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. Author manuscript; available in PMC: 2023 May 1.
Published in final edited form as: Am J Biol Anthropol. 2022 Mar 8;178(1):182–190. doi: 10.1002/ajpa.24502

Dietary Protein Source Matters for Changes in Inflammation Measured by Urinary C-Reactive Protein in Rural Polish Women

Hayley E Ban 1, Katharine MN Lee 1,†, Mary P Rogers-LaVanne 1,‡, Katarzyna Zabłocka-Słowińska 2, Andrzej Galbarczyk 3, Grazyna Jasienska 3, Kathryn BH Clancy 1,4,*
PMCID: PMC9718368  NIHMSID: NIHMS1803946  PMID: 36466441

Abstract

Objectives:

Multiple macronutrients have been shown to affect systemic inflammation, a well-known predictor of chronic disease. Less often, varying sources of these macronutrients are examined. Different subsistence environments lead to varying access to protein sources which, combined with physical activity patterns, may lead to different relationships than among more typically studied sedentary, industrialized populations. This study hypothesizes an association between dietary protein intake and urinary C-Reactive Protein (CRP) concentration in women from a rural, agrarian Polish community.

Materials and Methods:

We assessed protein intake and their sources for 80 nonsmoking, premenopausal Polish women who were not pregnant, nursing, or on hormonal birth control during the study or within the previous six months. Each participant completed multiple 24-hour dietary recalls during one menstrual cycle. Participants collected morning void urinary samples daily over one menstrual cycle for urinary CRP analysis. We analyzed relationships between plant and animal protein intake and CRP over the menstrual cycle by multiple linear regression.

Results:

Plant protein in cereal foods was significantly positively associated with cycle-average urinary CRP concentrations (p<0.05) after controlling for body fat percent, total energy intake, and dietary fiber. Foods containing animal protein were not significantly associated with CRP.

Discussion:

Contents of this population’s main plant and animal protein sources differ from those of more commonly studied industrialized populations. Within the context of a population’s typical diet, more emphasis may need to be placed on particular source of protein consumed, beyond plant versus animal, in order to understand relationships with CRP.

Keywords: Inflammation, C-Reactive Protein, Dietary Protein, Menstrual Cycle

INTRODUCTION

Diet plays an important role in regulating chronic low-grade inflammatory status (Giugliano, Ceriello, & Esposito, 2006), and markers of inflammation like C-Reactive Protein (CRP) are associated with chronic disease in a population-specific way (e.g., Shattuck-Heidorn et al., 2020). Because inflammation plays an important role in chronic disease risk and is influenced by various social, economic, and behavioral factors that are often population-specific, it is critical to understand the impact of these factors on health across varying populations (McDade et al., 2010). For instance, different subsistence environments lead to varying sources of protein and can result in unique relationships between protein source and inflammation (Hodgson, Ward, Burke, Beilin, & Puddey, 2007, van Woudenbergh et al., 2012). Thus, we investigate the relationship between dietary sources of protein and CRP in a sample of Polish women living in a transitioning, subsistence agricultural environment.

In addition to the well-established link between diet and inflammation, different populations transition across subsistence patterns at various paces, and changes observed in dietary and activity patterns often associated with Westernization can have significant implications (Dufour & Piperata, 2018). An increased consumption of refined starches, sugar, and saturated fats coupled with a low consumption of fruits, vegetables, and whole grains may result in activation of the innate immune system via an increased production of proinflammatory cytokines and a decreased production of anti-inflammatory cytokines (Giugliano et al., 2006). One such dietary-related change observed with Westernization is a rise in consumption of foods associated with cardiovascular disease through inflammation (Galanis, McGarvey, Quested, & Sio, 1999, World Health Organization, 2003).

