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Abstract

Research into ageing and its underlying molecular basis enables us to develop and implement targeted interventions to ameliorate or cure its consequences. However, the efficacy of interventions often differs widely between individuals, suggesting that populations should be stratified or even individualized. Large-scale cohort studies in humans, similar systematic studies in model organisms as well as detailed investigations into the biology of ageing can provide individual validated biomarkers and mechanisms, leading to recommendations for targeted interventions. Human cohort studies are already ongoing, and they can be supplemented by in silico simulations. Systematic studies in animal models are made possible by the use of inbred strains or genetic reference populations of mice. Combining the two, a comprehensive picture of the various determinants of ageing and 'health span' can be studied in detail, and an appreciation of the relevance of results from model organisms to humans is emerging. The interactions between genotype and environment, particularly the psychosocial environment, are poorly studied in both humans and model organisms, presenting serious challenges to any approach to a personalized medicine of ageing. To increase the success of preventive interventions, we argue that there is a pressing need for an individualized evaluation of interventions such as physical exercise, nutrition, nutraceuticals and calorie restriction mimetics as well as psychosocial and environmental factors, separately and in combination. The expected extension of the health span enables us to refocus health care spending on individual prevention, starting in late adulthood, and on the brief period of morbidity at very old age.
E-Mail karger@karger.com
Clinical Section / Viewpoint
Gerontology
DOI: 10.1159/000442746
Living Long and Well: Prospects for a
Personalized Approach to the Medicine
of Ageing
Georg Fuellen
a
Paul Schofield
l, m
Thomas Flatt
k
Ralf-Joachim Schulz
e
Fritz Boege
g
Karin Kraft
b
Gerald Rimbach
h
Saleh Ibrahim
i
Alexander Tietz
f
Christian Schmidt
c
Rüdiger Köhling
d
Andreas Simm
j
a
Institute for Biostatistics and Informatics in Medicine und Ageing Research (IBIMA),
b
Chair of Complementary
Medicine,
c
Office of the Medical Director, and
d
Oscar Langendorff Institute of Physiology, Rostock University
Medical Center, Rostock ,
e
Department of Geriatric Medicine, St. Marien-Hospital, and
f
Gesellschaft für Gesundes
Altern und Prävention, Cologne ,
g
Institute of Clinical Chemistry and Laboratory Diagnostics, Medical Faculty,
Heinrich Heine University, Düsseldorf ,
h
Institute of Human Nutrition and Food Science, University of Kiel, Kiel ,
i
Lübeck Institute of Experimental Dermatology, University of Lübeck, Lübeck , and
j
Clinic for Cardiothoracic Surgery,
University Hospital Halle, Halle (Saale) , Germany;
k
Department for Ecology and Evolution, University of Lausanne,
Lausanne , Switzerland;
l
Department of Physiology, Development and Neuroscience, University of Cambridge,
Cambridge , UK;
m
The Jackson Laboratory, Bar Harbor, Maine , USA
are made possible by the use of inbred strains or genetic ref-
erence populations of mice. Combining the two, a compre-
hensive picture of the various determinants of ageing and
‘health span’ can be studied in detail, and an appreciation of
the relevance of results from model organisms to humans is
emerging. The interactions between genotype and environ-
ment, particularly the psychosocial environment, are poorly
studied in both humans and model organisms, presenting
serious challenges to any approach to a personalized medi-
cine of ageing. To increase the success of preventive inter-
ventions, we argue that there is a pressing need for an indi-
vidualized evaluation of interventions such as physical exer-
Key Words
Health span · Healthy ageing · Cohort study ·
Model organism · Bioinformatics
Abstract
Research into ageing and its underlying molecular basis en-
ables us to develop and implement targeted interventions
to ameliorate or cure its consequences. However, the effi-
cacy of interventions often differs widely between individu-
als, suggesting that populations should be stratified or even
individualized. Large-scale cohort studies in humans, similar
systematic studies in model organisms as well as detailed
investigations into the biology of ageing can provide indi-
vidual validated biomarkers and mechanisms, leading to
recommendations for targeted interventions. Human cohort
studies are already ongoing, and they can be supplemented
by in silico simulations. Systematic studies in animal models
Received: July 17, 2015
Accepted: November 25, 2015
Published online: December 17, 2015
Georg Fuellen
Institute for Biostatistics and Informatics in Medicine and Ageing Research (IBIMA)
Rostock University Medical Center, Ernst-Heydemann-Strasse 8
DE–18057 Rostock (Germany)
E-Mail fuellen
@ alum.mit.edu
© 2015 S. Karger AG, Basel
0304–324X/15/0000–0000$39.50/0
www.karger.com/ger
This paper is derived from a discussion at the ‘Hauptstadtkongress
2014’ (http://www.hauptstadtkongress.de/index.php?id=76&id_pro-
gramm=103) on ‘Regeneration and slowing down ageing in the world
of tomorrow’.
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DOI: 10.1159/000442746
2
cise, nutrition, nutraceuticals and calorie restriction mimetics
as well as psychosocial and environmental factors, separate-
ly and in combination. The expected extension of the health
span enables us to refocus health care spending on individ-
ual prevention, starting in late adulthood, and on the brief
period of morbidity at very old age.
© 2015 S. Karger AG, Basel
Introduction
The need for ageing research is growing rapidly.
