Jeszka et al.
Journal of Physiological Anthropology(2024) 43:12
https://doi.org/10.1186/s40101-024-00359-2

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

Allostatic load and frailty do not covary
significantly among older residents of Greater
PolandJan Jeszka1, Darian Hummel2, Malgorzata Woźniewicz1, Tomoko Morinaka3, Yoshiaki Sone3,4 andDouglas E. Crews5*   

Abstract
Background Physiological dysregulation/allostatic load and the geriatric syndrome frailty increase with age. Asa neurophysiological response system, allostasis supports survival by limiting stressor-related damage. Frailty reflects
decreased strength, endurance, and physical abilities secondary to losses of muscle and bone with age. One suggestion, based on large cohort studies of person’s ages 70 + years, is that frailty contributes to allostatic load at older ages.
However, small community-based research has not confirmed this specific association.
Methods To further explore possible associations between allostatic load and frailty, we enrolled 211 residentsof Greater Poland aged 55–91 years living in a small village (Nekla, N = 104) and an urban center and capital of Greater
Poland (Poznan, N = 107). For each, we recorded age, self-reported sex, and residence and estimated a 10-biomarker
allostatic load score (ALS) and an 8-biomarker frailty index. We anticipated the following: higher ALS and frailty
among men and rural residents; for frailty but not ALS to be higher at older ages; significant associations of ALS
with sex and place of residence, but not with age or frailty. The significance of observed associations was evaluatedby t-tests and multivariate regression.
Results ALS did not vary significantly between men and women nor between Nekla and Poznan residents overall. However, women showed significantly higher frailty than men. Nekla men showed significantly higher ALS
but not frailty, while Nekla women showed nonsignificantly higher ALS and lower frailty than Poznan. In multivariate
analyses, neither age, nor sex, nor residence was associated with ALS. Conversely, age, sex, and residence, but not ALS,
are associated significantly with frailty. In Nekla, both age and sex, but in Poznan only age, are associated with ALS.
Among women, both age and residence, but among men, neither associated with ALS. In no case did ALS associatesignificantly with frailty.
Conclusion In this sample, lifestyle factors associated with residence, age, and sex influence stress-related physiology, less so in women, while ALS and frailty do not covary, suggesting their underlying promoters are distinct. Similarcomplex associations of physiological dysregulation with frailty, age, sex, and residence likely exist within many local
settings. Knowledge of this variation likely will aid in supporting health and healthcare services among seniors.
Keywords Aging, Allostatic load, Frailty, Poland, Rural, Seniors, Urban*Correspondence:
Douglas E. Crews
Crews.8@osu.edu
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Jeszka et al. Journal of Physiological Anthropology(2024) 43:12

Background
Organisms experience multiple constant and intermittent stressors: e.g., temperature extremes, hunger/ thirst,
infections, injury, parasites, predators, physical attacks,
social constraints; over their lifespans. All have evolved
responses to limit stressor-related somatic damage andsupport their continued survival and reproduction.
Among mammals, stressors initiate allostasis, an evolutionary ancient neurophysiological and somatic defensive and protective system that enhances survival andreproductive success [1–3]. Allostatic response adjusts
one’s neurological, physiological, and somatic milieu
to meet current environmental and internal demands,
thereby maintaining a dynamic, yet relatively consistentinternal state [1, 4]. Whether predictable or not, stressors perturb neurological and somatic sensory systems
inducing cognitive uncertainty (stress) and activating
allostatic response [5]. By regulating our internal physiological milieu (e.g., blood pressure, oxygen saturation,
cortisol level, digestion) in response to stressors, allostasis mitigates related damage, thereby maintaining organismal integrity and function. When stressors are notfully resolved, they may continue to perturb and damage
somatic systems, leading to neurophysiological dysregulation and allostatic load.
Once mature, allostatic responses are rapid and hierarchical, altering neurophysiology in response to experienced stressors [2, 3, 6, 7]. Illness, undernutrition, orabuse during critical periods may limit growth or development, permanently imprinting individuals and altering adult physiological dysregulation [1, 2, 8, 9]. Jointly,internal and external environments, variable DNA, sociocultural settings, lifestyles, and experiences influence

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awareness, rapidity, length and strength of allostatic
responses, and subsequent stressor-related damages[2, 7, 10–14]. Allostatic response does not prevent allstressor-related damage, physiological dysregulation,
nor debilitation. Continual allostatic response to major
and minor stressors alters physiological integrity, contributing to dysregulation [2, 10–14]. Although allostasis is not directly measurable, biomarkers of stressor
response and systemic losses secondary to stressors andallostatic responsiveness are. By combining 10 biomarkers of neurophysiological and somatic variation available from the McArthur Studies of Successful Aging (seeTable 1), in 1997, Seeman and colleagues developed thefirst composite estimate of individual allostatic load [10],with cortisol, norepinephrine, dehydroepiandrosterone
sulfate (DHEAs), and epinephrine, viewed as primarybiomarkers of allostatic response, and six secondary
mediators reflecting detrimental stressor-related physiology (Table 1). This original index, along with other composites, has been shown to associate significantly withmorbid and mortal outcomes across populations [10–12,
15–18].
Describing frailty as a systemic geriatric syndrome,in 2001, Fried and colleagues combined five physicalassessments, muscle strength and endurance, walking
speed, physical activity, and recent unintended weight
loss, into the first physical frailty index [19, 20]. Biomarkers reflecting decreased physical abilities with age occurring largely secondary to sarcopenia (loss of muscle cells).
As individual phenotypic assessments, frailty indices provide tools for assessing clinical and geriatric treatment
and care, and research exploring elder health, well-being,
and housing needs. Today, a variety of frailty indices are

