OBM Geriatrics is an international peer-reviewed Open Access journal published quarterly online by LIDSEN Publishing Inc. The journal takes the premise that innovative approaches – including gene therapy, cell therapy, and epigenetic modulation – will result in clinical interventions that alter the fundamental pathology and the clinical course of age-related human diseases. We will give strong preference to papers that emphasize an alteration (or a potential alteration) in the fundamental disease course of Alzheimer’s disease, vascular aging diseases, osteoarthritis, osteoporosis, skin aging, immune senescence, and other age-related diseases.

Geriatric medicine is now entering a unique point in history, where the focus will no longer be on palliative, ameliorative, or social aspects of care for age-related disease, but will be capable of stopping, preventing, and reversing major disease constellations that have heretofore been entirely resistant to interventions based on “small molecular” pharmacological approaches. With the changing emphasis from genetic to epigenetic understandings of pathology (including telomere biology), with the use of gene delivery systems (including viral delivery systems), and with the use of cell-based therapies (including stem cell therapies), a fatalistic view of age-related disease is no longer a reasonable clinical default nor an appropriate clinical research paradigm.

Precedence will be given to papers describing fundamental interventions, including interventions that affect cell senescence, patterns of gene expression, telomere biology, stem cell biology, and other innovative, 21st century interventions, especially if the focus is on clinical applications, ongoing clinical trials, or animal trials preparatory to phase 1 human clinical trials.

Papers must be clear and concise, but detailed data is strongly encouraged. The journal publishes a variety of article types (Original Research, Review, Communication, Opinion, Comment, Conference Report, Technical Note, Book Review, etc.). There is no restriction on the length of the papers and we encourage scientists to publish their results in as much detail as possible.

Publication Speed (median values for papers published in 2023): Submission to First Decision: 5.7 weeks; Submission to Acceptance: 17.9 weeks; Acceptance to Publication: 7 days (1-2 days of FREE language polishing included)

Current Issue: 2024  Archive: 2023 2022 2021 2020 2019 2018 2017
Open Access Concept Paper

Biological Age versus Chronological Age in the Prevention of Age Associated Diseases

Gian Andrea Rollandi 1, *, Aldo Chiesa 1, Nicoletta Sacchi 1, Mauro Castagnetta 1, Matteo Puntoni 1, Adriana Amaro 2, Barbara Banelli 2, Ulrich Pfeffer 2, *

1. Ente Ospedaliero Ospedali Galliera, Genova, Italy

2. IRCCS Ospedale Policlinico San Martino, Genova, Italy

Correspondences: Gian Andrea Rollandi, Ulrich Pfeffer

Academic Editor: Michael Fossel

Special Issue: Perspectives on Telomeres and Aging

Received: January 31, 2019 | Accepted: April 17, 2019 | Published: May 05, 2019

OBM Geriatrics 2019, Volume 3, Issue 2, doi:10.21926/obm.geriatr.1902051

Recommended citation: Rollandi GA, Chiesa A, Sacchi N, Castagnetta M, Puntoni M, Amaro A, Banelli B, Pfeffer U. Biological Age versus Chronological Age in the Prevention of Age Associated Diseases. OBM Geriatrics 2019; 3(2): 051; doi:10.21926/obm.geriatr.1902051.

© 2019 by the authors. This is an open access article distributed under the conditions of the Creative Commons by Attribution License, which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is correctly cited.


Aging is associated with an increased incidence of major diseases, including cancer, cardiovascular, neurodegenerative, metabolic and autoimmune diseases. Primary prevention and early diagnosis of these diseases have a dramatic impact on incidence, outcome, quality of life and are commonly applied as age-dependent indications based on evidence of efficacy for specific groups of the aging population. They likely contribute to the observed increase in life expectancy through the reduction of incidence and the retardation of the onset of age-associated diseases. In the present article, we develop the hypothesis that age-dependent preventative measures and diagnostic screenings might perform better if biological age were used instead of chronological age. This is based on the observation that there are individual differences in age-associated decline in performance that are reflected by measurable biological indicators, such as telomere length, signal joint T-cell receptor rearrangement excision circles, and specific DNA-methylation and gene expression events.

