1. The impact of malnutrition
on bone micro-anatomy
Robert R. Paine, Texas Tech University
&
Barrett P. Brenton, St. John’s University
For
The 76th annual meeting of the American Association of Physical Anthropologists

2. The micro-anatomy of a rib from a 45 year old female, suffering from malnutrition.

3. Goal:
To reveal micro skeletal indicators for individuals suffering from malnutrition using an autopsy sample.

4. Rib Samples
26 Autopsied 20th century Black South African remains from the Raymond Dart skeletal collection, housed at the University of Witwatersrand Medical School, Johannesburg, South Africa were examined.

5. Secondary osteon (intact and fragmentary) #’s
Research Goals for the assessment of rib micro-anatomy:
To reveal micro-skeletal indicators for individuals suffering from malnutrition.
Micro-skeletal indicators:
Secondary osteon area
Rib Cortical area
Haversian canal area
Secondary osteon (intact and fragmentary) #’s
(OPD) Osteon population density

6. Predications for how dietary deficiencies might influence bone remodeling rates have been around since the 1960’s, with H. Frost’s (1966) work as the most note worthy of the time.
In his book chapter, Frost suggested that malnutrition; specifically calcium & protein along with scurvy will affect the micro-anatomy of bone.
Frost’s work in 1985 challenged anthropologists to assess human skeletal population histologically for health and disease related criteria.

7. But in fact, understanding of skeletal tissue changes by malnutrition (Pellagra) was suggested by Gillman & Gillman (1951)
(1) Radiological studies showed marked osteoporosis;
(2) A negative mineral balance,
(3) skeletal histology
(4) An examination of costo-chondral junctions,
a) similar to scurvy with the fracturing of the
trabeculae and chondroperiosteal angle,
b) unmistakably rickets, and
c) created a zone of ossification where the trabeculae were seen to be covered with osteoid.

8. In Sam Stout’s dissertation and later in his publication with Teitelbaum 1976, he suggested that diet and disease might influence remodeling rates in at least two ways, Hyperparathyroidism would increase remodeling rates and scurvy might decrease it. His comments on scurvy have been ignored.

9. -Richman et al., 1979; histology & protein in take
Since then there has been a host of folks discussing the subject of how diet affects remodeling rates but they offer little direct evidence or means for these predictions.
-Richman et al., 1979; histology & protein in take
- Martin et al., 1981; disease and dietary problems might
lead to an increase in remodeling rates.
- Martin & Armelagos 1985; malnutrition and young
reproducing females
- Hanson & Buikstra 1987; Hyperparathyroidism
- M Cook et al., 1988; Hyperparathyroidism, Roman period
- Burr et al., 1990; remodeling rates are lower in
prehistoric populations
- Schultz 1993: Vit-D & rickets
- Foldes et al., 1995; calcium deficiency in Bedouin women
- Pfeiffer 2000: paleo-histology and health

10. A review of Stout & Paine’s (1992) rib equation by several researchers has found
1) That the formula under-aged historic burials. Dudar, Pfeiffer & Saunders, JFS,
2) That there are lower remodeling rates for prehistoric populations (e.g. Gibson & Ledders) Stout & Lueck,1995. AJPA.

11. The crossroads of forensic anthropology and the study of health in past populations come together is an odd way beginning in at the AAPA meeting. Salt Lake City, UT. With the presentation:
The skeletal biology of pellagra with intensive maize horticultural in the New World.
Followed later by the publication in 2006 “Dietary health does effect age assessment: an evaluation of the Stout & Paine (1992) age estimation equation using secondary osteons from the rib.” The Journal of Forensic Sciences. Vol. 51

12. Age prediction using the equation:
natural log age = (rib OPD).
PELLAGRINS
Specimen # age opd esti. age difference in years A A A A A A A A A A
MEAN DIFFERENCE IN YEARS 36.7 under-aged

13. NON-SPECIFIC MALNUTRITION
Age prediction using the equation: natural log age = (rib OPD).
NON-SPECIFIC MALNUTRITION
SPECIMAN # AGE OPD EST. AGE DIFFERENCE IN YEARS A A A A A A A A A A A A A A A A
MEAN DIFFERENCE = 24.4 YEARS under-aged.