Trends predicted from the EUROPOP survey suggest
that the proportion of the population aged 65 years and
over will rise from 17% in 2010 to 30% in 2060, with those
aged over 80 years increasing from 5 to 12% over the
same period (http://futurage.group.shef.ac.uk/road-
map.html). The economic and social consequences of
the fact that the population is ageing therefore cannot be
overestimated. Slowing down the deleterious processes
of ageing itself would allow significant benefits beyond
those of eradicating specific diseases – which, e.g. in the
case of cancer and stroke, amount to life span extensions
of just a few years
[1] . Diseases of age, whether cardio-
vascular, neoplastic, pulmonary or cognitive, are increas-
ing in frequency and will be the top 4 causes of death
worldwide by 2020; 75% of all deaths from these diseases
occur in people aged 60 years and over, and their inci-
dence rises with age. In other words, for a host of non-
communicable diseases, there is a clear link between the
underlying processes of ageing and the age-dependent
accumulation of risk, so that the eradication of one dis-
ease merely makes way for the occurrence of another dis-
ease slightly later
[2–4] . Slowing down ageing itself, and
addressing its root mechanisms, is expected to increase
‘health spans’ and to compress the period of age-related
morbidity, thus tackling goals considered much more
worthwhile than simply extending the chronological life
span
[3, 4] . Moreover, for any interventions, the effects
of genotype and environment (biological and psychoso-
cial) and the interaction between the underlying mecha-
nisms are most important, and their combinatorial ap-
plication should be considered ( fig.1 ).
Therefore, based on the recent convergence of per-
sonalized medicine and ageing research in human and
model organisms, we suggest in this viewpoint paper
that a successful research agenda for the next decade
should be based on three pillars ( fig.2 ): (1) extending,
Inter-
vention
Activity
Nutrition
• Exercise
• Social
participation
• Nutraceuticals
• MediterrAsian
diet
• CR
mimetics
• Statins, etc.
Mechanistic
validation in
various model
organisms
Supplementing
existing cohort
studies in humans
Systematic studies
in mice (e.g.
Collaborative
Cross)
Fig. 1. Health span extension includes activity, diet and other in-
terventions, each of which are expected to be most effective if per-
sonalized (alone and in combination). For the ‘MediterrAsian’
diet, see
[67] . CR = Calorie restriction.
Fig. 2. Robust research on health span extension requires a solid
base of systematic studies in humans and animals and an under-
standing of the biology of ageing, that is, of the mechanisms un-
derlying molecular ageing processes.
Color version available online
Color version available online
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A Personalized Approach to the Medicine
of Ageing
Gerontology
DOI: 10.1159/000442746
3
complementing and integrating the knowledge base as-
sembled in existing human cohort studies; (2) running
closely similar studies in animal models, and (3) under-
standing the biology of ageing through the detailed in-
vestigation of findings in humans and animals to gain a
mechanistic understanding of biomarkers and interven-
tions.
Our agenda rests on the biomarker concept. Baker
and Sprott
[5] defined a biomarker of ageing as ‘a bio-
logical parameter of an organism that either alone or in
some multivariate composite will, in the absence of dis-
ease, better predict functional capability at some late age,
than will chronological age’; the American Federation
for Aging Research has proposed more detailed criteria
for biomarkers of ageing aimed at estimating biological,
not chronological age
[6] , essentially adding their close
relation to processes that underlie ageing, not disease,
their ease of measurement and their cross-species rele-
vance. However, while many biomarkers of ageing were
described in animal or cross-sectional human studies,
most of them failed in the few long-term human studies
available
[7] . One problem lies in technical limitations:
human marker measurements are rarely comparable
across decades. Also, by selecting blood as the most eas-
ily assayable biological fluid, other organs affected by age
are ignored. Moreover, there are major variations during
the day or the year, as e.g. the amount of daylight will
have an impact on many markers. Also, some markers
such as a low body mass index or blood pressure may in-
dicate a lower biological age for younger people and the
opposite for the very old
[8, 9] . Finally, while biomarkers
should describe biological age, there is no true ‘gold stan-
dard’ – which would need to be based on a comprehen-
sive longitudinal study in humans running for almost a
century. Studies of populations at an advanced age, such
as the Leiden or Newcastle 85+ studies
[10, 11] , necessar-
ily focus on old-age multimorbidity rather than on the
full spectrum of ageing processes over a human life
course. Nevertheless, listings of biomarkers validated for
humans in longitudinal studies were compiled; they in-
clude interleukin 6 (IL-6) and some hormones
[7, 12] ,
as well as, more recently, galactosylated N-glycans
[13] ,
plasma N-terminal pro-B-type natriuretic peptide
[14]
and epigenetic markers
[15, 16] .
‘Personalized’ approaches to medicine are gaining
ground in mainstream medical research. The most well-
known of these involve cancer therapeutic agents with a
companion diagnostic gene test, such as Herceptin
TM
and
Gleevec
TM
[17] . More comprehensive, ‘omics’-based at-
tempts at personalizing diagnostics and therapy are be-
ing tested [18] . Moreover, molecular markers and inter-
ventions have to be integrated with biographical ones
[19] . Assembling sufficiently large human data sets in or-
der to allow a differentiation and classification of patients
within cohorts is the key to personalized medicine. Lon-
gitudinal cohort studies, such as the Framingham Heart
Study
[20] and the Study of Health in Pomerania (SHIP)
[21] or the upcoming German National Cohort [22] ,
therefore attempt to identify disease mechanisms, risk
factors, prevention strategies and early markers in the
general population; systematic integration of such data is
also being attempted (http://www.chancesfp7.eu/).