Graphical abstract


Signal joint T-cell receptor rearrangement excision circle; telomeres; DNA-methylation; gene expression; cellular senescence; aging

1. Introduction

Age is a major risk factor for many diseases such as cancer [1,2], cardiovascular diseases [3,4], diabetes [5], and neurodegenerative syndromes [6], as these diseases show clear age-related incidence rates. The age-related risk can be explained by: i) the age-dependent number of stem cell divisions for specific tissues [7], likely not only for cancer; ii) life-long exposure to environmental insults, including diet [8,9], radiation [10] and chemical factors [11,12], and lifestyle [13,14]; and iii) the, at least in part, genetically determined [15,16] efficacy of endogenous repair systems such as the DNA-repair machinery [17,18]. Chronic inflammation increases with age and is likely both a cause and a consequence of age-related (patho) physiological decline [19,20]. The psychological stress that accumulates over a lifetime and the related resilience also contribute to aging [21]. A genetic predisposition for age-related diseases strongly affects longevity [22], an effect that is difficult to discern from true genetically determined longevity that is not mediated by disease susceptibility. Nonetheless, genome-wide association studies have delivered several genetic variants that independently affect longevity [23,24,25,26]. Differences in longevity, whether determined by endogenous genetic factors or environmental exposure, likely correlate with the physical performance state at any given age. Some people may “look” (perform) younger or older than the mean population of the same chronological age. Genetic variability and history of exposure to environmental stresses probably determine these differences, but this has not yet been studied in a systematic way. In this hypothesis article, we argue that a systematic analysis of differences in individual performance status can and should affect medical decision making. Diagnostic and therapeutic interventions, especially in the preventative setting, are often prescribed by applying age-thresholds. To mention a few, cardiovascular preventative drugs are described for the population over 50 years of age [27], mammographic breast cancer screening is recommended for women over 50 while screening of the younger population is highly debated [28,29], and colonoscopy for the prevention of colon cancer is reserved for the elderly [30]. Age thresholds are implemented on a rationale considering objective health effects, associated costs, [31] and the risk of overdiagnosis [32]. Personalization of preventative screening invariably includes consideration of the person’s age [31]. Here we postulate that personalized medicine should consider “biological age” instead of “chronological age,” especially for the personalization of preventative measures in healthy subjects who do not present disease specific biomarkers.

2. The Concept of Biological Age

We define “biological age” as the individual physiological performance status that imperfectly correlates with “chronological age.” Simply, chronological age equals the years a person has lived and biological age is how old he or she seems, performs and/or feels. Alex Comfort was the first to address aging differences in the “aging rate” in men in 1969 [33]. The following search for biomarkers of aging has, however, met with limited success [34,35], although it was clear from the beginning that biological age is associated with disease risk [36]. The complexity of aging, considering endogenous and environmental factors, has stimulated the development of multifactorial models [37,38,39,40,41,42,43]. The aim of these attempts was the prediction of mortality with limited applicability to prevention medicine (for a recent review see [44]).

Many factors show some association with aging and might serve for constructing models for the prediction of biological age. As discussed above, these factors can be intrinsic or environmental. Intrinsic, genetic factors have been identified but are not strong. Many of them are correlated with the risk for specific diseases that might limit one's lifespan, whereas very few show any disease independent effects on longevity. The latter are expected to correlate with biological age. Markers of exposure can be useful but their effect is limited by the fact that a double correlation is considered; the exposure correlates with the marker and with lifespan and/or performance limitations but the marker might not necessarily correlate in a significant manner with lifespan or performance due to the lack of transitivity of correlation [45] (Figure 1).

3. Which Markers?

Research on biological age markers has proposed many different markers and marker combinations (for a recent review see [46]). Usually, the markers are better if they are causally associated with the event for which they are a marker. If a specific type of cancer depends on the presence of a specific mutation, then the mutation is the best marker. Yet causal relations are rarely so straightforward. The complex nature of biological systems and their potential perturbations limit the value of markers that measure just one perturbation that might well be compensated by other features of the complex system. We, therefore, propose to abandon markers that are associated with lifespan and performance limiting environmental factors and to use instead of a set of markers that are directly associated with chronological age. Markers of chronological age, widely used in forensic science to estimate the age of biological specimens [47], try to closely match chronological age but fail to do this perfectly. We hypothesize that the discrepancy between age and marker estimated age (1- correlation efficient) is due to noise in the measure and to differences in biological age.

Figure 1 Markers for biological age determination. (A) Exposure to environmental risk factors correlates with lifespan and with exposure markers. Since these correlations are not perfect and since correlations are not transitive, exposure markers do not necessarily correlate with lifespan. It is therefore necessary to develop markers that directly correlate with life expectancy. (B) The concept of biological versus chronological age. Markers of biological age correlate with chronological age, yet single subjects show some variation. Within a confidence interval, variation must be considered “noise,”; above and beneath this confidence interval we find subjects who show discordance between biological and chronological age. This discordance is expected to be associated with risk for age-related diseases and longevity.

4. Forensic Markers of Chronological Age

We propose here the introduction of chronological age forensic markers for the determination of biological age in a manner that is as much as possible independent of the processes that limit lifespan by increasing the risk for specific diseases. Recently, forensic science has introduced new markers that can be analyzed by minimally invasive methods in a cost-effective manner.

