A Historical Perspective on Skeletal Frailty in Medieval Poland: Evidence from an Early

Piast Dynasty Site

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the

Graduate School of The Ohio State University

By

Alexandra Christianne Tuggle

Graduate Program in Anthropology

The Ohio State University

2018

Thesis Committee

Dr. Douglas E. Crews, Advisor

Dr. Mark Hubbe

Dr. Samuel Stout

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Copyrighted by

Alexandra Christianne Tuggle

2018

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Abstract

In humans, stress and health are fundamentally intertwined. Recently, Marklein et al. (2016) proposed a skeletal frailty index (SFI) based upon clinical research methods.

Such SFIs estimate somatic frailty by assessing acute and chronic skeletal lesions. My hypothesis is that skeletal frailty, as estimated using a SFI, differed by both age and sex at the medieval Polish site Kałdus (n=108). During the 10th to 13th centuries, Kałdus was a regional economic center of Poland with a population expressing a variety of religious and cultural practices. Relying on 11 biomarkers of frailty, this sample was analyzed for skeletal markers of lifetime stressors used to estimate skeletal frailty. All skeletons were assigned to an age group: 18-25 (n=21), 26-35 (n=31), 36-45 (n=31), and >45 (n=25). In the full sample, SFI averaged 4.13 (range 0-9). Among men (n=56), SFI averaged 4.45

(sd = 1.90; range 1-8); among women (n=52), it was 3.79 (sd = 2.03; range 0-9; p =

0.044). SFI was lowest in the youngest age group, 2.38 (sd = 1.83; range 0-6) and highest in the oldest, 5.48 (sd = 1.50; range 2-9; p < 0.001). In these medieval skeletons, an 11- biomarker SFI shows a significant difference between those ascribed as male or female.

Additionally, frailty differs significantly by age. Skeletal frailty as estimated from biomarkers of skeletal stress suggests these individuals were exposed to a harsh environmental setting during their lives.

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Dedication

This work is dedicated to my parents Gerald and Sandra Tuggle, my number one cheerleaders and source of encouragement. I could not have accomplished this without your unwavering support.

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Acknowledgments

First and foremost, I would like to acknowledge my advisor Dr. Douglas Crews.

This project would not have been possible without his guidance, support, and countless hours spent helping me improve my writing. I would also like to thank Katy Marklein for taking me under her wing and providing invaluable advice and direction, and for helping me dream this project into existence. Your pep talks and constant encouragement kept me going through all my doubts. Additionally I would like to thank my committee members

Dr. Mark Hubbe and Dr. Sam Stout for their invaluable input and advice through all stages of this research. Importantly, I would like to thank Dr. Rick Steckel and Dr.

Tomasz Kozłowski for granting me access to work on this exceedingly fascinating collection.

Many thanks go to all my past educators in Arizona at Mountain View High

School and the University of Arizona for fostering my love of anthropology and science and encouraging me to pursue my curiosity. Much gratitude goes out to my fellow cohort members and graduate students in the Department of Anthropology, this work would not have been possible without your friendship and support. Lastly, I would like to thank all my friends and family in Arizona and across the world for their confidence and reassurance when I needed it most.

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Vita

2009………………………………………….Mountain View High School

2012………………………………………….B.S. Anthropology, University of Arizona

2016………………………………………….University Fellow, The Ohio State

University

2017 to present………………………………Graduate Teaching Associate, The Ohio

State University

Fields of Study

Major Field: Anthropology

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Table of Contents

Abstract ...... ii Dedication ...... iii Acknowledgments ...... iv Vita ...... v List of Tables ...... viii List of Figures ...... x Chapter 1. Introduction ...... 1 Background ...... 4 Biocultural Context: Medieval Poland ...... 4 Burial Context ...... 9 Diet at Kałdus: Historical and isotopic evidence ...... 13 Purpose and Hypotheses ...... 15 Chapter 2. Materials and Methods ...... 17 Materials ...... 17 11-biomarker SFI construct ...... 18 6-biomarker SFI construct ...... 19 Methods ...... 21 11-biomarker SFI construct ...... 21 6-biomarker SFI construct ...... 32 Statistical analyses ...... 33 Chapter 3. Results ...... 34 Gross Prevalence ...... 34 11-biomarker SFI construct ...... 35 6-biomarker SFI construct ...... 38 Chapter 4. Discussion ...... 41 Gross prevalence ...... 41 vi

Sex ...... 43 Age ...... 44 Limitations ...... 46 Chapter 5. Conclusions ...... 47 References ...... 49 Appendix A. Skeletal biomarkers of frailty included in the 11-biomarker SFI (N=108) . 55 Appendix B. Skeletal biomarkers of frailty included in the 6-biomarker SFI (N=176) ... 60

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List of Tables

Table 1. Sex and age distribution of individuals in skeletal sample from Kałdus site 4 used in the 11-biomarker SFI...... 18

Table 2. Sex and age distribution of individuals in skeletal sample from Kałdus site 4 used in a 6-biomarker SFI...... 20

Table 3. Skeletal biomarkers of frailty incorporated into the 11-biomarker skeletal frailty index with designated scores and measurements for frailty and frailty cut-off scores (table adapted from Marklein et al., 2016)...... 22

Table 4. Skeletal biomarkers of frailty incorporated into the non-metric 6-biomarker skeletal frailty index...... 32

Table 5. Average femoral lengths (mm), femoral head diameters (mm), and gross prevalence values of skeletal and dental lesions among males and females at Kałdus site 4

(N=108)...... 35

Table 6. Results for skeletal frailty by sex (Welch two sample t-test) and by sex with age as a covariate (ANCOVA) for Kałdus site 4 using an 11-biomarker skeletal frailty index

(N=108)...... 36

Table 7. Results for skeletal frailty by sex within each age cohort (Welch two sample t- test) and between all age cohorts (ANOVA) for Kałdus site 4 using an 11-biomarker skeletal frailty index (N=108)...... 37

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Table 8. Results for skeletal frailty in the under 35 and over 35 age cohorts by sex accounting for age at Kałdus site 4 using an 11-biomarker skeletal frailty index (N=108).

...... 38

Table 9. Results for skeletal frailty by sex (Welch two sample t-test) and by sex with age as a covariate (ANCOVA) for Kałdus site 4 using a 6-biomarker skeletal frailty index

(N=176)...... 39

Table 10. Results for skeletal frailty by sex within each age cohort (Welch two sample t- test) and between all age cohorts (ANOVA) for Kałdus site 4 using a 6-biomarker skeletal frailty index (N=176)...... 40

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List of Figures

Figure 1. Map of Poland with location of Kałdus site (Image by Joseph Lanning) ...... 8

Figure 2. Early Romanesque basilica in the stronghold at Kałdus (W. Chudziak; digital processing: M. Trzeciecki) ...... 8

Figure 3. Burial styles at Kałdus site 4...... 11

Figure 4. Distribution of sex by each age category (A=18-25, B=26-35, C=36-45,

D=>45) in the Kałdus site 4 sample (N=108) utilized in the 11-biomarker SFI...... 19

Figure 5. Distributions by sex and age category (A=18-25, B=26-35, C=36-45, D=>45) for Kałdus site 4 sample (N=176) utilized in a 6-biomarker SFI...... 20

Figure 6. Fragment of parietal bone showing porous lesions characteristic of porotic hyperostosis (grave 72) (photo T. Kozłowski)...... 25

Figure 7. Chronic active osteoperiostitis on the tibial shaft (grave 52) (photo T.

Kozłowski)...... 27

Figure 8. Advanced degenerative changes of the femoral head characteristic of degenerative joint disease (grave 365) (photo T. Kozłowski)...... 29

Figure 9. Healed, slanting fracture of left ulna (grave 339) (photo T. Kozłowski)...... 30

Figure 10. Sharp force trauma characteristic of decapitation from left side of dens on second cervical vertebrae (grave 256B) (photo T. Kozłowski)...... 31

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Figure 11. Sharp force trauma on frontal bone (possibly caused by sword or axe) with no observable healing (grave 254) (photo T. Kozłowski)...... 31

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Chapter 1. Introduction

For all species, the environment can be a significant stressor, and changing environments have an effect on health. As such, all organisms have evolved ways to halt and delay stressors sufficiently long enough to reproduce (Stearns, 1992). However, over time they all succumb to multiple processes, many of which leave their traces on skeletons that indicate severe trauma, illness, deprivations, and abuses during their lives.

Jointly these skeletal indicators give us a representation of stressors an individual was exposed to during their lifetime. These cumulative stressor-induced changes result in the development of a frail phenotype related to adverse health outcomes (Fried et al., 2001).

Phenotypically, frailty is not solely due to aging and senescence, rather frailty is a specific phenotype occurring secondary to stressors and morbidity experienced during life (Crews & Ice, 2012; Ice & James, 2012). In living people, the frail phenotype results from processes leading to weight loss, weakness, exhaustion, reduced walking speed, and decreased physical activity (Fried et al., 2001; Crews, 2005; Walston, 2005).

Such outcomes are not observable in bioarchaeological samples of skeletal remains. We must rely on skeletal and dental markers associated with pathological, degenerative, or chronic conditions to evaluate the relationships between stress, health, morbidity, and mortality in the past. Many methods have been proposed and utilized with the shared goal of understanding these relationships. In 1984, Cohen and Armelagos

1 published their seminal work Paleopathology at the Origins of Agriculture that explored the transition to an agricultural lifestyle and its effects on population health in the Middle

East, North America, and East Asia. This volume outlined an effective methodology using skeletal biomarkers to answer bioarchaeological questions. Since this publication, measurement standards for skeletal data collection have been established and applied worldwide (Buikstra & Ubelaker, 1994; Ortner & Putschar, 1985). Despite universal measurement standards, the way skeletal data are interpreted in relation to questions of health, frailty, and stress has differed between researchers. Many approaches exist to assessing health in the past (DeWitte & Hughes-Morey, 2012; Steckel & Rose, 2002;

Wilson, 2014; Wood et al., 1992).

In 1992, Wood et al. raised a debate concerning the paradoxical way health can be interpreted in the bioarchaeological record when it is based on such biomarkers of stress.