While the mechanisms of ageing are complex
[4, 23,
24]
, evidence is accumulating that ageing is a potential-
ly modifiable risk factor
[25] for morbidities associated
with it. Moreover, longitudinal cohort studies in hu-
mans (see above) and primates
[26, 27] , human genome-
wide association studies
[28] as well as longitudinal
studies, genetic manipulation and intervention testing
programs for rodents
[29–31] have yielded many in-
sights in recent years. Some of them converge on exer-
cise and diet and their associated pathways. In particu-
lar, one of the recurring themes is that of pathways re-
lated to energy and nutrient sensing and production
[32] , and dietary restriction has emerged as the most ro-
bust means of extending life spans and health spans alike
[26] . Dietary restriction may be the best path towards
this goal, even though its long-term effects on humans
are ultimately unknown. Pragmatically speaking, its
downside is that it requires behavioral modifications
and great willpower, triggering the search for calorie-
restriction mimetics, i.e. small molecules that produce
comparable effects, with some of them promising early
results
[33] . Importantly, the effects of dietary restric-
tion are not uniform; in the case of mice and primates,
the results of dietary restriction vary by genotype (or
strain or subspecies), diet and/or environment, and di-
etary restriction was sometimes found to be detrimental
[34] , just as the effects of its mimetics vary [35] . The ef-
fects of dietary components vary as well; for instance,
whole-grain bread tends to have positive effects mostly
in Northern European populations and less in Mediter-
ranean people
[36] . Similarly, the effects of fish oil in
mice and humans depend on the APOE genotype
[37] .
Thus, we may expect to find a high degree of heteroge-
neity in the informativity of biomarkers, or the efficacy
of interventions, for humans and outbred animals alike.
Moreover, studies of the underlying molecular mecha-
nisms in terms of pathways may also wish to take into
account individual variability.
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Personalized Medicine and Ageing Research
Are Now Starting to Come Together,
Aided by the Explorative and Confirmatory Power of
High-Throughput Data Sets
The most visible sign of the convergence of personal-
ized medicine and ageing research is the recent start-up
of Human Longevity Inc. (http://www.humanlongevity.
com/) by Craig Venter, aiming at finding genomic, me-
tabolomic, microbiomic and other determinants of health
in hundreds of thousands of volunteers. Along similar
lines, the Institute for Systems Biology in Seattle is now
pursuing the 100K Wellness Project (http://research.
systemsbiology.net/100k/). As time goes by, longitudinal
cohort studies will by necessity be developing into studies
of ageing, and a few are explicitly gathering data with the
aim of fostering a better understanding of ageing process-
es
[38, 39] . Longitudinal studies in model organisms per-
mit a systematic dissection of the molecular architecture
of ageing. For example, around 30 strains of mice have
recently been studied at the Nathan Shock Center of the
Jackson Laboratory
[29] , and phenotypic and/or genetic
data are now being analyzed together with life span data
[9, 40, 41] . Efforts such as the Collaborative Cross [42] al-
low genome-wide association study-like trials in mice as
well as the subsequent detailed study of mechanistic in-
sights and, more generally, the modeling of approaches to
personalized medicine in animals. Here, we can investi-
gate in great detail the individual differences in the biol-
ogy of ageing on the cell, tissue and organ level. On each
of these levels, the speed of ageing can vary substantially,
and this may, for instance, be reflected epigenetically
[15,
16]
. More generally, as described in the Introduction, bio-
markers of ageing are usually found while investigating
subpopulations (such as people aged 85 years and older),
and these biomarkers also allow the stratification of large
populations according to the biology of ageing.
Whilst association studies may provide information
on personal risks for specific morbidities as well as on
their severity and timing, many of these risks are turning
out to be modified by subjects’ psychosocial environment
and individual history, which in themselves need to be
included not only as part of the risk analysis but also as a
guide to potential therapies
[43] . Many associations with
ageing and age-related diseases such as Alzheimer’s are
complex, often with low effect sizes of individual variants,
and it is highly likely that at least some of the missing
heritability is due to environmental interactions
[44] . For
example, in a mouse model, the disease risk in predis-
posed strains was shown to be attenuated by environmen-
tal factors when Alzheimer-prone mice were placed in a
rich and naturalistic environment, showing reduced be-
havioral effects despite increased plaque density
[45] .
Moreover, the induction of a neuroinflammatory re-
sponse was related to chronic unpredictable stress
[46] .
Conversely, dopamine receptor D
4
(DRD4) knockout
mice do not show the increased longevity observed when
background strain mice are brought up in a rich environ-
ment, showing them to be refractory to the positive effects
of a rich environment. This study showed consistency
with a parallel human cohort, presenting an excellent par-
adigm for future work
[47] . An individual environmental
impact may be reflected epigenetically
[15] . Such epige-
netic individuality is influenced in part by biographical
parameters, reflecting the psychosocial environment, so-
cial participation and education, and the way this allo-
static load has been handled by the individual as part of
her or his stress response. In turn, targeted interventions
may be used to ameliorate the environment
[19] .
Apart from ‘omics’ data processing and analysis, com-
putational studies permit well-founded comparisons of
human and animal data, as well as simulation studies,
particularly on the molecular level. At its simplest, the
parallelogram approach, originally developed in toxicol-
ogy
[48] , suggests the use of data from diseased animal
tissue to extrapolate them to the often inaccessible human
diseased tissue, aided by e.g. blood data available for dis-
eased and healthy individuals. Moreover, controlled vo-
cabularies and ontologies, describing the formal relation-
ships between concepts and entities, are developed to al-
low the systematic comparison of human and animal data
[49] . For example, on the (cell) anatomical and physio-
logical level, we can then integrate data and analyze the
relationship between phenotypes of humans and model
species, yielding estimates for the extrapolation of data
and insights from model organisms to humans. Formal
data semantics is also useful to systematically mine elec-
tronic health record data in order to describe phenotypes
and diseases
[50] . Furthermore, recent promising devel-
opments in systems biology and systems medicine in-
clude simulation studies of ageing-related pathways and
multilevel modeling of the large number of interacting
processes involved
[51] . Such studies help to disentangle
the network of interdependent biological processes that
underlie ageing, and to distinguish between correlation
and causality, following the example of cancer research,
where computational studies help to distinguish ‘passen-
ger’ from ‘driver’ mutations
[52] . Whereas many cancers
are characterized by gross modifications of cell and organ
physiology (e.g. due to chromosomal aberrations), ageing
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processes are subtler, triggering weaker patterns and sig-
nals in terms of phenotype and molecular mechanisms,
and on a longer timescale. Therefore, the sound integra-
tion of data using the techniques of data semantics and
ontologies is important in ageing research
[53–56] to
maximize our chances of detecting meaningful patterns
and signals.