4.1 Telomere Length

Telomere length has been associated with the replicative history of human cells [48] as well as with aging and longevity [49,50], but this association is still not clear from the epidemiological point of view [51]. The relationship between cell division and net alteration in telomere length is not a constant one and distinct patterns of telomere length change can predominate [52]. Telomere length depends on the activity of the enzyme that replicates telomeres, the telomerase, which is present with variable activities in the population [53]. Telomere length can be determined through a luminescence-based hybridization method after DNA restriction [54]. More recently, polymerase chain reaction (PCR)-based assays have been introduced [55,56].

4.2 Signal Joint T-Cell Receptor Excision Circles

Human individual age can accurately and reliably be estimated through the analysis of T-cell DNA rearrangements in blood cells [57,58]. T-lymphocytes express T-cell receptors (TCR) that are adapted for the specific recognition of antigens through DNA-rearrangements. In this process, intervening DNA sequences in the TCR loci are deleted and circularized into episomal DNA molecules, also called signal joint TCR excision circles (sjTRECs) [59]. The δRec-ψJα sjTREC arises through an intermediate rearrangement in the TCRD/TCRA locus in developing TCRαβ+ T-lymphocytes. The number of sjTRECs declines in a log-linear fashion with increasing human age, as a consequence of thymus involution that starts shortly after birth [60]. sjTRECs thus correlate with age although there is some influence of diseases and their therapies that alter thymic function and eventually lead to thymic regeneration [61,62]. SjTREC levels can be monitored on archival DNA by a simple Taqman qPCR protocol in which the sjTREC DNA is amplified in comparison to the single copy albumin (ALB) gene following the protocol described by Zubakov et al. [57]. Figure 2 shows the analysis of sjTRECs for 82 healthy bone marrow donors collected at the Galliera Hospital in Genova, Italy. The inverse correlation of the relative amount of sjTREC DNA molecules with chronological age is evident (r2= 0.63); however, it is also clear that the values for single individuals greatly scatter around the central diagonal of perfect correlation.

Figure 2 sjTREC as a marker of biological age. (A) Linear regression analysis shows an inverse correlation between sjTREC molecules, as determined by semi-quantitative polymerase chain reaction performed as described by Zubakov et al. [57], and chronological age (linear regression: coeff. = -8.04, p <0.001, r2 = 0.63). Data were obtained from DNA samples of 82 healthy bone marrow donors, ranging from 18 to 55 years of age. For data points related to those under 18 and over 55 years of age, samples are obtained from donors' relatives or from patients tested for HLA-related pathology susceptibility upon informed consent. Considerable deviation from a perfect correlation is observed for many subjects likely indicating discordance between biological and chronological age. Similar correlations are expected for the other markers discussed that should be combined to determine biological age. (B) Exploratory analysis of agreement, by means of Bland-Altman plot, between chronological age and ΔCT (relative amount measured by qPCR) of sjTREC, rescaled to age. The mean difference (red line) is -0.86 (95%CI: -3.99, 2.27), the correlation between difference and mean values is not significant (Pitman’s Test of difference in variance: r=.16, p=.2). A larger difference is strongly noticeable at the center of the distribution, while at the borders (youngest and oldest subjects), the difference between chronological age and ΔCT seems to be lower. Limits of agreement (reference range for difference): -29.330 to 27.613; mean difference: -0.86 (CI -3.99 to 2.27); range: 3.5 to 80.

4.3 DNA Methylation and Transcription

Biological aging is a consequence of developmental programs and organ maintenance that imply, or may even rely on, DNA methylation events. Hence, DNA methylation can be used as an age estimator. Although the precise mechanisms that link aging and DNA methylation are not entirely clear, intracellular alterations leading to a loss of cellular identity and alterations affecting the number and viability of stem cells and DNA methylation are thought to be interrelated. DNA methylation therefore is an independent marker of all types of aging [63,64]. Several sets of age-associated DNA methylation markers have been developed [65,66,67,68,69,70,71] and the association of DNA methylation determined biological age with human diseases has been shown [71,72,73,74,75,76,77]. DNA methylation appears to outperform other markers of biological age [46]. The biological “clocks” developed by Levine [71], Horvath [68], Hannum [63], and their co-workers are based on 513, 353 and 71 methylation sites (CpGs), respectively.

Less numerous panels that have been developed for forensic science appear more practical for routine use. Zubakov's team has recently described the human age estimation using DNA methylation in cells from peripheral blood [58]. They identified 75 significantly differentially methylated CpG sites [58] in addition to the already well validated markers in the ELOVL2 and FHL2 genes [78,79]. Yi and colleagues identified several age-associated DNA-methylation markers and developed a multigene score that strongly correlated with chronological age in the cohort analyzed (r=0.96) [80]. DNA-methylation levels can be assessed by differential digestion by methylation sensitive restriction enzymes or, more accurately, by bisulfite (pyro-) sequencing [81]. A high throughput sequencing approach combined with machine learning for age prediction has allowed for the identification of 16 age-associated methylation markers with a mean error of 3.8 years. Marioni and co-workers established a multigene DNA-methylation signature that is strongly associated with aging [82] and could be validated in independent cohorts [74].