Table 1 Biomarkers of allostatic load, descriptive statistics, and quartile bounds among 211 residents of Greater Poland
Biomarker

Mean

sd

Range

4th ­quartilea

Urinary cortisol, nmol/g creatinine

84.94

59.11

11.4–321

112.0

Urinary norepinephrine, mg/g creatinine

35.54

15.18

9.6–104

44.2

Urinary epinephrine, mg/g creatinine

6.02

6.26

0.0–35.7

9.3

Waist- hip ratio

.889

.079

.66–.108

0.94

Percent HbA1c

6.22

0.75

.80–10.6

6.4

Dehydroepiandrosterone sulfate, mg/dl

125.41

81.75

15.8–499

1.3

Systolic blood pressure, mmHg

138.77

18.62

97–192151.0

Diastolic blood pressure, mmHg

78.41

12.88

7–123

86.0

HDL /total cholesterol ratio

29.33

23.22

11.1–347

21.1

LDL cholesterol, mg/dl LDLc

125.6539.63

8.2–22

153.0

Waist-hip ratio

.889

.079

.66–1.08.94

Glycated hemoglobin

6.22

.75

.80–10.6

6.4

Age

69.63

7.18

55–91

HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterola

Dehydroepiandrosterone sulfate and HDL /total cholesterol ratio lowest quartileJeszka et al. Journal of Physiological Anthropology(2024) 43:12

applied across disciplines beyond elder care, including
biological and physiological anthropology, bioarchaeology, epidemiology, public health, and human biology [19–25]. Their strength lies in aggregating multiple
aspects of age-related losses into a single metric assessing somatic declines across multiple systems [19–28]. Asa phenotypic state reflecting reduced physical strength,
resilience, capabilities, and responsiveness to stressors, frailty accumulates as age increases, and is hypothesized to contribute to increasing allostatic load with age[26–28]. Here, we estimate frailty by determining muscle
strength and resilience, walking speed over 50 feet, andresponses to five questions on physical activities and abilities among 211 Polish citizens. We then determine associations of this frailty index with place of residence, age,sex, and our estimated allostatic load.
Contemporary environmental, socioeconomic, and cultural settings differ significantly from those experienced
by our ancestors. Still, many stressors experienced today:
adverse fetal, infant, and childhood experiences, disruptive interpersonal relations and social settings, insecure
economies, stressful ecological settings, status differentials, hunger, thirst, fear, and anxiety, continue to influence today’s populations [29–33]. Long-term stressor
exposures may promote chronic allostatic activation,
somatic and physiological wear and tear, and systemic
damage to allostatic systems, and contribute to higher
allostatic load [2, 3, 6, 11, 12]. Over a lifespan, short- andlong-term activation of allostasis leads to dampened allostatic responsiveness, increased vulnerability to future
stressors, and greater physiological dysregulation, whichis assessable as greater allostatic load [2, 7]. Theoretically, by measuring one’s primary stress-mediating hormones, along with stress-related cardiovascular, immune,
somatic, and metabolic function and damage, allostaticload indices estimate systemic physiological dysregulation [34].
Determining how social, cultural, environmental, andbiological factors influence and interactively determine
phenotypic variation over the lifespan are shared goals
across physiological and biological anthropology, humanbiology, clinical medicine, and elder care. Allostatic load
and frailty during later life are outcomes of these processes; assessing them improves our understanding ofhealth and well-being not only in the present but also inthe past [30, 31]. Throughout human evolution and historically, individuals have needed to respond adaptively
when their somatic stability or survival were threatenedby physical and sociocultural stressors. Then and now,
multiple environmental, physical, and social factors
constrain individual health, behavior, and adaptability,
while modulating individual stress experiences, allostatic
responsiveness, and late-life frailty.

Page 3 of 12

Study location and background

Greater Poland is the fifth largest state in Poland
encompassing 29,826 ­km2 with a total population of
about 3.5 million, 55% of whom reside in urban settings, reflecting closely the national average of 60%[35, 36]. Nekla, one of 111 towns in Greater Poland,
includes an area of only 19.8 ­km2, therein 3712 persons
resided at time of survey; by contrast, Poznan included
541,561 people within 261.9 ­km2 [35]. During the latetwentieth and early twenty-first centuries, transnational
corporations, international investments, and privatization transformed rural Polish landscapes into urban
economic centers [36–38]. Nekla, located about 36 km
east of Poznan, is among the smallest towns in Greater
Poland. Residents experience a significantly poorer economic setting than observed in larger urban settings
like Poznan. However, Nekla also is located convenient to international rail routes (e.g., Berlin-Moscow)
and domestic highways (i.e., A-2 motorway) providing opportunities for economic development and easytransport [37–39]. Still, Nekla’s infrastructure remains
transitional, with about 30% of residents lacking modern sewer systems and gas supply lines [39]. At the timeof this fieldwork, attainment of college education differed significantly between Nekla (13.2%) and Poznan(31.7%), with a greater percent of women college-educated than men in both settings [40].
Poznan’s robust economy, along with its museums,
parks, and historic monuments, provide an economic,
tourism, and cultural hub for Greater Poland. Poznan’s
citizens receive a monthly income of 4549 PLN with aregistered unemployment rate of only 2.4%, while otherareas within Greater Poland average 406 PLN and a 11.9%
unemployment rate [40]. Differences in physical activity,
income, achieved education, infrastructure, and development between urban Poznan and rural Nekla provide
an ideal setting to explore influences of variable lifestyles
and lifetime activity patterns on allostatic load and frailty
among Poland’s citizenry.
Given this theoretical background and variable local
socioeconomic settings, we expected older residents(ages 55–91) of Poznan, capital of Greater Poland, would
differ in their estimated allostatic load and frailty fromthose residing in Nekla, a more rural setting within
Greater Poland. We also predicted our estimates of allostatic load and frailty would covary significantly with age
and self-reported sex in this older sample. Further, contrary to results from several cohort analyses [26–28] andbased on previous research among rural and urban Japanese showing no association between estimated allostaticload and frailty or age among those 55–89 years [41, 42],
we did not expect estimated allostatic load and frailty tocovary significantly in this sample.