The Implementation of Any Recommendations for
Health Span Extension Must Be Easy and Safe
Many people show close adherence to moderate mod-
ifications of exercise and dietary patterns, motivated by
their personal instinct or subjective feelings of benefit.
The correct use of and long-term adherence to changes in
dietary composition, nutraceuticals and food supple-
ments is more difficult, though.
1
Healthy and health-con-
scious individuals consuming high amounts of fruit and
vegetables (>400 g/day) display a more robust organismal
antioxidant defense system
[57] and a better cognitive
performance
[58] , independent of their age and gender,
compared to subjects consuming <100 g/day, although
a good plasma micronutrient status can be achieved
through targeted counseling
[59] . However, as the correct
use of nutraceuticals and food supplements is complicat-
ed
[60] , most of the supplementation trials with single
compounds and/or single lifestyle preventive strategies
against age-related diseases have largely been unsuccess-
ful so far
[61] . Furthermore, an immediate subjective feel-
ing of benefit with nutraceuticals is not usually attained,
while their possible physiological impact may be signifi-
cant (on the positive as well as on the negative side). This
also applies to long-term small-molecule interventions
such as calorie-restriction mimetics. Additionally, the
quality and safety of nutraceuticals and food supplements
are not controlled as strictly as drugs. Here, subjective
feelings have to be supplemented with or substituted by
sound scientific evidence regarding benefits, subject to a
personalized approach. The polypill concept
[62] is often
criticized exactly because it does not consider the specif-
ics of the individual. It consists of intensively tested drugs
at low dosage, the benefits of which have been shown in
large-scale studies. Specifically, it aims to reduce the risk
of heart attacks and strokes, employing one statin and
three blood pressure-reducing agents at around half the
standard dose; in a personalized instantiation, it can be
considered a model for active interventions to stay healthy
for longer periods of time.
Sound Scientific Evidence for Health Span Effects of
Interventions in Humans Is Becoming Available
Conclusive evidence for therapeutic or prophylactic
effects of interventions on the human health span is going
to be difficult to establish, since longitudinal intervention
studies (starting in midlife) would take around half a cen-
tury to complete. Moreover, interventions designed for
presumably healthy people need specific justification and
should have no discernible negative side effects. Howev-
er, a significant postponement of ageing-associated dis-
ease and morbidity is a distinctly positive aim that should
not be abandoned without due consideration. Fortunate-
ly, there are a couple of convincing arguments that indi-
cate a high likelihood of success in finding valid means
for achieving health span extensions
[25] , with people in
their late adulthood as the target group. First, very ‘mild’
forms of health span-extending interventions have al-
ready been practiced for a long time; their systematic and
personalized improvement is already half the battle. Such
interventions include exercise, diet and nutraceuticals, as
well as indication-based interventions such as drug-based
blood pressure reduction, cholesterol modulation and os-
teoporosis prevention. Also, for many individuals, a fur-
ther significant extension of their health span can be ex-
pected from improvements in their psychosocial envi-
ronment, social participation and education. While
consistent good parental care in the early years is a good
foundation, psychosocial lifestyle interventions can still
be effective in adulthood
[19] . Second, as a proof of con-
cept, dietary restriction has already been demonstrated to
extend health spans in numerous animal species includ-
ing mammals – benefitting rhesus monkeys, for example
(see above) – and has been shown to improve biomarkers
of ageing in humans in late adulthood as well
[63] . More-
over, as described above, pharmacological mimetics of di-
etary restriction have shown promising results, at least in
mouse studies
[33] . Combining interventions is impor-
tant, though, since most of the supplementation trials
with single compounds and/or single lifestyle preventive
strategies have largely been unsuccessful so far
[61] .
Third, centenarians frequently feature very late onsets of
age-related diseases and disabilities
[64] , demonstrating
that the goal of health span extension can indeed be ac-
complished at a very old age.
1
For example, a 6-year study on the prevention of dementia failed to show
positive effects, possibly due to increasing nonadherence
[68] (fig.2 therein).
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Conclusions and Prospects
We propose a realistic research agenda with distinctive
positive advances within one decade:
We need to augment ongoing and future clinical stud-
ies, measuring as many ageing-related parameters as
possible, and to couple them with closely similar animal
studies, which feature far shorter execution times and
more possibilities for experimental intervention and
detailed study. Here, one main aim is to discover and
validate new markers of ageing which may assist in the
stratification of populations with regard to the efficacy
of therapeutic and prophylactic interventions. It is crit-
ical to record environmental parameters as well as the
characteristics of stress, activity and personal history for
human studies and to conduct detailed analyses of the
effects of the environment for model organism studies.
We need to systematically validate the evidence gained
from model animal studies in humans and vice versa.
Here, we need an in-depth understanding of the mo-
lecular processes that are supposed to be the targets of
an intervention. Mechanistic studies in mice are essen-
tial, and studies in humanized mice, (human) cell lines
and other model organisms should be undertaken as
well, always selecting the most informative approach.
Finally, given the range of interventions likely to be-
come validated and available, we should aim for a com-
binatorial approach through the establishment of a
modular system, from which the most appropriate
combination of interventions ( fig.1 ) can be selected by
any individual.
Such an agenda can be expected to yield validated person-
alized prescriptions for many people within a decade, en-
abling them to extend their health span and to shorten the
period of their life that is spent in ill health.
