. The results are consistent with the hypothesis that patterns of T-cell subsets vary among mice in a manner than can predict longevity in middle age, and they suggest that these subsets may prove to be useful for further studies of the genetics of aging and age-sensitive traits.

J Gerontol A Biol Sci Med Sci. 1999 Dec;54(12):B549-66.
Behavioral biomarkers of aging: illustration of a multivariate approach for detecting age-related behavioral changes.
Markowska AL, Breckler SJ.
Department of Psychology, Johns Hopkins University, Baltimore, MD 21218, USA.

The goal of the current project is to develop a multivariate statistical strategy for the formation of behavioral indices of performance and, further, to apply this strategy to establish the relationship between age and important characteristics of performance. The strategy was to begin with a large set of measures that span a broad range of behaviors. The behavioral effects of the following variables were examined: Age (4, 12, 24, and 30 months), genotype [Fischer 344 and a hybrid (F1) of Fischer 344 and Brown Norway (F344xBN)], gender (Fischer 344 males and Fischer 344 females), long-term diet (ad lib diet or dietary restriction beginning at 4 months of age), and short-term diet (ad lib diet or dietary restriction during testing). The behavioral measures were grouped into conceptually related indicators. The indicators within a set were submitted to a principal component analysis to help identify the summary indices of performance, which were formed with the assumption that these component scores would offer more reliable and valid measures of relevant aspects of behavioral performance than would individual measures taken alone. In summary, this approach has made a number of important contributions. It has provided sensitive and selective measures of performance that indicated contributions of all variables: psychological process, age, genotype, gender, long-term and short-term diet and has increased the sensitivity of behavioral measures to age-related behavioral impairment. It has also improved task-manageability by decreasing the number of meaningful variables without losing important information, consequently providing a simplification of the pattern of changes.

J Gerontol A Biol Sci Med Sci. 1999 Nov;54(11):B492-501.
Growth curves and survival characteristics of the animals used in the Biomarkers of Aging Program.
Turturro A, Witt WW, Lewis S, Hass BS, Lipman RD, Hart RW.
Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079-9502, USA.

The collaborative Interagency Agreement between the National Center for Toxicological Research (NCTR) and the National Institute on Aging (NIA) was aimed at identifying and validating a panel of biomarkers of aging in rodents in order to rapidly test the efficacy and safety of interventions designed to slow aging. Another aim was to provide a basis for developing biomarkers of aging in humans, using the assumption that biomarkers that were useful across different genotypes and species were sensitive to fundamental processes that would extrapolate to humans. Caloric restriction (CR), the only intervention that consistently extends both mean and maximal life span in a variety of species, was used to provide a model with extended life span. C57BI/6NNia, DBA/2JNia, B6D2F1, and B6C3F1 mice and Brown Norway (BN/RijNia), Fischer (F344/NNia) and Fischer x Brown Norway hybrid (F344 x BN F1) rats were bred and maintained on study. NCTR generated data from over 60,000 individually housed animals of the seven different genotypes and both sexes, approximately half ad libitum (AL) fed, the remainder CR. Approximately half the animals were shipped to offsite NIA investigators internationally, with the majority of the remainder maintained at NCTR until they died. The collaboration supplied a choice of healthy, long-lived rodent models to investigators, while allowing for the development of some of the most definitive information on life span, food consumption, and growth characteristics in these genotypes under diverse feeding paradigms.

J Gerontol A Biol Sci Med Sci. 1999 Nov;54(11):B466-77.
Lesion biomarkers of aging in B6C3F1 hybrid mice.
Lipman RD, Dallal GE, Bronson RT.
Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, Massachusetts 02111, USA.

