Reliability Chicago 2005

7/31/2019 Reliability Chicago 2005

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Reliability Theoryof Aging and Longevity

Dr. Leonid A. Gavrilov, Ph.D.Dr. Natalia S. Gavrilova, Ph.D.

Center on AgingNORC and The University of Chicago

Chicago, Illinois, USA


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Why Do We Need Reliability Theory

for Aging Studies ?

Why Not To Use Evolutionary

Theories of Aging?:

mutation accumulation theory

(Peter Medawar) antagonistic pleiotropy theory(George Williams)


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Diversity of ideas and theories isuseful and stimulating in science(we need alternative hypotheses!)

Aging is a very general phenomenon!

Evolution through Natural selection

(and declining force of natural selectionwith age) is not applicable to aging cars!


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Aging is a Very General Phenomenon!


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Particular mechanisms of aging may bevery different even across biologicalspecies (salmon vs humans)

BUT

General Principles of Systems Failure and

Aging May Exist(as we will show in this presentation)


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What Is Reliability Theory?

Reliability theory is a generaltheory of systems failure.


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Reliability Theory

Reliability theory was historicallydeveloped to describe failure and agingof complex electronic (military)

equipment, but the theory itself is a verygeneral theory.


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Applications of Reliability Theory to

Biological Aging

(Some Representative Publications)


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Gavrilov, L., Gavrilova, N.

Reliability theory ofaging and longevity.In: Handbook of theBiology of Aging.

Academic Press, 6thedition (forthcoming inDecember 2005).


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The Concept of Systems Failure

In reliability theoryfailure is defined asthe event when arequired function is

terminated.


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Failures are often classified into

two groups:

degradation failures, where

the system or component nolonger functions properly

catastrophic or fatal failures -the end of system's orcomponent's life


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Definition of aging and non-agingsystems in reliability theory

Aging: increasing risk of failure withthe passage of time (age).

No aging: 'old is as good as new'(risk of failure is not increasing withage)

Increase in the calendar age of asystem is irrelevant.


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Mortality in Aging and Non-aging Systems

Age

0 2 4 6 8 10 12

Riskofdeath

1

2

3

Age0 2 4 6 8 10 12

RiskofDeath

0

1

2

3

non-aging system aging system

Example: radioactive decay


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According to Reliability Theory:

Aging is NOT just growing oldInstead

Aging is a degradation to failure:becoming sick, frail and dead

'Healthy aging' is an oxymoron likea healthy dying or a healthy disease

More accurate terms instead of'healthy aging' would be a delayedaging, postponed aging, slow aging,or negligible aging (senescence)


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Further plan of presentation

Empirical laws of failure and aging inbiology

Explanations by reliability theory

Links between reliability theory andevolutionary theories


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Empirical Laws of SystemsFailure and Aging


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Stages of Life in Machines and Humans

The so-called bathtub curve fortechnical systems

Bathtub curve for human mortality asseen in the U.S. population in 1999has the same shape as the curve for

failure rates of many machines.


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Failure (Mortality) Laws in Biology

1. Gompertz-Makeham law of mortality

2. Compensation law of mortality

3. Late-life mortality deceleration


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The Gompertz-Makeham Law

(x) = A + R ex

A Makeham term or background mortality

R ex age-dependent mortality; x - age

Death rate is a sum of age-independent component

(Makeham term) and age-dependent component

(Gompertz function), which increases exponentially

with age.

risk of death


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Gompertz-Makeham Law of Mortalityin Flour Beetles

Based on the life table for400 female flour beetles(Tribolium confusumDuval). published by Pearland Miner (1941).

Source: Gavrilov, Gavrilova,

The Biology of Life Span1991


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Compensation Law of Mortality

(late-life mortality convergence)

Relative differences in death

rates are decreasing with age,

because the higher initial death

rates are compensated by lower

pace of their increase with age


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Compensation Law of MortalityConvergence of Mortality Rates with Age

Source:Gavrilov, Gavrilova,

The Biology of

Life Span 1991


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Compensation Law of Mortality in

Laboratory Drosophila1 drosophila of the Old Falmouth,

New Falmouth, Sepia and EaglePoint strains (1,000 virginfemales)

2 drosophila of the Canton-Sstrain (1,200 males)

3 drosophila of the Canton-Sstrain (1,200 females)

4 - drosophila of the Canton-S

strain (2,400 virgin females)Mortality force was calculated for

6-day age intervals.

Source: Gavrilov, Gavrilova,

The Biology of Life Span 1991


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Mortality deceleration at

advanced ages. After age 95, the

observed risk ofdeath [red line]

deviates from thevalue predicted byan early model, theGompertz law [blackline].

