Biodemography
of Human Longevity
Dr. Leonid A. Gavrilov, Ph.D.
Center on Aging
NORC and University of Chicago
Chicago, Illinois, USA
What is Biodemography?
Biodemography is a multidisciplinary
approach, integrating biological
knowledge (studies on human biology
and animal models) with demographic
research on human longevity and
survival
Why do we need Biodemography?
Biodemographic studies are important for:
• understanding the driving forces of current
longevity revolution (dramatic increase in
human life expectancy)
• forecasting the future of human longevity
• identification of new strategies for further
increase in healthy and productive life span.
Two Illustrative Examples of the
Recent Longevity Revolution in
Industrialized Countries:
• France
• Japan
Historical Changes in Survival
from Age 90 to 100 years. France
Percent Surviving from Age 90 to 100
6
5
Females
Males
4
3
2
1
0
1900
1920
1940
1960
Calendar Year
1980
2000
Historical Changes in Survival
from Age 90 to 100 years. Japan
Percent Surviving from Age 90 to 100
10
Females
Males
8
6
4
2
0
1950
1960
1970
1980
Calendar Year
1990
2000
What are the representative
examples of biodemograhic
studies and publications?
Brief history of Biodemography
and brief bibliography
What are the Major
Biodemograhic Findings?
Biodemographic studies found a remarkable
similarity in survival dynamics between
humans and laboratory animals.
1. Gompertz-Makeham law of mortality
2. Compensation law of mortality
3. Late-life mortality deceleration.
The Gompertz-Makeham Law
The Gompertz-Makeham law states that death rate is
a sum of age-independent component (Makeham
term) and age-dependent component (Gompertz
function), which increases exponentially with age.
μ(x) = A + R0exp(α x)
A – Makeham term or background mortality
R0exp(α x) – age-dependent mortality
Exponential Increase of Death Rate
with Age in Fruit Flies
(Gompertz Law of Mortality)
Linear dependence of
the logarithm of
mortality force on the
age of Drosophila.
Based on the life table
for 2400 females of
Drosophila melanogaster
published by Hall (1969).
Mortality force was
calculated for 3-day age
intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
Age-Trajectory of Mortality in Flour Beetles
(Gompertz-Makeham Law of Mortality)
Dependence of the
logarithm of mortality force
(1) and logarithm of
increment of mortality force
(2) on the age of flour
beetles (Tribolium confusum
Duval).
Based on the life table
for 400 female flour beetles
published by Pearl and
Miner (1941). Mortality
force was calculated for 30day age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span”
1991
Age-Trajectory of Mortality in Italian Women
(Gompertz-Makeham Law of Mortality)
Dependence of the
logarithm of mortality
force (1) and logarithm of
increment of mortality
force (2) on the age of
Italian women.
Based on the official
Italian period life table for
1964-1967. Mortality force
was calculated for 1-year
age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span”
1991
The Compensation Law
of Mortality
The Compensation law of mortality (late-life
mortality convergence) states that the relative
differences in death rates between different
populations of the same biological species are
decreasing with age, because the higher initial
death rates are compensated by lower pace of their
increase with age
Compensation Law of Mortality
Convergence of Mortality Rates with Age
1 – India, 1941-1950, males
2 – Turkey, 1950-1951, males
3 – Kenya, 1969, males
4 - Northern Ireland, 1950-1952,
males
5 - England and Wales, 19301932, females
6 - Austria, 1959-1961, females
7 - Norway, 1956-1960, females
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
Compensation Law of Mortality
in Laboratory Drosophila
1 – drosophila of the Old Falmouth,
New Falmouth, Sepia and Eagle
Point strains (1,000 virgin
females)
2 – drosophila of the Canton-S strain
(1,200 males)
3 – drosophila of the Canton-S strain
(1,200 females)
4 - drosophila of the Canton-S strain
(2,400 virgin females)
Mortality force was calculated for 6day age intervals.
