1. The impact of malnutrition on bone micro-anatomy: Does this influence how histological age at death techniques produce results?
R.R. Paine1 and B.P. Brenton2
1Department of Sociology & Anthropology, Texas Tech University, Lubbock, TX; 2 2 Dept. of Sociology and Anthropology, St. John's University
Introduction
The potential for using the dynamic nature of bone modeling and remodeling to extract a life history from dry bone samples of archaeological populations has been suggested by Frost (1985) but not yet fully realized. Although his publication is not the only one to make this assertion (see Frost, 1966, 1973; Stout and Teitelbaum, 1976; Stout, 1978; Richman et al., 1979) it was the first to outline a general criterion for doing so. One of the criteria offered was that remodeling (secondary osteon formation) should react predictably and be sensitive to nutritional problems. Typically, bone will either respond with a decrease or increase in secondary osteon formation (Frost, 1985). Few skeletal biologists working on archaeological samples attempt to examine dry bone histologically for this evidence and those that have (e.g., Stout and Teitelbaum, 1976) offer only tentative ideas as to how they have viewed the response of bones to dietary problems. On the other hand, while working out problems using histology to create a demographic profile from burial samples, Weinstein et al. (1981) realized that there was a problem between age assessment and the affects of metabolic disturbances on bone microstructure. They caution that changes due to age and disease (dietary or infectious) may not be separable and that one influence might compound or affect the assessment of the other.
The purpose of this poster is to present a continuation of work that has been conducted on the issue of malnutrition and bone biology, specifically that of pellagra (a niacin deficiency disease) and non-specific general malnutrition (Brenton and Paine, 2000, 2007; Paine and Brenton, 2006a, 2006b). We offer specific data on osteon population densities (OPD) both observed and expected for age-at-death of autopsied individuals with known dietary stress and age
Results
Even the Cho et al., 2002 equations under-estimated the age of the dart samples, (57.7 % of them are under-aged) showing that the error in age assessment is not race specific (Table 3).
Table 2. Two-tailed t-test results of expected and observed OPD values for the Dart rib samples.
Sample* n
Exp OPD mean
Obs OPD mean
P**
Total samples 26
29.80
13.54
0.0001
Pellagra 10
32.78
13.24
Malnutrition 14
28.20
13.95
*A separate t-test of the two scurvy victims was not performed given the small sample size.
**All t-tests are highly significant at a 95% confidence interval, P =.05.
Table 3. Comparison of Stout and Paine (1992) with Cho et al., (2002) equations for estimating the Age of the Dart samples.
Photo 1. Typical rib section with secondary
osteons (see red arrow) with Haversian
canals (see blue arrow). Source: The Raymond Dart Skeletal Collection
Formula
Difference in age estimation
Ratio of under/over-aged individuals
% of under-aged samples
% of over-aged samples
% Est. age +/- 5 years
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 EUAM
26.9
25/1
96.1 (25)
Cho UND
23.9
Photo 2. A rib section with extremely thin
cortical bone. The cortical wall is not much thicker than the diameter of a normal size secondary osteon. This image is from a 70-year-old female who died from pellagra. Source: The Raymond Dart Skeletal Collection
Background
As early as the 1950’s medical doctors (Gillman & Gillman, 1951) and later on in the 1960’s skeletal histologists (Bassan et al., 1963) realized that nutritional deficiencies might have acted as causal factors in the pathogenesis of osteoporosis. They specifically stated that vitamin C is an important component for maintaining the integrity of bone. This work suggested that osteoporotic bone might accompany the outcome of scurvy-related health problems. Jaworski et al. (1981) agree with this basic assessment and the idea that nutrients (what they call "permissive factors") like vitamin D and C [we would add niacin, B3, to this list], calcium, and phosphorus have a considerable impact on the formation of bone and on the onset of osteoporosis and other skeletal lesions. Since 1985, a number of anthropologists/skeletal histologists have taken up the challenge issued by members of the medical establishment to use anthropological methods to help to evaluate the interaction among dietary deficiencies and skeletal health (Frost, 1985). Our work is a continuation of this endeavor.