This study of B6C3F1 hybrid mice was designed to determine the effects of caloric restriction (CR) on age-related pathologic changes. These changes accompany and may, in part, account for the apparent effect of CR in slowing the rate of aging. The study also explored whether lesions observed in groups of animals killed at 6 month intervals can serve as biomarkers of aging. Approximately 30 mice of each sex and each of two diet groups--CR and ad libitum fed (AL)--and each of six age groups--6, 12, 18, 24, 30, and 36 months of age--from the Biomarkers of Aging Program of the National Institute on Aging were killed and all tissues from each were examined for the histological presence or absence of lesions. A total of 209 distinct lesions were observed, of which 51 occurred in at least 10% of the AL or CR mice. The average number of lesions per mouse increased linearly with age in all diet-sex groups except in male CR mice. This increase was significantly smaller in CR than in AL mice of both sexes. The number of distinct lesions also increased with age in both diet groups but much less rapidly in CR mice. Nearly all lesions, including neoplastic, and nonneoplastic proliferative and degenerative ones, occurred significantly less often in CR than in AL mice at all ages. Foci of leukocytes in the liver, however, occurred more frequently in CR mice. Lung adenomas in old female mice occurred with equal frequency in both diet groups. A parsimonious classification tree analysis showed that diet groups could have been distinguished at each age by an evaluation of relatively few lesions and tissues. Altogether, this study suggests strongly that the prevalence of many individual lesions, the total lesion burden, and the total types of lesions are good biomarkers of aging because they increase with age and reflect the effect of CR in slowing the aging process. The study also shows that lesions occur stochastically, randomly, and independently in genetically homogeneous mice raised in a nonvariable environment.

Ann N Y Acad Sci. 1998 Nov 20;854:277-90.
Immunohistochemical and ELISA assays for biomarkers of oxidative stress in aging and disease.
Onorato JM, Thorpe SR, Baynes JW.
Department of Chemistry and Biochemistry, University of South Carolina, Columbia 29208, USA.

Oxidative stress is apparent in pathology associated with aging and many age-related, chronic diseases, including atherosclerosis, diabetes mellitus, rheumatoid arthritis, and neurodegenerative diseases. Although it cannot be measured directly in biological systems, several biomarkers have been identified that provide a measure of oxidative damage to biomolecules. These include amino acid oxidation products (methionine sulfoxide, ortho-tyrosine (o-tyr) and dityrosine, chlorotyrosine and nitrotyrosine), as well as chemical modifications of protein following carbohydrate or lipid oxidation, such as N epsilon-(carboxymethyl)lysine and N epsilon-(carboxyethyl)lysine, and malondialdehyde and 4-hydroxynonenal adducts to amino acids. Other biomarkers include the amino acid cross-link pentosidine, the imidazolone adducts formed by reaction of 3-deoxyglucosone or methylglyoxal with arginine, and the imidazolium cross-links formed by the reaction of glyoxal and methylglyoxal with lysine residues in protein. These compounds have been measured in short-lived intracellular proteins, plasma proteins, long-lived extracellular proteins, and in urine, making them valuable tools for monitoring tissue-specific and systemic chemical and oxidative damage to proteins in biological systems. They are normally measured by sensitive high-performance liquid chromatography or gas chromatography-mass spectrometry methods, requiring both complex analytical instrumentation and derivatization procedures. However, sensitive immunohistochemical and ELISA assays are now available for many of these biomarkers. Immunochemical assays should facilitate studies on the role of oxidative stress in aging and chronic disease and simplify the evaluation of therapeutic approaches for limiting oxidative damage in tissues and treating pathologies associated with aging and disease. In this article we summarize recent data and conclusions based on immunohistochemical and ELISA assays, emphasizing the strengths and limitations of the techniques.

Exp Gerontol. 1998 Aug;33(5):421-43.
A strategy for identifying biomarkers of aging: further evaluation of hematology and blood chemistry data from a calorie restriction study in rhesus monkeys.
Nakamura E, Lane MA, Roth GS, Ingram DK.
Division of Natural Environmental Sciences, Faculty of Integrated Human Studies, Kyoto University, Japan.

