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

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

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

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.

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

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.

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.

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.

-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

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, 1993. JFS, 2) That there are lower remodeling rates for prehistoric populations (e.g. Gibson & Ledders) Stout & Lueck,1995. AJPA.

The crossroads of forensic anthropology and the study of health in past populations come together is an odd way beginning in 1998 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

Age prediction using the equation: natural log age = 2.343 + 0.050877 (rib OPD). PELLAGRINS Specimen # age opd esti. age difference in years A1917 38 7.84 15 23 A2319 40 13.29 20 20 A3325 45 13.70 20.9 24 A2735 47 6.90 15 32 A3141 50 16.96 24.5 26 A2382 52 20.1 29 23 A1419 67 11.99 19 48 A1351 70 16.7 24 46 A2153 75 10.73 18 57 A2469 89 14.19 21 68 MEAN DIFFERENCE IN YEARS 36.7 under-aged

NON-SPECIFIC MALNUTRITION Age prediction using the equation: natural log age = 2.343 + 0.050877 (rib OPD). NON-SPECIFIC MALNUTRITION SPECIMAN # AGE OPD EST. AGE DIFFERENCE IN YEARS A2393 16 5.31 13.5 2.5 A2933 29 10.80 18 11 A3421 30 14.79 22 8 A2212 38 13.84 21 17 A 466 39 12.10 19 20 A1558 39 14.70 22 17 A 162 40 12.30 19 21 A3696 43 14.78 22 21 A 730 46 14.37 21.6 24 A2455 50 14.19 21 29 A 980 50 12.58 20 30 A 2863 58 16.67 24 34 A3546 60 19.46 28 32 A2919 64 13.33 20.5 43.5 A3131 65 18.21 29.6 35.4 A3423 65 12.28 19.4 45.6 MEAN DIFFERENCE = 24.4 YEARS under-aged.

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 = 2.343 + 0.050877 (Exp. rib OPD) (38 yrs) = 3.369 logage 3.369 - 2.343 / 0.050877 = Exp. rib OPD Obs. OPD Exp. OPD 7.84 25.44

This photo is of a 40 year old Male suffering from scurvy; log age = 3.659 Obs OPD = 12.30 exp OPD= 26.450 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

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 3.916 50.2 13.54 30.917 17.377 56.2% less

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

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.

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

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

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

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

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.

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).

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

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.

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 !!