Economic Implications
Slowing ageing and extending health spans has pro-
found economic implications. Importantly, maintaining
health and fitness for a longer time period allows later
retirement and more senior-level contributions to society
(http://www.healthyageing.eu/). Furthermore, with the
growing shortage of young employees there is a great
need for people working until the age of 70 years or more,
especially in service industries such as medicine. Most
importantly, however, increases in health span are among
the few contributors to lowering health care costs in a
predictable way by postponing most of the demand until
very old age
[65] . The current repair-oriented approaches
adopted in cancer, cardiovascular diseases, neurodegen-
eration and other areas may then slowly but steadily be
refocused on serving populations at an increasingly ad-
vanced age who stay healthy well beyond their 90s. In
summary, the social, economic and health-related bene-
fits resulting from prolonging health spans are ‘longevity
dividends’
[66] .
Acknowledgements
G.F. was supported in part by the BMBF Verbundprojekt
ROSAge, FKZ 0315892A and the EU Horizon 2020 research and
innovation program under grant agreement No. 633589; A.S.
was supported in part by the state of Saxony-Anhalt.
Disclosure Statement
The authors declare that they have no conflicts of interest.
References
1 Olshansky SJ, Carnes BA, Cassel C: In search
of Methuselah: estimating the upper limits to
human longevity. Science 1990;
250: 634–640.
2 Niccoli T, Partridge L: Ageing as a risk factor
for disease. Curr Biol 2012;
22:R741–R752.
3 Partridge L: Intervening in ageing to prevent
the diseases of ageing. Trends Endocrinol
Metab 2014;
25: 555–557.
4 Lopez-Otin C, Blasco MA, Partridge L, Ser-
rano M, Kroemer G: The hallmarks of aging.
Cell 2013;
153: 1194–1217.
5 Baker GT 3rd, Sprott RL: Biomarkers of ag-
ing. Exp Gerontol 1988;
23: 223–239.
6 Johnson TE: Recent results: biomarkers of ag-
ing. Exp Gerontol 2006;
41: 1243–1246.
7 Simm A, Nass N, Bartling B, Hofmann B, Sil-
ber RE, Navarrete Santos A: Potential bio-
markers of ageing. Biol Chem 2008;
389: 257–
265.
8 Moeller M, Hirose M, Mueller S, Roolf C, Bal-
trusch S, Ibrahim S, Junghanss C, Wolken-
hauer O, Jaster R, Köhling R, Kunz M, Tiedge
M, Schofield PN, Fuellen G: Inbred mouse
strains reveal biomarkers that are pro-longev-
ity, antilongevity or role switching. Aging Cell
2014;
13: 729–738.
9 Martin-Ruiz C, von Zglinicki T: Biomarkers
of healthy ageing: expectations and valida-
tion. Proc Nutr Soc 2014;
73: 422–429.
10 Lagaay AM, van Asperen IA, Hijmans W: The
prevalence of morbidity in the oldest old, aged
85 and over: a population-based survey in
Leiden, The Netherlands. Arch Gerontol
Geriatr 1992;
15: 115–131.
11 Collerton J, Barrass K, Bond J, Eccles M, Jag-
ger C, James O, Martin-Ruiz C, Robinson L,
von Zglinicki T, Kirkwood T: The Newcastle
85+ Study: biological, clinical and psychoso-
cial factors associated with healthy ageing:
study protocol. BMC Geriatr 2007;
7: 14.
12 Lara J, Cooper R, Nissan J, Ginty AT, Khaw
KT, Deary IJ, Lord JM, Kuh D, Mathers JC: A
proposed panel of biomarkers of healthy age-
ing. BMC Med 2015;
13: 222.
Downloaded by:
Univ.-Bibliothek Rostock
149.126.78.1 - 12/17/2015 8:51:18 AM
A Personalized Approach to the Medicine
of Ageing
Gerontology
DOI: 10.1159/000442746
7
13 Dall’Olio F, Vanhooren V, Chen CC, Slag-
boom PE, Wuhrer M, Franceschi C: N-gly-
comic biomarkers of biological aging and lon-
gevity: a link with inflammaging. Ageing Res
Rev 2013;
12: 685–698.
14 van Peet PG, de Craen AJ, Gussekloo J, de
Ruijter W: Plasma NT-proBNP as predictor
of change in functional status, cardiovascular
morbidity and mortality in the oldest old: the
Leiden 85-plus Study. Age (Dordr) 2014;
36:
9660.
15 Horvath S: DNA methylation age of human
tissues and cell types. Genome Biol 2013;
14:R115.
16 Weidner CI, Lin Q, Koch CM, Eisele L, Beier
F, Ziegler P, Bauerschlag DO, Jöckel KH, Er-
bel R, Mühleisen TW, Zenke M, Brümmen-
dorf TH, Wagner W: Aging of blood can be
tracked by DNA methylation changes at just
three CpG sites. Genome Biol 2014;
15:R24.
17 Hamburg MA, Collins FS: The path to per-
sonalized medicine. N Engl J Med 2010;
363:
301–304.
18 Snyder M: iPOP and its role in participatory
medicine. Genome Med 2014;
6: 6.
19 McEwen BS, Getz L: Lifetime experiences, the
brain and personalized medicine: an integra-
tive perspective. Metabolism 2013; 62(suppl 1):
S20–S26.
20 Mahmood SS, Levy D, Vasan RS, Wang TJ:
The Framingham Heart Study and the epide-
miology of cardiovascular disease: a historical
perspective. Lancet 2014;
383: 999–1008.
21 Völzke H, Alte D, Schmidt CO, et al: Cohort
profile: the study of health in Pomerania. Int
J Epidemiol 2011;
40: 294–307.
