1. American Association of Physical Anthropologists.
Challenging population-based variation in bone-turnover rates: Implications for interpreting the impact of diet and disease, growth and development, and the aging process on rib microanatomy.
Robert R. Paine1, Barrett P. Brenton2. 1Department of Sociology and Anthropology, Texas Tech University, 2Department of Sociology and Anthropology, St. John's University.
American Association of Physical Anthropologists.
Minneapolis, MN
April

2. 35 Autopsied 20th century Black South Africans remains from the
Skeletal Samples
35 Autopsied 20th century Black South Africans remains from the
Raymond Dart skeletal collection, housed at the Witwatersrand
University Medical School, Johannesburg, South Africa.
Micro-skeletal examination
14 pellagrin cases
21 nonspecific malnutrition cases

3. Previous histological work includes a comparison
of rib samples from the Raymond Dart with ribs
collected for Paine’s Master’s thesis (1985).
Rib samples:
1) RRP, recent USA autopsies, n = 45 age range 14-88, mean age 34 years
2) South African autopsies with pellagra, n = 10, age range 38-89, mean age 57 years
3) South African autopsies with non-specific malnutrition, n = 16, age range 16-70, mean age 46 years

4. Here is our success in sharing this perspective on bone histology and health assessment. However we have had difficulty in challenging the current paradigm in bone histology, specific to aging assessment and how this related to health assessment.
2007 “Reevaluating the health and nutritional status of maize-dependent populations: Evidence for the impact of pellagra on human skeletons from South Africa”. Ecology of Food and Nutrition. BP Brenton & RR Paine, 46(5/6):
2006 “The paleopathology of Pellagra: Investigating the impact of prehistoric and historic dietary transitions to maize.” RR Paine & BP Brenton. The Journal of Anthropological Sciences Vol 84. pp
2006 “Dietary health does effect age assessment: an evaluation of the Stout & Paine (1992) age estimation equation using secondary
osteons from the rib.” RR Paine & BP Brenton. The Journal of Forensic Sciences. Vol. 51, no.3: pp

5. Challenges have included the persistent suggestion of using “more appropriate samples to compare the Dart material with, specifically using the African American equation from (Cho et al., 2002).”
At first, we resisted doing this for several reasons, specifically, the idea that we should take a population specific “race” based approach to skeletal histology did not sit well with us.

6. known age = 2.343 + 0.050877 (expected rib OPD).
Additional challenges arose when we decided to use the Stout & Paine 1992 rib age predicting equation using known ages to determine expected OPD numbers as we to compared this with Dart material .
known age = (expected rib OPD).

7. OPD differences with South African Pellagrins
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

8. We also applied this observation to a well studied archaeological skeletal series from
Ledders and Gibson, by using data provided by 3 established skeletal biologists.
Buikstra JE Hopewell in the lower Illinois River Valley: A regional study in human biological variability and prehistoric mortuary behavior. Ph.D. dissertation. University of Chicago, IL.
Cook DC Pathologic states and disease process in Illinois woodland populations: An epidemiologic approach. Ph.D. dissertation. University of Chicago, IL.
Stout SD Histomorphometrics analysis of archaeological bone. Ph.D. dissertation, Department of Anthropology, Washington University, St. Louis.

9. A sample from this work Gibson site, Early Woodland burials.
AAPA meeting
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

10. Cho H, Stout SD, Madsen RW, and Streeter MA. 2002
Cho H, Stout SD, Madsen RW, and Streeter MA Population-specific histological age-estimating method: A model for known African-American and European-American skeletal remains. For Sci 47(1):
We used several of their equations: group 0 = African-American; 1 = European American
African-American
age= (OPD) – (OnAr *0) (CtAr/TtAr) (CtAr/TtAr *0).
European –American
Age = (OPD) – (OnAr *1) (CtAr/TtAr) (CtAr/TtAr *1).
Unknown ethnicity
Age = (OPD) (CtAr/TtAr) – (OnAr)

11. Formula
Mean Absolute Difference in Years
Ratio of Under/Over-aged
%Under-aged
% Over-aged
%Est.Age /- 5 yrs
Stout & Paine
29.2
26/0
100% (26)
0% (0)
3.9% (1)
Cho AFAM
11.5
11/15
57.7% (15)
42.3% (11)*
38.5% (10)*
Cho EURO
26.9
25/1
96.1% (25)
Cho UND
23.9
N= 26 samples from the Dart Collection.
*Three of the individuals were within one year of the actual known age (all were over-aged).

12. Challenging Population-Based Variation in Bone-Turnover Rates
There is a dearth of evidence in the literature for the existence of any population-based genetic differences in bone formation and turnover.
Similar to the Stout and Paine aging formula, the Cho et al. equations clearly show that metabolic disturbances have a significant impact on the accuracy of the methods by under-aging individuals.

13. Challenging Population-Based Variation in Bone-Turnover Rates
Some may argue that the Cho AfAm formula is more “accurate” because of its relevancy to the South African population (although still highly unreliable with this sample).
We strongly argue that the ability of the Cho AfAm formula to “more accurately” predict age has nothing to do with population-based genetic similarities but is linked to comparable phenotypic expression in populations with high levels of health disparities.

14. Challenging Population-Based Variation in Bone-Turnover Rates
Emerging epigenetic and developmental
models of disease epidemiology call for a more broadened approach to the durable role that environments have on patterns of biology and health (Kuzawa and Sweet 2009).
This requires our discipline to rethink our current understanding of bone biology and the application of histological methods.