Jeszka et al. Journal of Physiological Anthropology(2024) 43:12

Page 4 of 12

Our research included four main hypotheses. Due tomore frequent farm/agricultural work, strenuous andphysically demanding labor activities, along with differential lifeways in rural Nekla compared to Poznan, we
hypothesized allostatic load and frailty would be greater
among Nekla residents than Poznan. Similarly, as men
in Poland tend to engage in more physically demanding lifeways than women, we hypothesized both allostatic load and frailty would be higher among men than
women in Nekla, Poznan, and the full sample. Third, we
hypothesized frailty would associate significantly withage, while allostatic load would not in Nekla, Poznan, or
the full sample. Last, given previous research comparing
elders across local populations [41, 42], we hypothesized
allostatic load and frailty would not be associated significantly with one another.

Allostasis and allostatic loadMethods
To evaluate these hypotheses, we recruited 211 participants aged 55–91 years old from two locations in thestate of Greater Poland, Nekla a rural village and Poznan
an urban city and regional capital. All participants dwelt
in one of these communities. Within Nekla, participants
were recruited using flyers posted in the local health station, churches, and markets, and through personal contacts. In Poznan, we recruited participants through flyers
posted at the Poznan Center for Geriatrics and Gerontology and personal contacts. Collaborating with the
Nekla Cultural Center, Nekla Medical Center, the Poznan
Center for Geriatrics and Gerontology, and the University of Poznan Life Sciences Department, we obtained
fasting blood samples and overnight urine collections,assessed weight, grip strength, height, systolic and diastolic blood pressure, and health information from 104
residents of Nekla and 107 from Poznan who completed
all protocols including a self-report questionnaire. Age
was documented with state-issued IDs. Sex was selfreported. Examining these data, we determine if allostatic load and frailty associate significantly with sex, age,or residential location, and if frailty is associated significantly with allostatic load in bivariate and multivariate
analyses.

To explore variability in allostatic load and frailty, we
enrolled 104 residents of Nekla and 107 of Poznan ages
55–91 years, including 53 women and 51 men from
Nekla and 51 women and 36 men from Poznan. Each
person participated in anthropometric and physiological assessments, provided blood samples, completed
overnight urine collections, and responded to questions regarding their age, health, physical activities, andabilities. Participants were recruited through personal
contacts, flyers at the Nekla Health Clinic and at an
adult activity center in Poznan, along with advertisements on local bulletin boards. Those who responded
were informed of the details and significance of the
project upon first contact. Once agreeing to participate,each completed an Informed Consent Form approvedby the University of Poznan IRB (Number 425/13). Our
research protocols included health, social, and activity
questions, including the 36-item Short-Form Health
Survey (SF-36) [43–47], a 10 ml blood draw, assessmentof walking speed, muscle strength, and exhaustion,
and an overnight urine collection. The SF-36 is an evidence-based, self-administered health survey designed
to assess the general health of responders [43–45]. The
SF-36 includes questions regarding physical and social
functioning, general health, bodily pain, and activity,
providing estimates of physical abilities, activity levels,and mental health [45–47].

Frailty

Here, we estimate an 8-biomarker frailty index including measures of walking speed, strength, and endurance, along with self-reported physical activity based on
5 questions from the 36-item Short-Form Health Survey(SF-36) scored on a Likert scale from 1 “limited a lot” to 3
“not limited at all,” indicating no issues/problems [43, 44].
The SF-36 is an evidence-based, multipurpose, and selfadministered health survey designed to assess the general
health of responders [43, 44].

While frailty is directly measurable as physical declines,
allostatic load is estimated based on neuroendocrine, metabolic, cardiovascular, and immune systembiomarkers of stress-related damage and systemic
responses [10–12, 15–18]. Since first described, multiple variable composite estimates of allostatic load
have been published, most showing significant associations with childhood stressors, lifestyle factors,
lifelong stressor exposures, psychological health, andmorbid and mortal outcomes across populations andage groups [10–12, 15–18, 26–28, 34, 41, 42]. Here, weestimate an ALI based on the ten biomarkers originallyproposed by Seeman and colleagues [10] (Table 1).
Sampling

Measurement protocols

All research activities adhered to Helsinki Declaration
guidelines and were approved by the Research Ethics
Committee Poznan University of Life Sciences (Number
425/13). Phlebotomy, anthropometry, interviews, andall assessments followed approved standard protocols.
These were incorporated into standard physical exams