Source: Gavrilov, Gavrilova,
“The Biology of Life Span” 1991
The Late-Life Mortality Deceleration
(Mortality Leveling-off, Mortality Plateaus)
• The late-life mortality deceleration law states that death
rates stop to increase exponentially at advanced ages and
level-off to the late-life mortality plateau.
• An immediate consequence from this observation is that
there is no fixed upper limit to human longevity - there is no
special fixed number, which separates possible and
impossible values of lifespan.
• This conclusion is important, because it challenges the
common belief in existence of a fixed maximal human life
span.
Mortality at Advanced Ages
Source: Gavrilov L.A., Gavrilova N.S. The Biology of Life Span:
A Quantitative Approach, NY: Harwood Academic Publisher, 1991
M. Greenwood, J. O. Irwin. BIOSTATISTICS OF SENILITY
Survival Patterns After Age 90
Percent surviving (in log scale) is
plotted as a function of age of Swedish
women for calendar years 1900, 1980,
and 1999 (cross-sectional data). Note
that after age 100, the logarithm of
survival fraction is decreasing without
much further acceleration (aging) in
almost a linear fashion. Also note an
increasing pace of survival improvement
in history: it took less than 20 years
(from year 1980 to year 1999) to repeat
essentially the same survival
improvement that initially took 80 years
(from year 1900 to year 1980).
Source: cross-sectional (period) life
tables at the Berkeley Mortality
Database (BMD):
http://www.demog.berkeley.edu/~bmd/
Non-Gompertzian Mortality Kinetics
of Four Invertebrate Species
Non-Gompertzian mortality
kinetics of four invertebrate
species: nematodes,
Campanularia flexuosa,
rotifers and shrimp.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure kinetics
of manufactured products.
AGE, 1979, 2: 74-76.
Non-Gompertzian Mortality Kinetics
of Three Rodent Species
Non-Gompertzian
mortality kinetics of
three rodent species:
guinea pigs, rats and
mice.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure
kinetics of manufactured
products. AGE, 1979, 2:
74-76.
Mortality Leveling-Off in Drosophila
Non-Gompertzian
mortality kinetics of
Drosophila
melanogaster
Source: Curtsinger
et al., Science, 1992.
Non-Gompertzian Mortality Kinetics
of Three Industrial Materials
Non-Gompertzian
mortality kinetics of three
industrial materials: steel,
industrial relays and
motor heat insulators.
Source: A. Economos.
A non-Gompertzian
paradigm for mortality
kinetics of metazoan
animals and failure
kinetics of manufactured
products. AGE, 1979, 2:
74-76.
Aging is a Very General Phenomenon!
What Should
the Aging Theory Explain:
• Why do most biological species deteriorate with age?
• Specifically, why do mortality rates increase exponentially
with age in many adult species (Gompertz law)?
• Why does the age-related increase in mortality rates vanish
at older ages (mortality deceleration)?
• How do we explain the so-called compensation law of
mortality (Gavrilov & Gavrilova, 1991)?
Redundancy Creates Both Damage Tolerance
and Damage Accumulation (Aging)
Damage
Defect
No redundancy
Death
Damage
Defect
Redundancy
Damage accumulation
(aging)
Differences in reliability structure between
(a) technical devices and (b) biological systems
Each block diagram represents a system with m serially connected blocks (each being critical for
system survival, 5 blocks in these particular illustrative examples) built of n elements connected in
parallel (each being sufficient for block being operational). Initially defective non-functional elements
are indicated by crossing (x).
The reliability structure of technical devices (a) is characterized by relatively low redundancy in
elements (because of cost and space limitations), each being initially operational because of strict
quality control. Biological species, on the other hand, have a reliability structure (b) with huge
redundancy in small, often non-functional elements (cells).
Statement of the HIDL hypothesis:
(Idea of High Initial Damage Load )
"Adult organisms already have an
exceptionally high load of initial damage,
which is comparable with the amount of
subsequent aging-related deterioration,
accumulated during the rest of the entire
adult life."
Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span:
A Quantitative Approach. Harwood Academic Publisher, New York.
Why should we expect high initial
damage load ?