A common approach to assessing biomarkers in a skeletal sample is through gross prevalence counts of individual pathological conditions and mean age at death. Wood et al. (1992) argued there are three main conceptual problems inherent to this approach that can potentially confound any interpretation of health. The first is demographic nonstationarity, wherein populations are subject to migration, shifts in age distributions, and are very sensitive to changes in fertility, but not mortality. Though not terribly intuitive, this means that mean age at death in a skeletal sample is more accurately a measure of fertility than mortality. This leads to the second issue, selective mortality, where bias is fundamentally built into the data. We can never access the total sample of individuals in the population who were at risk of disease or mortality. Thus, any

2 prevalence of a pathological condition will be an overestimation of its true prevalence in the general population. Lastly, hidden heterogeneity presents a hurdle that must be considered in any analysis of health in the past. This concept is based on the fact that a population is made up of a mix of individuals for whom their underlying frailty is unknown. Frailty, and health in general, is affected by genetics, socioeconomic status, nutrition, behaviors, environmental variation, and temporal trends, among other factors.

Therefore, any aggregate-prevalence values must be interpreted with caution as to suggesting underlying variation in susceptibility to disease and death (Wood et al., 1992).

As a consequence, individuals exhibiting some lesions may actually be less frail than others in the sample without those lesions. One example is periosteal new bone.

Individuals exhibiting healed periosteal lesions have been shown to be more resilient in fighting the responsible infection than those with active lesions, representing those individuals who could not fight off the infection and subsequently died (DeWitte, 2014).

Additionally, individuals with no lesions at all could represent those with relatively high frailty, as they were not alive long enough for any pathological condition to leave its mark on their bones (DeWitte, 2014).

Following Wood et al. (1992), other methods were proposed to circumvent the paradoxical nature of health in the past. Hazard models (DeWitte, 2010; DeWitte,

Boulware, & Redfern, 2013; Wilson, 2014) elucidate an individual view of health rather than a population average. Hazard models rely on the principle that skeletal samples are comprised of the frailest individuals, so frailty is conceptualized in terms of highest

3 mortality risk (DeWitte, 2010). The statistical methods utilized by hazard models thus diminish the issues of selective mortality and hidden heterogeneity.

Recently, Marklein et al. (2016) reported a skeletal frailty index (SFI) based upon theory, research, and methods for assessing frailty in living samples. They applied this

SFI to bioarchaeologically recovered samples from medieval London. Their original index included 13 biomarkers indicative of stressors experienced during life. Here, modified versions of the SFI using 11 and 6 biomarkers, are applied to a medieval Polish sample. These are reflective of low prevalences of conditions such as rickets and neoplasms, high prevalences of conditions such as cribra orbitalia and porotic hyperostosis in this sample, and the categorization of pathology utilized in the database.

The 6-biomarker SFI is applied here to evaluate the explanatory power of a reduced, non- metric index in comparison to the expanded 11-biomarker index. Thus far, the SFI has been applied only to samples from medieval London. It is yet unclear if it will be as useful when applied to other samples such as the one analyzed here.

Background

Biocultural Context: Medieval Poland

Poland is positioned at the boundary between Eastern and Western Europe, its many rivers leading to the Baltic Sea. Poland never was conquered by the Romans, and as such, a written record of Poland’s history up until the fourteenth century is sparse. The first documentation of Poland’s origins was written by a chronicler known as “Gallus

Anonymous” in the early 12th century. Thus, much of Poland’s early history comes from a few written accounts by bishops, clergymen, and travelers, as well as archaeological

4 records (Lukowski & Zawadzki, 2001). The beginning of the medieval period is generally attributed to the year 966 AD, with the rise and baptism of Piast duke Mieszko

I. This resulted in the consolidation of the Polish State and the beginning of

Christianization in the area (Gieysztor et al., 1979). Prior to this time, Poland was occupied by a diverse tribal, largely pagan population of native Polanes and Slavs who practiced agriculture and animal husbandry. Inherited from an ancient past, their beliefs were centered on sun worship and often involved animal sacrifices and large feasts

(Gieysztor et al., 1979). The transition that began with Mieszko I in 966 was the beginning of a considerable social, religious, and economic upheaval that affected relations within Poland for the next few centuries (Buko, 2008).

During the second half of the tenth century, many castle-towns began to arise to meet the needs of an extensive, more organized state. Each town was governed by a duke and a group of lords, who benefitted from the labor of the associated peasant population.

This network of towns served as economic, administrative, judicial, and defensive centers of the new Polish state under the ruling Piast dynasty (Buko, 2008; Gieysztor et al.,

1979). Christianization was likewise a political tool used to consolidate the diverse ethnic groups and integrate them into the new organization. However, Christianity was not accepted meekly by all and often had to be imposed with military force. Complete

Christianization did not occur until the 12th and 13th century with the construction of monasteries across Poland. For the period in between, the church was seen as a deeply unpopular and foreign institution forced upon the populace by the ruling class for political gain (Górecki, 2007; Lukowski & Zawadzki, 2001).

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Although Mieszko I succeeded in uniting the Polish state, the reigns of his successors Mieszko II (1025-34), Bolesław II (1058-81), and Bolesław III (1102-38) were hostile and marked by revolt and invasion (Lukowski & Zawadzki, 2001). The Piast state lacked a clear means of succession, and thus the periods in between rulers were times of political strife and upheaval, often leaving Poland with no ruler at all. At the beginning of the 13th century, Poland’s political organization began to fracture. A total of five Piast duchies existed in 1202. This number grew to nine by 1250 and seventeen by

1288. As order devolved, it became harder and harder to defend the north-eastern borders against the onslaught of pagan Prussian tribes. In 1227, Conrad I duke of Masovia (1202-

1247) positioned the Teutonic Knights, a German Catholic military order, in Chełmno along the left bank of the Vistula River as defense (Lukowski & Zawadzki, 2001). The

Teutonic Order became a formidable military force and succeeded in subduing the

Prussian tribes over the next 50 years. At home, they imposed German Law or

“Ordenstaat” that contributed to the increasingly German character of the area (Gieysztor et al., 1979).

Although political turmoil was abundant during the 12th and 13th centuries, rural settlements experienced increases in population growth attributed to improved economic conditions and substantial advances in agricultural technology. The use of improved farming tools, most notably the wheeled plow, decreased workloads while the introduction of the three-field system increased land-use efficiency. This method replaced the previous system in which cultivated fields were alternately allowed to lie fallow during growing season (Gieysztor et al., 1979). Centers of territorial power, or

6 strongholds, developed rapidly, with corresponding regional economic development. The main strongholds, known as sedes regni principales, built sacral structures accompanied by a church administrative system. These centers were surrounded by many ancillary settlements that produced the goods necessary to sustain the strongholds. These centers and their associated auxiliary settlements became vibrant hubs for trade, craftsmanship, and urban development (Buko, 2008).

One of these early urban centers was Kałdus. Kałdus is located in modern-day

Chełmno, Poland on the banks of the Vistula River (Fig. 1) on the border between former

West Prussia and Pomerania. Kałdus is located near a large man-made mound called

Mount Saint Lawrence, or St. Lawrence’s Hill, which was a sacred place for pre- medieval pagan gatherings during the 10th century (Chudziak, 1997; 2003). Two spring- fed ponds are linked with a pagan altar and nearby evidence of offerings including animal remains, corn, and even a young man. The excavator, Wojciech Chudziak, posits that the area around St. Lawrence’s Hill was sparsely populated prior to medieval times, yet held considerable sacral importance for the local tribal community (Chudziak, 2003). After

Christianization began in the area around the 11th century, a large basilica (Fig. 2) was erected atop the pagan altar. However, the basilica was never completed and was likely abandoned during a pagan revolt in 1030 AD (Chudziak 1997; 2010).

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Figure 1. Map of Poland with location of Kałdus site (Image by Joseph Lanning)

Figure 2. Early Romanesque basilica in the stronghold at Kałdus (W. Chudziak; digital processing: M. Trzeciecki)

According to Chudziak (2003), this basilica was built with the intent to offset the pagan associations with the area. It is the northernmost religious building of the Piast state, and underlies the importance of the area in the Christianization of Prussian territory. Its careful construction involved multiple techniques in the style of the large 8 early cathedrals at Poznań and Gniezno. Chudziak (2000) postulates that the investment made into the basilica’s construction coupled with the large area of the stronghold and surrounding settlements indicates that Kałdus was once one of the sedes regni principales. However, despite grandiose plans and focused religious and military influence, Kałdus never gained the projected status indicated by its regional importance in the early Piast state period. Kałdus’ economic importance gradually declined over the medieval period with the expansion of nearby settlements such as Torún, although it certainly fulfilled an important role in the early state system (Buko, 2008). In addition,

Kałdus is home to two of the richest and most remarkable cemeteries in medieval Poland.

The burials reveal an exceptional diversity of social and ethnic backgrounds including

Christian, pagan, and Scandinavian-style burials, and have been the focus of much recent archaeological research.

Burial Context

The cemeteries at Kałdus have been the focus of research since excavations began in 1996 (Chudziak, 1997; 2003; 2010; Mackowiecki, 2010; Reitsema, 2012; Reitsema et al., 2017). The cemetery at site 4 is located about 300 meters south of St. Lawrence’s

Hill. The burials date from the 10th to 13th centuries, but most are from the 10th and 11th centuries. It is characterized by diverse burial styles, including Christian, pagan, and

Scandinavian style burials both with and without grave goods (Chudziak, 2010). With the introduction of Christianity and the conversion of many of Kałdus’ residents came a transition in burial style. Until the late 10th century, cremation was a popular burial custom, but was replaced by inhumations of varying lavishness during the earliest period

9 of Christianization in the 11th and 12th centuries (Biermann, 2008; Janowski &

Kurasiński, 2003).

A typical medieval Christian burial is placed along an East-West axis in a supine position. They are simple, often without grave goods (Biermann, 2008). At Kałdus, there is a clear tendency to place females with their heads facing the West, and males towards the East (Fig. 3A). Pagan-style burials are more varied, often in flexed or fetal positions on a North-South axis. Many contain elaborate grave goods including large bronze bowls and buckets, jewelry, weapons, coins, and flint (Fig. 3B) (Chudziak, 2010). Some burials are topped with heavy stones or wooden objects, presumably as a defense against the dead rising from the grave, as is documented in medieval lore (Fig. 3C) (Barber, 1988).

Additionally, there are some large wooden chamber graves and double graves representative of Scandinavian immigrants or cultural influence (Fig. 3D).

The burials at Kałdus site 4 can be separated into three distinct phases, each with their own characteristics reflective of social change. Dates for all burials were previously obtained by radiocarbon dates and analysis of coins present in the graves (Chudziak,

2010). The cemetery’s first phase (late 10th century – first half of 11th century) coincided with the first Christianization efforts in the region. The earliest graves from this phase were large, Scandinavian-style chamber graves with elaborate grave goods. These individuals are thought to be local elite and of foreign origin. The later graves from the first phase are largely Christian-style burials in line with the Romanesque basilica. The majority of these Christian-style burials were interned in clogs – open faced “coffins” formed by hollowing out a large log. Lidded coffins from this time period are rare.

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Figure 3. Burial styles at Kałdus site 4.

A) Christian-style burial (grave 374, adult male, E-W); B) Pagan-style burial (grave 65, adult male, N-S); C) Pagan-style burial with large stone on body (grave 48, adult female, E-W); D) Scandinavian-style double burial with grave goods (grave 13, adult male and female, E-W) (Images from Chudziak, 2010).

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Although in the Christian style, these burials were more generously outfitted than a typical medieval Christian burial containing jewelry, coins, knives, swords, and vessels.

The orientation of these later first phase burials indicates their close connection to the basilica, however the presence of “anti-vampire” burials, lavish grave goods, as well as

Christian religious elements suggests a blending of cultural influences (Chudziak, 2010).

Burials in the second phase date from the second half of the 11th century to the first half of the 12th century. During this phase, chamber graves completely disappeared and clogs were replaced by lidded coffins. Burial orientations were more variable during this time period and not necessarily in line with the basilica, which was partially destroyed in 1030. New areas of the cemetery were also utilized. Additionally, the lavish grave goods seen in the previous phase largely disappeared, replaced by simple graves placed in rows according to Christian style. While general features of the graves confirm their Christian character, many variable features such as burial orientation and limb positioning lack a unified adherence to burial practices prescribed by the church

(Chudziak, 2010). The political situation was turbulent at this time on the borderlands between the pagans and Christians, which might be reflected in these burial characteristics.

During the third and final phase, burials date from the second half of the 12th century to the beginning of the 13th century when use of the cemetery largely ceased.

These graves are characterized by regular row configurations and greater adherence to formal Christian burial practices. Furthermore, the deceased’s heads were turned only toward the West, indicating rigorous church influence (Chudziak, 2010). 12

The heterogeneity of burial styles at Kałdus site 4 describes a transitional period in the region’s history. It follows the adoption of Christian burial practices as well as resistance to these practices, a compromise between old and new traditions, and the influence of immigrant populations. The burials at nearby Kałdus site 1 date to the 12th and early 13th century, and reveal a more complete transition to Christian burial customs.

The majority of burials at this site are East-West oriented and supine in the Christian- style (Chudziak, 2003).

Diet at Kałdus: Historical and isotopic evidence

As social instability and shifting political power influence access to resources, nutrition, disease transmission, and ultimately health, dietary trends can reveal much about how stressors actually affected people at a local level (Steckel, 2004). The diet of the general populace at Kałdus centered on cereals such as wheat, millet, and sown manna. However, sown manna was relatively scarce and difficult to harvest, and thus likely an elite food (Reitsema et al., 2017). Meat from terrestrial animals such as pork, beef, and poultry, was commonly consumed and organ meat was often utilized. Under

Christianity, the Polish Church enforced meatless days and days of fasting on

Wednesdays and Fridays. However, medieval church records reveal these rules were commonly ignored by a large portion of the populace (Dembińska, 1999). Dairy products were consumed, although less commonly than meat due to the small size of most dairy animals in medieval Polish settlements. Additionally, eggs from domesticated fowl would have formed an important part of the diet (Dembińska, 1999). Pigs are the most prevalent

13 domesticated animal found in faunal assemblages. Cows, sheep, goats, and chickens make up the remaining proportion (Mackowiecki, 2006).

Fish remains, including freshwater fish from the nearby Vistula River and marine fish imported from the Baltic Sea, are extraordinarily abundant at Kałdus site 4 in comparison to other contemporaneous medieval Polish sites (Mackowiecki, 2003; 2006).

At Kałdus site 4, fish comprise 28.6% of all faunal remains. This is in stark contrast to

Kałdus site 1, where fish make up only 2.9% of faunal assemblages. Most fish remains found at Kałdus site 4 are from freshwater fish such as pike and sturgeon, while marine fish such as herring account for less than 3% (Mackowiecki, 2010).

Kałdus was an economic center, not an agricultural one. Thus, their dietary trends differ correspondingly from other nearby agricultural settlements such as Gruczno (Fig.

1). Residents of Kałdus likely procured more of their subsistence from trade markets than surrounding agricultural regions, and utilized the river as a convenient source of fish to feed a large community (Mackowiecki, 2001).

Recently, Reitsema et al. (2017) released a detailed isotopic analysis of individuals found at Kałdus sites 1 and 4, as well as other surrounding sites such as

Gruczno. They considered the role of individual resilience and agency in dietary patterns at Kałdus, as its inhabitants experienced considerable political upheaval, social instability, and religious conversion. To determine if dietary patterns were influenced more by broader national or smaller local trends, they performed analyses using stable carbon and nitrogen isotopes drawn from 133 adults. They found these settlements largely did not reflect expectations attributed to national trends, but revealed local-scale

14 resilience to outward forces in the diet. Kałdus experienced a decrease in millet and fish consumption over time, and an increase in terrestrial meat consumption as its economic importance and dominant position in the trade economy waned.

Comparing burial contexts, Reitsema et al. (2017) found Christian and pagan burials to have similar stable isotope values, while Scandinavian-style burials showed elevated values of both carbon and nitrogen stable isotopes. Christianization would have encouraged replacing meat in the diet with alternatives such as fish or vegetarian meals on certain days, but the lack of isotopic differences between Christian and pagan-style burials indicates this was an insignificant influence on overall diet. Notably, Reitsema et al. (2017) posit that not all individuals introduced to Christianity would have embraced

Christian burial customs, evidenced by lack of difference in isotopic values as well as the presence of Christian graves retaining pagan elements. Additionally, they found no significant isotopic differences between males and females at Kałdus site 4. The only significant sex differences found were in stable carbon isotopes in the Gruczno sample.

Purpose and Hypotheses

Health, at its core is deeply intertwined with all aspects of life, including social, economic, political, environmental, and lifestyle changes. Thus, health in the past is best understood in historical context. The purpose of this research is to conceptualize the relationships between stress and health in rural medieval Poland by putting them in biocultural context. Poland’s early history is rife with political upheaval, war, social change, migration, religious conflict, subsistence transition, and outside cultural

15 influences. These together make for a fascinating look into the connections between health and history.

My hypothesis is that skeletal frailty, as estimated using a skeletal frailty index, will differ by sex and age at the medieval Polish Kałdus site 4. Although there is no evidence of significant dietary differences between males and females at Kałdus, elsewhere in medieval Europe a sex differential in skeletal frailty has been documented

(DeWitte, 2010). Therefore, I expect to see a similar sex differential in frailty at Kałdus.

Following a general correlation of age and frailty (Fried et al., 2001), I expect to find increasing frailty with age. It is my viewpoint that this research will add a new dimension to the previously gathered knowledge about Kałdus by connecting health to historical context, burial context, and dietary trends. Considerable research has illuminated the settlement at Kałdus, but it has not yet been connected to a pathological assessment of its residents. I will analyze the pathological conditions utilizing a skeletal frailty index previously developed by Marklein et al. (2016).

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Chapter 2. Materials and Methods

Materials

Medieval Polish skeletons from Kałdus were collected by Wojciech Chudziak from 1996-2006. Data are currently collected as part of the Global Health History Project

(GHHP) and are used here with permission from Professors Richard Steckel and Tomasz

Kozłowski. All determinations of pathological lesions and skeletal measurements were completed by Tomasz Kozłowski from the Nicolaus Copernicus University in Torún,

Poland. This research utilized assembled data from the GHHP database, the author neither diagnosed nor assigned pathological lesions to skeletons. Dr. Kozłowski also estimated sex and age using macroscopic aspects of the pelvis and/or cranium for sex determination (Buikstra & Ubelaker, 1994), and macroscopic changes in the pubic symphysis (Buikstra & Ubelaker, 1994). Individuals with indeterminate sex were excluded from this analysis.

The sample here comes from Kałdus site 4, one of two fully excavated cemeteries from medieval Kałdus. Kałdus site 4 dates to the medieval period, including the second half of the 10th century to the beginning of the 13th century AD. Most burials included here date to the 10th and 11th centuries, a period characterized by Christianization and state-formation in Poland (Buko, 2008; Gieysztor et al., 1979; Reitsema, 2012). Burials at site 4 are heterogeneous. Most burials are supine, without grave goods, and oriented in an east-west direction – definitive features of Christian burial customs (Biermann, 2008). 17

Others are flexed, oriented along the north-south axis, and with grave goods characteristic of Scandinavian cultural influences. Other graves also contain pagan elements, including burials in the fetal position with overlaying rocks or other heavy objects; these likely were placed to protect the living from the dead rising from their graves (Barber, 1988).

11-biomarker SFI construct

The total sample from Kałdus site 4 is 601 individuals in the GHHP database.

This includes the 56 males and 52 females used here (Table 1, Fig. 4). Of 601 total skeletons from Kałdus site 4, only 108 are included in this analysis, as they all included measures of the 11 biomarkers, both metric and nonmetric, examined here. Individuals in the sample are assigned to one of four age categories (18-25, 26-35, 36-45, and >45) following Marklein et al. (2016), thereby representing a sequence from young adult to old ages. Additionally, the 11-biomarker SFI was assessed within age cohorts of under 35 and over 35, to ascertain additional associations between sex, age, and skeletal frailty.

Table 1. Sex and age distribution of individuals in skeletal sample from Kałdus site 4 used in the 11-biomarker SFI.

Male Female Total 18-25 3 18 21 26-35 14 17 31 33-45 20 11 31 >45 19 6 25 Total 56 52 108

18

SEX F M 80 60 40 Percentage 20 0

A B C D

Age Category

Figure 4. Distribution of sex by each age category (A=18-25, B=26-35, C=36-45, D=>45) in the Kałdus site 4 sample (N=108) utilized in the 11-biomarker SFI.

6-biomarker SFI construct

Using 6 biomarkers in a reduced nonmetric SFI, the available sample size is increased to 176 individuals, including the original 108 (Table 2, Fig. 5). Results with this nonmetric 6-biomarker SFI are compared to the 11-biomarker construct across four age categories and sex.

19

Table 2. Sex and age distribution of individuals in skeletal sample from Kałdus site 4 used in a 6-biomarker SFI.

Male Female Total 18-25 5 21 26 26-35 25 30 55 33-45 36 18 54 >45 25 16 41 Total 91 85 176

SEX F M 80 60 40 Percentage 20 0

A B C D

Age Category

Figure 5. Distributions by sex and age category (A=18-25, B=26-35, C=36-45, D=>45) for Kałdus site 4 sample (N=176) utilized in a 6-biomarker SFI.

20

Methods

Following criteria outlined by Marklein et al. (2016), a modification of the 13- biomarker SFI is examined here. For every individual, eleven frailty biomarkers, including both skeletal and dental, were included in the dataset and scored according to the GHHP Data Collection Codebook (Steckel et al., 2006) (Table 3). From the total sample of 601 individuals in the GHHP database, 108 individuals had information for all eleven biomarkers. Biomarkers used here capture four major categories of lifetime stressors: growth, nutritional deficiency and infection, activity, and trauma. Each nonmetric biomarker was scored based on presence (1) or absence (0). For metric variables (i.e. femoral length and head diameter) the lowest quartile including the shortest lengths and smallest diameters was scored 1, all other quartiles were scored 0. Once all biomarkers were scored for an individual, their assigned zeroes and ones were added to estimate their SFI.

11-biomarker SFI construct

The purpose of the SFI is to quantify individual frailty rather than population frailty by accumulating multiple indicators of lifetime stressors to compare between individuals for multiple variables. As described by Marklein and colleagues (Marklein et al., 2016; Marklein & Crews, 2017), the skeletal frailty index (SFI) utilizes skeletal indicators of stressful conditions that the person experienced during life. They described four broad categories of frailty markers: growth, nutrition and infection, activity, and trauma (Table 3).

21

Table 3. Skeletal biomarkers of frailty incorporated into the 11-biomarker skeletal frailty index with designated scores and measurements for frailty and frailty cut-off scores (table adapted from Marklein et al., 2016).

*Male femoral length quartiles: x ≤ 434 mm, 434 mm ≤ x < 456 mm, 456 mm ≤ x < 478 mm, x ≥ 478 mm; female femoral length quartiles: x ≤ 394.75 mm, 394.75 mm ≤ x < 426.5 mm, 426.5 mm ≤ x < 458.25 mm, x ≥ 458.25 mm. **Male femoral head diameter quartiles: x ≤ 45 mm, 45 mm ≤ x < 48 mm, 48 mm ≤ x < 51 mm, x ≥ 51 mm; female femoral head diameter quartiles: x ≤ 40.5 mm, 40.5 mm ≤ x < 44 mm, 44 mm ≤ x < 47.5 mm, x ≥ 47.5 mm.

Scores and Frailty score Stress category Frailty variables measurements “1” Growth Femoral length Length Lowest quartile* Femoral head Diameter Lowest quartile** diameter Linear enamel Present/absent Present hypoplasia Nutrition and Cribra orbitalia Present/absent Present infection Porotic hyperostosis Present/absent Present Periodontal disease Present/absent Present Osteoperiostitis Active/healing/absent Active Activity Osteoarthritis Present/absent Present Degenerative joint Present/absent Present disease (limbs) Degenerative joint Present/absent Present disease (vertebrae) Trauma Fracture Present/absent Present

During growth and development, severe stressors may alter or disrupt skeletal and dental growth and imprint the skeleton. During critical growth periods, stressor exposures may lead to shorter stature, decreased skeletal robusticity, and linear enamel hypoplasias

(LEH) observable at later ages (Goodman & Armelagos, 1985; Ortner, 2003; Ruff et al.,

1997). Such biomarkers of early life stressors are inversely and significantly related to later mortality risks (Bogin et al., 1989). Stature is acknowledged as a general indicator 22 of health in adulthood in bioarchaeological samples (Steckel et al., 2002) and femoral length is used as a proxy for adult stature in analyses (Bogin & MacVean, 1983; Bogin et al., 1989; Ruff et al., 2003; Saunders & Hoppa, 1993; Steckel, 1995; Steckel et al., 2002).

Because there is considerable variation between sexes in stature, and thus femoral length due to sexual dimorphism, here quartiles for femoral length are determined within sexes.

Femoral head diameter is used as a proxy for bone robusticity (Ruff et al., 1997).

In the living, relatively low bone robusticity is a component of the frailty phenotype, and is associated with poor walking performance, low physical activity, and precedes poor health outcomes (Fried et al., 2001) and increased risk of falling (Xue et al., 2011).

Individuals with femoral length and femoral head diameter in the first or lowest quartile for their population’s sex-specific distributions were scored “1”, all others zero. The first quartile is used for the SFI because it represents those individuals in the population who most likely suffered undernourishment during childhood resulting in shorter stature and decreased bone robusticity. Research on modern populations shows individuals who are short-statured relative to others in their population suffer poorer health outcomes and increased morbidity later in life (Cameron & Demerath, 2002; Cameron, 2007).

Linear enamel hypoplasias (LEH) are lines in teeth secondary to disrupted enamel formation following both mild and severe stressors experienced during tooth formation

(Goodman & Rose, 1990; Guatelli-Steinberg, 2015; Guatelli-Steinberg et al., 2012;

Hillson, 1996; Ortner, 2003). Rarely are LEH attributed to genetic factors (Goodman &

Rose, 1991). Rather, they occur in response to malnutrition during early life, and also due

23 to variable factors including social and metabolic stress, as well as systemic poor health

(Armelagos et al., 2009). Presence of any LEH was scored “1”, otherwise zero.

When nutrition is poor or the body is fighting infection, immune function is compromised. Thus, the body cannot effectively ward off disease, fight infection, and retain normal function, making poor nutrition an indicator of the frailty phenotype

(Ortner, 2003; Scrimshaw, 2003). Here, evidence of nutritional and infectious stress is recorded as the presence of cribra orbitalia, porotic hyperostosis, periodontal disease, and osteoperiostitis. Cribra orbitalia and porotic hyperostosis (Fig. 6) are areas of macroscopic pitting on the orbital roofs and external cranial vault, respectively (Ortner,

2003). Previously treated as evidence of iron-deficiency anemia, recent research suggests these conditions more likely reflect age-related changes in marrow production caused by hemolytic and megaloblastic anemias (Walker et al., 2009). These two conditions are often combined into one category or both referred to as porotic hyperostosis, however here they are separated into two separate biomarkers as clinical evidence suggests they have separate etiologies (Augustin et al., 2007; Ma’luf et al., 2002; Walker et al., 2009).

Though cribra orbitalia and porotic hyperostosis are not caused by iron-specific deficiencies, they result from considerable nutritional stress during growth and development, poor sanitation, and infectious disease burden. Specifically, cribra orbitalia is associated with severe vitamin C deficiency and porotic hyperostosis is associated with severe vitamin B12 deficiency. These deficiencies stem from nutritionally deficient plant- based diets (and thus nutrient-deficient milk and weaning foods), childhood diarrheal

24 diseases, and intestinal parasites. Here, presence of cribra orbitalia and porotic hyperostosis were scored “1” for present and “0” for absent.

Figure 6. Fragment of parietal bone showing porous lesions characteristic of porotic hyperostosis (grave 72) (photo T. Kozłowski).

Periodontal disease is the clinical characterization for destruction of the periodontium—the oral structure containing the teeth, composed of both fibrous (gingival and periodontal ligament) and mineralized (cementum and alveolar bone) tissues

(Hillson, 1996). Periodontal disease has a multifactorial etiology, but is primarily associated with inflammatory host response resulting from bacterial infection of periodontal tissues (Hillson, 1996; Martelli et al., 2017). Martelli et al. (2017) suggest the aggressiveness of subgingival plaque biofilm along with individual host immune

25 response influence progression and severity of periodontal disease. However, both also are affected by genetic and epigenetic context and multiple environmental factors (e.g., age, sex, smoking, oral hygiene). Presence of periodontal disease precedes multiple systemic disorders, including cancer, diabetes, rheumatoid arthritis, cardiovascular diseases, and preterm birth (see Martelli et al., 2017). When untreated, periodontal disease produces significant resorption of alveolar bone. This often results in tooth loss when associated with a compromised immune system (Hillson, 1996). Here, periodontal disease is scored “1” when associated with antemortem tooth loss.

Osteoperiostitis, a non-specific skeletal lesion wherein membranes surrounding the bone (periosteum) become inflamed, causes macroscopic changes to bone surfaces

(DeWitte, 2014; Ortner, 2003; Ortner & Putschar, 1985; Larsen, 2015). Active osteoperiostitis (Fig. 7) is suggested as indicative of frailty and associated with higher mortality risk, whereas healing lesions likely represent healthier or less frail phenotypes

(DeWitte, 2014). Therefore, osteoperiostitis was scored “1” for active but not healing lesions. Healing lesions and those with no sign of osteoperiostitis were scored “0”. The tibia is one of the bones most commonly affected by osteoperiostitis, possibly due to lack of soft tissue covering the bone and thus less environmental buffering (Ortner, 2003).

Additionally, the tibia is not as vascularized as other bones. Consequently, immune responses tend to manifest slowly making it a better area to view chronic rather than

26

Figure 7. Chronic active osteoperiostitis on the tibial shaft (grave 52) (photo T. Kozłowski).

27 acute infection (Klaus, 2014). For this reason, osteoperiostitis was only scored if present on the tibia.

Repetitive motion stresses joints and leads to painful, sometimes immobilizing conditions. Activity biomarkers are included in this SFI because of the immobilizing and painful nature of conditions such as osteoarthritis, degenerative joint disease (Fig. 8), and intervertebral disc disease (Waldron, 2009). These conditions inhibit the completion of daily tasks and quality of life (Anderson & Loeser, 2010; Fried et al., 2001; Ling &

Bathon, 1998). In the GHHP database, degenerative joint disease of the vertebrae was scored, here it is treated as intervertebral disc disease. Because the severity of these three conditions is difficult to assess from skeletal remains, the presence of a condition across any joint is scored “1”.

Evidence of skeletal trauma suggests interpersonal conflict or traumatic accidents during life, possibly disrupting normal functioning and daily lives (Knüsel & Smith,

2014). Trauma is highly prevalent in this sample (Figs. 9-11), possibly due to increasing social stress throughout the medieval period in Poland. Trauma suggests either stressful accidents or interpersonal violence, based on the type of injuries, both of which suggest conditions conducive to stress (Knüsel & Smith, 2014). Any sign of trauma on the skeleton is scored “1” due to the likely physically limiting nature of injuries that impact skeletons, regardless of location.

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Figure 8. Advanced degenerative changes of the femoral head characteristic of degenerative joint disease (grave 365) (photo T. Kozłowski).

29

Figure 9. Healed, slanting fracture of left ulna (grave 339) (photo T. Kozłowski).

30

Figure 10. Sharp force trauma characteristic of decapitation from left side of dens on second cervical vertebrae (grave 256B) (photo T. Kozłowski).

Figure 11. Sharp force trauma on frontal bone (possibly caused by sword or axe) with no observable healing (grave 254) (photo T. Kozłowski).

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6-biomarker SFI construct

Use of metric biomarkers to assess skeletal frailty may significantly limit sample size (Marklein & Crews, 2017). This is the case at Kałdus site 4. In a follow-up paper,

Marklein & Crews (2017) showed that nonmetric SFIs including 6 or more biomarkers correlate significantly (r > 0.9) with metric-based SFIs. An added benefit is with fewer biomarkers overall and no metric biomarkers, sample size is increased significantly.

Marklein and Crews (2017) suggested a 6-biomarker non-metric SFI may be the smallest index that retains analytical power similar to the 13-biomarker metric SFI.

To test this finding, a six biomarker SFI, suggested by Marklein & Crews (2017), is applied to the Kałdus site 4 sample (Table 4). This non-metric SFI represents each of the four stress categories noted earlier and biomarkers are scored following criteria and methods already presented, but without metric biomarkers. The relationship between the

11- and 6-biomarker SFIs are assessed for retention of analytical power.

Table 4. Skeletal biomarkers of frailty incorporated into the non-metric 6-biomarker skeletal frailty index.

Scores and Frailty score Stress category Frailty variables measurements “1” Linear enamel Growth Present/absent Present hypoplasia Nutrition and Periodontal disease Present/absent Present infection Osteoperiostitis Active/healing/absent Active Activity Osteoarthritis Present/absent Present Degenerative joint Present/absent Present disease (vertebrae) Trauma Fracture Present/absent Present

32

Statistical analyses

To begin, descriptive statistics were determined for all 11 biomarkers in the full sample (N=601), the sample for the 11-biomarker SFI (N=108), and for the 6-biomarker

SFI (N=176). From these data, both the 6- and 11-biomarker SFIs, standard deviations, ranges, and averages by sample, sex, and age category were determined. To determine the significance of differences in SFIs by sex and age category, Welch’s two sample t- tests were performed by sex in the whole sample as well as within each age cohort, while differences by age cohort were examined using ANOVA. Lastly, ANCOVA was applied to distinguish influences of sex on skeletal frailty while controlling for the possible association of frailty with age. Additionally, multiple linear regression was used when the sample (N=108) was split into age cohorts of under age 35 and over age 35 to ascertain additional associations between age, sex, and skeletal frailty.

In addition, Spearman’s correlation was applied to determine association between

11- and 6-biomarker SFIs only between the original 108 individuals. For the 11- biomarkers in the SFI (N=108), gross prevalence was recorded and t-tests (for metric biomarkers) or chi-square tests (for non-metric biomarkers) were performed between males and females for each condition. All statistical analyses were performed using R version 3.4.3 (R Foundation for Statistical Computing, 2017).

33

Chapter 3. Results

Gross Prevalence

While gross prevalence does not describe individual health, it does provide an overview of stress-related conditions within a sample and identify conditions contributing most to skeletal frailty. Here, the most prevalent conditions are periodontal disease, osteoperiostitis, osteoarthritis, and degenerative joint disease of both the limbs and vertebrae (Table 5). Femoral length and femoral head diameter are significantly smaller in females (both p < 0.001), while porotic hyperostosis (p = 0.035) and degenerative joint disease of the vertebrae (IVD) (p = 0.007) occur more frequently in males. Fractures (p =

0.065) also occur more frequently in males although not significantly so.

It is useful to compare gross prevalence to skeletal frailty indices here to determine which conditions are contributing most to skeletal frailty. The most prevalent conditions in this sample commonly are associated with chronic lifetime nutritional and activity-related stressors (Anderson & Loeser, 2010; DeWitte, 2014, Ortner, 2003;

Waldron, 2009; Walker, 2012).

34

Table 5. Average femoral lengths (mm), femoral head diameters (mm), and gross prevalence values of skeletal and dental lesions among males and females at Kałdus site 4 (N=108).

* Results significant at the 0.05 level

Male (N= 56) Female (N=52) T/C2 Value P-value Femoral length 453 418 t = -154.19 <0.001* (mm) Femoral head 48.5 42.5 t = -100.93 <0.001* diameter (mm) Linear enamel 0.34 0.37 X2 = 0.081 0.777 hypoplasia Cribra orbitalia 0.04 0.06 X2 = 0.295 0.587 Porotic hyperostosis 0.30 0.13 X2 = 4.453 0.035* Periodontal disease 0.50 0.60 X2 = 1.006 0.316 Osteoperiostitis 0.57 0.44 X2 = 1.799 0.180 Osteoarthritis 0.70 0.60 X2 = 1.671 0.196 Degenerative joint 0.70 0.62 X2 = 1.187 0.276 disease (limbs) Degenerative joint 0.64 0.38 X2 = 7.202 0.007* disease (vertebrae) Fracture 0.30 0.15 X2 = 3.398 0.065

11-biomarker SFI construct

For the total sample (N=108) at Kałdus site 4 skeletal frailty averaged 4.13 (sd =

1.98; range = 0-9) (Table 6). Skeletal frailty averaged 4.45 (sd = 1.90; range 1-8) among males, higher than among females, who averaged 3.79 (sd = 2.03; range 0-9; p = 0.09), but not significantly so. Across age cohorts skeletal frailty generally is higher at older ages being lowest in the youngest age group, 2.43 (sd = 1.91; range 0-6) and highest in the oldest, 5.28 (sd = 1.43; range 2-9, p < 0.001) as expected. To account for age-related variation in frailty and higher proportions of males in older age categories and females in younger categories, ANCOVA was applied with age as a covariate. In this analysis,

35 skeletal frailty varied significantly by sex (p = 0.044). Further examining this sex differential within age categories revealed frailty differed significantly only in the 26-35 year age group (p = 0.032) (Table 7). This age group is the only one with a similar number of males and females (14 and 17, respectively).

The sample (N=108) was then split into the age cohorts of under 35 (N = 45) and over 35 (N = 63) to further explore frailty associations with sex and age (Table 8). In males under the age of 35, skeletal frailty averaged 2.92 (sd = 1.38; range 1-5), lower than among females, who averaged 3.00 (sd = 1.89; range 0-6; p = 0.289), but not significantly so. In males over the age of 35, skeletal frailty averaged 4.86 (sd = 1.81; range 1-8), lower than among females, who averaged 5.16 (sd = 1.50; range 3-9). Within sexes, frailty was significantly higher in older males than in younger males (p=0.00054).

Frailty was also significantly higher in older females than in younger females

(p=0.00004).

Table 6. Results for skeletal frailty by sex (Welch two sample t-test) and by sex with age as a covariate (ANCOVA) for Kałdus site 4 using an 11-biomarker skeletal frailty index (N=108).

* Results significant at the 0.05 level

Mean Standard p-value (t- p-value N Range SFI Deviation test) (ANCOVA) Total 108 4.13 1.98 0-9 Sample

Sex 0.086 0.044* Male 56 4.45 1.90 1-8 Female 52 3.79 2.03 0-9

36

Table 7. Results for skeletal frailty by sex within each age cohort (Welch two sample t-test) and between all age cohorts (ANOVA) for Kałdus site 4 using an 11-biomarker skeletal frailty index (N=108). * Results significant at the 0.05 level

Males Females Total p-value Age Cohort N Mean SFI SD N Mean SFI SD N Mean SFI SD Range

18-25 3 1.33 1.15 18 2.61 1.97 21 2.38 1.83 0-6 0.186 26-35 14 2.86 1.83 17 4.35 1.84 31 3.61 1.71 1-7 0.032* 36-45 20 4.30 1.78 11 5.09 1.22 31 4.74 1.65 1-8 0.156 >45 19 5.21 1.27 6 5.50 1.97 25 5.48 1.50 2-9 0.747 Total <0.001*

37

Table 8. Results for skeletal frailty in the under 35 and over 35 age cohorts by sex accounting for age at Kałdus site 4 using an 11-biomarker skeletal frailty index (N=108). * Results significant at the 0.05 level

Males Females Age p-value Cohort N Mean SFI SD Range N Mean SFI SD Range 1.8 Under 35 12 2.92 1.38 1-5 33 3.00 0-6 0.289 9 1.5 Over 35 44 4.86 1.81 1-8 19 5.16 3-9 0.562 0 p-value 0.00054* 0.00004*

6-biomarker SFI construct

To determine whether the nonmetric 6-biomarker SFI is a reasonable proxy for the metric 11-biomarker SFI, Spearman’s correlation was determined within the original sample of 108 individuals. These two estimates are highly and significantly correlated (r

= 0.898; p < 0.0001). A nonsignificant correlation would indicate the reduced SFI produces a different representation of frailty for the sample and may not be as informative as is the 11-biomarker SFI. Their high correlation suggests they also likely capture the same variation as the 11-biomarker SFI. However, a 0.898 r-value indicates only about 0.81 R2-value, suggesting a significant portion of the variation in the two SFIs

(~19%) is not explained by the other.