22 Wichmann HE, Kaaks R, Hoffmann W, Jö-
ckel KH, Greiser KH, Linseisen J: The Ger-
man National Cohort (in German). Bundes-
gesundheitsblatt Gesundheitsforschung Ge-
sundheitsschutz 2012;
55: 781–787.
23 Gems D, Partridge L: Genetics of longevity in
model organisms: debates and paradigm
shifts. Annu Rev Physiol 2013;
75: 621–644.
24 Behrens A, van Deursen JM, Rudolph KL,
Schumacher B: Impact of genomic damage
and ageing on stem cell function. Nat Cell Biol
2014;
16: 201–207.
25 Kirkland JL: Translating advances from the
basic biology of aging into clinical applica-
tion. Exp Gerontol 2013;
48: 1–5.
26 Colman RJ, Anderson RM, Johnson SC, Kast-
man EK, Kosmatka KJ, Beasley TM, Allison
DB, Cruzen C, Simmons HA, Kemnitz JW,
Weindruch R: Caloric restriction delays dis-
ease onset and mortality in rhesus monkeys.
Science 2009;
325: 201–204.
27 Mattison JA, Roth GS, Beasley TM, Tilmont
EM, Handy AM, Herbert RL, Longo DL, Al-
lison DB, Young JE, Bryant M, Barnard D,
Ward WF, Qi W, Ingram DK, de Cabo R: Im-
pact of caloric restriction on health and sur-
vival in rhesus monkeys from the NIA study.
Nature 2012;
489: 318–321.
28 Nebel A, Kleindorp R, Caliebe A, Nothnagel
M, Blanché H, Junge O, Wittig M, Ellinghaus
D, Flachsbart F, Wichmann HE, Meitinger T,
Nikolaus S, Franke A, Krawczak M, Lathrop
M, Schreiber S: A genome-wide association
study confirms APOE as the major gene influ-
encing survival in long-lived individuals.
Mech Ageing Dev 2011;
132: 324–330.
29 Yuan R, Peters LL, Paigen B: Mice as a mam-
malian model for research on the genetics of
aging. ILAR J 2011;
52: 4–15.
30 Bartke A: Single-gene mutations and healthy
ageing in mammals. Philos Trans R Soc Lond
B Biol Sci 2011;
366: 28–34.
31 Miller RA, Harrison DE, Astle CM, Floyd RA,
Flurkey K, Hensley KL, Javors MA, Leeuwen-
burgh C, Nelson JF, Ongini E, Nadon NL,
Warner HR, Strong R: An Aging Interven-
tions Testing Program: study design and in-
terim report. Aging Cell 2007;
6: 565–575.
32 Johnson SC, Rabinovitch PS, Kaeberlein M:
mTOR is a key modulator of ageing and age-
related disease. Nature 2013;
493: 338–345.
33 Martin-Montalvo A, Mercken EM, Mitchell
SJ, Palacios HH, Mote PL, Scheibye-Knudsen
M, Gomes AP, Ward TM, Minor RK, Blouin
MJ, Schwab M, Pollak M, Zhang Y, Yu Y,
Becker KG, Bohr VA, Ingram DK, Sinclair
DA, Wolf NS, Spindler SR, Bernier M, de
Cabo R: Metformin improves healthspan and
lifespan in mice. Nat Commun 2013;
4: 2192.
34 Liao CY, Johnson TE, Nelson JF: Genetic vari-
ation in responses to dietary restriction – an
unbiased tool for hypothesis testing. Exp
Gerontol 2013;
48: 1025–1029.
35 Miller RA, Harrison DE, Astle CM, Baur JA,
Boyd AR, de Cabo R, Fernandez E, Flurkey K,
Javors MA, Nelson JF, Orihuela CJ, Pletcher
S, Sharp ZD, Sinclair D, Starnes JW, Wilkin-
son JE, Nadon NL, Strong R: Rapamycin, but
not resveratrol or simvastatin, extends life
span of genetically heterogeneous mice. J
Gerontol A Biol Sci Med Sci 2011;
66: 191–201.
36 Kyrø C, Olsen A, Landberg R, et al: Plasma
alkylresorcinols, biomarkers of whole-grain
wheat and rye intake, and incidence of
colorectal cancer. J Natl Cancer Inst 2014;
106:djt352.
37 Gardener SL, Rainey-Smith SR, Barnes MB,
Sohrabi HR, Weinborn M, Lim YY, Har-
rington K, Taddei K, Gu Y, Rembach A,
Szoeke C, Ellis KA, Masters CL, Macaulay SL,
Rowe CC, Ames D, Keogh JB, Scarmeas N,
Martins RN: Dietary patterns and cognitive
decline in an Australian study of ageing. Mol
Psychiatry 2015;
20: 860–866.
38 Collerton J, Martin-Ruiz C, Kenny A, Barrass
K, von Zglinicki T, Kirkwood T, Keavney B:
Telomere length is associated with left ven-
tricular function in the oldest old: the New-
castle 85+ Study. Eur Heart J 2007;
28: 172–
176.
39 Bertram L, Böckenhoff A, Demuth I, Düzel S,
Eckardt R, Li SC, Lindenberger U, Pawelec G,
Siedler T, Wagner GG, Steinhagen-Thiessen
E: Cohort profile: the Berlin Aging Study II
(BASE-II). Int J Epidemiol 2014;
43: 703–712.
40 Yuan R, Meng Q, Nautiyal J, Flurkey K, Tsaih
SW, Krier R, Parker MG, Harrison DE, Pai-
gen B: Genetic coregulation of age of female
sexual maturation and lifespan through circu-
lating IGF1 among inbred mouse strains.
Proc Natl Acad Sci USA 2012;
109: 8224–8229.