Jeszka et al. Journal of Physiological Anthropology(2024) 43:12

at the Nekla Health Clinic and at the Adult Community
Activity Center in Poznan.
We measured height using a metal anthropometer ora fixed wall stadiometer. Waist circumference was measured at the narrowest point of the torso above the iliac
crests and below the ribs. Hip circumference was measured at the point of greatest protuberance of the most
posterior aspect of the pubic symphysis and buttocks.
Circumferences were measured twice with a fiberglass
tape. Systolic and diastolic blood pressure, standard biomarkers of cardiovascular function, were determinedusing a Litman® stethoscope and Baumanometer® sphygmomanometer. The cuff was placed 1 inch above theantecubital fossa. Blood pressures were measured twicewith a minimum 3-min hiatus between and averaged foranalyses.
Participants arrived at study sites for in-person protocols following an overnight fast and urine collection.
Collection volumes were measured and samples werealiquoted and frozen or chilled onsite. After delivering
their overnight collections to research staff, each participated in a 10 ml blood draw. Following their blood draw,
participants were offered a breakfast of local foods andproducts. Blood samples were processed at the Laboratory Diagnostyka of Poznan University Life Sciences
Center for determinations of total serum cholesterol,low-density lipoprotein cholesterol (LDLc), high-density
lipoprotein cholesterol (HDLc), and glycated hemoglobin(HbA1c). The Laboratory Diagnostyka also assayedurine samples for DHEAs, cortisol, norepinephrine, andepinephrine. All assays followed International Laboratory Standards specific methods available upon request.
Results provided from urine and serum samples, alongwith anthropometric assessments were used to estimate
an allostatic load score (ALS).
Frailty index

Biomarkers to assess frailty commonly include strength,
endurance, physical activity, slow walking speed, poor
balance, and unexpected weight loss [19–28]. We used
three biomarkers of physical capabilities along with five
responses to activity levels from the SF-36 [43–47] toestimate frailty. Maximum hand grip strength, a reliable
proxy for muscle strength [48, 49], was assessed using a
dynamometer (JAMAR Model 5030 J1: Sammons, Preston, Rolyan, Bolingbrook, IL 60440). Those in the lowest quartile for grip strength by self-reported sex werescored one for frailty, otherwise 0. Muscle endurance wasestimated using a stopwatch to determine if the participant could retain any pressure on the dynamometer for
30 s. If not, they were scored one for lacking endurance,
and if yes, they were scored 0. Walking speed, a reliable
assessment of current function and mobility [19, 20], wasPage 5 of 12

measured as the number of seconds participants required
to walk 25 feet [7.62 m] on a flat surface. Those in thelowest quartile of their distribution (male, female) wereassigned a score of 1, otherwise 0, not frail.
Using the SF-36 Health Survey, participants self-evaluated their current health and physical capabilities andlimitations, health issues, bodily pain, general well-being,and somatic dysfunction as limited a lot, limited a little,
and not limited [43–47]. SF-36 questions were designed
to reflect personal impressions of current health, are easy
to administer, and are applied widely in clinical practice and research [45, 46]. In addition to walking speed,
strength, and endurance, we used responses to five elements of the SF-36 to assess frailty. These included thefollowing: Does your health limit you when: Lifting or
carrying groceries; Climbing several flights of stairs;
Bending and kneeling; Walking more than 1 mile; Bathing or dressing (SF-36 questions 5, 6, 8, 9, 12) (see [47]).
We estimated individual frailty by determining sample
distributions of quantitative biomarkers (walking speed,
grip strength, endurance) and scoring those in the slowest and lowest quartiles 1 (frail), and all others 0 (notfrail). Self-reported limitations for five physical activities
were assigned a 1 when reporting “limited a lot” (frail),otherwise 0. Frailty was estimated as the sum of 0/1
scores for walking speed, grip strength, endurance, andfive self-reported limitations, range 0–8.
Assessing allostatic load

Based on the MacArthur Studies of Successful Aging,
Seeman and colleagues [10] reported an aggregate index
of 10 physiological biomarkers consistently predicted
cognitive and physical health declines over a 7-year
follow-up. Since this 10-biomarker ALS was reported,
indices with fewer and many more clinical, behavioral,
and physiological measures, using different biomarker
aggregating systems, and statistical methods have beenreported [13–15, 18, 26–28, 34, 41, 42, 50]. The majority have followed the original aggregation methods withsample-specific quartile cutpoints for high risk [10].
Here, we also follow Seeman and colleagues’ [10] methodology, scoring the same 10 biomarkers as either high or
low risk, and assigning a score of one to the highest risk
quartile, all others 0. For these 10 biomarkers, excepting
DHEAs and HDLc, the highest risk occurs in the highest quartile of their distributions. For those eight biomarkers, when an individual’s assessment was at/above
the 3rd quartile cut point, it was scored 1, otherwise 0.
DHEAs and HDLc were scored 1 when falling in the lowest quartile of their respective distributions, otherwise 0.
Each participant’s allostatic load score was then determined by summing their biomarker scores, producing a
possible range of 0–10 for estimated ALS. Although theJeszka et al. Journal of Physiological Anthropology(2024) 43:12

original sample-specific quartile method for determining ALS provides accurate preclinical risk profiles within
local samples [10], the debate over the best methods fordetermining critical cut points for biomarker distributions continues [34, 50]. The quartile-based 0,1 index
method estimates underlying physiological dysfunction
and capturing subclinical variability while associating significantly with environmental settings, lifestyle attributes,
morbidity, and mortality across populations [10, 12–14,
41, 42, 50].
Statistical methods