Source: Gavrilov, Gavrilova,Why we fall apart.Engineerings reliabilitytheory explains humanaging. IEEE Spectrum.2004


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Mortality at Advanced Ages

Source: Gavrilov L.A., Gavrilova N.S. 1991. The Biology of Life Span


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Mortality Leveling-Off in House FlyMusca domestica

Based on life

table of 4,650male house fliespublished byRockstein &

Lieberman, 1959

Age, days

0 10 20 30 40

hazard

rate,log

sca

le

0.001

0.01

0.1


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Non-Aging Mortality Kinetics in Later Life

Source: A. Economos.A non-Gompertzianparadigm for mortalitykinetics of metazoananimals and failurekinetics of

manufacturedproducts. AGE, 1979,2: 74-76.


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Non-Aging Mortality Kinetics in Later Life

Source: A. Economos.A non-Gompertzian

paradigm formortality kinetics ofmetazoan animalsand failure kineticsof manufacturedproducts. AGE,1979, 2: 74-76.


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Mortality Deceleration in Animal Species

Invertebrates:

Nematodes, shrimps, bdelloidrotifers, degenerate medusae(Economos, 1979)

Drosophila melanogaster(Economos, 1979; Curtsingeret al., 1992)

Housefly, blowfly (Gavrilov,1980)

Medfly (Carey et al., 1992)

Bruchid beetle (Tatar et al.,1993)

Fruit flies, parasitoid wasp(Vaupel et al., 1998)

Mammals:

Mice (Lindop, 1961;Sacher, 1966; Economos,1979)

Rats (Sacher, 1966)

Horse, Sheep, Guinea pig(Economos, 1979; 1980)


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Non-Aging Failure Kineticsof Industrial Materials in Later Life

(steel, relays, heat insulators)

Source:

A. Economos.

A non-Gompertzianparadigm formortality kinetics ofmetazoan animals

and failure kinetics ofmanufacturedproducts. AGE, 1979,2: 74-76.


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Additional Empirical Observation:

Many age changes can be explained bycumulative effects of cell loss over time

Atherosclerotic inflammation - exhaustion

of progenitor cells responsible for arterialrepair (Goldschmidt-Clermont, 2003; Libby,2003; Rauscher et al., 2003).

Decline in cardiac function - failure of

cardiac stem cells to replace dyingmyocytes (Capogrossi, 2004).

Incontinence - loss of striated muscle cellsin rhabdosphincter (Strasser et al., 2000).


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Like humans,nematode

C. elegansexperiencemuscle loss

Body wall muscle sarcomeres

Left - age 4 days. Right - age 18 days

Herndon et al. 2002.

Stochastic and geneticfactors influence tissue-specific decline in ageingC. elegans. Nature419,808 - 814.

many additional cell types

(such as hypodermis andintestine) exhibit age-related deterioration.


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What Should

the Aging Theory Explain Why do most biological species

deteriorate with age?

The Gompertz law of mortality

Mortality deceleration and leveling-off at

advanced ages

Compensation law of mortality


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The Concept of Reliability Structure

The arrangement of components

that are important for systemreliability is called reliabilitystructure and is graphicallyrepresented by a schema oflogical connectivity


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Two major types of systems

logical connectivity Components

connected in

series

Components

connected inparallel

Fails when the first component fails

Fails whenall

componentsfail

Combination of two types Series-parallel system


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Series-parallel

Structure ofHuman Body

Vital organs areconnected in series

Cells in vital organsare connected inparallel


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Reliability Model

of a Simple Parallel System

Failure rate of the system:

Elements failrandomly and

independentlywith a constantfailure rate, k

n initialnumber ofelements

nknxn-1 early-life period approximation, when 1-e-kxkxk late-life period approximation, when 1-e-kx1

( )x =d S( )x

S( )x dx=

nk ek x( )1 e

k x n 1

1 ( )1 ek x n


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Failure Rate as a Function of Agein Systems with Different Redundancy Levels

Failure of elements is random


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Standard Reliability Models Explain

Mortality deceleration andleveling-off at advanced ages

Compensation law of mortality


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Standard Reliability ModelsDo Not Explain

The Gompertz law of mortalityobserved in biological systems

Instead they produce Weibull(power) law of mortalitygrowth with age


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An Insight Came To Us While WorkingWith Dilapidated Mainframe Computer

The complexunpredictable

behavior of thiscomputer couldonly be describedby resorting to such'human' conceptsas character,personality, andchange of mood.

Wh O ga is s Ma Be


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Why Organisms May BeDifferent From Machines?

