Age estimation based on parameters of the aorta in the recently deceased adult human

This is not a published paper, it has not been peer-reviewed and allthe data belongs to the Netherlands Forensic Institute.

Age estimation based onparameters of the aorta in therecently deceased adult human

L.A. Middendorp, BSc.

OBJECTIVE The purpose of this study was to determine the possibility ofestimating age by the aorta.

BACKGROUND Estimating age of an unidentified human cadaver can becrucial in the forensic process. Dental, skeletal or biochemical analysis is notalways sufficient, rapid, cost-effective or accurate. The possibility exists that

the aorta is a better predictor.

METHODS A total of 104 subjects, 71 males and 33 females, aged 19 to 91years of age were examined in the Netherlands Forensic Institute. During the

standard forensic autopsy the aorta was measured for circumference on 7locations, length over 3 segments and atherosclerotic score over 4 segments.

Additional secondary variables were collected from the preliminarypathologists report. Linear multiple regressions and paired T-tests were

performed.

RESULTS High correlations were found. The best fit for males, females andmixed respectively: rM=.961, rF=1.000 and rMF=.959. Standard deviation fromthe original database 4.29737 years in males, .00142 years in females and

4.98841 years in the mixed population.

1This is not a published paper, it has not been peer-reviewed and all

the data belongs to the Netherlands Forensic Institute.


CONCLUSION The aorta is a predictor of age. A functional method is derivedas well as multiple functions.




In the forensic world the age of a victim can be a crucial but unknown

factor. For some time we know how to estimate the victims age by

examining the skeleton, cartilage, dentals and biochemical analysis.

[1,2] However, accuracy and ease of these methods is not always

guaranteed. Short after death the tissue is still intact and a

different body part gives rise to believe it can be utilized as age

predictor, the aorta. There have been other studies attempting to

ascertain age by the aorta. The greater majority of these studies have

been performed ante mortem.[3-13] During life the aorta is subject to

many external factors. For example the blood that is being pressed into

the aortic arch under high pressures from the left ventricle up to and

including atherosclerosis and aneurysm formation. These pathologies

lead to dilatation of the aorta. Studies have shown that the aorta

diameter is measurable with CT or MRI scans, resulting in functions and

tables describing the characteristics of the aorta per age category.[3-

13] Studies describing the characteristics in children or exceptionally

tall people have also taken place.[3,14,15] Many of these studies only

describe the correlation between the parameters ante mortem. They do,

however, show that the size of the aorta changes during life. It is

therefore conceivable that the size of the aorta can be used for the

purpose of estimating the age of the unidentified victim[14].

Because measurements performed in live patients can differ from

measurements in the deceased, post mortem, the results from the studies

cannot be used with certainty. Due to the drop in blood pressure upon

discontinuation of the heart function the aorta cannot be identified on

CT or MRI whereby no measurements can be made post mortem. Furthermore,

one should take into account the pump phase the heart is in when the3

This is not a published paper, it has not been peer-reviewed and allthe data belongs to the Netherlands Forensic Institute.Hypothesis

The aorta is a predictor

of age in the adult human

and a function can be


ante mortem measurements are made. During systole the diameter is

larger than during diastole. It is also plausible that the ante mortem

diameter will at all times be larger than the post mortem diameter due

to the baseline blood pressure of approximately 80 mmHg being exerted

on the vessel wall during diastole. Therefore it is necessary to obtain

post mortem data to research if this is a practical method of

estimating age in the deceased. Horny et al created a method using the

pre-strain of the aorta to estimate the age.[16]In addition to

researching the mere possibility of estimating age on the basis of

aorta parameters, a practical method of calculating the age in the

forensic autopsy setting can be developed.

Experimental MethodAfter the pathologist had cut open the aorta the circumference was

measured over the following seven points: one cm proximal to the

brachiocephalic trunk, between the brachiocephalic trunk and the left

carotid artery, start of the descending aorta, fifth intercostal

artery, seventh intercostal artery, diaphragm and halfway between the

renal artery and the aortic bifurcation (Fig. 1). If the intercostal

arteries differed in height, the left marker was held. In addition the

length between one cm proximal to the brachiocephalic trunk to the

descending aorta, descending aorta to renal artery and renal artery to

aortic bifurcation was measured. Lastly an atherosclerosis score is

given to four parts, being the ascending aorta, the aortic arc,

descending aorta to renal artery and renal artery to aortic

bifurcation. A grading system was used to score each section. I being

no atherosclerosis, II being fatty streaks, III coded for fibrous

plaques and IV for complicated lesions. (Fig 2.)[17-21] The scoring was4


Hypothesis

The aorta is a predictor

of age in the adult human

and a function can be


done with the naked eye and not with a microscope. If a section had

even one lesion of a higher grade it would be scored as such.

Furthermore, the following details were gathered: sex, length of the

victim, weight of the victim, date of birth, date of death, date of

autopsy, cause of death, ethnicity, drug abuse, alcohol abuse and

resuscitation.

Figure 1. The measuring points. C1- one cm proximal to the brachiocephalic trunk C2 -

between the brachiocephalic trunk and the left Carotid artery. C3 - start of the

descending aorta C4 - 5th intercostal artery C5 - 7th intercostal artery C6 - diaphragm

C7 - halfway between the renal artery and the aortic bifurcation. L1- one cm proximal

to the brachiocephalic trunk to the descending aorta. L2- descending aorta to renal

artery L3- renal artery to aortic bifurcation. A1 - ascending aorta. A2 - the aortic

arc. A3 - descending aorta to renal artery. A4 - renal artery to aortic bifurcation.




SelectionAortas were included based on state of decomposition and integrity.

Cadavers with extensive decomposed aortas could not be measured and

were therefore excluded. If the aorta had been severely damaged or was

of special value to the pathologist in the forensic process no

measurements were made.

No discrimination on basis of ethnicity, cause of death or pre-existing

disease or habits were made. Only victims older than 18 years of age

were included.

Statistical MethodThe statistical research was performed in IBM SPSS Statistics version

20. A multiple regression, an approach to modeling the relationship

between a scalar dependent variable and multiple explanatory

variables, was used creating a regression function . Two

methods were used: the Enter and Stepwise, forward selection. Enter

stipulates which variables are to be entered into the regression, while

stepwise, forward selection, starts with no variables in the model

adding significant variables until there are no more. Only forward

selection was used due to similar results generated by both forward and

backward selection.

Prerequisites for the linear regression were met: All variables were

quantitative or converted to dummy scores, the data was linear and the

relation was theoretically causal. The data was tested for

multicollinearity with Pearson’s correlation and none scored above 0.9.

[22]



http://en.wikipedia.org/wiki/Explanatory_variable

http://en.wikipedia.org/wiki/Explanatory_variable

http://en.wikipedia.org/wiki/Dependent_variable


Continuous variables were: Circumference of the aorta, length of the

aorta sections, length of victim, weight of victim, Post-mortem

Interval, alcohol abuse, resuscitation. For categorical variables dummy

variables had to be made, which consisted of the following:

Atherosclerosis score, cause of death, ethnicity. For atherosclerosis

the first two symbols, A1 to A4, code for the area measured. The third

symbol, 0 to 3, is the score that position received. A22 is area A2

which received score 2, fibrous plaques. In the A1 score 3 was the

least common while in A2 to A4 score 1 was least common which is why

those were treated as reference.

The calculations were made both for males and females independently and

males and females combined. Both Enter and Stepwise, forward selection,

were used to try as many combinations of variables as possible. For the

male population 29 combinations were tried, for the female population 8

and the combined population only 2. Paired T-Tests were performed to

ascertain correlation and supplementary statistics.

Table 1. Descriptives of the population

Number % MeanMedia

n

Upper

range

Lower

rangeTotal

population104

Male 7168.

3

Female 3331.

7Male age in

years

41.4

639 19 84




Female age in

years

46.1

245 19 91

Male length in

cm

179.

53179.5 160 203

Female length

in cm

168.

27168 151 183

Male Weight in

kg

83.1

082 50 163

Female Weight

in kg75.8 70 50 110

ResultsFor both male as female a function was found to predict the victims

age. Both the Stepwise as the Enter method gave good results. First the

male population will be discussed.

Male populationNear equal results were generated with both the stepwise as the enter

method, but the enter method proved slightly better. The difference is

not significant, but with the enter method the correlation is closer to

1 and the standard deviation of the function compared to the original

database is smaller. (Table 2) The best fit in the male population is

the Enter9m, with a correlation of rM=.961, standard deviation of 4.30

years. The function contains all seven ‘circumference of the aorta’

variables, three ‘length of the aorta’ variables, all atherosclerosis

scores and length of the victim (Function 1). The second best fit, as

can be seen in Table 2, is Enter8m. The function is near identical with

exception of the victim’s length. (Appendix)




Table 2. Comparison of functions with the original data in the male population. Enter8m

consists of all seven ‘circumference of the aorta’ variables, three ‘length of the

aorta’ variables and all atherosclerosis scores. Enter9m contains all seven

‘circumference of the aorta’ variables, three ‘length of the aorta’ variables, all

atherosclerosis scores and length of the victim too. Stepwise6m is made up of C5, C7,

all the atherosclerosis scores and the victim’s length. Stepwise A124m contains C5, C7

and only A1, A2 and A4, of the atherosclerosis scores. Stepwise A234m is the same but

with A2, A3 and A4 of the atherosclerosis scores.

Numbe

r

Correlat

ion

Std.

Deviation(years

)Original vs. Enter8m 51 .954 4.66499Original vs. Enter9m 51 .961 4.29737Original vs.

Stepwise6m70 .942 5.16334

Original vs.

StepwiseA124m70 .944 5.04320

Original vs.

StepwiseA234m70 .944 5.05887

Function 1. The function for the male population, also known as Enter9m. C1 to C7 are

the circumference or the aorta variables, L1 to L3 are the length of the aorta

variables, Length is the body length of the victim and the A variables are the dummy

variables for the four atherosclerosis scores.




Female populationFor the female population the same regressions were performed as for

the male population but the population was much smaller. However there

was a remarkable result. In near all cases, both enter and stepwise,

the correlation was higher, and in one case, known as Enter1f, the

correlation was rF=1.0. Its standard deviation is .00142 years. The

function contains all seven ‘circumference of the aorta’ variables,

three ‘length of the aorta’ variables and all atherosclerosis scores

(Function 2). This function gives a near perfect prediction of the

victim’s age.




Table 3. Comparison of functions with the original data in the female population.

Enter1f contains all seven ‘circumference of the aorta’ variables, three ‘length of the

aorta’ variables and all atherosclerosis scores. Enter2f contains only the

‘circumference of the aorta’ variables and the three ‘length of the aorta’ variables.

Enter3f contains C1, C4, L1 and L3. Stepwise1f is consists of C4 only. Stepwise2f

contains C4 and all atherosclerosis scores. Stepwise3f adds the victim’s race to the

function.

Numbe

r

Correlat

ion

Std.

Deviation(years

)Original vs.

Enter1f22 1.000 .00142

Original vs.

Enter2f22 .971 5.02013

Original vs.

Enter3f22 .968 5.35083

Original vs.

Stepwise1f30 .950 6.01754

Original vs.

Stepwise2f30 .963 5.34438

Original vs.

Stepwise3f32 .948 6.83969




Function 2. The function for the female population, also known as Enter1f. C1 to C7 are

the circumference or the aorta variables, L1 to L3 are the length of the aorta

variables and the A variables are the dummy variables for the four atherosclerosis

scores.

Combined populationAttempts to create a function for both sexes were also successful. This

method was not researched intensively, only one Enter and one Stepwise

were performed. Both functions contain the sex variable. (Function 3.)

Enter gives the best fit with a correlation of rMF=.959 and a standard

deviation of 4.98841 years.