Methylation can directly affect gene expression and it is possible to exploit their inverse correlation to develop a transcriptomic signature that also contemplates the expression of genes with differential, age-associated methylation [83]. The age-associated gene expression signature was shown to be associated with biological features linked to aging, such as blood pressure, cholesterol levels, fasting glucose, and body mass index [83]. Yet in contrast to the aforementioned measures, transcriptomics requires isolation of RNA that is much less stable than DNA and therefore presents a logistic challenge in everyday practice (RNase free environment and procedures).

4.4 SNVs

Many studies have addressed genetic determinants of longevity and genome-wide association studies have identified many single nucleotide variants (SNV) that are associated with long life expectancy. Several of these SNVs have been confirmed in thorough validation studies [23,24,25]. A variant of the telomerase RNA component (TERC, rs3772190) has been found to be associated with leukocyte telomere length and longevity [26]. We hypothesize that the association with longevity might be determined by slower aging for the carriers of the longevity alleles. Hence, these alleles would also be associated with a lower biological age, yet this has not been shown so far. This hypothesis is sustained by the finding that the difference between biological and chronological age appears to develop gradually. Its accumulation determines longevity and reflects lifetime extending interventions such as dietary restriction [84].

4.5 Immunosenescence and Inflamm-Aging

Interleukin-6 (IL-6), a pro-inflammatory and pro-angiogenic cytokine, is present in the plasma at higher levels in elder as compared to younger subjects [85]. Similarly, other pro-inflammatory cytokines, coagulation factors, homocysteine, acute phase proteins, stress hormones, reactive oxygen species, and lipoprotein-A are present at elevated levels in the elderly [20]. Inflammation plays a crucial role in almost all age-related diseases, like cancer, atherosclerosis, cardiovascular disease, type II diabetes, and neurodegeneration, yet inflammation markers, in particular, IL-6, are present at elevated levels even in healthy centenarians. IL-6 or other inflammation markers could therefore contribute to the definition of biological age but they are too heavily influenced by the actual inflammation state of the subjects, for example infections, to yield a reliable marker.

4.6 Other Markers of Aging

Immunoglobulins (Ig) are post-translationally modified by glycosylation that regulates their function. After analyzing a cohort of over 5,000 subjects, Kristic and colleagues described an alteration of the complex pattern of immunoglobulin glycosylation associated with age that explained 58% of the variation in chronological age [86]. Ig glycosylation affects Ig function, thus contributing to immunosenescence [87]. The sensitivity of this method allows for age determination using bloodstains [88]. The analysis of the gut microbiome allowed for the identification of bacterial patterns associated with biological age [89] that might reflect age-dependent decline in gastro-intestinal functions [90].

5. Conclusions

The introduction of biological age instead of chronological age could affect medical decisions that rely on age thresholds. Age is associated with risk for disease, and preventative measures like diagnostic procedures or pharmacological therapy are usually applied in consideration of the persons’ age. Age as a risk factor is mediated by an age-related performance that varies within a given age group. Biological age is therefore expected to be more closely associated with risk.

Guidelines for mammographic screenings to prevent metastasization of breast cancer generally recommend screening of women over 50 years of age and restrict screening of women between 40 and 49 to those with additional risk factors [91]. Pharoah et al. have proposed the introduction of genetic risk factors to personalize screening programs [92,93]. Introduction of biological instead of chronological age could further personalize mammographic screenings and it could do so in a cost-neutral manner since the inclusion of women under 50, yet “older” than their chronological age, would likely be compensated by women over 50 that are “younger” than their age. At the same time, the astonishingly limited effect of mammographic screening [94] could be enhanced because of a risk-oriented personalization. The same would apply to other screening programs such as colonoscopy for colorectal or prostate cancer prevention as well as pharmacological prevention such as low-dose NSAIDS for cardiovascular disease prevention. At the same time, biological age could become a powerful tool for the physician to induce changes in the lifestyles and diets of patients who, in the absence of acute disease, present an increased biological age. It is therefore justified to think that in the future, clinical decision making can replace chronological age by the assessment of the biological age of patients.

Author Contributions

All authors contributed to the conceptual development and read, revised an approved the final manuscript. AC, AA, BB, MC performed pilot-experiments to sustain the hypothesis, MP analyzed data and performed simulations, GAR, NS and UP supervised the discussion, UP wrote a draft manuscript.