OPD = number of osteons (Pi + Pf) / cortical area mm2;
**LnY = rib (OPD) (Stout and Paine, 1992);
†(Obs OPD/Exp OPD) x 100 provides an indication of how far off secondary osteon production
is for each individual specific to their age. For example, Case # 1 is calculated as follows:
(7.84/25.44) x 100 = Therefore, case # 1 shows only 30.8% of the osteons per cortical
area of bone expected for her age.
Table 1. Demographics for the Raymond Dart rib samples.
n = 26 Dart rib specimens. * Three of the individuals were within one year of the actual known age (all were over-aged).
Case #
Cause of Death*
Sex
Age
Est.
age
Age difference
LnY**
Obs. OPD
Expected OPD
OPD
differences
(Obs OPD/ Exp OPD) x 100† 1
Pellagra F 38 15 23
3.639
7.84
25.440
17.600
30.8% 2 M 40 20
3.689
13.29
26.540
13.160
50.0% 3 45
20.9
24.1
3.807
13.70
28.768
15.070
47.6% 4 47 32
3.850
6.90
29.623
22.720
23.3% 5 50
24.5
25.5
3.912
16.96
30.839
13.879
54.9% 6 52 29
3.951
20.10
31.610
11.510
63.6% 7 67 19 48
4.201
11.99
36.592
24.60
32.8% 8 70 24 46
4.248
16.70
37.452
20.750
44.5 % 9 75 18 57
4.317
10.73
38.809
28.080
27.6% 10 89 21 68
4.489
14.19
42.173
27.987
33.6% 11
Malnutr. 16
13.5
2.5
2.772
05.31
8.443
3.133
62.9% 12
Malnutr
3.367
10.80
20.132
9.332
53.6% 13 30 22
3.401
14.79
20.799
6.009
71.1% 14 17
3.637
13.84
11.600
54.4 % 39
3.663
14.70
25.955
11.255
56.6% 43
3.761
14.78
27.875
13.095
53.0%
21.6
24.4
3.829
14.37
29.200
14.830
49.2%
16.649
46.0%
12.58
18.259
40.8% 58 34
4.060
16.67
33.756
17.080
49.4% 60 28
4.094
19.46
34.423
14.963
56.5% 64
20.5
43.5
4.159
13.33
35.691
22.361
37.3% 65
29.6
35.4
4.174
18.21
35.990
17.780
50.6%
19.4
45.6
12.28
23.710
34.1% 25
Scurvy
12.10
13.855
45.5% 26
3.659
12.30
26.450
14.150
46.5%
Conclusions
In challenging the population based variation in bone remodeling and OPD production dogma, we suggest that anthropologists begin to recognize the emerging perspectives that epigenetic and developmental models of disease epidemiology be applied to how we interpret our findings (Kuzawa and Sweet, 2009). We content that epigenetic influences play an important role that affects human patterns of biology and health. That the poor results of histological equations created with autopsy samples are poorly estimating the ages of individuals from the past is in part related to poor dietary health and other related metabolic disorders.
Specific to our work with 20th century Black South African autopsied samples, we are confident that malnutrition has a definite impact on human bone metabolism that can be histologically observed and recorded. We argue that a rethinking of the use of histology for evaluating human bone is required for an adequate evaluation of skeletal material specific to dietary problems. Why? As Frost (1985) implied and as Gillman and Gillman (1951) observed, remodeling (OPD) is clearly affected by metabolic disruptions related to dietary problems. If one can determine observed OPD and match it to an expected OPD then one can begin to see the impact of diet on bone health.
Three assumptions are required for this research to be accepted for the evaluation of archaeological and forensic samples:
1) Gross osteological aging techniques provide accurate means for assessing the age of archaeological specimens;
2) Rib histological formulae provide an accurate assessment of age; and
3) When there is disagreement between age assessments, the difference should be interpreted in the context of metabolic influences such as dietary deficiencies.