We examined a dataset derived from a battery of hematology and blood chemistry tests to identify candidate biomarkers of aging in a sample of 33 male rhesus monkeys (Macaca mulatta) ranging in age from 4-27 years. About half this sample comprised an experimental group subjected to 30% calorie restriction for six to seven years compared to the control group fed the same nutritionally fortified diet to approximate ad lib levels. Variables that met the following criteria were selected: (1) longitudinal change within the cohorts of control monkeys; (2) cross-sectional correlation with age across the adult lifespan in the control group; (3) stability of individual differences within all groups; and (4) no obvious redundancy with other selected variables. Five variables emerged from this step-wise selection, including the percentage lymphocytes, and serum levels of alkaline phosphatase, albumin, creatinine, and calcium. These variables were then submitted to a principal component analysis, which yielded a single component accounting for about 58% of the total variance. Based on this marked degree of covariance, these candidate biomarkers of aging could be combined into a biological age score (BAS) for the control and experimental groups. When chronological age was regressed onto BAS, the slopes of the control and experimental groups could be compared. Although a trend toward a slower aging rate in calorie-restricted monkeys was apparent, this analysis did not detect a statistically significant difference in the rate of aging between these groups estimated by this index. Despite this result, a logical strategy was confirmed for expanding the search for candidate biomarkers of aging to apply to this and to other studies assessing interventions that purport to affect the rate of aging in long-lived species.

Exp Gerontol. 1997 Jan-Apr;32(1-2):87-94.
Biomarkers of age and aging.
McClearn GE.
Center for Developmental and Health Genetics, Pennsylvania State University, University Park 16802, USA.

The escalating interest in research on interventions that may affect aging processes has necessarily focussed attention on the outcome measures. The desirable characteristics of these "biomarker variables" have been widely discussed. This article offers some reflections on validity, reliability, and generalizability of biomarkers. It is argued that our comprehension of aging will evolve iteratively from application of a diversity of biomarker variables. Each of these will have strengths and shortcomings from methodological and measurement points of view. The siren song that a "gold standard" index of aging can be found should be ignored.

J Gerontol A Biol Sci Med Sci. 1997 Jan;52(1):B39-47.
Candidate biomarkers of aging: age-sensitive indices of immune and muscle function covary in genetically heterogeneous mice.
Miller RA, Bookstein F, Van der Meulen J, Engle S, Kim J, Mullins L, Faulkner J.
Department of Pathology, University of Michigan School of Medicine, Ann Arbor, USA.

A longitudinal experiment was designed to test the hypothesis that individual mice differ in their aging rate and to validate candidate biomarkers proposed to measure the rate of aging. Mice were bred as the genetically heterogeneous progeny of a cross between CB6F1 mothers and C3D2F1 fathers. Half of the mice were fed ad libitum (AL group), and the other half were subjected to 60% calorie restriction (CR group). Each mouse was tested at about 9 months of age using age-sensitive tests of immune status, and then again at about 12 months of age using age-sensitive tests of muscle function. The data were then analyzed using the method of partial least squares to determine the combinations of test weights that maximize the covariance of the weighted sum of immune measures with the weighted sum of muscle function measures. Both AL and CR mice exhibited a statistically significant relation between the immune status tests and the muscle function tests. Maximal covariance was obtained with a set of weighting coefficients consistent with our working hypothesis: mice with high levels of CD4 memory T cells (which increase with age) also had relatively low levels of muscle strength and endurance. Low strength was associated with low CD8 cells in the AL mice, with high numbers of CD8 memory cells in the CR mice and with low CD3 cells in both diet groups. The partial least squares method generates composite indices of immune status and muscle function that can be evaluated as biomarkers of aging rate in these mice. Further work will be needed to assess whether these tests predict either longevity or the trajectory of change in other age-sensitive molecular and physiological traits.

Am J Physiol. 1995 Feb;268(2 Pt 2):R536-48.
Plasma proteins as biomarkers of the aging process.
Vranckx R, Savu L, Lambert N, de Conchard GV, Grosse R, Mourey MS, Corman B.
Institut National de la Sante et de la Recherche Medicale U 224, Centre National de la Recherche Scientifique, Faculte Xavier Bichat, Paris, France.