41 Bogue MA, Peters LL, Paigen B, Korstanje R,
Yuan R, Ackert-Bicknell C, Grubb SC,
Churchill GA, Chesler EJ: Accessing data re-
sources in the mouse phenome database for
genetic analysis of murine life span and health
span. J Gerontol A Biol Sci Med Sci 2014,
Epub ahead of print.
42 Threadgill DW, Churchill GA: Ten years of
the Collaborative Cross. G3 (Bethesda) 2012;
2: 153–156.
43 Nithianantharajah J, Hannan AJ: The neuro-
biology of brain and cognitive reserve: mental
and physical activity as modulators of brain
disorders. Prog Neurobiol 2009;
89: 369–382.
44 Tosto G, Reitz C: Genome-wide association
studies in Alzheimer’s disease: a review. Curr
Neurol Neurosci Rep 2013;
13: 381.
45 Lewejohann L, Reefmann N, Widmann P,
Ambree O, Herring A, Keyvani K, Paulus W,
Sachser N: Transgenic Alzheimer mice in a
semi-naturalistic environment: more plaques,
yet not compromised in daily life. Behav
Brain Res 2009;
201: 99–102.
46 Barnum CJ, Pace TW, Hu F, Neigh GN, Tan-
sey MG: Psychological stress in adolescent
and adult mice increases neuroinflammation
and attenuates the response to LPS challenge.
J Neuroinflammation 2012;
9: 9.
47 Grady DL, Thanos PK, Corrada MM, Barnett
JC Jr, Ciobanu V, Shustarovich D, Napoli A,
Moyzis AG, Grandy D, Rubinstein M, Wang
GJ, Kawas CH, Chen C, Dong Q, Wang E,
Volkow ND, Moyzis RK: DRD4 genotype pre-
dicts longevity in mouse and human. J Neu-
rosci 2013;
33: 286–291.
48 Kienhuis AS, van de Poll MC, Wortelboer H,
van Herwijnen M, Gottschalk R, Dejong CH,
Boorsma A, Paules RS, Kleinjans JC, Stierum
RH, van Delft JH: Parallelogram approach
using rat-human in vitro and rat in vivo
toxicogenomics predicts acetaminophen-
induced hepatotoxicity in humans. Toxicol
Sci 2009;
107: 544–552.
49 Hoehndorf R, Hiebert T, Hardy NW, Scho-
field PN, Gkoutos GV, Dumontier M: Mouse
model phenotypes provide information about
human drug targets. Bioinformatics 2014;
30:
719–725.
50 Machado CM, Rebholz-Schuhmann D, Frei-
tas AT, Couto FM: The semantic web in trans-
lational medicine: current applications and
future directions. Brief Bioinform 2015;
16:
89–103.
51 Kriete A, Lechner M, Clearfield D, Bohmann
D: Computational systems biology of aging.
Wiley Interdiscip Rev Syst Biol Med 2011;
3:
414–428.
Downloaded by:
Univ.-Bibliothek Rostock
149.126.78.1 - 12/17/2015 8:51:18 AM
Fuellen etal.
Gerontology
DOI: 10.1159/000442746
8
52 Pon JR, Marra MA: Driver and passenger mu-
tations in cancer. Annu Rev Pathol 2015;
10:
25–50.
53 Sundberg JP, Berndt A, Sundberg BA, Silva
KA, Kennedy V, Bronson R, Yuan R, Paigen
B, Harrison D, Schofield PN: The mouse as a
model for understanding chronic diseases of
aging: the histopathologic basis of aging in
inbred mice. Pathobiol Aging Age Relat Dis
2011;
1: 10.3402/pba.v1i0.7179.
54 Johnson SC, Dong X, Vijg J, Suh Y: Genetic
evidence for common pathways in human
age-related diseases. Aging Cell 2015;
14: 809–
817.
55 Cen W, Freitas AA, de Magalhaes JP: Predict-
ing the pro-longevity or anti-longevity effect
of model organism genes with new hierarchi-
cal feature selection methods. IEEE/ACM
Trans Comput Biol Bioinform 2015;
12: 262–
275.
56 Callahan A, Cifuentes JJ, Dumontier M: An
evidence-based approach to identify aging-
related genes in Caenorhabditis elegans . BMC
Bioinformatics 2015;
16: 40.
57 Anlasik T, Sies H, Griffiths HR, Mecocci P,
Stahl W, Polidori MC: Dietary habits are ma-
jor determinants of the plasma antioxidant
status in healthy elderly subjects. Br J Nutr
2005;
94: 639–642.
58 Polidori MC, Praticó D, Mangialasche F, Ma-
riani E, Aust O, Anlasik T, Mang N, Pient-
ka L, Stahl W, Sies H, Mecocci P, Nelles G:
High fruit and vegetable intake is positively
correlated with antioxidant status and cogni-
tive performance in healthy subjects. J Alz-
heimers Dis 2009;
17: 921–927.
59 Polidori MC, Carrillo JC, Verde PE, Sies H,
Siegrist J, Stahl W: Plasma micronutrient sta-
tus is improved after a 3-month dietary inter-
vention with 5 daily portions of fruits and veg-
etables: implications for optimal antioxidant
levels. Nutr J 2009;
8: 10.
60 Mecocci P, Tinarelli C, Schulz RJ, Polidori
MC: Nutraceuticals in cognitive impairment
and Alzheimer’s disease. Frontiers Pharmacol
2014;
5: 147.
61 Polidori MC, Schulz RJ: Nutritional contri-
butions to dementia prevention: main issues
on antioxidant micronutrients. Genes Nutr
2014;
92: 382.
62 Wald DS, Morris JK, Wald NJ: Randomized
Polypill crossover trial in people aged 50 and
over. PLoS One 2012;
7:e41297.
63 Rickman AD, Williamson DA, Martin CK,
Gilhooly CH, Stein RI, Bales CW, Roberts S,
Das SK: The CALERIE Study: design and
methods of an innovative 25% caloric restric-
tion intervention. Contemp Clin Trials 2011;
32: 874–881.