Way of systemcreation

Assembly bymacroscopic agents

Self-assembly

Machines Biological systems

Opportunities to pre-test components

Expected litteringwith initial defects

Demand for highredundancy to be

operationalExpected system

redundancy

Demand for high initialquality of each element

Size of components

Degree of elementsminiatiruzation

Total number ofelements in a system


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Reliability structure of

(a) technical devices and (b) biological systems

Low redundancy

Low damage load

High redundancy

High damage load

X - defect


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Models of systems with

distributed redundancy

Organism can be presented as a systemconstructed ofmseries-connected blockswith binomially distributed elements withinblock (Gavrilov, Gavrilova, 1991, 2001)


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Model of organism

with initial damage loadFailure rate of a system with binomially distributedredundancy (approximation for initial period of life):

x0= 0 - ideal system, Weibull law of mortality

x0>> 0 - highlydamaged system,Gompertz law of mortality

( )x Cmn ( )q kn 1 q

q kx+

n 1

= ( )x0 x+n 1

where - the initial virtual age of the systemx0 =1 q

q k

The initial virtual age of a system defines the law ofsystems mortality:

Binomiallaw ofmortality


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People age more like machines built with lots of

faulty parts than like ones built with pristine parts.

As the numberof badcomponents,

the initialdamage load,increases[bottom to top],machine failure

rates begin tomimic humandeath rates.


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Statement of the HIDL hypothesis:(Idea of High Initial Damage Load )

"Adult organisms already have anexceptionally high load of initial damage,

which is comparable with theamount of subsequent aging-relateddeterioration, 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.


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Why should we expect high initial damage load inbiological systems?

General argument:-- biological systems are formed by self-assemblywithout helpful external quality control.

Specific arguments:

1. Most cell divisions responsible for DNA copy-errorsoccur in early development leading to clonal expansionof mutations

2. Loss of telomeres is also particularly high in early-life

3. Cell cycle checkpoints are disabled in early development


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Birth Process is a PotentialSource of High Initial Damage

Severe hypoxia and asphyxia justbefore the birth.

oxidative stress just after the birthbecause of acute reoxygenationwhile starting to breathe.

The same mechanisms that produceischemia-reperfusion injury and therelated phenomenon, asphyxia-reventilation injury known incardiology.


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Spontaneous mutant frequencieswith age in heart and small intestine

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35

Age (months)

Mutantfrequen

cy(x10-5)

Small Intestine

Heart

Source: Presentation of Jan Vijg at the IABG Congress, Cambridge, 2003

Practical implications from


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Practical implications fromthe HIDL hypothesis:

"Even a small progress in optimizing theearly-developmental processes canpotentially result in a remarkableprevention of many diseases in later life,postponement of aging-related morbidityand mortality, and significant extension

of healthy lifespan."

Source: Gavrilov, L.A. & Gavrilova, N.S. 1991. The Biology of Life Span:

A Quantitative Approach. Harwood Academic Publisher, New York.

Lif E t d M th f Bi th


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Month of Birth

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

life

expectancyata

ge80,years

7.6

7.7

7.8

7.9

1885 Birth Cohort1891 Birth Cohort

Life Expectancy and Month of Birth

Data source:Social SecurityDeath Master File


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Evolution in the Direction

of Low Mortality at Young Ages

This could be

easily achievedby simpleincrease in theinitial redundancylevels (e.g., initialcell numbers).

Lo

g

risk

ofdeath

Age


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Reliability of Birds vs Mammals

Birds should be veryprudent in redundancy oftheir body structures(because it comes with aheavy cost of additionalweight).

Result: high mortality atyounger ages.

Flight adaptation shouldforce birds to evolve in adirection of high reliability

of their components(cells).

Result: low rate ofelements (cells) damageresulting in low mortalityat older ages


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Effect of extrinsic mortality on theevolution of senescence in guppies.

Reznick et al. 2004. Nature431, 1095 - 1099

Reliability-theory

perspective:

Predators ensureselection for betterperformance and

lower initial damageload.

Hence life span wouldincrease in high

predator localities.

Solid line high predator locality

Dotted linelow predator locality


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Conclusions (I)

Redundancyis a key notion for understandingaging and the systemic nature of aging inparticular. Systems, which are redundant innumbers of irreplaceable elements, do

deteriorate (i.e., age) over time, even if theyare built of non-aging elements.

An apparent aging rate or expression of aging

(measured as age differences in failure rates,including death rates) is higher for systems withhigher redundancy levels.


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Conclusions (II)

Redundancy exhaustionover the life course explains theobserved compensation law of mortality (mortalityconvergence at later life) as well as the observed late-lifemortality deceleration, leveling-off, and mortalityplateaus.

Living organisms seem to be formed with a high load ofinitial damage, and therefore their lifespans and agingpatterns may be sensitive to early-life conditions that

determine this initial damage load during earlydevelopment. The idea of early-life programming of agingand longevity may have important practical implicationsfor developing early-life interventions promoting healthand longevity.


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Acknowledgments

This study was made possiblethanks to:

generous support from theNational Institute on Aging, and

stimulating working environmentat the Center on Aging,NORC/University of Chicago


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For More Information and Updates

Please Visit OurScientific and Educational Websiteon Human Longevity:

http://longevity-science.org

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Reliability Chicago 2005

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