This work has generously been supported by a grant from the Compagnia di San Paolo to NS (grant #20460).

Competing Interests

The authors have declared that no competing interests exist.


  1. Whelan SL, Ferlay J. Cancer incidence in five continents. Age-specific and standardized incidence rates. IARC Sci Publ. 1992: 178-861.
  2. Moolgavkar SH, Stevens RG, Lee JA. Age and breast cancer incidence. Eur J Cancer Clin Oncol. 1984; 20: 1453-1454. [CrossRef]
  3. Sarink D, Nedkoff L, Briffa T, Shaw JE, Magliano DJ, Stevenson C, et al. Trends in age- and sex-specific prevalence and incidence of cardiovascular disease in western australia. Eur J Prev Cardiol. 2018; 25: 1280-1290. [CrossRef]
  4. Chrysant SG, Chrysant GS. The age-related hemodynamic changes of blood pressure and their impact on the incidence of cardiovascular disease and stroke: New evidence. J Clin Hypertens. 2014; 16: 87-90. [CrossRef]
  5. Chen L, Magliano DJ, Zimmet PZ. The worldwide epidemiology of type 2 diabetes mellitus—present and future perspectives. Nat Rev Endocrinol. 2011; 8: 228. [CrossRef]
  6. Mayeux R. Epidemiology of neurodegeneration. Ann Rev Neurosci. 2003; 26: 81-104. [CrossRef]
  7. Tomasetti C, Vogelstein B. Cancer etiology. Variation in cancer risk among tissues can be explained by the number of stem cell divisions. Science. 2015; 347: 78-81. [CrossRef]
  8. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: Systematic review. Int J Obes. 2011; 35: 891-898. [CrossRef]
  9. Ames BN, Shigenaga MK, Hagen TM. Oxidants, antioxidants, and the degenerative diseases of aging. Proc Natl Acad Sci U S A. 1993; 90: 7915-7922. [CrossRef]
  10. Lucas RM, McMichael AJ, Armstrong BK, Smith WT. Estimating the global disease burden due to ultraviolet radiation exposure. Int J Epidemiol. 2008; 37: 654-667. [CrossRef]
  11. Jarup L, Berglund M, Elinder CG, Nordberg G, Vahter M. Health effects of cadmium exposure--a review of the literature and a risk estimate. Scand J Work Environ Health. 1998; 1: 1-51.
  12. Vaiserman A. Early-life exposure to endocrine disrupting chemicals and later-life health outcomes: An epigenetic bridge?. Aging Dis. 2014; 5: 419-429.
  13. Minois N. Longevity and aging: Beneficial effects of exposure to mild stress. Biogerontology. 2000; 1: 15-29. [CrossRef]
  14. Paffenbarger RS, Jr., Lee IM. Physical activity and fitness for health and longevity. Res Q Exerc Sport. 1996; 67: S11-S28. [CrossRef]
  15. Shen MR, Jones IM, Mohrenweiser H. Nonconservative amino acid substitution variants exist at polymorphic frequency in DNA repair genes in healthy humans. Cancer Res. 1998; 58: 604-608.
  16. Goode EL, Ulrich CM, Potter JD. Polymorphisms in DNA repair genes and associations with cancer risk. Cancer Epidemiol Biomarkers Prev. 2002; 11: 1513-1530.
  17. Mohrenweiser HW, Jones IM. Variation in DNA repair is a factor in cancer susceptibility: A paradigm for the promises and perils of individual and population risk estimation?. Mutat Res. 1998; 400: 15-24. [CrossRef]
  18. Weissman L, Jo DG, Sorensen MM, de Souza-Pinto NC, Markesbery WR, Mattson MP, et al. Defective DNA base excision repair in brain from individuals with alzheimer's disease and amnestic mild cognitive impairment. Nucleic Acids Res. 2007; 35: 5545-5555. [CrossRef]
  19. Khansari N, Shakiba Y, Mahmoudi M. Chronic inflammation and oxidative stress as a major cause of age-related diseases and cancer. Recent Pat Inflamm Allergy Drug Discov. 2009; 3: 73-80. [CrossRef]
  20. Franceschi C, Bonafe M, Valensin S, Olivieri F, De Luca M, Ottaviani E, et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann N Y Acad Sci. 2000; 908: 244-254. [CrossRef]
  21. Feder A, Nestler EJ, Charney DS. Psychobiology and molecular genetics of resilience. Nat Rev Neurosci. 2009; 10: 446. [CrossRef]
  22. Jazwinski SM. Longevity, genes, and aging. Science. 1996; 273: 54-59. [CrossRef]
  23. Broer L, Buchman AS, Deelen J, Evans DS, Faul JD, Lunetta KL, et al. Gwas of longevity in charge consortium confirms apoe and foxo3 candidacy. J Gerontol A Biol Sci Med Sci. 2015; 70: 110-118. [CrossRef]
