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					Limits on Human Lifespan and Molecular Effects on Ageing
Leonid A. Gavrilov Natalia S. Gavrilova
Center on Aging, NORC/University of Chicago, 1155 East 60th Street, Chicago, IL 60637 gavrilov@longevity-science.org

Questions of Scientific and Practical (Actuarial) Significance
• How far could mortality decline go? (absolute zero seems implausible) • Are there any „biological‟ limits to human mortality decline, determined by „reliability‟ of human body? (lower limits of mortality dependent on age, sex, and population genetics) • Were there any indications for „biological‟ mortality limits in the past? • Are there any indications for mortality limits now?

The Gompertz-Makeham Law

μ(x) = A + R0exp(α x)
A – Makeham term or background mortality R0exp(α x) – age-dependent mortality

Historical Changes in Mortality for 40-year-old Swedish Males
1. Total mortality 2. Background mortality 3. Age-dependent mortality
• Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality for 40-year-old Women in Norway and Denmark
1. Norway, total mortality 2. Denmark, total mortality 3. Norway, age-dependent mortality 4. Denmark, agedependent mortality
Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality for 40-year-old Italian Women and Men
1. Women, total mortality 2. Men, total mortality 3. Women, agedependent mortality 4. Men, age-dependent mortality
Source: Gavrilov, Gavrilova, “The Biology of Life Span” 1991

Historical Changes in Mortality Swedish Females
1

0.1

1925 1960 1980 1999

Log (Hazard Rate)

0.01

0.001

0.0001 0 20 40 60 80 100

Age

Historical Changes in Survival from Age 90 to 100 years. France
6

Percent Surviving from Age 90 to 100

5

Females Males

4

3

2

1

0

1900

1920

1940

1960

1980

2000

Calendar Year

Historical Changes in Survival from Age 90 to 100 years. Japan
10

Percent Surviving from Age 90 to 100

8

Females Males

6

4

2

0 1950 1960 1970 1980 1990 2000

Calendar Year

Extension of the GompertzMakeham Model through the Factor Analysis of Mortality Trends
Mortality force (age, time) =
= a0(age) + a1(age) x F1(time) + a2(age) x F2(time)

Factor Analysis of Mortality Swedish Females
4

Factor 1 ('young ages') Factor 2 ('old ages')

3

2

Factor score

1

0

-1

-2

1900

1920

1940

1960

1980

2000

Year

Preliminary Conclusions
• There was some evidence for ‘ biological’ mortality limits in the past, but these ‘limits’ proved to be responsive to the recent technological and medical progress. • Thus, there is no convincing evidence for absolute ‘biological’ mortality limits now.
• Analogy for illustration and clarification: There was a limit to
the speed of airplane flight in the past (‘sound’ barrier), but it was overcome by further technological progress. Similar observations seems to be applicable to current human mortality decline.

Molecular Effects on Ageing
New Ideas and Findings by Bruce Ames:
• The rate of mutation damage is NOT immutable, but it can be dramatically decreased by very simple measures: -- Through elimination of deficiencies in vitamins and other micronutrients (iron, zinc, magnesium, etc). • Micronutrient deficiencies are very common even in the modern wealthy populations • These deficiencies are much more important than radiation, industrial pollution and most other hazards

Our hypothesis:
Remarkable improvement in the oldest-old survival may reflect an unintended retardation of the aging process, caused by decreased damage accumulation, because of improving the micronutrient status in recent decades

Micronutrient Undernutrition in Americans
Nutrient
Minerals Iron Women 20-30 years Women 50+ years 18 mg 8 mg 75% 25% 25% 5-10%

Population Group RDA

% ingesting

< RDA

% ingesting < 50% RDA <50% RDA

Zinc Vitamins B6

Men; Women 50+ years

11; 8 mg

50%

10%

Men; Women

1.7; 1.5 mg

50%

10% 25%; 50% 5; ~10-25%

Folate**
B12

Men; Women
Men; Women

400 mcg
2.4 mcg

75%
10-20; 25-50 %

C

Men; Women

90; 75 mg

50%

25%

•Wakimoto and Block (2001) J Gerontol A Biol Sci Med Sci. Oct; 56 Spec No 2(2):65-80. ** Before U.S. Food Fortification Source: Presentation by Bruce Ames at the IABG Congress

Molecular Effects on Ageing (2)
Ideas and Findings by Bruce Ames:
• The rate of damage accumulation is NOT immutable, but it can be dramatically decreased by PREVENTING INFLAMMATION: Inflammation causes tissue damage through many mechanisms including production of Hypochlorous acid (HOCl), which produces DNA damage (through incorporation of chlorinated nucleosides). Chronic inflammation may contribute to many age-related degenerative diseases including cancer

Hypothesis:
Remarkable improvement in the oldest-old survival may reflect an unintended retardation of the aging process, caused by decreased damage accumulation, because of partial PREVENTION of INFLAMMATION through better control over infectious diseases in recent decades

Characteristic of our Dataset
• Over 16,000 persons belonging to the European aristocracy • 1800-1880 extinct birth cohorts • Adult persons aged 30+ • Data extracted from the professional genealogical data sources including Genealogisches Handbook des Adels, Almanac de Gotha, Burke Peerage and Baronetage.

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

•
p=0.006 p=0.02

Lifespan Difference (yr)

2

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.

•
1

0

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.