This study was designed to characterize the rat serum proteins as biomarkers of the normal aging process. Crossed immunoelectrophoresis or electroimmunodiffusion quantitation of proteins was performed in rats aged 6, 12, 24, and 30 mo. Selection of healthy animals was based on confrontation of crossed immunoelectrophoresis patterns with those of experimentally inflamed young adults and with individual anatomopathological data. Convergence of inflammatory patterns and severe histological lesions was the exclusion criterion. Senescence-induced decrease was demonstrated for eight proteins [negative senescence reactants (SRs-)] and increase for six proteins [positive SRs (SRs+)]. Most SRs belonged to the class of proteins responsive to acute inflammation [acute phase reactants (APRs)]. One SR+, the thyroxine-binding globulin, a high-affinity thyroid hormone binder, emerged as a particularly reliable senescence biomarker, showing the highest aging-related variation (8-fold increase from 6 to 30 mo) and not belonging to the APR class. Chronic treatment with perindopril, an angiotensin I-converting enzyme inhibitor used in heart and renal disease therapy, significantly enhanced thyroxine-binding capacity, possibly by preventing age-related alterations of serum lipids. Serum protein patterns prove valuable both as indexes for selecting aging animals free from superimposed pathologies and as parameters of senescence-induced changes in protein biosynthesis.

Mutat Res. 1993 Dec;295(4-6):247-63.
Biomarkers of aging: correlation of DNA I-compound levels with median lifespan of calorically restricted and ad libitum fed rats and mice.
Randerath K, Zhou GD, Hart RW, Turturro A, Randerath E.
Department of Pharmacology, Baylor College of Medicine, Houston, TX 77030.

I-compounds are species-, tissue-, genotype-, gender-, and diet-dependent bulky DNA modifications whose levels increase with animal age. While a few of these DNA modifications represent oxidation products, the majority of I-compounds appear to be derived from as yet unidentified endogenous DNA-reactive intermediates other than reactive oxygen species. Circadian rhythms of certain I-compounds in rodent liver imply that levels of these DNA modifications are precisely regulated. Caloric restriction (CR), the currently most effective method available to retard aging and carcinogenesis, has been previously shown to elicit significant elevations of I-compound levels in tissue DNA from Brown-Norway (BN) and F-344 rats as compared to age-matched ad libitum fed (AL) animals. The present investigation has extended this work by examining liver and kidney DNA I-compound levels in three genotypes of rats (F-344, BN, and F-344 x BN) and two genotypes of mice (C57BL/6N and B6D2F1) under identical experimental conditions in order to determine whether correlations exist between I-compound levels, measured in middle-aged animals, and median lifespan. Levels of a number of liver and kidney I-compounds were found to display genotype- and diet-dependent, statistically significant positive linear correlations with median lifespan in both species. In particular, the longer-lived hybrid F-344 x BN rats and B6D2F1 mice tended to exhibit higher I-compound levels than the parent strains. CR enhanced I-compound levels substantially in both rats and mice. Thus, I-compounds, measured at middle age, reflected the functional capability ('health') of the organism at old age, suggesting their predictive value as biomarkers of aging. The positive linear correlations between levels of certain I-compounds (designated as type I) and lifespan suggest that these modifications may be functionally important and thus not represent endogenous DNA lesions (type II), whose levels would be expected to correlate inversely with lifespan.

Ann N Y Acad Sci. 1992 Dec 26;673:1-8.
The reliability and stability of biomarkers of aging.
McClearn GE.
Center for Developmental and Health Genetics, College of Health and Human Development, Pennsylvania State University, University Park 16802-6501.

Different types of stability of a biomarker are important properties, influencing the degree of predictability across age (ordinal stability) and the interpretation of quantitative and qualitative change with age (structural stability). These properties may be expected to differ from biomarker to biomarker and may change with age. Any age-related process with individual differences in time of onset of change or in rate of change will necessarily display reduced ordinal stability. Another source of reduced correlation across occasions is the short-term fluctuance of individuals due to cyclic processes and to responsiveness to environmental displacements of biomarker values and recovery therefrom. Structural stability of composite variates may be quite high across relatively short intervals but sufficiently low across longer intervals as to suggest the inappropriateness of simple description of mean changes or differences across these longer time spans. The outcome with multivariate composites raises the issue that single biomarkers may have quite different meanings at different parts of the life span.