  24. Deelen J, Beekman M, Uh HW, Broer L, Ayers KL, Tan Q, et al. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age. Hum Mol Genet. 2014; 23: 4420-4432. [CrossRef]
  25. Deelen J, Beekman M, Uh HW, Helmer Q, Kuningas M, Christiansen L, et al. Genome-wide association study identifies a single major locus contributing to survival into old age; the apoe locus revisited. Aging Cell. 2011; 10: 686-698. [CrossRef]
  26. Soerensen M, Thinggaard M, Nygaard M, Dato S, Tan Q, Hjelmborg J, et al. Genetic variation in tert and terc and human leukocyte telomere length and longevity: A cross-sectional and longitudinal analysis. Aging Cell. 2012; 11: 223-227. [CrossRef]
  27. Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: The jnc 7 report. JAMA. 2003; 289: 2560-2571. [CrossRef]
  28. Djulbegovic B, Lyman GH. Screening mammography at 40–49 years: Regret or no regret?. Lancet. 2006; 368: 2035-2037. [CrossRef]
  29. Moss SM, Cuckle H, Evans A, Johns L, Waller M, Bobrow L, et al. Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years' follow-up: A randomised controlled trial. Lancet. 2006; 368: 2053-2060. [CrossRef]
  30. Baron TH, Kimery BD, Sorbi D, Gorkis LC, Leighton JA, Fleischer DE. Strategies to address increased demand for colonoscopy: Guidelines in an open endoscopy practice. Clin Gastroenterol Hepatol. 2004; 2: 178-182. [CrossRef]
  31. Schousboe JT, Kerlikowske K, Loh A, Cummings SR. Personalizing mammography by breast density and other risk factors for breast cancer: Analysis of health benefits and cost-effectiveness. Ann Intern Med. 2011; 155: 10-20. [CrossRef]
  32. Jorgensen KJ, Gotzsche PC. Overdiagnosis in publicly organised mammography screening programmes: Systematic review of incidence trends. BMJ. 2009; 339: b2587. [CrossRef]
  33. Comfort A. Test-battery to measure ageing-rate in man. Lancet. 1969; 294: 1411-1415. [CrossRef]
  34. Johnson TE. Recent results: Biomarkers of aging. Exp Gerontol. 2006; 41: 1243-1246. [CrossRef]
  35. Sprott RL. Biomarkers of aging and disease: Introduction and definitions. Exp Gerontol. 2010; 45: 2-4. [CrossRef]
  36. Furukawa T, Inoue M, Kajiya F, Inada H, Takasugi S. Assessment of biological age by multiple regression analysis. J Gerontol. 1975; 30: 422-434. [CrossRef]
  37. Takeda H, Inada H, Inoue M, Yoshikawa H, Abe H. Evaluation of biological age and physical age by multiple regression analysis. Med Inform (Lond). 1982; 7: 221-227. [CrossRef]
  38. Kroll J, Saxtrup O. On the use of regression analysis for the estimation of human biological age. Biogerontology. 2000; 1: 363-368. [CrossRef]
  39. Bae CY, Kang YG, Kim S, Cho C, Kang HC, Yu BY, et al. Development of models for predicting biological age (ba) with physical, biochemical, and hormonal parameters. Arch Gerontol Geriatr. 2008; 47: 253-265. [CrossRef]
  40. Nakamura E, Miyao K, Ozeki T. Assessment of biological age by principal component analysis. Mech Ageing Dev. 1988; 46: 1-18. [CrossRef]
  41. Nakamura E, Miyao K. A method for identifying biomarkers of aging and constructing an index of biological age in humans. J Gerontol A Biol Sci Med Sci. 2007; 62: 1096-1105. [CrossRef]
  42. MacDonald SW, Dixon RA, Cohen AL, Hazlitt JE. Biological age and 12-year cognitive change in older adults: Findings from the victoria longitudinal study. Gerontology. 2004; 50: 64-81. [CrossRef]
  43. Klemera P, Doubal S. A new approach to the concept and computation of biological age. Mech Ageing Dev. 2006; 127: 240-248. [CrossRef]
  44. Levine ME. Modeling the rate of senescence: Can estimated biological age predict mortality more accurately than chronological age?. J Gerontol A Biol Sci Med Sci. 2013; 68: 667-674. [CrossRef]
  45. Langford E, Schwertman N, Owens M. Is the property of being positively correlated transitive?. Am Stat. 2001; 55: 322-325. [CrossRef]
  46. Jylhava J, Pedersen NL, Hagg S. Biological age predictors. EBioMedicine. 2017; 21: 29-36. [CrossRef]
  47. Kayser M, de Knijff P. Improving human forensics through advances in genetics, genomics and molecular biology. Nat Rev Genet. 2011; 12: 179-192. [CrossRef]