Month of Birth

Season of Birth and Female Lifespan
6,517 females from European aristocratic families born in 1800-1880 Seasonal Differences in Adult Lifespan at Age 60
2

•
p=0.008

Lifespan Difference (yr)

p=0.04

Life expectancy of adult women (60+) 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.

1

•

0

FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB.

Month of Birth

Mean Lifespan of Females Born in December and February as a Function of Birth Year
80

Mean Lifespan, years

75

70

65

60

Born in February Born in December
Linear Regression Fit

• Life expectancy of adult women (30+) as a function of year of birth

1800

1820

1840

1860

1880

Year of Birth

Daughters' Lifespan (30+) as a Function of Paternal Age at Daughter's Birth
6,032 daughters from European aristocratic families born in 1800-1880
1
•

0

Lifespan Difference (yr)

Life expectancy of adult women (30+) as a function of father's age when these women were born (expressed as a difference from the reference level for those born to fathers of 40-44 years). The data are point estimates (with standard errors) of the differential intercept coefficients adjusted for other explanatory variables using multiple regression with nominal variables. Daughters of parents who survived to 50 years.

-1

•
-2

-3
p = 0.04

•
-4
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Paternal Age at Reproduction

Daughters' Lifespan (60+) as a Function of Paternal Age at Daughter's Birth
4,832 daughters from European aristocratic families born in 1800-1880
1
•

0

Lifespan Difference (yr)

Life expectancy of older women (60+) as a function of father's age when these women were born (expressed as a difference from the reference level for those born to fathers of 40-44 years). The data are point estimates (with standard errors) of the differential intercept coefficients adjusted for other explanatory variables using multiple regression with nominal variables. Daughters of parents who survived to 50 years.

•
-1

-2
p = 0.004

•

-3
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59

Paternal Age at Reproduction

Paternal Age as a Risk Factor for Alzheimer Disease
40

Parental age at childbirth (years)

p = 0.04

35
NS
p=0.04

• MGAD - major gene for Alzheimer Disease
NS NS NS

30

25

Paternal age

Maternal age

Sporadic Alzheimer Disease (low likelihood of MGAD) Familial Alzheimer Disease (high likelihood of MGAD) Controls

• Source: L. Bertram et al. Neurogenetics, 1998, 1: 277-280.

Paternal Age and Risk of Schizophrenia
• Estimated cumulative incidence and percentage of offspring estimated to have an onset of schizophrenia by age 34 years, for categories of paternal age. The numbers above the bars show the proportion of offspring who were estimated to have an onset of schizophrenia by 34 years of age.

•

Source: Malaspina et al.,
Arch Gen Psychiatry.2001.

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)?

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

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

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.

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.

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

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
40

Mutant frequency (x10 )

35 30 25 20 15 10 5 0 0

-5

Small Intestine Heart

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.

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.

Daughter's Lifespan
(Mean Deviation from Cohort Life Expectancy)

as a Function of Paternal Lifespan
6

4

2

0

-2

40

50

60

70

80

90

100

• Offspring data for adult lifespan (30+ years) are smoothed by 5-year running average. • Extinct birth cohorts (born in 1800-1880) • European aristocratic families. 6,443 cases

Daughter's Lifespan (deviation), years

Paternal Lifespan, years

Offspring Lifespan at Age 30 as a Function of Paternal Lifespan
Data are adjusted for other predictor variables

p=0.0003

Lifespan difference, years

Lifespan difference, years

4

4

p=0.001

p<0.0001

p=0.001

2

2

p=0.006

p=0.05

0

0

-2 -2

40

50

60

70

80

90

100

40

50

60

70

80

90

100

Paternal Lifespan, years

Paternal Lifespan, years

Daughters, 8,284 cases

Sons, 8,322 cases

Offspring Lifespan at Age 60 as a Function of Paternal Lifespan
Data are adjusted for other predictor variables
4 4

Lifespan difference, years

p=0.0001

Lifespan difference, years

p=0.0003

2
p=0.04 p=0.04

2
p=0.004 p=0.006

0

0

-2

-2

40

50

60

70

80

90

100

40

50

60

70

80

90

100

Paternal Lifespan, years

Paternal Lifespan, years

Daughters, 6,517 cases

Sons, 5,419 cases

Offspring Lifespan at Age 30 as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4 4
p=0.0004 p=0.02

Lifespan difference, years

Lifespan difference, years

2

2
p=0.05 p=0.01

0

0

-2

-2

40

50

60

70

80

90

100

40

50

60

70

80

90

100

Maternal Lifespan, years

Maternal Lifespan, years

Daughters, 8,284 cases

Sons, 8,322 cases

Offspring Lifespan at Age 60 as a Function of Maternal Lifespan
Data are adjusted for other predictor variables
4 4
p<0.0001 p=0.04

Lifespan difference, years

Lifespan difference, years

2

2
p=0.01 p=0.01

0

0

-2

-2

40

50

60

70

80

90

100

40

50

60

70

80

90

100

Maternal Lifespan, years

Maternal Lifespan, years

Daughters, 6,517 cases

Sons, 5,419 cases

Person‟s Lifespan as a Function of Spouse Lifespan
Data are adjusted for other predictor variables
6 6

Lifespan difference, years

4

Lifespan difference, years
40 50 60 70 80 90 100

4

2

2

0

0

-2 -2 -4

40

50

60

70

80

90

100

Spouse Lifespan, years

Spouse Lifespan, years

Married Women, 6,442 cases

Married Men, 6,596 cases

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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