Oncology (Huntingt). 1992 Feb;6(2 Suppl):39-44.
Biological and functional definition of the older patient: the role of biomarkers of aging.
Mooradian AD.
Department of Internal Medicine, St. Louis University School of Medicine.

The rate of aging is not uniform among all individuals. Thus, determination of the biological age of an individual and possibly the biological age of his or her organ systems poses a special challenge to the gerontologist. This could be accomplished if specific biomarkers of aging were available, which would allow standardization of studies, help us understand the various determinants of aging, monitor the impact of various interventions on the rate of aging, and possible allow estimates of life expectancy and predictions of future morbidity. Specific criteria need to be developed for accepting a parameter as a biomarker of aging, since an age-related alteration in a biological parameter does not necessarily qualify. Potential biomarkers of human aging include in vitro proliferative capacity of fibroblasts, glycation of collagen, and DNA unwinding rate.

Biomed Environ Sci. 1991 Jun;4(1-2):144-65.
Neurobehavioral biomarkers of aging: influence of genotype and dietary restriction.
Forster MJ, Lal H.
Department of Pharmacology, Texas College of Osteopathic Medicine, Fort Worth 76107.

Because of the importance of central nervous system (CNS) functions to productive capacity and quality of life, biomarkers of these functions will play a key role in evaluating the success of interventions targeting aging processes. The CNS biomarkers may also be useful for predicting aging in other systems and in the organism as a whole. Age-related behavioral changes, the products of CNS aging, have content and predictive validity with respect to human functional capacities and may, therefore, represent important "neurobehavioral" markers of functional aging. This article presents a discussion of some behavioral paradigms which are currently being considered as neurobehavioral biomarkers of aging in mice and the experimental approaches being employed in the assessment of their validity. Studies conducted in the authors' laboratory using dietary restriction and genetic comparisons to evaluate the validity of neurobehavioral biomarkers have revealed several methodological concerns, and hypothetical and empirical examples of these pitfalls are described and discussed. In spite of those concerns, it is concluded that approaches to validity using genetic comparisons and dietary restriction can be successfully implemented and should ultimately lead to identification of valid and useful neurobehavioral biomarkers of aging.

J Gerontol. 1990 Nov;45(6):B183-6.
Biomarkers of aging: do we know what to look for?
Mooradian AD.
Division of Restorative Medicine, University of Arizona College of Medicine.

The identification of specific biomarkers of aging would be an important milestone in gerontologic research. In this communication, the goals of identifying biomarkers of aging are summarized and some criteria for defining biomarkers are suggested. An age-related alteration in a biological parameter is not necessarily a biomarker of aging. None of the previously observed age-related changes satisfies all the criteria. Potential biomarkers that are applicable to human aging include in vitro proliferative capacity of fibroblasts, glycation of collagen, and DNA unwinding rate. Future research should focus on identifying age-related changes that are not merely expressions of aging, but also have some causal link to aging.

Exp Gerontol. 1988;23(4-5):309-25.
Biomarkers of aging: tissue markers. Future research needs, strategies, directions and priorities.
Harrison DE, Archer JR.
Jackson Laboratory, Bar Harbor, Maine 04609.

Objective tests that allow early detection of deleterious changes with age are necessary to develop treatments enhancing the health span--the length of healthy life. Here we report tests of eight biological systems that can be performed in mice with no harm to the subjects. Male and female B6, CBA and F1 mice were used. While most test results correlated with chronological age in most genotypes, none predicted subsequent longevities in more than two genotypes. Surprisingly, the open field activity test that most consistently predicted longevities, did not correlate with chronological age. Six tests predicted beneficial effects of food restriction in F1 males, but only one correctly predicted the deleterious effects of the same food restriction regimen in B6 males. These results suggest that different biological systems age at different rates, that rates are affected by genotype and that an anti-aging treatment beneficial in one genotype may be harmful in another.