64 Andersen SL, Sebastiani P, Dworkis DA, Feld-
man L, Perls TT: Health span approximates
life span among many supercentenarians:
compression of morbidity at the approximate
limit of life span. J Gerontol A Biol Sci Med
Sci 2012;
67: 395–405.
65 Goldman DP, Cutler D, Rowe JW, Michaud
PC, Sullivan J, Peneva D, Olshansky SJ: Sub-
stantial health and economic returns from de-
layed aging may warrant a new focus for med-
ical research. Health Aff (Millwood) 2013;
32:
1698–1705.
66 Olshansky SJ, Perry D, Miller RA, Butler RN:
Pursuing the longevity dividend: scientific
goals for an aging world. Ann NY Acad Sci
2007;
1114: 11–13.
67 Pallauf K, Giller K, Huebbe P, Rimbach G:
Nutrition and healthy ageing: calorie restric-
tion or polyphenol-rich ‘MediterrAsian’ diet?
Oxid Med Cell Longev 2013;
2013: 707421.
68 Jerant A, Chapman B, Duberstein P, Robbins
J, Franks P: Personality and medication non-
adherence among older adults enrolled in a
six-year trial. Br J Health Psychol 2011;
16:
151–169.
Downloaded by:
Univ.-Bibliothek Rostock
149.126.78.1 - 12/17/2015 8:51:18 AM
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Aging is the single largest risk factor for chronic disease. Studies in model organisms have identified conserved pathways that modulate aging rate and the onset and progression of multiple age-related diseases, suggesting that common pathways of aging may influence age-related diseases in humans as well. To determine whether there is genetic evidence supporting the notion of common pathways underlying age-related diseases, we analyzed the genes and pathways found to be associated with five major categories of age-related disease using a total of 410 genomewide association studies (GWAS). While only a small number of genes are shared among all five disease categories, those found in at least three of the five major age-related disease categories are highly enriched for apoliprotein metabolism genes. We found that a more substantial number of gene ontology (GO) terms are shared among the 5 age-related disease categories and shared GO terms include canonical aging pathways identified in model organisms, such as nutrient-sensing signaling, translation, proteostasis, stress responses, and genome maintenance. Taking advantage of the vast amount of genetic data from the GWAS, our findings provide the first direct evidence that conserved pathways of aging simultaneously influence multiple age-related diseases in humans as has been demonstrated in model organisms. © 2015 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
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Background Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans. Results We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation. Conclusions We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0469-4) contains supplementary material, which is available to authorized users.
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Understanding the source of genetic variation in aging and using this variation to define the molecular mechanisms of healthy aging require deep and broad quantification of a host of physiological, morphological, and behavioral endpoints. The murine model is a powerful system in which to understand the relations across age-related phenotypes and to identify research models with variation in life span and health span. The Jackson Laboratory Nathan Shock Center of Excellence in the Basic Biology of Aging has performed broad characterization of aging in genetically diverse laboratory mice and has placed these data, along with data from several other major aging initiatives, into the interactive Mouse Phenome Database. The data may be accessed and analyzed by researchers interested in finding mouse models for specific aging processes, age-related health and disease states, and for genetic analysis of aging variation and trait covariation. We expect that by placing these data in the hands of the aging community that there will be (a) accelerated genetic analyses of aging processes, (b) discovery of genetic loci regulating life span, (c) identification of compelling correlations between life span and susceptibility for age-related disorders, and (d) discovery of concordant genomic loci influencing life span and aging phenotypes between mouse and humans. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America.
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Increases in human lifespan worldwide have revealed that advancing age is the predominant risk factor for major life-threatening diseases. Recent work has shown that ageing in diverse animals, including humans, is malleable to specific types of genetic mutation, diet, and drugs that can extend lifespan and improve health during ageing. These findings point to the prospect of broad-spectrum preventive medicine for the diseases of ageing based on intervention in relevant aspects of the ageing process itself.
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Ageing is a highly complex biological process that is still poorly understood. With the growing amount of ageing-related data available on the web, in particular concerning the genetics of ageing, it is timely to apply data mining methods to that data, in order to try to discover novel patterns that may assist ageing research. In this work, we introduce new hierarchical feature selection methods for the classification task of data mining and apply them to ageing-related data from four model organisms: Caenorhabditis elegans (worm), Saccharomyces cerevisiae (yeast), Drosophila melanogaster (fly), and Mus musculus (mouse). The main novel aspect of the proposed feature selection methods is that they exploit hierarchical relationships in the set of features (Gene Ontology terms) in order to improve the predictive accuracy of the Naïve Bayes and 1-Nearest Neighbour (1-NN) classifiers, which are used to classify model organisms' genes into pro-longevity or anti-longevity genes. The results show that our hierarchical feature selection methods, when used together with Naïve Bayes and 1-NN classifiers, obtain higher predictive accuracy than the standard (without feature selection) Naïve Bayes and 1-NN classifiers, respectively. We also discuss the biological relevance of a number of Gene Ontology terms very frequently selected by our algorithms in our datasets.
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Next-generation sequencing has allowed identification of millions of somatic mutations and epigenetic changes in cancer cells. A key challenge in interpreting cancer genomes and epigenomes is distinguishing which genetic and epigenetic changes are drivers of cancer development. Frequency-based and function-based approaches have been developed to identify candidate drivers; we discuss the advantages and drawbacks of these methods as well as their latest refinements. We focus particularly on identification of the types of drivers most likely to be missed, such as genes affected by copy number alterations, mutations in noncoding regions, dysregulation of microRNA, epigenetic changes, and mutations in chromatin modifiers. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease Volume 10 is January 24, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.