First, we determined biomarker averages, standard deviations, ranges, and lower/upper fourth quartile values
for all quantitative measures, and estimated allostaticload and frailty for the full sample (Table 1). Based on
t-tests, women showed significantly higher norepinephrine (p = 0.006) and LDLc (p = 0.011) than men but lower
DHEAs (p < 0.0001) systolic blood pressure (p 0.038),
and waist-hip ratio (p < 0.0001). These differences were
adjusted for when estimating allostatic load scores withthe use of sex-specific quartile cutpoints for these biomarkers. Next, we compared allostatic load and frailty
between men (N = 87) and women (N = 124) using t-tests
to determine if observed differences were statisticallysignificant; we also compared men and women in Nekla(N = 51 and N = 53) and Poznan (N = 36 and N = 71) with
one another to determine if allostatic load and frailty varied significantly by residence. Last, we examined independent associations of age, sex (men = 0, women = 1),
and area of residence (Nekla = 0, Poznan = 1) with ALS
and frailty; then we examined associations of these three
independent variables and frailty with ALS using multivariate regression. All statistical analyses were completed
in SPSS 24.

Results
Biomarker variation

Sampled women (69.5 years; sd 6.62; range 55–87 years)
and men (69.8 years; sd 7.95; range 55–91 years) show
almost identical mean ages (Table 2). Systolic blood pressure averages 138.8 mmHg, the upper range for prehypertension (120–139 mmHg), while diastolic averages

Page 6 of 1278.4 mmHg, slightly below prehypertension (80–89
mmHg) [51] (Table 1). Both the 4th quartile cut point
for systolic blood pressure (151 mmHg) and for diastolicblood pressure (86 mmHg) fall within hypertension stage
1 (140–159 mmHg; 90–99 mmHg; see (Table 1) [51].
Average percent HbA1c (6.22%; Table 1) falls within theupper clinical range for prediabetes (5.7–6.4%), with afourth quartile lower bound (6.4%) at the upper boundary for prediabetic [51].
Assessed LDLc (125.7 mg/dl) falls just within the normal clinical range (< 130 mg/dl), yielding a 4th quartile
cut point of 153 mg/dl, within the elevated range (> 130
mg/dl) [52]. The total-cholesterol to high-density lipoprotein (HDLc) cholesterol ratio average (5.3:1) exceeds
the borderline high clinical ratio of 5:1, and the optimal
ratio of 3.5:1 [53]. Cortisol averages 83.9 nmol/g creatinine (Table 1), within normal clinical ranges (men 1–119
nmol/g creatinine; women 0.7–85 nmol/g creatinine; the4th quartile cut point for cortisol (112.0 nmol/g creatinine) falls just within the normal clinical range for men,but not women (Table 1) [52]. Average epinephrine (6.02
mg/g creatinine) and norepinephrine (35.54 mg/g creatinine) both fall within normal clinical ranges (2–20 mg/g
and 15–80 mg/g) [54]. The upper quartile lower bounds
for both (9.3 mg/g and 44.2 mg/g creatinine) also fallwithin normal clinical standards [54]. DHEAs average
125.4 mg/dl within the normal clinical range for men andwomen (130–550 mg/dl; 60–330 mg/dl; Table 2) [53].
Within this sample, waist-hip ratio averages 0.89, at clinical obesity standards for men (> 0.90), exceeding themslightly among women (> 0.85) [53].
Allostatic load score

The 10-biomarker ALS averages 2.50 (sd 1.62), ranging from 0 to 8 (Table 3). Within this sample, ALS isslightly higher among men 2.70 (sd 1.74; range 0–8) than
women 2.36 (sd 1.5; range 0–7; Table 3), although notsignificantly so (t-test p 0.146; Table 3). Among men from
Nekla, ALS averages 2.94 (sd 1.76), significantly higher
than observed among Poznan men 2.17 (sd 1.67; t-test
p 0.039; Table 3). Among Nekla women, allostatic load
averages 2.57 (sd 1.39), slightly above Poznan women

Table 2 Average age of total sample by self-reported sex and residential location
Men

Women

Mean

sd

Range

Average age

69.80

7.95

55–91

Nekla

69.12

8.49

Poznan

70.67

7.14

Method: t-test; sd standard deviation, p p-values

p

0.360

Mean

sd

Range

p

69.53

6.62

55–87

0.828

68.23

6.82

70.51

6.34

0.600

Jeszka et al. Journal of Physiological Anthropology(2024) 43:12

Page 7 of 12

Table 3 Estimated allostatic load and frailty among 211 residents of Greater Poland: full sample, by sex, and by residence
Full sample N = 211

Mean

sd

Range

Mean

sd

Range

p

Allostatic load

2.50

1.62

0–8

Frailty

2.42

1.70

0–7

By sexMen N = 87
2.70

1.75

0–8

2.36

1.50

0–7

0.146

2.05

1.46

0–6

2.68

1.82

0–7

0.006

Allostatic load
Frailty

Women N = 124

By residence
Men

Nekla N = 51

Poznan N = 36

2.94

1.76

2.17

1.67

0.039

Frailty

2.22

1.42

1.81

1.51

0.205

Women

Nekla N = 36
2.57

1.39

2.38

1.74

0.510

3.00

1.78

2.44

1.82

0.086

Allostatic load

Allostatic load
FrailtyPoznan N = 88

Method: t-test; sd standard deviation, p p-values

Table 4 Multivariate associations of age, sex, and residence withallostatic load among older residents of Greater Poland based
multivariate regression

Poznan although not significantly so (t-test p 0.086 and p
0.205, respectively; Table 3).