Exp Gerontol. 1988;23(4-5):297-307.
Cellular biomarkers of aging.
Cristofalo VJ.
Wistar Institute, Philadelphia, Pennsylvania 19104.

Normal human fibroblast-like cells show a declining proliferative capacity with age. Eventually the cultures become senescent and incapable of replicating. Loss of proliferative capacity is characterized by a declining fraction of cells which synthesize DNA in a defined time period, a gradually increasing cell cycle time, and a declining saturation density. For 36 sublines of WI-38 cells and 17 sublines of IMR-90 cells, we have characterized the fraction of cells synthesizing DNA and the saturation density throughout their life spans. These parameters were both related in a regular and consistent way with the percent life span completed and determined retrospectively by cell counts at each subcultivation until phase-out. Thus, these two determinations serve as independent biomarkers for cell culture aging as they relate to one functional parameter--proliferative capacity. As such, they can be used to assess functional age independently of chronological age.

Exp Gerontol. 1988;23(4-5):429-34.
Key questions in developing biomarkers of aging.
Ingram DK.
Molecular Physiology and Genetics Section, National Institute on Aging, Baltimore, Maryland 21224.

A series of questions is presented regarding a logical strategy for developing biomarkers of aging. The questions pertain to the conceptualization process in determining how to define aging and what extraneous and possibly confounding variables must be controlled in measuring this epiphenomenon. In addition, the investigator must consider the degree of generalization that is intended to apply to a candidate biomarker of aging. Empirical questions are also to be considered. Specifically, how will reliability and validity of the candidate biomarker be quantified and assessed? What statistical methods will be applied in this process? The need for biomarkers of aging as research tools in gerontology is argued, but the greater need for agreement on how to direct the conceptualization of this effort is also emphasized.

on the Adriatic Coast
The Anti-Aging Fasting Program consists of a 7-28 days program (including 3 - 14 fasting days). 7-28-day low-calorie diet program is also available .
More information
    The anti-aging story (summary)
Introduction. Statistical review. Your personal aging curve
  Aging and Anti-aging. Why do we age?
    2.1  Aging forces (forces that cause aging
Internal (free radicals, glycosylation, chelation etc.) 
External (Unhealthy diet, lifestyle, wrong habits, environmental pollution, stress, poverty-change "poverty zones", or take it easy. etc.) 
    2.2 Anti-aging forces
Internal (apoptosis, boosting your immune system, DNA repair, longevity genes) 
External (wellness, changing your environment; achieving comfortable social atmosphere in your life, regular intake of anti-aging drugs, use of replacement organs, high-tech medicine, exercise)
    2.3 Aging versus anti-aging: how to tip the balance in your favour!
    3.1 Caloric restriction and fasting extend lifespan and decrease all-cause mortality (Evidence)
      Human studies
Monkey studies
Mouse and rat studies
Other animal studies
    3.2 Fasting and caloric restriction prevent and cure diseases (Evidence)
Hypertension and Stroke
Skin disorders
Mental disorders
Neurogical disorders
Asthmatic bronchitis, Bronchial asthma
Bones (osteoporosis) and fasting
Arteriosclerosis and Heart Disease
Cancer and caloric restriction
Cancer and fasting - a matter of controversy
Eye diseases
Chronic fatigue syndrome
Sleeping disorders
Rheumatoid arthritis
Gastrointestinal diseases
    3.3 Fasting and caloric restriction produce various
      biological effects. Effects on:
        Energy metabolism
Lipids metabolism
Protein metabolism and protein quality
Neuroendocrine and hormonal system
Immune system
Physiological functions
Reproductive function
Cognitive and behavioral functions
Biomarkers of aging
    3.4 Mechanisms: how does calorie restriction retard aging and boost health?
        Diminishing of aging forces
  Lowering of the rate of gene damage
  Reduction of free-radical production
  Reduction of metabolic rate (i.e. rate of aging)
  Lowering of body temperature
  Lowering of protein glycation
Increase of anti-aging forces
  Enhancement of gene reparation
  Enhancement of free radical neutralisation
  Enhancement of protein turnover (protein regeneration)
  Enhancement of immune response
  Activation of mono-oxygenase systems
  Enhance elimination of damaged cells
  Optimisation of neuroendocrine functions
    3.5 Practical implementation: your anti-aging dieting
        Fasting period.
Re-feeding period.
Safety of fasting and low-calorie dieting. Precautions.
      3.6 What can help you make the transition to the low-calorie life style?
        Social, psychological and religious support - crucial factors for a successful transition.
Drugs to ease the transition to caloric restriction and to overcome food cravings (use of adaptogenic herbs)
Food composition
Finding the right physician
    3.7Fasting centers and fasting programs.
  Food to eat. Dishes and menus.
    What to eat on non-fasting days. Dishes and menus. Healthy nutrition. Relation between foodstuffs and diseases. Functional foods. Glycemic index. Diet plan: practical summary. "Dr. Atkins", "Hollywood" and other fad diets versus medical science