• General argument:
-- In contrast to technical devices, which are built from pretested high-quality components, biological systems are formed by
self-assembly without helpful external quality control.
• Specific arguments:
1. Cell cycle checkpoints are disabled in early
development (Handyside, Delhanty,1997. Trends
Genet. 13, 270-275 )
2. extensive copy-errors in DNA, because most cell
divisions responsible for DNA copy-errors occur in
early-life (loss of telomeres is also particularly high in
early-life)
3. ischemia-reperfusion injury and asphyxia-reventilation
injury during traumatic process of 'normal' birth
Spontaneous mutant frequencies with
age in heart and small intestine
Small Intestine
Heart
35
-5
Mutant frequency (x10 )
40
30
25
20
15
10
5
0
0
5
10
15
20
Age (months)
25
30
35
Source: Presentation of Jan Vijg at the IABG Congress, Cambridge, 2003
Birth Process is a Potential
Source of High Initial Damage
•
During birth, the future child is deprived
of oxygen by compression of the
umbilical cord and suffers severe
hypoxia and asphyxia. Then, just after
birth, a newborn child is exposed to
oxidative stress because of acute
reoxygenation while starting to breathe.
It is known that acute reoxygenation
after hypoxia may produce extensive
oxidative damage through the same
mechanisms that produce ischemiareperfusion injury and the related
phenomenon, asphyxia-reventilation
injury. Asphyxia is a common
occurrence in the perinatal period, and
asphyxial brain injury is the most
common neurologic abnormality in the
neonatal period that may manifest in
neurologic disorders in later life.
Practical implications from
the HIDL hypothesis:
"Even a small progress in optimizing the early-developmental
processes can potentially result in a remarkable prevention of
many diseases in later life, postponement of aging-related
morbidity and mortality, and significant extension of healthy
lifespan."
"Thus, the idea of early-life programming of aging and longevity
may have important practical implications for developing earlylife interventions promoting health and longevity."
Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span:
A Quantitative Approach. Harwood Academic Publisher, New York.
Season of Birth and Female Lifespan
8,284 females from European aristocratic families
born in 1800-1880
Seasonal Differences in Adult Lifespan at Age 30
3
•
Life expectancy of adult
women (30+) as a function of
month of birth (expressed as
a difference from the
reference level for those
born in February).
•
The data are point estimates
(with standard errors) of the
differential intercept
coefficients adjusted for
other explanatory variables
using multivariate
regression with categorized
nominal variables.
p=0.006
Lifespan Difference (yr)
p=0.02
2
1
0
FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.
Month of Birth
Failure Kinetics in Mixtures of Systems with
Different Redundancy Levels
Initial Period
The dependence of
logarithm of
mortality force
(failure rate) as a
function of age in
mixtures of parallel
redundant systems
having Poisson
distribution by
initial numbers of
functional elements
(mean number of
elements,  = 1, 5,
10, 15, and 20.
Strategies of Life Extension
Based on the Reliability Theory
Increasing redundancy
Maintenance and repair
Increasing durability
of components
Replacement and repair
Conclusions (I)
•
Redundancy is a key notion for understanding
aging and the systemic nature of aging in
particular. Systems, which are redundant in
numbers of irreplaceable elements, do deteriorate
(i.e., age) over time, even if they are built of nonaging elements.
•
An actuarial aging rate or expression of aging
(measured as age differences in failure rates,
including death rates) is higher for systems with
higher redundancy levels.
Conclusions (II)
•
Redundancy exhaustion over the life course explains the
observed ‘compensation law of mortality’ (mortality
convergence at later life) as well as the observed late-life
mortality deceleration, leveling-off, and mortality plateaus.
•
Living organisms seem to be formed with a high load of
initial damage, and therefore their lifespans and aging
patterns may be sensitive to early-life conditions that
determine this initial damage load during early
development. The idea of early-life programming of aging
and longevity may have important practical implications
for developing early-life interventions promoting health
and longevity.
Acknowledgments
This study was made possible thanks to:
• generous support from the National
Institute on Aging, and
• stimulating working environment at the
Center on Aging, NORC/University of
Chicago
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