Table 4. Comparison of functions with the original data in the combined population.

Enter1mf consists of all seven ‘circumference of the aorta’ variables, three ‘length of

the aorta’ variables, length of the victim, sex and all atherosclerosis scores.

Stepwise1mf contained C4, C5, L3, length of the victim, sex and all the atherosclerosis

scores.

NumberCorrelati

on

Std. Deviation

(years)Original vs.

Enter1mf73 .959 4.98841

Original vs.

Stepwise1mf97 .949 5.31826

Function 3. The function for the combined population, also known as Enter1mf. C1 to C7

are the circumference or the aorta variables, L1 to L3 are the length of the aorta

variables and the A variables are the dummy variables for the four atherosclerosis

scores. Male is the dummy variable for sex.

In the appendix an overview of the three best fits for the male, female

and combined populations is presented, as well as a graph portraying

the values predicted by the equations plotted against the original age.




DiscussionThe aim of this study was to research if it is possible to predict the

victim’s age by the parameters of its aorta. For males this can be done

with a deviation of 4.29737 years, a window of 8.6 years, and for women

this window is less than half a year. A practical method has been

developed and created in the Netherlands Forensic Institute which can

be used to determine age in the deceased. The method has not been

verified.

Mixed versus split genderIn this study both the split population, male and female, as the mixed

population have been researched. Both gave rise to good results. Due to

a different morphology in males and females and better results in the

split population it is advised to use the different equations instead

of the combined equation. The combined equation has not been researched

as thoroughly as the split and could therefore give a less accurate

prediction.

VariablesThe circumference of the aorta, length of the aorta, atherosclerosis

score and length of the victim are the only variables that proved

predictive for the victim’s age. It is not very surprising to see that

the circumference and length of the aorta are predictive variables

because these describe the aorta itself. If the aorta would have

prognostic value than the aorta itself should be represented almost

entirely. The atherosclerosis score is also a variable specific to the

aorta and it increases with age. [4,10,23-28]Length of the victim is

linked to length of the aorta which explains its predictive value.




Weight of the victim is not predictive. It can be argued that the

atherosclerosis score is linked to the weight of the victim but this

does not seem to be necessarily the case. The regressions that included

the victim’s weight showed a lower correlation and standard deviation.

BMI was tested briefly but did not prove influential. The Post-mortem

interval was not high enough to have effect on deterioration of the

aorta. Only in 5% of the victims the interval was higher than 4 days.

Alcohol abuse was excluded for two reasons. On the one hand it was very

difficult to test because the more victims were alcoholics than was

reported. Secondly, the regressions that were performed showed worse

outcomes than the regressions in which alcohol abuse was excluded.

Resuscitation was excluded for the same reasons. The cause of death

gave rise to similar problems. The cause of death was taken from the

preliminary report because the final report was not available in all

cases creating a bias. Furthermore, in 25 cases, the cause of death was

unknown. Cause of death therefore was excluded. Ethnicity was the last

variable excluded due to problems determining ethnicity objectively.

The majority, 85%, scored ‘white’, which was too vague a term to use.

Field of LiteratureThere have been many studies examining the aorta, but these are ante

mortem.[3-13] None of the post mortem studies, to the author’s best

knowledge, have utilized the method described in this study. Horny et

al did attempt a post mortem method but with pre-strain. Horny et al.

also confirms the high correlation between age and abdominal aorta

circumference and age and atherosclerosis. [16] However, no other

studies have been performed to verify the findings in this study.




Study Strengths and LimitationsThe greatest strength is that this study was aimed to be practical,

giving a quick answer to the question of how old the victim was. This

is in contrast to other studies which used more precise methods, inter

alia the microscope, to obtain the measurements.[16,29] Those methods

have higher precision but take more time. In this study the age of the

victim can be calculated within minutes after the forensic autopsy.