  48. Chang E, Harley CB. Telomere length and replicative aging in human vascular tissues. Proc Natl Acad Sci U S A. 1995; 92: 11190-11194. [CrossRef]
  49. Kappei D, Londono-Vallejo JA. Telomere length inheritance and aging. Mech Ageing Dev. 2008; 129: 17-26. [CrossRef]
  50. Rizvi S, Raza ST, Mahdi F. Telomere length variations in aging and age-related diseases. Curr Aging Sci. 2014; 7: 161-167. [CrossRef]
  51. Sanders JL, Newman AB. Telomere length in epidemiology: A biomarker of aging, age-related disease, both, or neither?. Epidemiol Rev. 2013; 35: 112-131. [CrossRef]
  52. Hodes RJ. Telomere length, aging, and somatic cell turnover. J Exp Med. 1999; 190: 153-156. [CrossRef]
  53. Xie Z, Jay KA, Smith DL, Zhang Y, Liu Z, Zheng J, et al. Early telomerase inactivation accelerates aging independently of telomere length. Cell. 2015; 160: 928-939. [CrossRef]
  54. Cawthon RM. Telomere measurement by quantitative pcr. Nucleic Acids Res. 2002; 30: e47. [CrossRef]
  55. Lai TP, Zhang N, Noh J, Mender I, Tedone E, Huang E, et al. A method for measuring the distribution of the shortest telomeres in cells and tissues. Nat Commun. 2017; 8: 1356. [CrossRef]
  56. O'Callaghan N, Dhillon V, Thomas P, Fenech M. A quantitative real-time pcr method for absolute telomere length. BioTechniques. 2008; 44: 807-809. [CrossRef]
  57. Zubakov D, Liu F, van Zelm MC, Vermeulen J, Oostra BA, van Duijn CM, et al. Estimating human age from t-cell DNA rearrangements. Curr Biol. 2010; 20: R970-R971. [CrossRef]
  58. Zubakov D, Liu F, Kokmeijer I, Choi Y, van Meurs JB, van IWF, et al. Human age estimation from blood using mrna, DNA methylation, DNA rearrangement, and telomere length. Forensic Sci Int Genet. 2016; 24: 33-43. [CrossRef]
  59. Breit TM, Verschuren MC, Wolvers-Tettero IL, Van Gastel-Mol EJ, Hahlen K, van Dongen JJ. Human t cell leukemias with continuous v(d)j recombinase activity for tcr-delta gene deletion. J Immunol. 1997; 159: 4341-4349.
  60. Douek DC, McFarland RD, Keiser PH, Gage EA, Massey JM, Haynes BF, et al. Changes in thymic function with age and during the treatment of hiv infection. Nature. 1998; 396: 690-695. [CrossRef]
  61. Sun DP, Wang L, Ding CY, Liang JH, Zhu HY, Wu YJ, et al. Investigating factors associated with thymic regeneration after chemotherapy in patients with lymphoma. Frontiers Immunol. 2016; 7: 654. [CrossRef]
  62. Glowala-Kosinska M, Chwieduk A, Smagur A, Fidyk W, Najda J, Mitrus I, et al. Thymic activity and t cell repertoire recovery after autologous hematopoietic stem cell transplantation preceded by myeloablative radiotherapy or chemotherapy. Biol Blood Marrow Transplant. 2016; 22: 834-842. [CrossRef]
  63. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013; 49: 359-367. [CrossRef]
  64. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19: 371-384. [CrossRef]
  65. Rakyan VK, Down TA, Maslau S, Andrew T, Yang TP, Beyan H, et al. Human aging-associated DNA hypermethylation occurs preferentially at bivalent chromatin domains. Genome research. 2010; 20: 434-439. [CrossRef]
  66. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger DJ, Shen H, et al. Age-dependent DNA methylation of genes that are suppressed in stem cells is a hallmark of cancer. Genome Res. 2010; 20: 440-446. [CrossRef]
  67. Koch CM, Wagner W. Epigenetic-aging-signature to determine age in different tissues. Aging. 2011; 3: 1018-1027. [CrossRef]
  68. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012; 13: R97. [CrossRef]
  69. Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012; 8: e1002629. [CrossRef]
  70. Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, Horvath S, et al. Epigenetic predictor of age. PloS One. 2011; 6: e14821. [CrossRef]
  71. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018; 10: 573-591. [CrossRef]
  72. Cimato TR. Biological age and circulating progenitor cell levels as predictors heart disease events. Circ Res. 2017; 120: 1053-1054. [CrossRef]