14. We decided to look at this issue from the view that all of these individuals were under-aged because of a metabolic disturbance, that could be associated with a dietary deficiency, much as Stout & Teitelbaum 1976 suggested with Scurvy. This approach differs from those who simply feel the equation is a poor predictor of age of archaeological samples.
To do this we reversed the Stout & Paine 1992 age predicting equation and used known age to determine expected OPD numbers.
known age = (Exp. rib OPD)
(38 yrs) = logage
/ = Exp. rib OPD
Obs. OPD Exp. OPD

15. This photo is of a 40 year old Male suffering from scurvy;
log age = 3.659
Obs OPD = exp OPD= difference in OPD = 14.15,
Or 53.5% less than the expected OPD
Notice the large secondary osteons, the vast amount of primary bone and the lack of fragmentary osteons

16. OPD differences with pellagra
Sex
lnage
age
Obs. OPD
Expected OPD
OPD Diff.
Turn over rate diff.
3.639 38
7.84
25.44
17.60
69.2% less M
3.689 40
13.29
26.54
13.16
50% less
3.807 45
13.70
28.768
15.07
52.4% less
3.850 47
6.90
29.623
22.72
76.7% less
3.912 50
16.96
30.839
13.889
45.1% less
3.951 52
20.10
31.610
11.51
36.4% less
4.201 67
11.99
36.592
24.6
67.2% less F
4.248 70
16.70
37.452
20.75
55.5% less
4.317 75
10.73
38.809
28.08
72.4% less
4.489 89
14.19
42.173
27.987
66.4% less
Means % less

17. OPD differences with malnutrition
Sex
lnage
age
Obs. OPD
Expected OPD
OPD
Diff.
Turn over rate diff. M
2.772 16
05.31
8.443
3.133
37.1% less
3.367 29
10.80
20.132
9.332
46.4% less
3.401 30
14.79
20.799
6.009
28.9% less
3.637 38
13.84
25.44
11.6
45.6% less
3.663 39
12.10
25.955
13.855
54.5% less F
14.70
11.255
43.4% less
3.659 40
12.30
26.450
14.15
53.5% less
3.761 43
14.78
27.875
13.095
47% less
3.829 46
14.37
29.200
14.83
50.8% less
3.912 50
14.19
30.839
16.649
54% less
12.58
18.259
59.2% less
4.060 58
16.67
33.756
17.080
50.6% less
4.094 60
19.46
34.423
14.963
43.5% less
4.159 64
13.33
35.691
22.361
62.7% less
4.174 65
18.21
35.990
17.780
49.4% less
12.28
23.710
65.9% less
Scurvy
Scurvy

18. We used histological and marco-anatomy indicators
of age for Gibson & Ledders burials.
Observed OPD readings were made by Sam Stout (1976)
Osteological age estimations are provided by Jane Buikstra & Della Cook.
These are 2 sample populations of Native Americans that were from the Early & Late Woodland periods and the latter (Ledders) being increasingly maize dependent.
Cook (1976) has shown that nearly all of these individuals were suffering of metabolic problems including dietary deficiencies.