Full sample

Age, sex, area of residence, allostatic load, and frailty

Variable

β

p

Age

0.014

0.696

Residence − 0.132

0.063

Sex

0.696

By sexAge− 0.027
Men N = 87
− 0.037

Residence − 0.221
By residence
Age
Sex

Nekla N = 51

0.729

β

Women N = 124

0.057

0.045 − 0.068

0.024

0.538

− 0.1200.464

p

Poznan N = 36

0.538

0.464

0.025

0.988

0.059

0.545

Method: multivariate regression; β standardized regression coefficient, p p-value

2. 38 (sd 1.74), but not significantly so (t-test p 0.510;
Table 3).
Age, sex, area of residence, and allostatic load

With age, sex, and place of residence included in multivariate regression, none were associated significantlywith ALS (Table 4). Only place of residence even marginally associated with the ALS (p 0.063; Table 4). When
subdivided by sex, residence was associated significantlywith ALS (p 0.045) among men, but not among women(Table 4). When subdivided by residence, neither age nor
sex was associated significantly with ALS.
Frailty

Within the total sample, frailty averaged 2.42 (range 0–7;
sd 1.70; Table 3). Frailty was significantly higher among
women 2.68 (range 0–7; sd 1.82) than among men 2.05
(range 0–6; sd 1.46; t-test p 0.006; Table 3). Among both
women and men, frailty was higher in Nekla than in

In multivariate regression analysis, neither in the fullsample nor by sex nor within either community did allostatic load and frailty show any significant associations
among this sample of older Poles (Table 4). Conversely,age, sex, and residence were all associated independently
with frailty in the full sample (Table 5). Among women,
when controlling for allostatic load, both age (p 0.001)
and residence (p 0.023) were associated significantly withfrailty (Table 5), while among men, only age showed evena borderline association with frailty. In Nekla, both age (p0.025) and sex (p 0.009) were associated positively withfrailty (Table 5). Among residents of Poznan, only age
was associated significantly with frailty (p 0.006; Table 5).

Discussion
As dynamic phenotypes, allostatic load and frailty
reflect long-term consequences of lifelong exposuresand responses to stressors, making their direct assessment difficult. Compositing multiple biomarkers ofstressor-related physiological dysregulation into multisystem allostatic load indices and measures of phenotypic losses of muscle strength and physical abilities
into frailty indices mitigates some such challenges [2, 10,
13, 14, 19–21]. Today, allostatic load and frailty indices
are widely applied across clinical and congregate care,research, and nursing settings. Both indices tend to varyacross social and residential settings and by age and sex.
A continuing question is whether frailty covaries with or
may even in some degree underlie allostatic load [26–28].
Based on previous research in Japan, among two unrelated local populations [41, 42], we expected frailty toJeszka et al. Journal of Physiological Anthropology(2024) 43:12

Page 8 of 12

Table 5 Multivariate associations of age, sex, residence, and allostatic load with frailty among older residents of Greater Poland
Full sample

VariableβpAllostatic load

0.021

0.755

Age

0.237

< 0.0001

Sex

0.215

0.001

− 0.177

0.009

Residence
By sexAllostatic load

Men N = 87βWomen N = 124p− 0.028

0.801

0.047

0.591

Age

0.179

0.099

0.291

0.001

Residence− 0.1620.144− 0.201

0.023

By residence
Allostatic load

Nekla N = 51

Poznan N = 36

0.020

0.834

0.027

0.773

Age

0.216

0.025

0.263

0.006

Sex

0.254

0.009

0.174

0.067

Method: multivariate regression; β standardized regression coefficient

vary significantly with age, sex, and residential setting,and for allostatic load to vary by sex and residential setting, but neither by age nor frailty. Therefore, we tested
hypotheses regarding frailty and allostatic load among
211 older Polish men ages 55–91 and women ages 55–87.
We expected allostatic load and frailty would be higher
among men than women and would be greater amongresidents of Nekla than Poznan, Poland; that frailty but
not allostatic load would covary with age; and that allostatic load would not be associated significantly withfrailty in this sample.
No observed age differences between men and womenin this sample nor in Nekla or Poznan achieved statisticalsignificance (Table 2). Unexpectedly, men do not show
significantly higher estimated ALS (2.70) than women(2.36; Table 3). Although the average ages of sampled men
and women are almost identical, converse to our expectations, frailty is significantly greater among women (2.68)
than men (2.05; Table 3). Nekla women exhibit the highest average frailty score observed in this study (3.0), but
do not differ significantly from Poznan women (2.44). As
expected, Nekla men have a significantly higher allostaticload (2.94) than Poznan men (2.17; Table 3). Neither
women nor men differ significantly in frailty between settings, although Nekla women are over one point higher
than Poznan.
Our third hypothesis, allostatic load would associatesignificantly and independently with residential location and sex, but not age was supported in part. In thefull sample, neither age nor sex nor residence was associated significantly with allostatic load (Table 4). Amongmen, residence was associated significantly with allostatic load, but age did not. Among women, neither age
nor residence was associated significantly with allostaticload. Within Nekla and Poznan individually, neither age