Materials
In this paper we present histological data from the ribs of twenty-six autopsied 20th century Black South Africans from the Raymond Dart Skeletal Collection, housed in the Department of Anatomical Sciences, University of Witwatersrand Medical School, Johannesburg, South Africa (Dayal et al., 2009). Age, sex, and cause of death are documented for each case (Table 1). All of the individuals in this sample died from some form of nutritional deficiency (pellagra, scurvy or non-specific general malnutrition).
Methods
Histological data from the sixth rib of twenty-six individuals whose cause of death was pellagra, non-specific general malnutrition, or scurvy were gathered (Table 1). No macro-lesions or pathologies were noted on the ribs sectioned for microscopic examination. To prepare the ribs for histological analysis we employed the methods outlined by Stout and Paine (1992) and Paine (2007) and had bone wafers embedded in epoxy before they were ground down to a thickness of 75 microns. Cortical area and secondary osteons (intact and fragmentary) were read for the entire cross-section of cortical bone. Similar to the original work done by Paine (1985), the entire cross-section of the rib was read. This was done to avoid sampling error or bias during the collection of secondary osteon data.
As used in previously published anthropological reports (Stout and Paine 1992; Stout and Paine 1994), the following histological variables are discussed and defined:
1. Cortical area (AC), the area of actual cortical bone found in a cross section, recorded in mm2;
2. The number of intact secondary osteons (Pi);
3. The number of fragmentary secondary osteons (Pf); and
4. Osteon population density (OPD) is the number of intact and fragmentary secondary osteons per mm2 of bone:
Pi + Pf/ AC = OPD
Secondary osteon count (OPD) is used to estimate the age of each individual using the rib equation (Stout and Paine, 1992):
LnY = rib (OPD), LnY = natural log age in years
We decided to exam the Dart material with the Cho et al., equation designed to be used on
material of African descent.
group 0 = African-American; 1 = European American
African-American equation..
Age= (OPD) – (OnAr *0) (CtAr/TtAr) (CtAr/TtAr *0).
European –American equation
Age = (OPD) – (OnAr *1) (CtAr/TtAr) (CtAr/TtAr *1).
Unknown ethnicity equation
Age = (OPD) (CtAr/TtAr) – (OnAr)
Selected References
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Brenton BP, and Paine RR Pellagra and paleonutrition: Assessing the diet and health of maize horticulturists through skeletal biology. Nutr Anthropol 23(1):2-9.
Brenton BP, and Paine RR Reevaluating the health and nutritional status of maize dependent populations: Evidence from the impact of pellagra on human skeletons. Eco Food Nutr 46:
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):
Dayal MR, Kegley ADT, Štrkalj G, Bidmos MA, and Kuykendall KL The history and composition of the Raymond A. Dart Collection of Human Skeletons at the University of the Witwatersrand, Johannesburg, South Africa. Am J Phys Anthropol 140:
Frost HM The “New Bone:” Some anthropological potentials. Yrbk Phys Anthropol 28:
Gillman, J. and T. Gillman, Perspectives in Human Malnutrition: A Contribution to the Biology of Disease from a Clinical and Pathological Study of Chronic Malnutrition and Pellagra in the African. New York: Grune & Stratton.
Jaworski ZFG, Duck B, and Sekaly G Kinetics of osteoclasts and their nuclei in evolving secondary Haversian systems. J Anat 133:
Paine RR Histological aging utilizing clavicles and ribs. Unpublished MA Research paper, Department of Anthropology, University Of Missouri-Columbia, MO.
Paine RR, and Brenton BP. 2006a. Dietary health does effect age assessment: An evaluation of the Stout & Paine (1992) age estimation equation using secondary osteons from the rib. J For Sci 51:
Stout SD, and Paine RR Histological age estimation using ribs and clavicles. Am J Phys Anthropol 87: