Received: 3 January 2016 | Revised: 9 May 2016 | Accepted: 9 May 2016 DOI 10.1002/ajpa.23019

RESEARCH ARTICLE

In sickness and in death: Assessing frailty in human skeletal remains

Kathryn E. Marklein1 | Rachael E. Leahy1 | Douglas E. Crews1,2

1Department of Anthropology, The Ohio State University, Columbus, Ohio 43210 Abstract 2College of Public Health, The Ohio State Stress plays an important role in the etiology of multiple morbid and mortal outcomes among the University, Columbus, Ohio 43210 living. Drawing on health paradigms constructed among the living augments our evolving knowl- Correspondence edge of relationships between stress and health. Therefore, elucidating relationships between Kathryn E Marklein, Department of stress and both chronic and acute skeletal lesions may help clarify our understanding of long-term Anthropology, The Ohio State University, health trends in the past. In this study, we propose an index of “skeletal frailty,” based on models Columbus, OH. ff Email:[email protected] of frailty used to evaluate the life-long e ects of stress on health among living populations. Here, Grant Sponsorship: None. we assess the possible applicability of frailty to archaeological populations. The skeletal frailty index (SFI) is proposed as a methodological liaison between advances made by biological anthro- pologists studying relationships between stress and health among the living and bioarchaeologists studying stress and health among the dead. In a case study examining skeletal stress in Medieval London, the SFI is applied to nonmonastic (N 5 60) and monastic (N 5 74) samples. We used analy- sis of variance/analysis of covariance to compare SFI values between nonmonastic-monastic groups, sexes, and age cohorts. Results indicate higher lifetime morbidity among monastic groups. These results complement previous bioarchaeological findings on the same London populations, wherein lower risks of mortality and longer lifespans were observed for monastic populations. SFI data reflect the morbidity-mortality paradox observed in modern populations and accompany recent findings in bioarchaeology of variation in Medieval monastic and nonmonastic “health.” Ulti- mately, this study demonstrates the SFI’s utility in bioarchaeology, through its application of commonly assessed skeletal biomarkers, its ease of applicability, and its potential usefulness for assessing changes in skeletal health over time and across specific geographies.

KEYWORDS bioarchaeology, morbidity–mortality paradox, skeletal health, stress markers

1 | INTRODUCTION To evaluate health among past populations, bioarchaeologists study the last biological vestiges of once living peoples, skeletonized or Among humans, stress and physiological health1 are intrinsically linked mummified human remains. Assessing dental and skeletal markers (Goodman & Leatherman, 1998; Reitsema & McIlvaine, 2014; Temple associated with various chronic, degenerative conditions and/or patho- & Goodman, 2014). Exposures to chronic and acute stressors lead to logical conditions on human remains is the best way to understand predictable and measurable hormonal and physiological responses that stress and physiological health among past populations. For bioarch- evolved to protect organisms from immediate damage (McEwen, aeologists, using a biological/physiological approach to measuring skel- 2000b; Sterling & Eyer, 1988). Continual stress responses over the life- etal stress is essential for understanding relationships between stress, span have been shown to weigh heavily on future health, morbidity, health, morbidity, and mortality (Goodman, Martin, & Armelagos, 1984; senescent decline, and mortality (McEwen & Seeman, 1999; Seeman, Goodman, Thomas, Swedlun, & Armelagos, 1988). Singer, Rowe, Horwitz, & McEwen, 1997). Therefore, being able to Washburn’s (1951) clarion call for comparative work in biological assess effects of stress provides significant insight into both current anthropology instigated decades of methodological research, promot- and future health status among living and deceased samples. ing cross-population studies within the discipline. Among human

208 | VC 2016 Wiley Periodicals, Inc. wileyonlinelibrary.com/journal/ajpa American Journal of Physical Anthropology 2016; 161: 208-225 MARKLEIN ET AL. | 209 biologists, this call inspired standardized measures for stress and health with a higher risk of mortality. In this way, hazard models mitigate con- to be applied universally across living populations (Ice & James, 2007). cerns of selective mortality and individual heterogeneity, while defining These measurements ultimately were adapted to quantifiable measure- frailty in terms of highest risk of mortality (DeWitte, 2010; Wilson, ments like allostatic load (AL) and frailty (Seeman et al., 1997; Seeman, 2014). McEwen, Rowe, & Singer, 2001; Rockwood, Mogilner, & Mitnitski, Life-history approaches to examining health in archaeological pop- 2004; Rockwood et al., 2005). For human osteologists to conduct ulations have not been adapted as yet into a comprehensive, quantita- inter-population comparative studies, etiological bases for frequently tive model considering multiple stressors simultaneously. At an observed skeletal and dental lesions had to be identified and contex- individual level, osteobiographical accounts provide the most compre- tualized from living populations. In the decades following the Wash- hensive study of individual health by gathering together and interpret- burn article, biological anthropologists and skeletal biologists tackled ing all skeletal variables in reference to each other within a rich this etiological issue within specific living and archaeological popula- archaeological, historical, and cultural context (Stodder & Palkovich, tions, illustrating clear trends in mortality and morbidity associated 2012). However, while these case studies allow researchers to under- with skeletal and dental lesions (Armelagos, et al., 2009; Armelagos, stand the relationships between stress biomarkers in a single person, it ffi Jacobs, & Martin, 1981; Cook & Buikstra, 1979; Goodman & Rose, remains di cult to compare these extensive individual life-histories on 1990; Goodman, Martinez, & Chavez, 1991; Goodman, Pelto, Allen, & a broader population level. Recent work, using newly adopted techni- Chavez, 1992). However, it was not until the conference on and subse- ques and data that facilitate comparisons of longitudinal stress quent publication of Paleopathology at the Origins of Agriculture (1984) between individuals (e.g., stable isotope analyses and dental micro- that regionally diverse osteological studies were collated into the first structural studies), have demonstrated the strength and depth of comprehensive volume addressing how adoption and intensification of knowledge to be gained from merging life history and population agriculture impacted the health of populations throughout North Amer- approaches (Fitzgerald, Saunders, Bondioli, & Macchiarelli, 2006; Sand- berg, Sponheimer, Lee-Thorp, & Van Gerven, 2014). ica, Eastern Asia, and the Levant. This seminal work demonstrated the Drawing from research in human biology, which routinely utilizes effective application of skeletal biomarkers to questions in human his- multiple biomarkers of stress in the models of AL and frailty, we pro- tory and prehistory (Cohen & Armelagos, 1984). Within a decade of pose a model of stress (skeletal frailty index [SFI]), composed of multi- the conference, standards for skeletal data collection had been estab- ple standardized indicators of stress frequently observed and assessed lished (Buikstra & Ubelaker, 1994; Ortner & Putschar, 1985). While in bioarchaeological research, to evaluate frailty in past human popula- these recording standards have since been universally utilized in bio- tions. The SFI is inspired by previously developed (Goodman et al., archaeology, how these data are analyzed and interpreted relative to 1984, 1988; Goodman & Martin, 2002) and widely used (Paine, Vargiu, health and frailty is not yet universally established. Multiple definitions Signoretti, & Coppa, 2009; Redfern & DeWitte, 2011; Steckel, Rose, for frailty and statistical approaches to quantifying frailty currently exist Larsen, & Walker, 2002) skeletal health indices in bioarchaeology. in bioarchaeological literature (DeWitte & Hughes-Morey, 2012; Piper- Merging the strengths of osteobiographical life-history and popula- ata, Hubbe, & Schmeer, 2014; Wilson, 2014; Wood, Milner, Harpend- tional approaches, this index establishes a quantitative assessment of ing, & Weiss, 1992). frailty for each individual in a population, which may then be compared Due to constraints of preservation and context, the most common between individuals and across sex, age, and social settings to help approach for analyzing skeletal data involves comparisons of gross and establish an overall portrait of population frailty. Unlike the statistical adjusted prevalence values for individual pathological conditions within practice of gross prevalence, which evaluates skeletal lesions sepa- a sample. Wood et al. (1992) exposed the inherent paradox of these rately, the SFI model accumulates multiple biomarkers of stress for an statistical interpretations, which apparently overlooked concerns of individual into a single frailty construct. The frailty score does not demographic non-stationarity, selective mortality, and individual heter- reflect susceptibility to death, a definition applied to current bioarch- ogeneity (see Goodman, 1993 for response). Since publication of the aeological translations and approaches to frailty. Rather, the index is osteological paradox, alternative methods for dealing with these ines- modeled on biological and epidemiological concepts of frailty in living capable issues in bioarchaeology have been proposed: hazard models human individuals (Fried et al., 2001, 2009). Through this theoretical (Boldsen, 2007; DeWitte, 2010; DeWitte & Wood, 2008; DeWitte, lens, frailty is studied as a fluctuating physical state based on exposures Boulware, & Redfern, 2013; Wilson, 2014) and life-history studies to and survival from diseases and other stressors. Those individuals (Wright & Yoder, 2003). Both approaches enable bioarchaeologists to with greater resilience to stressors live longer by surviving multiple study health among individuals rather than overall population propor- stressors over their lifetimes. At the same time, this increased resilience tions and ratios, which tend to mute population variability. The hazard to stressors often comes at the price of increased frailty, that is, a model is premised on the fact that all skeletal assemblages are com- morbidity-mortality paradox similar to those described for men and prised of the frailest individuals (i.e., the dead), so these skeletons women in many modern populations (Crimmins, 2001; Kulminski et al., reflect the least resilient (i.e., most susceptible to death) among their 2008; Oksuzyan et al., 2008). By implementing a similar definition of age and sex cohorts (Usher, 2000). Consequently, the pathological frailty into skeletal stress research, the SFI is intended to facilitate com- lesions and general demographics from these skeletal collections may parisons and discussions of health and stress as observed in the living be statistically analyzed to determine which variables are associated by human biologists and the dead by bioarchaeologists. 210 | MARKLEIN ET AL.

The proposed SFI follows past models indexing skeletal health and nating “fight or flight” responses (see Johnson, Kamilaris, Chrousos, & stress using both dental and whole body assessments (e.g., Cohen & Gold, 1992), a concept later expanded by Hans Selye (1936). Modern Armelagos, 1984; Goodman & Martin, 2002; Larsen, 2003, 2006; viewpoints conceptualize stress as a coordinated process involving Pechenkina & Delgado, 2006; Steckel, Kjellstrom,€ Wittwer-Backofen, & integrated responses from the neuroendocrine, central nervous, and Engel, 2009) by directly relating these biomarkers to aspects of frailty other physiological systems coping with psychosocial, environmental, in living people and building an index therefrom. The Museum of Lon- and physical stressors which challenge somatic homeostasis (Johnson don (MoL) Centre for Human Bioarchaeology’s(CHB)Human Osteology et al., 1992; McEwen, 2000b; Schulkin, 2004; Sterling, 2004). Method Statement and Global History of Health Project (GHHP), for example, have both constructed robust frameworks for such assess- 3 | MEASURING STRESS ments, considering a variety of skeletal indicators associated with health among the living, including cribra orbitalia, porotic hyperostosis, Among living populations, three primary approaches to measuring trauma, and non-specific periosteal lesions (Goodman & Martin, 2002; stress can be identified: environmental, psychological, and biological/ Powers, 2012; Steckel et al., 2005). The GHHP catalogs information physiological. Based on individuals’ identifications of key life events about individual skeletons and their relative health in comparison to and perceptions of the relative impacts of these events, both environ- other individuals from the same population, codifying information such mental and psychological measures of stress require communication that it may be compared both cross-culturally and through time. Never- with study participants and therefore have little possibility of being theless, how these data have been presented—mainly as gross preva- operationalized among archaeological populations. However, meas- lences and discrete skeletal lesions—may underscore the importance of uring stress using solely physiological and biomedical measurements a life-history model, which recognizes interactions between pathologi- such as frailty requires no input from participants. Given that frailty is cal lesions and focalizes on individual stress (Stodder & Palkovich, accessible to and frequently utilized by bioarchaeologists, we argue 2012; Williamson & Pfeiffer, 2003). Garnering standards from the MoL that a modeled approach to frailty will provide a novel and accurate Center for Human Bioarchaeology Database and GHHP suites of skele- means for assessing outcomes of life-long cumulative stress in skeletal tal stress biomarkers, our model develops a cumulative score from samples. these variables for each individual within a population. Subsequently, Building on definitions of stress emphasizing somatic reactions to we analyze these scores and their distributions in two samples. We stimuli (McEwen & Stellar, 1993; Seeman et al., 1997; Schulkin, 2004), present the SFI as a possible tool for bioarchaeologists to compare measures using frailty evaluate changes in physiological systems of our frailty levels between individuals of different age cohorts, sexes, and somas following repeated responses to stressors over the lifespan. Pro- socioeconomic classes within and among populations. posed physiological approaches to measuring stress focus either on associations between stress and an individual biomarker (e.g., cortisol, 2 | STRESS see Goldman et al., 2005; Glover, 2006; Strahler et al., 2010) or the association between stress and an index measure encompassing sev- This model of skeletal frailty is premised on the idea that studying den- eral biomarkers (e.g. AL, see Karlamangla, Singer, McEwen, Rowe, & tal and skeletal markers associated with various degenerative condi- Seeman, 2002; Karlamangla, Singer, & Seeman, 2006; Kusano et al., tions and/or pathologies does not lead us to a direct assessment of 2016; Maselko, Kubzansky, Kawachi, Seeman, & Berkman, 2007; See- illness so much as it results in a tally of skeletal stress markers serving man et al., 1997). as proxies for health. Health is a construction of biological, social, and cultural factors, so it always is a latent variable within any study of the 3.1 | Index biomarker assessments living or non-living. Under this premise, our model utilizes recent | advances made in stress research among the living to help inform stud- 3.1.1 Frailty in living human populations ies of past populations (Goodman et al., 1984; Goodman & Martin, Frailty, as studied in living humans, is considered a phenotype reflect- 2002). Capitalizing on the model’s utility requires a coherent definition ing decreased reserve capacity and reduced resistance to stressors, fol- of stress and a method for assessing effects of stress on the skeleton lowing cumulative declines across multiple physiologic systems (Fried comparable to those indices being used among the living. et al., 2001; Walston, 2004, 2005). Today, the most widely accepted To date, the most useful definitions identify stress in living individ- definition proposes frailty as a “biologic syndrome of decreased reserve uals as a continuous internal process rather than as an object (e.g., an and resistance to stressors, resulting from cumulative declines across external or internal pressure or change). Proponents of this viewpoint multiple physiologic systems, and causing vulnerability to adverse out- suggest stress is an ongoing process through which organisms respond comes” (Fried et al., 2001: M146). Higher frailty results from functional to risk factors (stressors) while utilizing somatic resources to resist dele- declines across multiple physiological systems and their synergistic terious outcomes (stress responses) to the best of their abilities (Crews, interactions as altered systems express a variety of frail and non-frail 2007; Crews et al., 2012; Sterling & Eyer, 1988). Walter Cannon phenotypes (Fried et al., 2001). Frailty is highly individualistic as well as (1932) was among the first researchers to identify stress as a process independent of chronic disease morbidity and chronological age (Fried when he recognized the role of the sympathoadrenal system in coordi- et al., 2009). Additionally, frailty is useful for assessing individuals’ MARKLEIN ET AL. | 211 current functional capacity and susceptibility to future adverse out- Our proposed index builds on previously proposed determinants comes (Crews, 2005, 2007). of skeletal health (Goodman et al., 1984, 1988; Goodman & Martin, In living human populations, frailty is operationalized as an index 2002), by uniquely analyzing and interpreting these biomarkers within measure that assesses multiple aspects of functional decline at the a model for stress/health applied to living human populations: frailty. somatic level. As research into stress has shown, measures that simul- By studying frailty as a syndrome in archaeological populations (as it is taneously assess multiple biomarkers consistently outperform meas- in living populations), and not exclusively as a measure of increased ures of individual biomarkers when assessing stress (Goldman et al., susceptibility to death, we evaluate skeletal lesions in terms of morbid- 2005; Glover et al., 2006; Maselko et al., 2007; Seeman et al., 2001; ity and establish frailty in archaeological populations as a variable state Schulkin, 2004; see also Leahy & Crews, 2012 for a recent review). A of cumulative stress. Of primary importance is whether components “Frailty Index,” comprising 70 deficits observable during clinical exami- commonly used to assess frailty in the living leave permanent altera- nation, has also been proposed to identify frail individuals (Rockwood tions on the skeleton. While none of the hormonal indicators of et al., 2007). These frailty indices have been shown to predict vulner- somatic dysregulation skeletonize, some key skeletal and dental indica- ability to disability (Fried et al., 1998, 2001; Walston et al., 2006; Xue, tors of stress are useful for our purpose (Steckel et al., 2002, 2009). Walston, Fried, & Beamer, 2011), decreased mobility (Fried et al., For example, dental enamel hypoplasias may indicate chronic childhood 2001), reduced capacity to complete activities of daily living (ADLs) stress; fractures and other traumatic lesions capture interpersonal as (Fried et al., 2001), falls (Xue et al., 2011), need for long-term care, and well as accidental stress events; and stature consistently proves to be a robust indicator of overall health during periods of childhood and mortality (Fried, Ferrucci, Darer, Williamson, & Anderson, 2004) in adolescent growth (Bailey et al., 1984; Bogin & MacVean, 1983; modern samples. Proposed markers of the “frailty phenotype” include Friedman & McEwen, 2004; Goodman & Rose, 1991; Griffin&Donlon, atrophy (sarcopenia, leading to loss of strength, and weight loss), loss 2007; Larsen, 1997, 2015; May, Goodman, & Meindl, 1993; McEwen, of endurance, decreased balance and mobility, slower performance, rel- 2000b; Steckel et al., 2002). Additional skeletal indicators of daily ative inactivity, and decreases in cognitive function (Fried et al., 2001, wear-and-tear are observed in archaeological human remains. For 2004, 2005). Other authors have proposed difficulty in completing example, slow walking speed is often, although not ubiquitously, asso- ADLs/IADLs (ADL/Instrumental ADL), incontinence, mobility, and unin- ciated with osteoarthritis and degenerative joint disease (DJD) during tentional weight loss as indicators of frailty (Rockwood et al., 1999; life (Ling & Bathon, 1998). Muscle strength, physical activity, and sarco- Crews, 2007). Frailty may be calculated, as is AL, by summing the num- penia may be reflected by osteoporosis and overall robusticity of long ber of components for which an individual’s values are in the highest bones (Fried et al., 2001; Siris et al., 2001). When an individual is immu- risk quartile. This may be the bottom (e.g., low albumin) or top (e.g., glu- nocompromised, the body is more susceptible to chronic inflammation, cose, triglycerides) quartile for respective parameters (Edes, Wolfe, & which may manifest skeletally as periosteal new bone (PNB) growth or Crews, in press). Frailty scores also may include as few as five bio- periodontal disease (DeWitte & Bekvalac, 2011). Certain morbid condi- markers (slow walking speed, weakness, weight loss, low activity, and tions, including infectious diseases and some forms of anemia are fatigue) (Walston et al., 2006) while other frailty indices include reflected in the skeleton as non-specific periosteal lesions and cribra upwards of 70 variables (Rockwood et al., 2005). orbitalia/porotic hyperostosis, respectively (Eyre-Brook, 1984; Simp- 3.1.2 | Skeletal frailty in past human populations son, 1985; Walker et al., 2009).

Currently, frailty is promoted as an assessment for evaluating influen- 3.1.3 | Operationalizing skeletal frailty ces of lifelong stress on health and disease risks in living populations Drawing from frailty models, which consider multiple somatic stress and measuring loss of somatic function secondary to sarcopenia and responses, the proposed SFI considers 13 cumulative and active skele- somatic wasting. Unfortunately, hormonal, physiological, and functional tal biomarkers of stress within four broad categories (Table 1): growth measures incorporated into frailty indices for the living are not repre- disruption, nutritional deficiency and infection, activity, and trauma. sented in archaeological assemblages, whether dealing with complete Early childhood stressors, which manifest as disrupted skeletal and ffi or incomplete skeletal materials. Perhaps this di culty has precluded dental growth, often leave individuals immunologically compromised bioarchaeologists from proposing indices of skeletal health modeled on and susceptible to infection in their adult years (McDade, 2003). Com- frailty indices for living samples. However, the model of skeletal frailty pounded with childhood physiological burdens are the stressors per- presented herein overcomes this pitfall by drawing on previous petually placed on the body by malnutrition and infection, when the research linking skeletal markers to their etiologies in the living and body reallocates energy away from normal maintenance (Scrimshaw, focusing on markers which mimic aspects of frailty measured among 2003). The stress of physical activity also eventuates in non-reversible modern samples (Goodman et al., 1984; Goodman & Martin, 2002). By wear-and-tear to the soma (specifically joints), which restricts mobility studying skeletal frailty as a syndrome among the living, and not as a and overall movement (Waldron, 2009). Finally, traumatic episodes measure of increased susceptibility to death, we acknowledge the dual result in lesions that add further strain to the body throughout the meaning of skeletal lesions both as representations of survival from healing and recovery process (Ortner, 2003). Echoing scoring standards past stressor events (resilience) and indicators of ongoing and cumula- for living humans (e.g., AL), all frailty variables are scored as “0” or “1” tive somatic stress at the time of death. according to stress impact. Cut-points for high risk quartiles are 212 | MARKLEIN ET AL.

TABLE 1 Skeletal biomarkers of stress incorporated into the SFI with designated variable scores and measurements for frailty and frailty cut- off scores

Stress category Frailty variables Scores and measurements Frailty score “1” a,b,c,d Growth Femoral Length Lengths in quadrants Shortest lengths (1=4)* e Femoral Head Diameter Diameters in quadrants Smallest diameters (1=4)** Linear Enamel Hypoplasiaf,g,h,i Present/absent Present Nutrition and Infection Periostitis/Osteomyelitisj,k,l Active, healing, absent Active Periodontal Diseasei,m,n Present/absent Present Porotic hyperostosis (PH)/Cribra Orbitalia (CO)o,p Present/absent Present Rickets/Osteomalaciaq,r Present/absent Present Neoplasms Present/absent Present Osteoporosis Present/absent Present Activity Osteoarthritiss,t,u Present/absent Present Intervertebral Disc Disease (IVD)n,v Present/absent Present Rotator Cuff Disorder (RCD)n Present/absent Present Trauma Fracturew,x,y Present/absent Present

Sources. aBogin and MacVean (1983). bBogin et al. (1989). cSaunders and Hoppa (1993). dSteckel (1995). eRuff et al. (1997). fGoodman and Armelagos (1984). gGoodman et al. (1991). hGoodman et al. (1992). iHillson (1996). jDeWitte (2014). kOrtner (2003). lOrtner and Putschar (1985). mHillson (2014). nWaldron (2009). oStuart-Macadam (1992). pWalker et al. (2009). qMays, Brickley, and Ives (2006). rReginato and Coquia (2003). sAnderson and Loeser (2010). tJurmain (1980). uLoughlin (2001). vWalker (2012). wLambert (1997). xMilner and Smith (1990). yWalker (1989). *Male femoral length quartiles: x  447 mm, 447 mm  x < 461 mm, 461 mm  x < 475 mm, x  475 mm; female femoral length quartiles: x  414 mm, 414 mm  x < 434 mm, 434 mm  x < 443 mm, x  443 mm (highest frailty measurements underlined). **Male femoral head diameter quartiles: x  46.7 mm, 46.7 mm  x < 48.4 mm, 48.4 mm  x < 49.9 mm, x  49.9 mm; female femoral head diameter quartiles: x  41.2 mm, 41.2 mm  x < 42.7 mm, 42.7 mm  x < 45.4 mm, x  45.4 mm (highest frailty measurements underlined).

determined based on the sample population distribution because pat- 3.1.3.1 | Femoral length terns of growth, development, and senescence vary substantially within Stature is directly affected by nutritional and environmental stressors and among populations in an age-independent, individualistic, and mul- in childhood and is acknowledged to be an indicator of general adult- tifactorial fashion (Arking, 2006; Crews, 2007). If, relative to the whole hood well-being (e.g., Bailey et al., 1984; Bogin & MacVean, 1983; population, a datum (e.g., osteoarthritis, stature) falls within the quartile Steckel et al., 2002). Undernourishment during childhood results in or category associated with highest risk/stress, an individual is scored shorter stature in adulthood as compared to individuals who receive “1” for this variable, while all other values receive a “0” (see Seeman adequate nourishment during growth periods (Bailey et al., 1984). This et al., 1997; Vidovic, Sharron, & Crews, 2015). For each individual, discrepancy is consistently observed despite undernourished children these scores are summed for all 13 variables, resulting in a possible SFI experiencing adolescent growth periods (catch-up growth) up to a year between 0 and 13. longer than adequately nourished children (Bailey et al., 1984). Studies MARKLEIN ET AL. | 213 among modern samples suggest the long-term consequences of com- uted to genetic predisposition (Goodman & Rose, 1991); instead, they promised longitudinal growth, with short-statured individuals suffering reflect general levels of physiological stress experienced by specific poorer health and increased morbidity in adult years relative to their populations and levels of systemic poor health (Armelagos, et al., 2009; taller contemporaries (Cameron & Demerath, 2002; Cameron, 2007). Griffin & Donlon, 2007; Skinner & Goodman, 1992; Yaussy, DeWitte, As it remains difficult to reconstruct stature in past populations, due to & Redfern, 2016). the disarticulated nature of most skeletons, femoral length provides a 3.1.3.4 | Non-specific periosteal lesions/osteomyelitis single element proxy for longitudinal growth (Ruff, 2003; Steckel et al., Non-specific PNB manifests as skeletal lesions implicating the perios- 2002). Due to disparities in longitudinal growth between males and teum, followed by cortical bone and, in extreme cases, the medullary females, the confounding variable of sexual dimorphism is avoided by cavity (Larsen, 1997, 2015; Ortner, 2003). Lesions are characterized as comparing femoral lengths within sexes only. For the SFI, sex must be osseous plaques with demarcated margins or irregular elevations of estimated from pelvic and cranial morphological characteristics (Buik- bone surfaces (Aufderheide & Rodriguez-Martin, 1998). When stimu- stra & Ubelaker, 1994), and not long bone dimensions, at the risk of lated by infection or injury, osteoblasts lining the subperiosteum confounding sex estimations with long bone longitudinal growth and quickly create unorganized woven bone along the periosteal surface robusticity components. (Eyre-Brook, 1984; Simpson, 1985). Recent studies have suggested 3.1.3.2 | Maximum femoral head diameter active (woven) rather than healing (lamellar) PNB, in particular, to be Similar to height, bone robusticity provides information on growth pat- associated with higher risk of mortality (DeWitte, 2014). Osteomyelitis terns, reportedly indicating body weight, diet, physiological health, work is the extension of lesions resulting from infection/injury to endosteal and activity patterns, and mobility (Ruff, Trinkaus, & Holliday, 1997; and periosteal bone surfaces, resulting in a restricted in diameter of the Steckel et al., 2002). Robusticity, as a proxy for several indicators of medullary cavity (Larsen, 1997, 2015). When not associated with bone frailty among modern populations, approximates walking performance, trauma (e.g., healing fractures), osteomyelitis is generally the result of physical activity, and strength (Fried et al., 2001). These indicators, localized infection induced by exogenous bacteria, e.g., Staphylococcus fl included in evaluations of frailty among modern populations, have been aureus (Waldron, 2009). These basic in ammatory responses are also shown to predict vulnerability to disability (Fried et al., 1998, 2001; Wal- attributed to leprosy, tuberculosis, and treponemal disease (Larsen, ston et al., 2006; Xue et al., 2011), decreased mobility (Fried et al., 1997, 2015; Ortner, 2003). As with active PNB, osteomyelitis is an ’ 2001), reduced capacity to complete ADLs (Fried et al., 2001), falls (Xue indicator of an individuals physiological stress at the time of death, et al., 2011), need for long-term care, and mortality (Fried et al., 2004). representing infectious morbidity or the physiological burden of This frailty index implements femoral head diameter as a simplified vari- responding to trauma. These direct somatic responses to a stressor par- allel the modeled mechanism for allostasis, whereby the body is con- able for body robusticity. Previous studies (Ruff et al., 1997; Stock & fronted with an insult to which it must respond quickly to reestablish Shaw, 2007) demonstrate the direct correlation between body mass and homeostasis (Beckie, 2012; Edes et al., in press). Active, disorganized femoral head diameter. As the femoral head often preserves in human periosteal bone reflects a stressful event to the soma in which the indi- archaeological skeletal populations, the maximum diameter presents a vidual did not completely recover, likely indicating an immunologically- recoverable and measurable variable for the SFI. However, femoral head compromised, dysfunctional state of health. diameters often represent a sexually dimorphic variable within popula- tions, so individuals should be separated according to sex and scored rel- 3.1.3.5 | Periodontal disease ative to quartiles established for males and females. Periodontal disease results in horizontal and vertical alveolar resorption and is associated with a chronically compromised immune system (Hill- 3.1.3.3 | Linear enamel hypoplasias son 1996, 2014). When untreated, periodontal disease leads to signifi- The secretory phase of amelogenesis can be disrupted in such a way as cant bone and tooth loss, eventuating in edentulousness. Among living to lead to reductions in enamel thickness, for example, linear enamel populations, periodontal disease has been shown to correlate signifi- hypoplasias (LEH) (Hubbard, Guatelli-Steinberg, & Sciulli, 2009; cantly with cardiovascular mortality (Ajwani, Mattila, Tilvis, & Ainamo, Guatelli-Steinberg, 2015). Across animals and humans, the function of 2003; DeStefano et al., 1993). DeWitte and Bekvalac (2011) confirmed ameloblasts, cells which secrete a protein matrix into which mineral is the severity of periodontal infection among Medieval London popula- embedded, is often reduced or impaired during periods of physiological tions, demonstrating higher risk of mortality among individuals with stress, and, notably, malnutrition (Goodman & Rose, 1991; Hillson, diagnosed periodontitis. 1996; May et al., 1993). In addition to nutritional stress and malnutri- tion, studies among modern humans and non-human primates associ- 3.1.3.6 | Porotic hyperostosis/cribra orbitalia ate formation of enamel hypoplasias with systemic disturbances, Identified as an area of pitting and porosity on the orbital roofs and metabolic stress, neonatal disruption, and life history events, including external surface of the cranial vault, cribra orbitalia, and porotic hyper- birth, parturition, and social stress (Bowman, 1991; Dobney, et al., ostosis, respectively, are among the most commonly reported patho- 2004; Hillson, 1996; Kreshover & Clough, 1953; Lukacs, 2001; logical conditions in archaeological collections (Walker, Bathurst, Pindborg, 1982; Skinner & Hung, 1989; Suckling, Elliot, & Thurley, Richman, Gjerdrum, & Andrushko, 2009). Generally thought to be a 1983, 1986; Suckling & Thurley, 1984). Rarely have LEH been attrib- result of marrow hyperplasia, cribra orbitalia and porotic hyperostosis 214 | MARKLEIN ET AL. are etiologically related to anemias linked to significant and sustained Albeit considered a condition of modern populations, bioarchaeological increase in red blood cell production (Walker et al., 2009) as well as studies have demonstrated the timelessness of osteoporosis through- metabolic diseases such as scurvy, rickets, syphilis, and cancer. Similar out human history, discrediting osteoporosis as a disease of modernity lesions can also result from vitamin D deficiency, chronic scalp infec- (Agarwal, 2008). Consequently, osteoporosis, functioning as a proxy for tions, and other infectious diseases (Lewis, 2007; Ortner, 2003). Both strength and physical activity, serves as a direct marker for frailty in have been documented in a variety of archaeological contexts, and both modern and archaeological samples (Fried et al., 2001). often are used to assess the health and nutritional status of these past 3.1.3.10 | Osteoarthritis populations (e.g., Cohen & Armelagos, 1984; El-Najjar, Ryan, Turner, & Bone modifications indicative of osteoarthritis/DJD are associated Lozoff, 1976; El-Najjar & Robertson, 1976; Mittler & van Gerven, with repetitive motions and/or old age. While we recognize that some 1994; Pechenkina, Benfer, & Zhijun, 2002; Steckel, Rose, Larsen, & individuals with osteoarthritis are asymptomatic, other individuals with Walker, 2002; Walker, 1985, 1986). Both are included in the model of this affliction suffer from pain and stiffness in the joints, limited mobil- skeletal frailty as indicators of physiological stress caused by increased ity, and decreased flexibility (Ling & Bathon, 1998). This marker is disease burden and disease itself. included in the model of skeletal frailty because these symptoms sug- 3.1.3.7 | Rickets/osteomalacia gest individuals with decreased mobility (Fried et al., 2001) and reduced Rickets (osteomalacia for adults) represents the skeletal manifestation capacity to complete ADLs (Fried et al., 2001), both of which are asso- of chronic vitamin D deficiency during formative juvenile and some- ciated significantly with higher levels of frailty. times adult years. Vitamin D is an essential element for the transforma- | tion of more amorphous osteoid into more rigid osteon structures 3.1.3.11 Intervertebral disc disease (Ortner, 2003). When a deficiency of vitamin D exists, juvenile bones Intervertebral disc disease (IVD) is diagnosed by the gradual deteriora- especially are more malleable and bend under body weight, resulting in tion of vertebral centra, ultimately bringing about ankylosis, or fusion, rickets’ characteristic bowing of femora and tibiae (Aufderheide & of vertebrae. Continuous movement on the back initially irritates and fl Rodriguez-Martin, 1998). While this condition may be rectified through in ames the outer portion of the vertebral disc, the annulus pulposus fl an introduction of adequate vitamin D, leading to femoral and tibial (Waldron, 2009). Over time, severe in ammation and degeneration shaft lengths within population averages, chronic rickets may cause (normal or pathological) of the vertebral discs results in macroporosity irreversible changes to the morphological structure of lower limb bones of the centra and bony growth (marginal osteophytes) along the central and, consequently, the entire skeleton (Waldron, 2009). These morpho- rims (Roberts, Evans, Trivedi, & Menage, 2006). This process eventu- logical changes to the skeleton often result in retarded growth and ates in compression of vertebral bodies and related neural and vascular muscle weakness, which inevitably affect limb movement and function- symptoms (Gilchrist, Slipman, & Bhagia, 2002; Waldron, 2009). As with ality (Hollick, 2006). osteoarthritis, although presenting similar effects from a differing etiol- ogy, IVD directly impacts an individual’s ability to engage in daily 3.1.3.8 | Neoplasms activities. Neoplasms, new, uncontrolled growths within the body, present either a long- or short-term stress to the soma. Although most neoplasms 3.1.3.12 | Rotator cuff disorder affect soft tissue and may not present skeletally, secondary metastases The rotator cuff is composed of four muscles—subscapularis, supraspin- from malignant tumors arise, and may be observed, throughout the atous, infraspinatous, and teres minor muscles—stabilizing the humeral skeleton. Sclerotic and lytic lesions throughout the axillary skeleton, for head within the shoulder joint. Through decades of use, the tendons of example, may attest to progressive lung, breast, or prostate cancer, these muscles become strained and weakened. Among modern popula- conditions which have prevailed throughout populations in history tions, by the seventh decade of the life, twenty-five percent of individ- (Aufderheide & Rodriguez-Martin, 1998; Waldron, 2009). While button uals suffer from rotator cuff disorder (RCD), and this percentage osteomas may only produce aesthetically-jeopardizing effects, neo- increases to fifty percent by the ninth decade (Tashjian, 2012). In plasms, such as metastasized ovarian cancer, seriously compromise an archaeological populations, RCD is a common pathological lesion individual’s daily movements and activities, consequences which may observed among even younger age cohorts, due to the continuous culminate in social and economic problems. stress placed on joints during daily labor (Waldron, 2009). RCD mani- fests as active porosity and marginal osteophytes on the humeral and 3.1.3.9 | Osteoporosis scapular muscle insertions of these muscles, contrasting with osteoar- Osteoporosis is a metabolic condition defined by the loss of bone den- thritis, whose inflammatory condition affects the joint capsule and sur- sity and characteristically is associated with menopause, extreme exer- face independent of RCD. As an irreversible condition, the presence of cise, and general deteriorative aging. Individuals suffering from this disease would only exacerbate with years and result in decreased osteoporosis, and its incipient condition osteopenia, suffer from skeletal movement or immobilization of the shoulder joint and arm. frailty, which heightens risks of non-traumatic and minimally traumatic fractures (Brickley & Ives, 2009). As increased vulnerability to fractures 3.1.3.13 | Fractures/trauma is associated with falls and decreased mobility, osteoporosis correlates Individuals who have experienced psychosocial trauma, including veter- with high frailty and mortality in modern populations (Siris et al., 2001). ans (Groer & Burns, 2009), mothers of pediatric cancer patients (Glover MARKLEIN ET AL. | 215 et al., 2006), and individuals with PTSD (Friedman & McEwen, 2004), well as our decision not to weigh biomarkers within the individual SFI are more likely to have higher stress than individuals who have not composite. For example, a high risk “1” score for trauma will be used to experienced a comparable form of trauma (Friedman & McEwen, 2004; describe one individual’s fractured hip that may have followed an acci- McEwen, 2000b). As it is not possible to observe the effects of psycho- dental fall, but also it is used to describe another individual’s multiple social stressors on the skeleton, we incorporate physical trauma (e.g., concussive fractures to the cranium. Both individuals must have fractures, projectile injuries, puncture wounds, and surgical operations diverted resources from general body functions toward survival recov- such as trepanations) into the skeletal frailty model. This is based upon ery mechanisms. The relative degree of physiological and psychological the hypothesis that physical trauma instigates as much, or more, physi- stress sustained by one fracture is very different from the stress experi- ological stress response as do psychosocial stressors (Schulkin, 2004; enced by another individual enduring multiple, repetitive strikes to the Sterling, 2004). head. The latter may have been living in an environment where inter- personal violence was endemic, and, unfortunately, this stress can nei- 3.1.4 | SFI applicability and limitations ther be evaluated in the skeleton nor discriminated from accidental The SFI allows us to compare individual frailty scores, thereby provid- traumas in the SFI. As with criticisms of AL biomarkers, that no current ing an estimated distribution of prior morbid events by sex and age scales exist for accurately assigning weights, bioarchaeologists may cohorts. By contrast, gross prevalence values exclusively compare per- debate our equal weighings of frailty biomarkers (Goldman, Turra, Glei, ff centages of a ected individuals relative to a total sample. Individuals Lin, & Weinstein, 2006). Among the nutrition- and infection-related within a sample are not treated discretely using a gross prevalence biomarkers, for instance, non-specific active PNB and malignant neo- analysis, as there is no comprehensive evaluation of health for each plasms receive equal weight in the total SFI score. Active periosteal individual skeleton. Rather, conditions are tallied for overall sample bone often is associated with systemic or localized infection, but even prevalences. Such approaches may miss subtle and demonstrative vari- simple insults to the body (e.g., bump on the leg or strain to the ankle) ation in frailty across a population. may incite a periosteal reaction. Relative to the impacts malignant As with previous methodological, statistical, and theoretical tumors have on the soma, these periosteal flare-ups arguably have a approaches to studying health and stress in human skeletal remains, smaller effect on overall somatic health. For this reason, it may be pro- the SFI poses inherent shortcomings. An immediate dilemma recog- posed that malignant tumors or bacterial infections like tuberculosis nized by any bioarchaeologist is the inescapable issue of skeletal pres- make a greater contribution to the SFI. However, the individual died ervation and context. Most bioarchaeologists are presented with highly while these lesions were active, so it is wrong to completely rule out decomposed or decontextualized skeletal remains either in the field or the effects of PNB on an individual’s death. At present, we offer an among previously excavated material. Commingled remains, for exam- equally-weighted comparative SFI as an option and springboard for ple, would be precluded from SFI analysis. Even with well-preserved others to conceptually approach and develop a multifactorial evaluation populations, a large portion of the population may be excluded based of skeletal health and frailty. on the absence of skeletal elements. In these cases, it may be advisable for bioarchaeologists to establish site-specific SFI based on observed 4 | CASE STUDY: FRAILTY IN MONASTIC preservation. Consequently, with this new method, interpopulation AND NONMONASTIC MEDIEVAL (1000– comparisons would only be possible when SFI include identical frailty 1500 CE) LONDON POPULATIONS biomarkers. Selecting and quantifying skeletal frailty biomarkers raises addi- In a recent article by DeWitte et al. (2013), monastic and nonmonastic tional issues requiring further bioarchaeological discussion. While we populations in Medieval London were evaluated through hazard mod- have presented all biomarkers as proxies for physiological decline or els to determine risk of mortality associated with these differing social dysregulation, some biomarkers, for example LEH, have been argued to environments. Results demonstrated a significant correlation between be evidence of relatively lower frailty. Among the Medieval Danish monastic lifestyles and decreased risk of mortality, a correlation populations of belholt and Næstved, higher prevalences of LEH were researchers (2013) attributed to the living conditions and resources observed among the older subadult (6.6–20.0 years; 0.45) age than available to the religious community. Applying the frailty index to these younger age (0–6.5 years; 0.024) cohorts, suggesting that individuals populations provides complementary information about the relative with LEH lesions maintained sufficient immunomodulatory capabilities morbidity of these discrete communities. to overcome the related period of chronic childhood stress; thus, higher LEH prevalence within this specific population was theorized to 4.1 | Materials be predictive of decreased frailty (Bennike, Lewis, Schutkowki, & Val- entin, 2005; Wood et al., 1992). These data fit the frailty model, fol- Skeletal data were obtained from the open-access MoL Wellcome lowed here, whereby LEH (and other skeletal lesions) may suggest Osteological Research Database, WORD (http://archive.museumoflon- greater resilience, and this is confirmed by our SFI findings among don.org.uk/Centre-for-Human-Bioarchaeology/Database/). Therefore, monastic and nonmonastic skeletal series. the authors did not diagnose and assign growth, nutritional/infectious, Of concern to bioarchaeologists may be how we quantify patho- activity-related, and traumatic pathological lesions for this study, but logical lesions into a relatively high risk (1) and other (0) categories, as assembled data observed and published by researchers and specialists 216 | MARKLEIN ET AL. at the MoL’sCHB.ThislaboratoryusestheHuman Osteology Method Statement (Powers, 2012) recording criteria, which utilize established skeletal and dental standards (Bass, 1987; Brothwell, 1981; Buikstra & Ubelaker, 1994; Hillson, 1996) to estimate age and sex and evaluate pathological conditions. For the current study, all published adult age categories, sex estimations, and pathological traits from the CHB’s WORD database were maintained throughout the analysis and discus- sion of variables. In accordance with DeWitte et al.’ (2013) study, the same monas- tic and nonmonastic populations were included in this study. However, only well-preserved skeletons—skeletons where all 13 SFI biomarkers could be observed—were selected for this case study, substantially reducing the sample sizes analyzed by DeWitte et al. From the 528 FIGURE 1 Distribution and mean values of frailty scores between individuals in monastic contexts, 70 skeletons met the SFI preservation monastic and nonmonastic Medieval (12th–16th centuries CE) Lon- criteria: St. Merton’sPriory(N 5 40) and Bermondsey Abbey (N 5 34). don populations (St. Merton’s Priory, Bermondsey Abbey, Guildhall Sixty-four of the 368 individuals in nonmonastic collections were Yard, Spital Square, St. Mary Graces, and St. Benet Sherehog) selected for examination: Guildhall Yard (N 5 12), Spital Square 5 5 5 (N 13), St. Mary Graces (N 33), and St. Benet Sherehog (N 2). active PNB was scored “1” while healing and no PBN were assigned “0” values. By contrast, periodontal disease and other nutritional 4.2 | Methods (porotic hyperostosis/cribra orbitalia, and rickets/osteomalacia) and neoplastic (tumors) conditions were recorded as present (1) or absent For every individual, the thirteen skeletal and dental frailty biomarkers (0). As the CHB records osteoporosis as present or absent, based upon were scored according to SFI criteria. All variables were combined into standards from Aufderheide and Rodríguez-Martín (1998), this condi- a total SFI score (0–13). Subsequent analysis of variance (ANOVA) and tion was also scored for presence (1) or absence (0). analysis of covariance (ANCOVA) tests were conducted on frailty scores with monastic/lay lifestyle, sex, and age as independent varia- 4.2.1.3 | Activity bles and covariates in SPSS 20. While the etiologies of various DJD maintain significant genetic com- ponents, strenuous and repetitive labor further stresses synovial joints 4.2.1 | Scoring variables in the SFI (Anderson & Loeser, 2010; Flores & Hochberg, 2003; Loughlin, 2001). | 4.2.1.1 Growth Stress on joints may exacerbate to debilitating conditions such as Skeletal growth perturbations capture infant and juvenile periods of osteoarthritis, IVD, and RCD. Continuous movement of these affected stress (Bogin, Sullivan, Hauspie, & MacVean, 1989). Within the skele- joints is often painful and leads to partial or complete immobilization of ton, these growth disruptions present as short stature, skeletal robus- the joint (Waldron, 2009). Although these conditions vary in their skel- ticity, and LEH, three variables inversely correlated with mortality risk etal manifestations, it is difficult to assess, for example, the severity of (Goodman & Armelagos, 1985; Goodman et al., 1991; Pelletier, 1994; each respective joint disease. Therefore, frailty scores were assigned in ff Ru et al., 1997). As short stature and diminutive body size often terms of presence (1) or absence (0). While osteoarthritis may occur at fl re ect compromised growth in childhood years, maximum femoral joints affected by IVD and RCD, identification of osteoarthritis will not length (proxy for stature) and femoral head diameter (proxy for body preclude these articulations (i.e., shoulders and vertebrae) from evalua- mass/skeletal robusticity) in the lowest data quartile were assigned “1” tion of IVD or RCD, as the etiologies of all three conditions are distinct frailty scores, and all others “0.” As adulthood antemortem tooth loss (Adams & Roughley, 2006; Frieman, Albert, & Fenlin, 1994; Waldron, and caries may limit the observed number of teeth within an individual, 2009). For example, the etiologies of RCD and shoulder OA are dis- LEH were scored for frailty as present (1)—regardless of number per tinct, although cases of shoulders with RCD and OA have been individual—or absent (0). reported in medical literature (e.g., Edwards et al., 2002), albeit uncom- monly. For this reason, we have excluded neither the shoulder nor ver- 4.2.1.2 | Nutrition and infection tebrae from the OA count. Nutritional deprivation inherently affects an individual’s susceptibility to disease, weakening immunological response to non-specificinfection 4.2.1.4 | Trauma and disease (McDade, 2003; Scrimshaw et al., 1968). In the skeleton, Episodes of intentional or accidental trauma present as unhealed and the body’s response to infection or disease stressors may include such healing fractures in skeletal series. Type and location of fracture may bony changes as non-specific periosteal lesions (i.e., PNB), osteomyeli- attest to instances of interpersonal violence, which suggest possible tis, and periodontal disease (DeWitte & Bekvalac, 2011; Waldron, psychosocial stressors within an individual’s daily environment (Knusel€ 2009). Recent studies have suggested active (woven) rather than heal- & Smith, 2014; Walker, 2001). However, due to the dearth of cases of ing (lamellar) PNB is associated with higher risk of mortality, so only interpersonal violence within the Medieval London population under MARKLEIN ET AL. | 217

FIGURE 2 Distribution and mean values of frailty scores between FIGURE 4 Distribution and mean values of frailty scores between – sexes from Medieval (12th 16th centuries CE) London populations adult age cohorts from Medieval (12th–16th centuries CE) London ’ (St. Merton s Priory, Bermondsey Abbey, Guildhall Yard, Spital populations (St. Merton’s Priory, Bermondsey Abbey, Guildhall Square, St. Mary Graces, and St. Benet Sherehog) Yard, Spital Square, St. Mary Graces, and St. Benet Sherehog)

— — study, any fracture regardless of location, type, or extent of healing egory 4 (over 46 years), 2.62 to 3.71, the youngest adult cohort (18– likely was associated with increased frailty and therefore scored as “1,” 25 years) exhibits a relatively higher SFI mean (3.00) than the subse- with absence of traumatic lesions scored as “0.” quent age cohort. Regardless, this high SFI average within the youngest

5 | RESULTS TABLE 2 ANOVA results for skeletal frailty indices by independent variables monastic/nonmonastic environments, sex, and age cohort Figures 1–4 and Table 2 show frailty score distributions between for Medieval (12th–16th centuries CE) London populations (St. ’ monastic-nonmonastic populations, sexes, and adult age cohorts. The Merton s Priory, Bermondsey Abbey, Guildhall Yard, Spital Square, St. Mary Graces, and St. Benet Sherehog) average SFI for the total sample (N 5 134) is 3.14 (standard deviation 1.47), with individual SFI ranging from 0 to 7. ANOVA indicates a sig- Independent Mean frailty index R2 p nificant difference between the frailty indices of monastic (SFI 5 3.42) variable (Std. deviation) -Value a and nonmonastic (SFI 5 2.80) assemblages, a difference for which life- Lifestyle 0.44 .015 5 style explains nearly half of the variation (R2 5 0.44, p 5 .015). While Monastic (N 74) 3.42 (1.41) Nonmonastic (N 5 60) 2.80 (1.47) no disparities were observed between sexes (SFIfemale 5 2.74, Sex 0.007 .33 SFImale 5 2.99), frailty indices between age cohorts were significantly different (p 5 .041; Table 2). However, the relationship between age Male (N 5 111) 3.20 (1.46) and frailty does not reflect a direct, positive correlation. Despite SFI Female (N 5 23) 2.87 (1.52) b increasing incrementally from age category 2 (26–35 years) to age cat- Age (years) 3.14 (1.47) 0.61 .041 18–25 (N 5 22) 3.00 (1.31) 26–35 (N 5 29) 2.62 (1.55) 36–45 (N 5 55) 3.18 (1.40) >46 (N 5 28) 3.71 (1.47) Females (N 5 23) 2.87 (1.52) 20.132 .853 18–25 (N 5 4) 2.75 (1.71) 26–35 (N 5 4) 2.00 (1.41) 36–45 (N 5 9) 3.00 (1.52) >46 (N 5 6) 3.33 (1.29) Males (N 5 111) 3.20 (1.46) 0.042 .70 18–25 (N 5 18) 3.06 (1.30) 26–35 (N 5 25) 2.72 (1.57) 36–45 (N 5 46) 3.22 (1.41) FIGURE 3 Distribution of age cohorts according to sex and ceme- >46 (N 5 22) 3.82 (1.44) tery from Medieval (12th–16th centuries CE) London populations (St. Merton’s Priory, Bermondsey Abbey, Guildhall Yard, Spital aResults significant at p 5 .05 level. Square, St. Mary Graces, and St. Benet Sherehog) bResults significant at p 5 .10 level. 218 | MARKLEIN ET AL.

TABLE 3 T-test results (p-values) comparing all age cohorts (a) and evaluation of mortality risks among monastic and nonmonastic popula- age cohorts according to sex (b) (females represented by white tions, these frailty indices demonstrate the paradoxical relationship boxes and males represented by black boxes) between morbidity and mortality documented elsewhere for men and (a) women in modern settings (Carey et al., 1995; Crimmins, 2001; Kul- 18–25 26–35 36–45 Over 45 minski et al., 2008; Oksuzyan et al., 2008). Although individuals within years years years years a monastic context experienced increased longevity relative to their 18–25 years 0.359 0.602 0.082b nonmonastic counterparts, during their longer lives they also incurred 26–35 years 0.096b 0.009a higher morbidity, that is, cumulative stress to their somas, which 36–45 years 0.113 resulted in higher frailty scores. However, while age distributions Over 45 years between populations are significantly different (v2 5 12.92, p < .01),

(b) ANCOVA results indicate that age is not the primary driving force of ff 18–25 26–35 36–45 Over 45 these SFI di erences, although monastic populations are represented years years years years by higher ages-at-death. When comparing the social and ecological 18–25 years 0.524 0.801 0.617 contexts in which monastic persons lived with those conditions of the 26–35 years 0.458 0.264 0.242 urban (or urbanizing) lay communities in London, this observed 36–45 years 0.673 0.177 0.690 morbidity-mortality paradox between the two samples is better con- Over 45 years 0.086b 0.016a 0.107 textualized. Throughout the Medieval period, and prior to the dissolu- aResults significant at p 5 .05 level. tion of the monasteries, monastic communities were sustainable bResults significant at p 5 .10 level. economic, as well as religious, institutions, with the financial and landed fi ff p-Values indicate signi cant di erences between oldest (over 45 years) means to support labored workers in addition to resident monks (Dick- and two youngest (18–25 years and 26–35 years) age groups for both populations and male subsamples. inson, 1961; Threlfall-Holmes, 2005). Concomitant with the concern placed on cleanliness of the soul, care was taken to ensure the salubri- age cohort is not significant when compared with any later age cohort ousness and health of the monastic residents. Fresh water supplies (Table 3). Despite the statistically insignificant decrease in frailty were critical considerations for the establishment and maintenance of between first and second age cohorts, regression equations show that the monasteries. If the community could not be situated near streams age accounts for 61% of observed SFI variance. Although age distribu- or springs, for example, pipe systems often were built to deliver fresh tions were relatively similar in monastic and nonmonastic populations, water to the community (Dickinson, 1961). Physical health for monks — an ANCOVA test nevertheless was performed with lifestyle as the was promoted through mandatory manual labor for example, carpen- — fi independent variable and age as the covariate. This analysis was neces- try, farming, or domestic duties as stipulated by the speci c religious sary for assessing the confounding variable of age, which almost invari- order (Lawrence, 1984). These laborious daily activities within ably impacts the emergence and progression of frailty (Fried et al., hygienically-engineered living and working quarters demonstrate the 2001). Results from this covariate analysis still yielded a significant cor- morbidity-mortality paradox (Crimmins, 2001) in practice: while monas- relation between SFI and lifestyle (p 5 .058), indicating that the lifestyle tic residents avoided earlier ages-at-death, resulting from fewer infec- model explained 4.9% of between-population frailty differences, and tions engendered by unsanitary conditions and poor water supply, these individuals accumulated greater chronic degenerative diseases, differential frailty between populations was not entirely the result of like osteoarthritis, with additional years of life and labor. The SFI distri- population age distributions. butions from our two samples suggest that monastic members better survived early episodes of stress and morbidity than did their nonmo- 6 | DISCUSSION nastic counterparts, but at the price of higher, later cumulative frailty. Among U.S. samples, this morbidity-mortality paradox has been 6.1 | Frailty differences between monastic and observed among women, who live longer than men but exhibit, on nonmonastic populations average, higher AL than do similar aged men (Crimmins et al., 1997; Comparison of frailty indices for Medieval London populations demon- Crimmins & Saito, 2001). This differential in male-female mortality pat- strates significant differences between monastic and nonmonastic pop- terns has been explained primarily by behavioral, social, genetic, and ulations (Figure 1). Despite increased access to higher quality foods environmental differences between the sexes (Verbrugge & Wingard, and living amenities (e.g., sanitation and medical care) (Hatcher et al., 1987; Waldron 1983a, 1983b, 1985). However, unlike patterns 2006), these conditions did not buffer the monastic community from observed in Western populations, this Medieval London sample indi- cumulative stress, although increased resources seem to have extended cates higher frailty and longer average lifespan for males than females the years of life for individuals residing in a monastic setting relative to within monastic and nonmonastic populations (Figure 2). This disparity a nonmonastic setting (DeWitte et al., 2013). In fact, the individuals with mortality and morbidity trends observed in living populations sug- associated with monastic environments apparently exhibited higher gests that the sociopolitical, economic, and cultural environment of morbidity with lower mortality. When interpreted with DeWitte et al.’ Medieval London enabled males to better buffer life’s stressors and MARKLEIN ET AL. | 219

TABLE 4 Average femoral lengths (mm) and gross prevalence values of skeletal and dental lesions among monastic and nonmonastic males and females in Medieval (12th–16th centuries CE) London populations (St. Merton’s Priory, Bermondsey Abbey, Guildhall Yard, Spital Square, St. Mary Graces, and St. Benet Sherehog)

Monastic Nonmonastic Male (N 5 68) Female (N 5 6) Male (N 5 43) Female (N 5 17) Femoral length (mm) 464 433 457 430 Femoral head diameter (mm) 48.5 43.5 47.5 43.2 LEH 0.76 0.83 0.58 0.87 Osteoarthritis 0.34 0.50 0.11 0.20 IVD 0.57 0.67 0.27 0.27 RCD 0.01 0.00 0.00 0.00 Osteoporosis 0.00 0.00 0.02 0.00 Periostitis 0.38 0.33 0.22 0.27 Periodontal disease 0.82 1.00 0.76 0.53 Cribra orbitalia/porotic hyperostosis 0.09 0.33 0.13 0.07 Rickets/osteomalacia 0.00 0.00 0.00 0.00 Neoplasm 0.03 0.00 0.00 0.00 Trauma 0.28 0.17 0.20 0.13

proceed to a frail phenotype than could contemporaneous females monastic, and subsequently sex-divided samples, show the youngest who, like nonmonastic individuals, may have died earlier with less frailty. (18–25 years) cohort to have a consistently higher SFI average than Such patterns have been reported among modern communities in South the succeeding (26–35 years) cohort. Thereafter, SFI increases incre- Asia, where men receive preferential food and healthcare resources over mentally from the second to fourth age cohort. This information con- women, enabling men to buffer any genetic or biological predispositions firms two precedents for frailty in bioarchaeology. First, this seemingly to infectious disease or mortality risks (Waldron, 1983a). This difference anomalous value for the youngest adult age group demonstrates how in SFI between males and females is not significant, nor is the difference frailty is not a function of senescence, although frailty and age often between average age at death among sexes. However, it should be are highly correlated (Fried et al., 2001). Frailty is a process synergisti- noted that these samples, especially the monastic cemeteries cally interacting with disability, comorbidity, and life’s stressors (e.g.,

(Nfemales 5 6), are disproportionately represented by male skeletons. Fur- infection, injuries, nutrition) (Fried et al., 2004, 2009); nonetheless, it ther comprehensive comparisons between sexes with the SFI in Medie- cannot be simplified to or conflated with aging. Additionally, frailty and val London will require a more equal representation of males and mortality are discrete concepts in health, which vary in relation to one females. To ensure that the disproportion of male skeletons was not con- another based upon biological, genetic, ecological, and sociocultural founding the monastic-nonmonastic covariate, we examined SFI accord- factors. As the data from Medieval London reveal, heightened frailty ing to sex and lifestyle. When male and female SFI distributions for may be associated with mortality among young as well as older age monastic and nonmonastic samples are observed separately, monastic classes. The initial decline in SFI from the first to second age cohorts affiliations still indicate higher morbidity and lower mortality. For lay and subsequent SFI increase to the fourth age cohort speak to the females, who died at earlier years than their monastic counterparts, the broader theoretical conundrum of the osteological paradox. One of the average SFI was 2.53, relative to 3.83 for monastic females; monastic tenets of the osteological paradox is the assumption that skeletal sam- males also showed a higher age-related distribution (SFI 5 3.38), relative ples represent the frailest individuals from the initial population. Work- to nonmonastic males (SFI 5 2.91) (Figure 3). These values suggest both ing from this base, the decrease in SFI from youngest to early-middle males and females living under nonmonastic conditions were less likely (26–35 years) adulthood reflects a logical progression of frailty with to survive morbid events and suffered higher mortality. In this regard, age: presumably, the frailest individuals within a population will be the the Medieval monastic environment was conducive to longer lifespans first to die, leading to lower frailty values in the next older cohort. among both men and women, as they better survived both acute and However, these data indicate that although the frailest may die the ear- chronic collateral damage to their skeletons, thereby increasing their SFI. liest, individuals living into older ages continue to amass frailty, exhibit- ing the highest SFI, not despite their longevity but because they have survived more stressors. These results run counter to Wood et al.’ 6.2 | Frailty patterns between ages in medieval (1992) interpretation of frailty, which would predict SFI to decrease populations with age. Instead, among monastic and nonmonastic samples, the high- Another enlightening result from this study is the observed relationship est mean SFI is exhibited by individuals over 45 years of age. As this between age and frailty (Figure 4). SFI averages for monastic and non- result may also be a consequence of the Medieval London biocultural 220 | MARKLEIN ET AL. environment, it is logical to test this age-frailty relationship within addi- biologists: namely, a process that promotes multiple individual pheno- tional skeletal series. types in response to variable external and internal stimuli. Adopting a SFI that is based upon results from living humans may aid in providing 6.3 | Evaluating the application of SFI today’s bioarchaeologists with an unambiguous definition of skeletal stress (frailty). The SFI may contribute to measuring stress and develop- Our results demonstrate an advantage to applying a SFI to archaeologi- ing predictive models mapping relationships between stress and health cal samples. The approach used by the SFI model seeks to quantify applicable across populations, geographic zones, and time periods. individual, cumulative stress that can be compared between individuals Analysis of skeletal remains is the most promising means of esti- and populations. While gross prevalence values provide an immediate mating and conceptualizing stress among past populations. For bioarch- and informative overview of the population, these values cannot aeologists interested in the physiological health of past populations, account for the individual. Bioarchaeologists must determine from a adopting a cumulative biological measure for stress, such as frailty, con- suite of gross prevalences and average skeletal metrics whether one tributes to improved understanding of relationships among stress, mor- subsample/sample is “more healthy” than another subsample/sample. bidity, and mortality within and between archaeological populations. This task may be difficult, as different variables may yield contradictory To this end, our proposed frailty index draws from previous palaeopa- information. To highlight this point, mean femoral length, mean femoral thological research linking skeletal markers to their etiologies in living head diameter, and gross prevalence values of conditions incorporated populations. While certain methodological problems must be into the SFI were tabulated for monastic and nonmonastic samples addressed, we propose the SFI as modeled here as a measurement (Table 4). Comparison of femoral lengths exclusively between samples technique with potential for addressing stress among archaeological suggests the nonmonastic community experienced higher frailty. How- populations. ever, when other variables (LEH, osteoarthritis, IVD, periostitis, peri- Implementation and application of the SFI to two Medieval Lon- odontal disease, and trauma) are considered, the monastic community don samples demonstrates the usefulness of the SFI, yielding results projects a more skeletally frail subpopulation. According to which skel- about population morbidity that closely reflect observations and pat- etal frailty variable is selected for comparison, relative frailty between terns of morbidity and mortality among living human populations. monastic and nonmonastic samples may readily fluctuate using only Unlike gross prevalence statistics, the frailty index condenses multiple gross prevalences of traits. Nevertheless, it is constructive to compare biomarkers of stress into a singular score for each individual. Theoreti- SFI with gross prevalences to establish which variables are contributing cally, this score quantifies individualized cumulative lifetime stress and the greatest to frailty differences. For example, osteoarthritis and IVD enables us to compare individual stressor life histories within and characterize DJD of synovial articulations and the vertebral column, between populations. The SFI, therefore, addresses and alleviates prob- respectively. These pathological lesions are observed within the lems when comparing gross prevalences, which always assess neither monastic communities twice as frequently as within the nonmonastic individuals nor the synergistic and antagonistic interactions of skeletal sample. Since DJD was not found to correlate with age in this study, stress biomarkers and health within individuals. daily activity and mobility may explain the variation in DJD between Based upon previous health models and indices established in bio- individuals. Differences in daily workload and physical movement archaeological research, we have developed a stress index that between monastic and nonmonastic populations in Medieval London uniquely accumulates biomarkers of life-time stressors into a single may explain prevalences within observed samples. To this end, we quantifiable, measurable unit for each individual. By assigning points to advocate the comparison of gross prevalence data with SFI for the individuals using the presence of a condition or highest-risk percentiles, most thorough assessment of health in skeletal samples. this proposed SFI score enhances interpretations of relationships Another variable that must be accounted for when applying the between stress and health in skeletal populations. Operationally, this SFI is age. As studies of modern human populations indicate, frailty model’s utility benefits from ease of applicability and its ability to often significantly correlates with age (Bergman, Wolfson, & Sourial, assess changes in patterns of skeletal health over time within a popula- 2006; Fried et al., 2001; Mitnitski, Graham, Mogilner, & Rockwood, tion and within specific geographic areas and zones over time. 2002). If a bioarchaeologist chooses to apply the SFI, a cumulative index for frailty, to test interpopulation differences in cultural, sociopolitical, or socioeconomic conditions, age must be considered as a confounding ENDNOTES variable through covariate analyses. Otherwise, especially in popula- 1 Throughout this article, the word “health” will be used in terms of its bio- tions, such as the present study, where significantly different distribu- logical and physiological components, unless specified otherwise. 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