In the expanded sample (N=176), skeletal frailty averaged 2.69 (sd 1.38; range =

0-6) (Table 9). Among males, estimated skeletal frailty averaged 2.80 (sd = 1.38; range 0-

6), higher than among females with mean 2.58 (sd = 1.38; range 0-5; p = 0.294), but not

38 significantly so. Across age cohorts skeletal frailty generally was higher at older ages.

Estimated skeletal frailty was lowest in the youngest age group, 1.31 (sd = 0.97; range 0-

3) and highest in the oldest, 3.71 (sd = 1.05; range 1-6; p < 0.001). To account for the age-related variation in frailty and the higher proportion of males in older age categories and females in younger categories, ANCOVA including age as a covariate was used to compare skeletal frailty by sex (p = 0.194). This ANCOVA result is not significant, indicating no disparity by sex in skeletal frailty even when accounting for the effects of age. Further examining this differential within each age category, frailty again did not differ significantly by sex in any of the four age cohorts (Table 10).

Table 9. Results for skeletal frailty by sex (Welch two sample t-test) and by sex with age as a covariate (ANCOVA) for Kałdus site 4 using a 6-biomarker skeletal frailty index (N=176).

Mean Standard p-value p-value N Range SFI Deviation (t-test) (ANCOVA) Total 176 2.69 1.38 0-6 Sample

Sex 0.281 0.194 Male 91 2.80 1.38 0-6 Female 85 2.58 1.38 0-5

39

Table 10. Results for skeletal frailty by sex within each age cohort (Welch two sample t-test) and between all age cohorts (ANOVA) for Kałdus site 4 using a 6-biomarker skeletal frailty index (N=176). * Results significant at the 0.05 level

Males Females Total p-value Age Cohort N Mean SFI SD N Mean SFI SD N Mean SFI SD Range

18-25 5 1.20 1.30 21 1.33 0.91 26 1.31 0.97 0-3 0.837

26-35 25 2.00 1.0 30 2.50 1.43 55 2.27 1.27 0-5 0.135 36-45 36 2.83 1.29 18 3.39 0.78 54 3.02 1.17 0-5 0.056 >45 25 3.89 1.01 16 3.44 1.09 41 3.71 1.05 1-6 0.203 Total <0.001*

40

Chapter 4. Discussion

Gross prevalence

Relatively high skeletal frailty observed among medieval residents of Kałdus as compared to another medieval sample (Marklein et al., 2016) indicates they experienced more severe social and/or environmental stressors during their lives. This is confirmed by the historical record. Because skeletal frailty indices quantify cumulative damage experienced by an individual’s skeleton during life, SFIs may be compared between individuals and across populations. Although gross prevalence counts provide a population overview of frailty, they do not allow estimates of frailty for individuals.

Comparisons of gross prevalence (Table 5) suggest males were more frail than females, as they expressed significantly higher prevalences for four of the eleven frailty biomarkers. Tabulation of an 11-biomarker metric SFI supports this conclusion. However a 6-biomarker non-metric SFI indicates little disparity between males and females in their skeletal frailty. Examination of gross prevalences are useful for determining how different biomarkers contribute to skeletal frailty. For example, osteoperiostitis, periodontal disease, osteoarthritis, and degenerative joint disease of the limbs and vertebrae are the most prevalent conditions observed among both males and females at

Kałdus site 4. By removing the only two available metric biomarkers, along with porotic hyperostosis (a condition significantly different between males and females), differences

41 between males and females were reduced leading to a non-significant difference in their total skeletal frailty.

The most prevalent conditions observed are indicative of nutritional stressors and damage from physical activity. Drawing on isotopic evidence (Reitsema et al., 2017) as well as historical records (Dembińska, 1999), the diet at Kałdus largely consisted of millet or similar cereal grains, fish, and terrestrial animal meat. Many medieval Polish cooking methods destroyed much of the nutritional value of the food being consumed and relied on the heavy addition of spices to cover up poor quality. Fruits were rare and likely available only to the elite. Additionally, the lower classes would have often consumed poor quality, fatty cuts of meat (Dembińska, 1999). Nutritional deficiencies from this diet could be severe as it lacks diversity and breadth and likely would be low in iron, sugar, and vitamins A, B12, C, E, and K (www.nutrition.gov). Furthermore, their reliance on trade routes to acquire their resources would have made the residents of Kałdus vulnerable to the flow of goods and shortages from other regions.

Additionally, the high prevalence of activity-related conditions suggest a lifestyle with high daily workloads. Although Kałdus was not an agricultural site, historical records indicate life for its residents was far from easy (Buko, 2008; Gieysztor, 1979). It is doubtful the significantly higher prevalence of IVD in males is meaningful and representative of a higher workload in males. Rather, this discrepancy may be a result of the age distribution of the sample (Fig. 4), wherein males are more represented at older ages and females at younger ages.

42

Sex

Comparisons of skeletal frailty from this site reveal a significant difference by sex using an 11-biomarker frailty index before (p=.09), and after accounting for possible confounding effects of age (p = 0.04). However, using a 6-biomarker index, skeletal frailty does not show a significant association with sex (p = 0.281 and p = 0.194, respectively). Although the 6-biomarker index is correlated significantly with the 11- biomarker index (r = 0.898) and increases sample size, it reduces ability to detect variability in this sample. Thus, strong evidence for a disparity in frailty between males and females at medieval Kałdus, revealed using a metric SFI with 11 biomarkers is lost when a 6-biomarker frailty index is used.

Using a 6-biomarker SFI increases the sample size available from bioarchaeological samples. However, at least in this sample an 11-biomarker estimate appears to provide a more accurate picture of skeletal frailty. Although the 11- and 6- biomarker constructs are highly correlated (r = 0.898; p < 0.0001), removing the metric variables as well as some of the rarer conditions such as porotic hyperostosis and cribra orbitalia may eliminate descriptive variation in the sample. As a result, the reduced index might instead represent a low common level of frailty in the population rather than distinguishing between uniquely hale and frail individuals. Metric variables may limit sample size, but also may be key in accessing subtle variations in frailty across this sample.

Based on mortality risk, DeWitte (2010) observed that females in a medieval

London cemetery were less frail than their male counterparts. Marklein and colleagues

43

(2016) also reported that males exhibited more frailty in a medieval London sample of monastic and nonmonastic individuals, and posited that this was due to a longer average lifespan for males in which to accumulate frailty and an environment that better buffered males from stressors. Using the 11-biomarker index, a significant sex difference in frailty is observed in the Kałdus site 4 sample. There also is a higher proportion of females in younger age groups and males in older age groups (Figs. 4 and 5), possibly reflective of selective mortality favoring longer lifespans for males, that also is associated with skeletal frailty.

In a further analysis, the individuals (N=108) were divided into those under and over the age of 35, representing those of younger and older ages, to additionally examine the associations between sex, age, and skeletal frailty at Kałdus site 4. Neither of these aggregate age cohorts revealed a significant difference in SFI by sex. This suggests, that the significant results reported previously from the 11-biomarker SFI may not be representative of the sample. In this case, it appears the 6-biomarker SFI, in expanding the sample, may actually illustrate frailty more accurately in affirming no sex disparity.

Looking closer within sexes, frailty was significantly higher in those age 35 or older in both males and females (Table 8), affirming the original hypothesis that SFI increases with age.

Age

ANOVA confirms the hypothesis that frailty is related significantly to age at

Kałdus site 4. Due to the degenerative nature of conditions associated with the frailty phenotype (e.g. osteoarthritis, degenerative joint disease), frailty and age often are

44 correlated significantly in both living and skeletal samples (Fried et al., 2001). However, many aspects of frailty also are reflective of childhood stressors, for example cribra orbitalia (Walker et al., 2009) and LEH (Hillson, 1996). Thus, frailty not only increases with age but also depends on early life conditions. In a sample from medieval London using the same age categories as here, Marklein et al. (2016) reported the youngest age cohort to have a consistently higher SFI than the next oldest age cohort, with frailty increasing incrementally thereafter. Thus, higher frailty is related to mortality among both the young and the old. However, by living longer older persons have more time to accumulate frailty as they survived even more of life’s stressors.

Although it is named the “skeletal frailty index,” it may more accurately be describing an intricate three-way interaction between frailty, health, and resilience. Those individuals who lived to old ages, although they attain a high score in the skeletal frailty index, may actually represent the most resilient individuals. Although they accumulate many skeletal indicators of stress by surviving more of life’s stressors, these indicators signify a resilience those who did not live long enough to develop the same skeletal signatures lacked. The older individuals with high SFIs at Kałdus may thus be labeled more resilient, rather than more frail in the face of marked social change.

45

Limitations

As is the nature of human remains, preservation is an issue in this sample. The final sample (N = 108), though relatively large compared to many bioarchaeological studies, was greatly diminished from the original number of individuals available from

Kałdus site 4 (N = 601). By using only individuals with all elements of study present, a large portion of the sample was excluded. Inherently, there might be descriptive variation in those individuals masked by their destruction and unable to be observed in this analysis.

Another limitation reflects the nature of the SFI methodology itself. Equal weighting of each biomarker and our inability to differentiate levels of severity even within each biomarker is inherently problematic, however, as yet no weighted scales exist for most biomarkers. With this methodology, there is no way to distinguish mild osteoperiostitis from decapitation, or cribra orbitalia from severe chronic intervertebral disc disease causing a significant challenge to mobility and decrease in well-being.

Additionally, one cannot discriminate between mild and severe, crippling forms of a condition such as osteoarthritis as both are equally weighted. With traumatic fractures, it was impossible to distinguish between accidental injuries and those inflicted by interpersonal violence, which would indicate a more chronic social stressor in a region rife with political, religious, and cultural tension. With closer analysis of the sample and more detailed differentiation in skeletal indicators of stress, a weighted index may be possible in the future.

46

Chapter 5. Conclusions

Utilizing a methodology recently proposed by Marklein et al. (2016) frailty was examined in a medieval Polish skeletal sample using skeletal biomarkers. Using an 11- biomarker SFI, skeletal frailty was significantly different between males and females, and was higher at older ages. Relatively high frailty in the full sample compared to another medieval sample (Marklein et al., 2016) indicates this population was exposed to multiple stressors on their bodies during a difficult life. Using a 6-biomarker SFI provided a larger sample but did not provide as clear an assessment of male-female differences observed using an 11-biomarker index.

An important next step in this research is to connect frailty to burial context and isotopic evidence to elucidate connections between diet, lifestyle, and health. These associations may better illustrate life in medieval Kałdus and the health and lifetime stressors of its inhabitants. Connecting frailty analyses to burial context would allow us to observe any relationships between religious influences and health, and any complex interactions that might have resulted from this unstable time of political and social change in Poland’s history. Connecting frailty to isotopic evidence would further illuminate the relationships between the local dietary trends at Kałdus and health.

For future research, additional samples from settlements surrounding Kałdus, such as Gruczno and Rogowo, should be analyzed. These data will allow us to determine a more complete picture of health in this region of Poland during the medieval period.

Results from the comparative analyses of settlements with different economies also will illuminate possible relationships between lifestyle and frailty. Finally, analyses of 47 isotopic data along with skeletal frailty will allow a more complete picture of relationships between lifestyles and health in this medieval setting.

48

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Appendix A. Skeletal biomarkers of frailty included in the 11-biomarker SFI (N=108)

(1 = Present, 0 = Absent) FL = femoral length, FHD = femoral head diameter, LEH = linear enamel hypoplasia, CO = cribra orbitalia, PH = porotic hyperostosis, PD = periodontal disease, OP = osteoperiostitis, OA = osteoarthritis, DJD = degenerative joint disease (of the limbs), IVD = degenerative joint disease of the vertebrae (treated as intervertebral disc disease), TR = trauma/fracture

Total Individual Age Sex FL FHD LEH CO PH PD OP OA DJD IVD TR SFI 3296 19 F 0 1 0 0 0 0 0 0 0 0 0 1 11240 20 F 0 0 0 0 0 0 1 0 0 0 0 1 8165 20 F 0 1 1 0 0 0 0 0 0 0 0 2 9902 20 F 0 0 0 0 0 0 0 0 0 0 0 0 9910 20 F 0 0 1 0 0 0 0 0 0 0 0 1 9936 22 F 1 1 1 1 0 1 1 0 0 0 0 6 3904 22.5 F 0 1 0 0 0 0 1 1 1 0 0 4 17155 22.5 F 0 1 0 1 0 0 1 1 1 0 0 5 19214 22.5 F 0 1 0 0 0 0 0 1 1 0 0 3 3789 22.5 F 0 0 0 0 0 0 0 0 0 0 0 0 3775 22.5 F 0 0 0 0 1 0 0 1 1 0 0 3 3464 22.5 F 0 0 0 0 1 0 0 0 0 0 0 1 4218 22.5 F 1 1 1 0 0 0 1 0 0 0 0 4

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10285 22.5 F 1 0 1 0 0 0 0 1 1 0 0 4 3370 22.5 F 0 1 0 0 1 1 1 1 1 0 0 6 3473 22.5 F 0 0 0 0 0 1 0 0 0 0 0 1 9730 24 M 0 0 0 1 0 0 1 0 0 0 0 2 9571 25 M 0 0 0 0 0 0 0 0 0 0 0 0 3416 25 M 0 0 0 0 0 0 1 0 0 1 0 2 10264 25 F 0 0 0 0 0 1 0 0 0 0 0 1 3297 25 F 1 1 0 0 0 1 0 0 1 0 0 4 18060 25.5 F 0 0 0 0 1 0 1 0 0 0 0 2 3946 27 M 0 0 0 1 1 0 1 0 0 0 1 4 17882 27.5 M 0 0 0 0 1 0 1 1 1 1 0 5 3289 27.5 M 0 0 1 0 1 0 0 0 0 1 1 4 3444 27.5 F 0 1 1 0 0 0 1 0 0 0 0 3 3941 27.5 F 0 0 1 0 0 0 0 1 1 0 0 3 10338 27.5 F 1 1 1 0 0 0 0 0 0 0 0 3 17212 27.5 F 0 1 0 0 0 0 0 0 0 1 0 2 9721 27.5 F 0 0 1 0 0 1 1 1 1 0 1 6 3458 27.5 F 0 1 0 0 0 1 0 1 1 0 0 4 8444 27.5 F 1 1 1 0 0 1 1 1 1 0 1 8 4222 27.5 F 0 0 1 0 0 1 1 1 1 1 0 6 3362 30 M 0 0 1 0 0 0 1 0 0 0 0 2 4750 30 M 0 0 0 0 0 0 0 0 0 0 0 0 4210 30 M 0 0 1 0 1 0 0 0 0 0 0 2 4203 30 M 0 0 0 0 1 0 1 1 1 1 0 5 10295 30 F 0 0 1 0 0 0 1 0 0 0 1 3 3329 30 F 0 1 0 0 1 1 0 0 0 0 0 3 56

11385 30 F 0 1 0 0 0 1 1 0 0 1 0 4 3192 30 F 0 1 0 0 0 1 1 1 1 1 1 7 3291 30 F 0 1 1 0 0 1 0 0 0 1 0 4 3931 30 F 0 0 1 0 0 1 0 1 1 1 0 5 3915 32.5 M 0 0 1 0 0 0 0 0 0 0 1 2 3445 32.5 M 0 0 0 0 0 1 0 0 0 0 0 1 4759 35 M 0 0 1 0 0 0 0 0 0 0 0 1 9859 35 M 0 0 0 0 0 0 1 1 1 1 0 4 3197 35 M 0 0 0 0 0 1 0 0 0 0 0 1 3354 35 M 0 0 0 0 1 1 1 1 1 1 0 6 3286 35 M 0 0 1 0 0 1 0 0 0 1 0 3 3922 35 F 0 0 1 0 0 1 0 1 1 0 0 4 12219 35 F 1 0 1 0 0 1 1 1 1 1 0 7 3947 37.5 M 0 0 1 0 0 0 1 1 1 1 1 6 4211 40 M 0 0 1 0 0 0 0 1 1 0 0 3 9574 40 M 0 0 1 0 0 0 0 0 0 0 0 1 3910 40 M 0 0 1 0 0 0 0 0 0 0 0 1 17041 40 M 0 0 0 0 0 0 1 1 1 1 0 4 18018 40 M 0 0 1 0 0 0 1 1 1 1 0 5 10467 40 M 0 0 1 0 1 1 1 1 1 0 0 6 11293 40 M 0 0 1 0 0 1 1 1 1 0 0 5 3777 40 M 0 0 0 0 0 1 0 1 1 0 0 3 3447 40 M 0 0 0 0 0 1 1 1 1 1 0 5 3373 40 M 0 0 0 0 1 1 1 1 1 1 1 7 3346 40 M 0 0 0 0 0 1 1 1 1 1 1 6 17231 40 M 0 0 0 0 0 1 1 1 1 1 0 5 57

3334 40 M 0 0 0 0 0 1 0 1 1 1 1 5 3351 40 M 0 0 0 0 1 1 0 1 1 1 0 5 11229 40 F 0 0 0 0 0 0 1 1 1 1 0 4 3916 40 F 0 0 1 0 0 0 0 1 1 1 1 5 11268 40 F 0 0 0 0 0 1 1 1 1 0 0 4 3322 40 F 1 1 1 0 0 1 1 1 1 0 0 7 3460 40 F 1 1 0 0 1 1 0 1 1 1 0 7 3352 40 F 1 0 0 0 0 1 0 1 1 1 0 5 3466 45 M 0 0 0 0 1 0 1 1 1 1 1 6 17360 45 M 0 0 0 0 0 0 1 1 1 1 0 4 3316 45 M 0 0 0 0 1 0 1 1 1 1 0 5 18045 45 M 0 0 0 0 0 0 0 0 0 1 0 1 8495 45 M 0 0 0 0 0 0 0 1 1 1 0 3 9703 45 F 1 1 1 0 0 1 0 0 0 0 0 4 11334 45 F 0 1 0 0 0 1 1 1 1 1 0 6 11247 45 F 0 1 0 0 0 1 0 1 1 1 1 6 8722 45 F 0 0 0 0 0 1 0 1 1 1 0 4 3330 45 F 0 0 0 0 0 1 0 1 1 1 0 4 9695 50 M 0 0 0 0 0 0 1 1 1 1 1 5 3471 50 M 0 0 0 0 1 0 0 1 1 1 1 5 3929 50 M 0 0 1 0 1 1 1 1 1 0 0 6 3804 50 M 0 0 1 0 0 1 0 0 0 0 0 2 11413 50 M 0 0 1 0 0 1 1 1 1 1 1 7 11139 50 M 0 0 0 0 0 1 1 1 1 1 0 5 17077 50 M 0 0 0 0 0 1 1 1 1 1 0 5 3934 50 M 0 0 0 0 0 1 0 1 1 1 0 4 58

17133 50 M 0 0 0 0 0 1 0 1 1 1 0 4 17128 50 F 0 0 0 0 0 1 1 1 1 1 0 5 17587 50 F 0 0 0 0 0 1 0 1 1 1 1 5 17773 55 M 0 0 0 0 0 0 1 1 1 1 0 4 3474 55 M 0 0 0 0 1 0 0 1 1 1 1 5 19217 55 M 0 0 1 0 1 1 1 1 1 0 0 6 11165 55 M 0 0 1 0 0 1 1 1 1 0 0 5 17151 55 M 0 0 1 0 1 1 1 1 1 1 1 8 12010 55 M 0 0 0 0 0 1 1 1 1 1 0 5 10267 55 M 0 0 0 0 0 1 1 1 1 1 1 6 3461 55 M 0 0 0 0 1 1 0 1 1 1 1 6 3323 55 F 0 0 0 0 1 1 1 1 1 1 0 6 9612 60 M 0 0 0 0 0 1 1 1 1 1 1 6 3462 60 M 0 0 0 0 0 1 0 1 1 1 1 5 3407 60 F 0 0 0 0 0 1 0 1 1 0 0 3 3534 60 F 0 0 0 0 0 1 1 1 1 1 0 5 17248 65 F 1 1 0 1 0 1 1 1 1 1 1 9

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Appendix B. Skeletal biomarkers of frailty included in the 6-biomarker SFI (N=176)

(1 = Present, 0 = Absent) LEH = linear enamel hypoplasia, PD = periodontal disease, OP = osteoperiostitis, OA = osteoarthritis, IVD = degenerative joint disease of the vertebrae (treated as intervertebral disc disease), TR = trauma/fracture Total Individual Age Sex LEH PD OP OA IVD TR SFI 3296 19 F 0 0 0 0 0 0 0 11240 20 F 0 0 1 0 0 0 1 9902 20 F 0 0 0 0 0 0 0 9910 20 F 1 0 0 0 0 0 1 3945 20 F 1 0 1 0 0 0 2 8165 20 F 1 0 0 0 0 0 1 8981 20 F 1 0 0 0 0 0 1 9936 22 F 1 1 1 0 0 0 3 9736 22.5 M 0 0 0 0 0 0 0 3789 22.5 F 0 0 0 0 0 0 0 3464 22.5 F 0 0 0 0 0 0 0 4218 22.5 F 1 0 1 0 0 0 2 19214 22.5 F 0 0 0 1 0 0 1 17155 22.5 F 0 0 1 1 0 0 2 10285 22.5 F 1 0 0 1 0 0 2 3904 22.5 F 0 0 1 1 0 0 2 3775 22.5 F 0 0 0 1 0 0 1 3473 22.5 F 0 1 0 0 0 0 1 3370 22.5 F 0 1 1 1 0 0 3 9730 24 M 0 0 1 0 0 0 1 9571 25 M 0 0 0 0 0 0 0 3416 25 M 0 0 1 0 1 0 2 8031 25 M 1 1 1 0 0 0 3 8024 25 F 1 0 0 1 0 0 2 3297 25 F 0 1 0 1 0 0 2 10264 25 F 0 1 0 0 0 0 1 18060 25.5 F 0 0 1 0 0 0 1 3946 27 M 0 0 1 0 0 1 2

60

9667 27.5 M 0 0 1 0 1 0 2 3289 27.5 M 1 0 0 0 1 1 3 3788 27.5 M 1 0 0 1 0 0 2 17882 27.5 M 0 0 1 1 1 0 3 8060 27.5 M 0 1 1 1 0 0 3 9715 27.5 M 0 0 1 1 0 0 2 3444 27.5 F 1 0 1 0 0 0 2 17212 27.5 F 0 0 0 0 1 0 1 10484 27.5 F 1 0 0 0 0 0 1 10338 27.5 F 1 0 0 0 0 0 1 3919 27.5 F 0 0 0 0 0 0 0 3941 27.5 F 1 0 0 1 0 0 2 3535 27.5 F 0 1 0 0 0 0 1 8444 27.5 F 1 1 1 1 0 1 5 4222 27.5 F 1 1 1 1 1 0 5 3385 27.5 F 0 1 0 1 0 0 2 3458 27.5 F 0 1 0 1 0 0 2 9721 27.5 F 1 0 1 1 0 1 4 3802 27.5 F 0 1 0 1 0 1 3 4750 30 M 0 0 0 0 0 0 0 11376 30 M 0 0 1 0 0 1 2 3918 30 M 1 0 0 0 1 0 2 3362 30 M 1 0 1 0 0 0 2 8406 30 M 0 0 0 0 0 0 0 4210 30 M 1 0 0 0 0 0 1 10431 30 M 1 0 1 0 0 0 2 4203 30 M 0 0 1 1 1 0 3 17127 30 M 0 0 1 1 1 0 3 10295 30 F 1 0 1 0 0 1 3 3355 30 F 1 0 1 0 0 0 2 3291 30 F 1 1 0 0 1 0 3 11385 30 F 0 1 1 0 1 0 3 3192 30 F 0 1 1 1 1 1 5 3931 30 F 1 1 0 1 1 0 4 3329 30 F 0 1 0 0 0 0 1 3915 32.5 M 1 0 0 0 0 1 2 3445 32.5 M 0 1 0 0 0 0 1 3463 32.5 F 0 1 0 0 0 0 1 3333 32.5 F 0 1 1 0 0 0 2 4759 35 M 1 0 0 0 0 0 1 9859 35 M 0 0 1 1 1 0 3 3197 35 M 0 1 0 0 0 0 1 61

7840 35 M 0 1 0 0 0 0 1 3383 35 M 0 1 0 1 1 0 3 3286 35 M 1 0 0 0 1 0 2 3354 35 M 0 1 1 1 1 0 4 3467 35 F 0 0 0 1 1 0 2 13130 35 F 1 0 0 1 0 0 2 17279 35 F 0 1 1 1 1 0 4 10505 35 F 1 1 0 1 1 0 4 12219 35 F 1 1 1 1 1 0 5 19209 35 F 0 1 0 0 1 0 2 3922 35 F 1 0 0 1 0 0 2 3336 37.5 M 1 0 0 1 1 0 3 3947 37.5 M 1 0 1 1 1 1 5 3910 40 M 1 0 0 0 0 0 1 9574 40 M 1 0 0 0 0 0 1 17435 40 M 0 0 0 0 1 0 1 4211 40 M 1 0 0 1 0 0 2 17041 40 M 0 0 1 1 1 0 3 18018 40 M 1 0 1 1 1 0 4 8950 40 M 0 0 0 1 1 0 2 3450 40 M 0 0 0 1 1 0 2 11341 40 M 0 1 0 1 1 0 3 3346 40 M 0 1 1 1 1 1 5 4223 40 M 0 1 0 1 1 0 3 3335 40 M 0 1 0 0 0 0 1 11293 40 M 1 1 1 1 0 0 4 3373 40 M 0 1 1 1 1 1 5 3447 40 M 0 1 1 1 1 0 4 10467 40 M 1 1 1 1 0 0 4 3777 40 M 0 1 0 1 0 0 2 3351 40 M 0 1 0 1 1 0 3 3927 40 M 0 0 0 0 0 0 0 3334 40 M 0 1 0 1 1 1 4 17231 40 M 0 1 1 1 1 0 4 3916 40 F 1 0 0 1 1 1 4 11229 40 F 0 0 1 1 1 0 3 3328 40 F 0 1 1 1 1 0 4 3322 40 F 1 1 1 1 0 0 4 3403 40 F 1 1 0 1 0 1 4 3304 40 F 0 1 0 1 1 0 3 3352 40 F 0 1 0 1 1 0 3 3460 40 F 0 1 0 1 1 0 3 62

11268 40 F 0 1 1 1 0 0 3 18045 45 M 0 0 0 0 1 0 1 3466 45 M 0 0 1 1 1 1 4 8495 45 M 0 0 0 1 1 0 2 17114 45 M 1 0 1 1 1 0 4 3316 45 M 0 0 1 1 1 0 3 17360 45 M 0 0 1 1 1 0 3 10435 45 M 0 0 0 1 1 0 2 3925 45 M 1 0 0 1 1 0 3 17547 45 M 0 1 0 1 1 0 3 3189 45 M 0 1 1 0 1 0 3 8143 45 M 0 1 0 1 1 1 4 8374 45 M 0 1 0 1 1 0 3 10031 45 M 0 0 0 1 0 0 1 11187 45 F 1 0 1 1 1 0 4 17021 45 F 1 1 1 1 1 0 5 9703 45 F 1 1 0 0 0 0 2 3468 45 F 0 1 0 1 1 0 3 11247 45 F 0 1 0 1 1 1 4 3330 45 F 0 1 0 1 1 0 3 11334 45 F 0 1 1 1 1 0 4 8722 45 F 0 1 0 1 1 0 3 17747 45 F 0 0 0 1 1 0 2 3471 50 M 0 0 0 1 1 1 3 9695 50 M 0 0 1 1 1 1 4 8461 50 M 0 0 1 1 0 1 3 3934 50 M 0 1 0 1 1 0 3 17102 50 M 0 1 1 1 1 1 5 3804 50 M 1 1 0 0 0 0 2 17077 50 M 0 1 1 1 1 0 4 11413 50 M 1 1 1 1 1 1 6 3929 50 M 1 0 1 1 0 0 3 11139 50 M 0 0 1 1 1 0 3 3917 50 M 1 0 0 1 1 0 3 17133 50 M 0 1 0 1 1 0 3 3542 50 F 0 1 0 0 1 0 2 17128 50 F 0 1 1 1 1 0 4 9858 50 F 1 1 1 1 1 0 5 3188 50 F 0 1 0 1 1 0 3 17587 50 F 0 1 0 1 1 1 4 3474 55 M 0 0 0 1 1 1 3 17773 55 M 0 0 1 1 1 0 3 63

19217 55 M 1 1 1 1 0 0 4 8574 55 M 0 1 1 1 1 0 4 3461 55 M 0 1 0 1 1 1 4 11165 55 M 1 1 1 1 0 0 4 12010 55 M 0 1 1 1 1 0 4 10267 55 M 0 1 1 1 1 1 5 17151 55 M 1 0 1 1 1 1 5 10496 55 F 0 0 0 1 1 0 2 8660 55 F 0 1 0 0 0 0 1 3323 55 F 0 1 1 1 1 0 4 3465 55 F 0 1 0 1 1 0 3 3938 55 F 0 1 1 1 1 0 4 3399 55 F 0 1 1 1 1 0 4 17819 60 M 0 0 1 1 1 1 4 9612 60 M 0 1 1 1 1 1 5 3462 60 M 0 1 0 1 1 1 4 9784 60 F 0 1 1 0 1 0 3 3325 60 F 0 1 1 1 1 0 4 3534 60 F 0 1 1 1 1 0 4 17206 65 M 1 1 1 1 1 1 6 9662 65 F 0 1 1 0 1 0 3 17248 65 F 0 1 1 1 1 1 5

64