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 12-16 2011

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

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

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):345-360. 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. 125-135.   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. 489-492.

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.

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 = 2.343 + 0.050877 (expected rib OPD).

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 3.916 50.2 13.54 30.917 17.377 56.2% less

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. 1972. 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. 1976. Pathologic states and disease process in Illinois woodland populations: An epidemiologic approach. Ph.D. dissertation. University of Chicago, IL. Stout SD. 1976. Histomorphometrics analysis of archaeological bone. Ph.D. dissertation, Department of Anthropology, Washington University, St. Louis.

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

Cho H, Stout SD, Madsen RW, and Streeter MA. 2002 Cho H, Stout SD, Madsen RW, and Streeter MA. 2002. Population-specific histological age-estimating method: A model for known African-American and European-American skeletal remains. For Sci 47(1): 12-18. We used several of their equations: group 0 = African-American; 1 = European American African-American age= 38.029 + 1.603 (OPD) – 882.210 (OnAr *0) -51.228 (CtAr/TtAr) + 57.441 (CtAr/TtAr *0). European –American Age = 38.029 + 1.603 (OPD) – 882.210 (OnAr *1) -51.228 (CtAr/TtAr) + 57.441 (CtAr/TtAr *1). Unknown ethnicity Age = 29.524 +1.560 (OPD) -4.786 (CtAr/TtAr) – 592.899 (OnAr)

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).

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.

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.

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.