  73. Lind L, Ingelsson E, Sundstrom J, Siegbahn A, Lampa E. Methylation-based estimated biological age and cardiovascular disease. Eur J Clin Invest. 2018; 48. [CrossRef]
  74. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC, et al. DNA methylation-based measures of biological age: Meta-analysis predicting time to death. Aging. 2016; 8: 1844-1865. [CrossRef]
  75. Kim SJ, Kim BJ, Kang H. Measurement of biological age may help to assess the risk of colorectal adenoma in screening colonoscopy. World J Gastroenterol. 2017; 23: 6877-6883. [CrossRef]
  76. Inamoto T, Matsuyama H, Ibuki N, Komura K, Fujimoto K, Shiina H, et al. Risk stratification by means of biological age-related factors better predicts cancer-specific survival than chronological age in patients with upper tract urothelial carcinoma: A multi-institutional database study. Ther Adv Urol. 2018; 10: 403-410. [CrossRef]
  77. Kresovich JK, Xu Z, O'Brien KM, Weinberg CR, Sandler DP, Taylor JA. Methylation-based biological age and breast cancer risk. J Natl Cancer Inst. 2019. [CrossRef]
  78. Zbiec-Piekarska R, Spolnicka M, Kupiec T, Makowska Z, Spas A, Parys-Proszek A, et al. Examination of DNA methylation status of the elovl2 marker may be useful for human age prediction in forensic science. Forensic Sci Int Genet. 2015; 14: 161-167. [CrossRef]
  79. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, et al. Methylation of elovl2 gene as a new epigenetic marker of age. Aging Cell. 2012; 11: 1132-1134. [CrossRef]
  80. Yi SH, Jia YS, Mei K, Yang RZ, Huang DX. Age-related DNA methylation changes for forensic age-prediction. Int J Legal Med. 2015; 129: 237-244. [CrossRef]
  81. Tost J, Gut IG. DNA methylation analysis by pyrosequencing. Nat Protoc. 2007; 2: 2265-2275. [CrossRef]
  82. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, Harris SE, et al. DNA methylation age of blood predicts all-cause mortality in later life. Genome Biol. 2015; 16: 25. [CrossRef]
  83. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, et al. The transcriptional landscape of age in human peripheral blood. Nat Commun. 2015; 6: 8570. [CrossRef]
  84. Petkovich DA, Podolskiy DI, Lobanov AV, Lee SG, Miller RA, Gladyshev VN. Using DNA methylation profiling to evaluate biological age and longevity interventions. Cell Metab. 2017; 25: 954-960 e956. [CrossRef]
  85. Franceschi C. Cell proliferation, cell death and aging. Aging (Milano). 1989; 1: 3-15. [CrossRef]
  86. Kristic J, Vuckovic F, Menni C, Klaric L, Keser T, Beceheli I, et al. Glycans are a novel biomarker of chronological and biological ages. J Gerontol A Biol Sci Med Sci. 2014; 69: 779-789. [CrossRef]
  87. Gudelj I, Lauc G, Pezer M. Immunoglobulin g glycosylation in aging and diseases. Cell Immunol. 2018; 333: 65-79. [CrossRef]
  88. Gudelj I, Keser T, Vuckovic F, Skaro V, Goreta SS, Pavic T, et al. Estimation of human age using n-glycan profiles from bloodstains. Int J Legal Med. 2015; 129: 955-961. [CrossRef]
  89. Maffei VJ, Kim S, Blanchard E 4th, Luo M, Jazwinski SM, Taylor CM, et al. Biological aging and the human gut microbiota. J Gerontol A Biol Sci Med Sci. 2017; 72: 1474-1482. [CrossRef]
  90. An R, Wilms E, Masclee AAM, Smidt H, Zoetendal EG, Jonkers D. Age-dependent changes in gi physiology and microbiota: Time to reconsider?. Gut. 2018; 67: 2213-2222. [CrossRef]
  91. Qaseem A, Snow V, Sherif K, Aronson M, Weiss KB, Owens DK, et al. Screening mammography for women 40 to 49 years of age: A clinical practice guideline from the american college of physicians. Ann Intern Med. 2007; 146: 511-515. [CrossRef]
  92. Pharoah PD, Antoniou A, Bobrow M, Zimmern RL, Easton DF, Ponder BA. Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet. 2002; 31: 33-36. [CrossRef]
  93. Pharoah PD, Antoniou AC, Easton DF, Ponder BA. Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med. 2008; 358: 2796-2803. [CrossRef]
  94. Keen JD, Keen JE. What is the point: Will screening mammography save my life?. BMC Med Inform Decis Mak. 2009; 9: 18. [CrossRef]
Download PDF Download Full-Text XML Download Citation
0 0