19. AGE X AGE OBS OPD EXP OPD DIF OPD 12-18 16 8.8 8.44 +0.36 15-20 17.5
Gibson site, Early Woodland burials slide 1.
AGE
X AGE
OBS OPD
EXP OPD
DIF OPD
12-18 16
8.8
8.44
+0.36
15-20
17.5
7.8
10.20
-2.4
17-21 19
11.9
11.82
+0.08
19-20
19.5
5.8
12.33
-6.53
18-21
19.1
+6.77
*19-21 20
7.7
12.83
-4.63
18-23
21.5
13.8
14.25
-0.45
22-35
28.5
19.79
-5.99
35-45 40
20.6
26.45
-5.85
18.9
-7.55
40-50 45
18.0
28.76
-10.76
17.9
-10.86
21.6
-7.16

20. Gibson site, Early Woodland burials, slide 2.
AGE
X AGE
OBS OPD
EXP OPD
DIF OPD
45-50
47.5
25.6
29.84
-4.24
44-55 50
28.4
30.84
-2.44
50+
19.9
-10.94 26
-4.84
22.2
-8.64
21.3
-9.54
18.9
-11.94
19.3
-11.54
37.5
+6.66
50++
21.2
-9.55
19.5
-11.34
21.6
- 9.24

21. The Ledders Late Woodland burials.
AGE
Mean AGE
OBS OPD
EXP OPD
DIF OPD
12-15
13.5
11.4
5.10
+6.30
16-17
16.5
14.6
9.04
+5.02
16-18 17
10.6
9.63
+0.97
18-20 19
13.7
11.82
+1.88
*22-26 24
23.3
16.41
+6.89
23-26
24.5
17.2
16.82
+0.38
25-28
26.5
18.36
-6.96
15.5
-2.86
26-28 27
20.7
18.73
+1.97
25-30
27.5
27.4
19.08
+8.32
26-30 28
16.2
19.44
-3.24
26-40 33
22.8
22.67
+0.13
30-42 44
21.1
28.3
-7.20
45-50
47.5
18.1
29.83
-11.73
47.4
18.5
-11.33
50+ 50
23.5
30.84
-7.34
17.4
-13.44
26.1
-4.74
50-60 55 25
32.7
-7.70

22. Of the 16 Ledders Late Woodland adult burials (those over 20 years), 62.5% of the were under-aged with less OPD using the Stout & Paine 1992 rib formula.
Of the 20 Gibson site adult burials 95% of them were under-aged.
We believe that these results match well to our expectations, that prehistoric populations with skeletal indictors of dietary and infectious lesions will exhibit slower than expected turn over rates which results in fewer secondary osteon produced per cortical area of bone.
The difference in obs. & exp. OPD for these archaeological samples is significant at
P = 0.001

23. Conclusions
Malnutrition has a definite impact on human bone therefore we suggest a rethinking of the assumptions about the use of histology. It may help to determine the impact of dietary problems for prehistoric peoples.
OPD is clearly effected by metabolic disturbances related to dietary problems. If one can determine the actual OPD and match it to an expected OPD then one can begin to examine the impact of diet on bone health in prehistoric populations.

24. Unlike many current attempts to understand why the Stout & Paine 1992 equation under-ages, we feel that it is an excellent indicator of age for modern samples but that it under ages prehistoric and historic samples because these individuals face poor dietary circumstances which appear to be life- long, chronic, possibly seasonal.
Here is a photo of a 70 year old male suffering from niacin deficiency (pellagra).

25. Our research could only have been done using individual samples of known ages. This is why the Dart collection is such an important resource.
Future work on the assessment of diet & health using the human micro-skeletal anatomy requires such studies.
Three assumptions are required for this research to progress:
1) that gross osteological aging techniques provide accurate means for aging.
2) that the rib histological formula also provides an accurate assessment of tissue age, and

26. 3) when there is a statistically significant disagreement between age assessments (determined by OPD values) the difference should be examine with the possibility that a metabolic influence such as dietary deficiency is at work.
A pellagrin with cortical bone of the rib nearly a single osteon thick.

27. Thank you !! ACKNOWLEDGEMENTS:
Dr. Brenda Baker, bringing the Raymond Dart skeletal collection to our attention.
Dr. Kevin Kuykendall, Witwatersrand University Medical School, Johannesburg South Africa, for providing the samples used the our research.
This research was supported impart by a Texas Tech University Graduate faculty Research Travel grant and a St. John’s University Faculty Development Grant.
Thank you !!