nor sex was associated significantly with allostatic load
(Table 4). Regarding our final hypothesis, based on multivariate regression, in the full sample (N = 211), age, sex,
and residential location are each associated significantly
and independently with frailty. However, allostatic load
did not (Table 5), supporting our final hypothesis. Neither among men nor women nor within Nekla or Poznan
did allostatic load and frailty show any significant associations (Table 5). Among women, both age and residence
were significantly associated with frailty, neither wereamong men.
The lack of significant association between allostaticload and age among older samples residing in different
local settings with variable lifestyles may not be uncommon [41, 42]. No significant association of an ALS withage among older Japanese aged 55–90 (X = 84 years)
residing on either Hizen-Oshima Island [41]or in Nagasaki and the Goto Islands was observed [42]. Jointly,
these results suggest age-related increases in allostaticload observed among the old and very old (for our purpose here ages 70–80 and 80 + years) may not characterize samples including the young-old (ages 55–69 years)
such as our current study sample with an age distributionof 55–91 years. Significant associations between age andallostatic load and between frailty and allostatic load havebeen reported for national/international cohorts aged
70 + years [26–28]. Our sample averages 67 years and iscomposed of local community members who live relatively similar lives and are sufficiently mobile and healthy
to attend a screening center and participate in study protocols. Conversely, aggregated national/internationallarge cohort studies of interactions among allostatic load,
frailty, age, and sex among elders may attenuate systemic
variability in local environments and socioeconomic settings within regional settings as reported here. WithinJeszka et al. Journal of Physiological Anthropology(2024) 43:12

this sample, frailty varied significantly and consistently
with age, sex, and area of residence, but in no case covaried with allostatic load (Table 5). Associations of frailty
with age, sex, and residence are significant; suggesting
the lack of significant association between frailty andallostatic load in this sample of older Poles is accurate
also.
Significantly higher allostatic load among men in Nekla(Table 4) may reflect variation in attained education, daily
activities, and social and cultural settings, along with variable occupational activities and differential exposuresand responses to experienced stressors (see [55–58]).
Across Poland, women have completed more formal education including college degrees, and engage more often
in nonlabor-intensive occupations than do men [35, 36,
40, 59–64]. In both Nekla and Poznan, although significant numbers of both men and women have completed
college, men tend to engage more in farming, herding,and labor-intensive activities than women [61–64]. Differences in lifestyles likely contribute to differential
stress-related physiology among men and women in both
Nekla and Poznan.
Nekla and Poznan provide a regional dichotomy within
Poland’s current socioeconomic settings. Differencesin economic pursuits, diets, and access to healthcare
likely expose Nekla men to greater physical stressors
than men residing in Poznan and even women in Nekla.
Recent structural transitions affecting financial security
and sociocultural stability across Poland also may haveaffected men more negatively than women particularly inmore rural settings [63, 64]. Additionally, changing social
norms, occupations, and perceptions of social security
and support, along with declining economic stability andlow employment opportunities for men in villages andtowns like Nekla, may also amplify experienced stressors[59–64].
Previous research on Polish men (1988–1993) observed
higher allostatic load among those experiencing more
economic stress along with reduced access to work andhealthcare [61, 62]. Higher allostatic load among men
than women in both Nekla and Poznan likely reflects differential stressor exposures among men common acrossPoland; while higher allostatic load among Nekla men
compared to Poznan men likely reflects their greater
lifetime exposures to stressors experienced in rural compared to urban regions of Poland. In this older sample,
age was not a significant predictor of allostatic load. In
the USA, samples of older residents often do not show
significant associations of age with allostatic load [65,66]. A lack of significant association between age andallostatic has also been reported for Japanese elders [41,42]. Those living their lives in local residential settingsand surviving to older ages may have experienced andPage 9 of 12

survived the same general stressors over their lifespans,
contributing to similarities in allostatic load at older ages.
In this sample, as in several others [41, 42], allostatic load
and frailty are not associated significantly. However, age,sex, and area of residence all associate positively andsignificantly with frailty. Frailty significantly increased
with age and was higher among women from both Nekla
and Poznan than among men. Overall, frailty was significantly higher in women (Table 3), while allostatic load
was higher, but not significantly so, among men, reflecting results from the USA and 11 European countries
wherein frailty was significantly higher among womenthan similar aged men [19, 25, 67]. One suggestion is thatfrailer men do not survive as well into older ages as frailer
women, leaving fewer frail men as survivors to later ages.
Another, men who do survive become frail at later ages
than women.
Further, our results do not conform to models suggesting allostatic load underlies frailty or frailty underlies allostatic load among older people. For example, in
the Women’s Health and Aging Studies (N = 728; 70–79
years), at baseline, the cross-sectional relationship ofallostatic load with frailty was statistically significant[26]. Authors suggested allostatic load reflected reduced
reserve capacity associated with older ages and measured
by frailty indices [26]. Results from this rural and urban
sample of older Poles fail to support this conjecture.
Here, assessed frailty shows no association with physiological dysregulation as assessed by an ALS (Table 5).
In the prospective Singapore Longitudinal Aging Studies, among 1298 participants aged 55 + years (mean 66.6
years) followed an average of 4.4 years for mortality outcomes, physiological dysregulation (allostatic load) andfrailty both associated independently with mortality,
functional and health outcomes [28]. This led authors tosuggest physiological dysregulation/allostatic load is not
only co-incident with but significantly influences physical impairments defining frailty [28]. Our results indicate
frailty and allostatic load are not correlated at the local
community level among those aged 55–91 years contrary
to results from samples representing broad nationally
representative and international population groupings.
In multivariate analyses, age, sex, and residence are independently associated with frailty, but allostatic load isnot. One suggestion is national and international databy aggregating local and regional variability in lifeways,
sociocultural interactions, demographic and economic
determinants of frailty and allostatic load lose underlying
local community context and variation. Data presented
here suggest frailty and allostatic load arise from different sources and stressors. Physical frailty represents agerelated declines in strength, resilience, endurance, andphysical capabilities mainly secondary to sarcopenia andJeszka et al. Journal of Physiological Anthropology(2024) 43:12

bone loss, a physiological process. Conversely, allostasis
is a neurophysiological response to experienced stressors.
As organisms respond to stressors over their lifespans,they incur physiological and somatic damages, resulting
in an allostatic load. In this context, frailty seems a more
specific correlate of somatic aging than does one’s allostatic load.
Limitations