Bread, cereals, pasta, fiber
Glycemic index
Meat and poultry
Sugar and sweet
Fats and oils
Dairy and eggs
Nuts and seeds
Food composition

  Anti-aging drugs and supplements
    5.1 Drugs that are highly recommended
      (for inclusion in your supplementation anti-aging program)
        Vitamin E
Vitamin C
Co-enzyme Q10
Lipoic acid
Folic acid
Flavonoids, carotenes
Vitamin B
Vinpocetine (Cavinton)
Deprenyl (Eldepryl)
    5.2 Drugs with controversial or unproven anti-aging effect, or awaiting other evaluation (side-effects)
        Phyto-medicines, Herbs
      5.3 Drugs for treatment and prevention of specific diseases of aging. High-tech modern pharmacology.
        Alzheimer's disease and Dementia
Immune decline
Infections, bacterial
Infections, fungal
Memory loss
Muscle weakness
Parkinson's disease
Prostate hyperplasia
Sexual disorders
Stroke risk
Weight gaining
    5.4 The place of anti-aging drugs in the whole
      program - a realistic evaluation
    6.1 Early diagnosis of disease - key factor to successful treatment.
      Alzheimer's disease and Dementia
Cataracts and Glaucoma
Genetic disorders
Heart attacks
Immune decline
Infectious diseases
Memory loss
Muscle weakness
Parkinson's disease
Prostate hyperplasia
Stroke risk
Weight gaining
    6.2 Biomarkers of aging and specific diseases
    6.3 Stem cell therapy and therapeutic cloning
    6.4 Gene manipulation
    6.5 Prosthetic body-parts, artificial organs
Bones, limbs, joints etc.
Heart & heart devices
    6.6 Obesity reduction by ultrasonic treatment
  Physical activity and aging. Experimental and clinical data.
        Aerobic exercises
Weight-lifting - body-building
Professional sport: negative aspects
  Conclusion: the whole anti-aging program
    9.1 Modifying your personal aging curve
      Average life span increment. Expert evaluation.
Periodic fasting and caloric restriction can add 40 - 50 years to your lifespan
Regular intake of anti-aging drugs can add 20-30 years to your lifespan
Good nutrition (well balanced, healthy food, individually tailord diet) can add 15-25 years to your lifespan
High-tech bio-medicine service can add 15-25 years to your lifespan
Quality of life (prosperity, relaxation, regular vocations) can add 15-25 years to your lifespan
Regular exercise and moderate physical activity can add 10-20 years to your lifespan
These approaches taken together can add 60-80 years to your lifespan, if you start young (say at age 20). But even if you only start later (say at 45-50), you can still gain 30-40 years

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    9.2 The whole anti-aging life style - brief summary 
    References eXTReMe Tracker
        The whole anti-aging program: overview

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