The greatest limitation was the size of the study population. Over a

year of measuring only 104 aortas were included in the study, of which

71 were of masculine victims and 33 of feminine victims. Not all of the

included aortas could be used due to incomplete measurements. The Enter

method, in contrast to the Stepwise method, requires all of the

individual measurements, for drafting the function. Fewer measurements

can therefore be used, only 51 and 22 masculine and feminine

respectively, instead of all the measurements. Especially in C1, C2 and

L1 are measurements often omitted due to how the aorta was cut during

autopsy.

Future studyA larger study population, better cut aortas, without the often omitted

measurements, should yield a higher accuracy. A more precise function

can pave the way to a less complicated function which can be utilized

with further ease. Different locations and segments can be tested for

better results.

ConclusionIt is possible to estimate the age of a human cadaver not long dead by

the parameters of its aorta within minutes. The primary variables are




the circumference of the aorta at 7 locations, the length of the aorta

over 3 segments and the atherosclerosis score over 4 segments. Length

and sex of the victim are valuable secondary variables which can

increase precision. All of these are easy and quick to measure without

the need for special tools.

AcknowledgementI thank my mentors Bela Kubat, Forensic Pathologist and

Neuropathologist, and Reza Gerretsen, MD, Senior Forensic Scientist,

Forensic Anthropologist, and the Netherlands Forensic Institute for the

opportunity they granted me.




Appendix

Table 1. For all populations the values of the equations that have the best fit. The

regression function, , is applicable, Y being age, A being the constant, B

the variables and x the measured values.

C1 to C7 are the ‘circumference of the aorta’ variables, L1 to L3 are the ‘length of

the aorta’ variables, and Length is the victim’s length. The A variables are the dummy

scores for atherosclerosis. Male is the dummy variable for sex.

Variab

lesMale Female Combined

Enter9

m

Enter8

mStepwiseA124m Enter1f

Enter

2f

Stepwis

e2f

Enter1

mf

Stepwise1

mf

Consta

nt-1.989

-

47.56711.049 -67.856

-

63.71

2

-72.920 9.524 10.057

C1 0.033 -0.853 3.312 2.599 0.816

C2 -0.654 -0.664 3.599-

0.564-1.143

C3 -1.773 -1.617 36.487 6.433 0.790

C4 2.996 3.771 -4.32916.10

024.720 5.990 8.012

C5 11.084 11.126 11.669 -1.096-

0.4417.351 7.355

C6 0.071 -0.598 -1.617 0.370 0.071

C7 4.155 3.425 5.264 3.312-

1.5051.385

L1 0.039 0.383 -2.011 0.252 0.563L2 0.689 0.485 -1.226 0.496 0.035




L3 1.289 0.558 -4.830-

1.9471.000 0.833

Length -0.330 -0.318 -0.297 -0.286A10 -2.223 -2.725 2.922 11.101 -2.960 -5.391 -4.355A11 0.682 -1.350 5.515 1.729 -5.868 -4.072 -3.701A12 -5.566 -6.771 -2.113 -7.384 -7.767 -8.832 -8.580A21 -2.529 -0.497 -2.618 30.948 18.810 -1.755 -2.922A22 0.214 4.227 0.466 26.511 15.900 -.064 -0.336A23 -3.822 -0.513 -2.420 21.389 13.132 -1.424 -1.426A31 7.746 9.396 -29.902 -13.230 1.504 1.709A32 7.752 5.531 -22.130 -8.478 2.432 2.445A33 9.792 11.515 -14.577 -3.280 9.033 9.253A41 -2.843 -5.263 4.604 6.126 -5.542 4.344 6.141A42 -6.195 -5.882 4.125 11.707 3.280 6.020 7.697A43 -3.681 -1.661 7.097 -5.992 -9.084 5.531 6.992Male -6.876 -6.951




Graph 1. The three best fits for male, female and combined. The lines are the values

predicted by the equations. The X-axis indicates case number. The lines for the methods

are intermittent due to missing values. The lines show the margin the method has.

Enter1f is near identical to the Original age while the stepwise methods are more

erratic.




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Age estimation based on parameters of the aorta in the recently deceased adult human

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