There are several limitations to this research. First, neither occupational information nor educational attainments were available for participants. Both allostaticload and frailty tend to vary by education and occupation, which influence lifestyles, stressor experiences,and income levels. Employing a rural-to-urban sampling strategy within a regional context for comparative
analyses provides some control for occupational variation as well as economic and social characteristics differing between settings, as they do in rural and urban areas
throughout Greater Poland [59–64]. In Poland today,
women are attaining more education, including college degrees, than men [61, 63, 64]. Participants in thisresearch were born 1926–1962. Likely, they attained significantly less formal education than observed today. In
addition, our data are cross-sectional. They allowed us todetermine how existing variation in age, sex, and place ofresidence interact to influence participants’ current levelsof allostatic load and frailty. However, they do not allow
us to explore temporal influences on participants’ frailty
or allostatic load. Follow-up and longitudinal data on thissample will provide information necessary for assessing how variability in allostatic load and frailty influence
health outcomes among rural- and urban-living Polishelders.

Conclusions
We explored interactions among self-reported sex, age,and place of residence, an 8-variable frailty index, anda 10-biomarker allostatic load score among older residents of Poland residing in Poznan and Nekla. Overall,
men and women did not differ significantly in average ALS, but women did show greater frailty. Amongmen, those residing in Nekla had significantly higher
ALS, but not frailty. Among women, neither ALS nor
frailty differed significantly between settings. Results
suggest locally variable lifestyles as identified by residence differentially influence frailty and allostatic load
among older Poles. This is particularly so among men
in our sample, conforming to results from other crosssectional research in local settings [41, 42]. Elsewhere,
in large cohort studies, ALS and frailty showed significant associations with one another [26–28]. Contrary
to such previous research on large cohorts of thosePage 10 of 12

aged 70 + years, among this sample of Poles ages 55–91
residing in both rural and urban settings, neither age
nor frailty associate significantly with allostatic load,
although age, sex, and residential location all associatesignificantly with frailty.
Estimating allostatic load and frailty from biomarkers
allows physiological and biological anthropologists to
explore demographic, environmental, and cultural influences on health disparities and adverse health outcomes
across populations and local environmental settings.
Such research aids the development of public health programs tailored to both individual and community needs.
That area of residence influences allostatic load in men inthis sample, but not women, while influencing frailty inwomen, but not men, emphasizes the need for a life history perspective when examining whether and how social
environments may mediate individual health, stressor
responses, allostatic load, and frailty. Future research on
health in rural and urban settings may be enriched byincorporating economic and educational factors whenassessing cumulative effects of environmental systems onhuman somatic dysregulation and frailty.
Abbreviations
ALS	Allostatic load score
DHEAs	Dehydroepiandrosterone sulfate
SF-36	Short-Form Health Survey
PLN	National currency of Poland: Polish zloty
LDLc	Low-density lipoprotein cholesterol
HDLc	High-density lipoprotein cholesterol
HbA1c	Glycated hemoglobin
Acknowledgements
The authors thank the residents of Nekla and Poznan, Poland, who participated in the research activities described here. We wish them the best andhope they are pleased to read our report.
Authors’ contributions
JJ participated in study design and proposal development and directed
fieldwork and laboratory analyses; DH participated in statistical analysis anddrafted original paper as part of her MA program; MW participated in study
design and proposal development; TM participated in study design; YS
participated in study design, proposal writing, directing fieldwork, and editing
manuscript; DEC participated in study design, proposal writing, fieldwork,
statistical analysis, and in conjunction with DH wrote the paper.
Funding
Japan Society for the Promotion of Science. KAKENHI Grant Number
JP22247038, PI Yoshiaki Sone.
Availability of data and materials
Data are available from the corresponding author on reasonable request.

Declarations
Ethics approval and consent to participate
This research was approved by the Research Ethics Committee Poznan University of Life Sciences (Number 425/13). All participants read and signed an
Informed Consent Form approved by the Research Ethics Committee Poznan
University of Life Sciences (Number 425/13).
Competing interests
The authors declare that they have no competing interests.

Jeszka et al. Journal of Physiological Anthropology(2024) 43:12

Author details
1
Department of Human Nutrition and Hygiene, Poznan University of Life
Sciences, Poznan, Poland. 2 Department of Anthropology, The Ohio State University, Columbus, OH, USA. 3 Graduate School of Human Life Science, Osaka
City University, Osaka, Japan. 4 Mimasaka University, Tsuyama, Okayama, Japan.
5
Department of Anthropology and School of Public Health, Smith Laboratory,
The Ohio State University, 174 W. 18Th Avenue, Columbus, OH 43210‑1106,
USA.
Received: 20 July 2023 Accepted: 28 March 2024

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