CRP, a widely used biomarker of inflammation, is potentially responsive to nutritional factors like dietary protein (Bartali et al., 2012, Prasad, 2006, Wells, Mainous III, & Everett, 2005), and different sources of protein may have differing relationships with CRP. Generally, high consumption of red meat, refined grains, high-fat foods, and full-fat dairy products is positively correlated with CRP (Fung et al., 2001). Substituting red meat with alternative protein sources may result in a healthier profile of inflammatory biomarkers (Ley et al., 2014). Conversely, some plant-based protein sources are associated with decreased levels of inflammatory biomarkers (Azadbakht et al., 2007, Baum et al., 1998, Hall et al., 2005, Jenkins et al., 2003, Prasad, 2006). Azadbakht and colleagues (2007) observed significantly lower CRP levels in postmenopausal women on a diet with soy protein and no red meat versus women on a diet that included red meat. Some have additionally hypothesized that plant-based diets may lead more generally to better health outcomes (Azadbakht et al., 2007, Azadbakht, Atabak, & Esmaillzadeh, 2008, Cao et al., 2017, Ley et al., 2014, McCarty, 2000, Montonen et al., 2013), although this relationship is typically studied when the main source of plant protein is soy (e.g., Allès et al., 2017, McCarty, 2000).

This study investigates whether plant protein intake correlates with systemic inflammation in a population where plant proteins derive primarily from cereal grains rather than soy. Therefore, we analyzed associations between dietary protein sources and urinary CRP concentrations in a sample of rural Polish women where soy is nearly absent from the diet. We hypothesized that in this setting: 1) dietary plant protein intake will be negatively associated with CRP and 2) dietary animal protein intake will be positively associated with CRP over the full menstrual cycle.

MATERIALS & METHODS

Study Population

Rural Polish women from the Mogielica Human Ecology Study Site in the Beskid Wyspowy region of southern Poland (Jasienska & Ellison, 2004) were recruited. In this transitioning subsistence agricultural environment, women experience moderate levels of physical activity and energetic expenditure (Colleran, 2014, Jasienska, Ziomkiewicz, Thune, Lipson, & Ellison, 2006, Lee, Rogers, Galbarczyk, Jasienska, & Clancy, 2019). The initial sample (n=129) consisted of healthy premenopausal Polish women with data collected in the 2014, 2015, and 2017 summer harvest seasons. Study protocols have been described elsewhere (Lee et al., 2019, Lee, Rogers-LaVanne, Galbarczyk, Jasienska, & Clancy, 2020, Rogers et al., 2020). Research protocols were approved by the University of Illinois at Urbana-Champaign Institutional Review Board (UIUC IRB #13856), and all participants provided informed consent. Participants missing nutrition or CRP data were excluded from the analysis for a final total of n=80. Women were non-smoking, reported experiencing menstrual cycles, and were not pregnant, nursing, or on hormonal birth control during the study or within the previous six months.

Surveys and Anthropometric Measurements

We administered demographic and health variable surveys. We used standard anthropometric techniques to measure body height and mass (Antón, Snodgrass, & Bones and Behavior Working Group, 2009) and measured body fat percentage with a Tanita bioimpedance scale (using the “female” setting) upon enrollment. Study data was managed using REDCap (Research Electronic Data Capture) tools hosted at the University of Illinois Urbana-Champaign (Harris et al., 2009, Harris et al., 2019).

Hormone Data Collection & Analysis

For one menstrual cycle, participants collected and immediately froze daily first morning void urine. Urinary estradiol metabolite estrone-3-glucuronide (E1G), pregnanediol glucuronide (PdG), and CRP concentrations were measured using custom Quansys Biosciences multiplex ELISA kits (n=114 assay plates). E1G and PdG were used to determine ovulation and align menstrual cycles. All samples were run at least in duplicate. For CRP, intra-assay variation ranged from 2.7% to 24.5%, with an average of 6.6%. A threshold of 15% was set for CV for inclusion in these analyses. Inter-assay coefficients of variability were calculated using multiple control samples and ranged from 19.5% to 60.5% (see supplemental Table 1). While inter-assay variation is high, it is within the range seen in multiplex assays (Bastarache et al., 2011; Chowdhury, Williams, & Johnson, 2009). All hormone concentrations were adjusted for specific gravity (Miller et al., 2004). A batch correction factor of 0.28 was multiplied to all CRP values based on manufacturer guidance to allow for comparability to other Quansys CRP concentration measurements. Cycle-average urinary CRP concentrations (ng/mL) were log-transformed to normalize the distribution. Log-transformed average urinary CRP did not significantly differ between menstrual cycle phase for this sample as assessed by ANOVA (F2, 237 = 0.001, p = 0.999, see supplemental Figure 1). Thus, log-transformed cycle-average urinary CRP served as the response variable for all statistical tests. While urinary CRP is not necessarily directly correlated with serum CRP, its presence represents a downstream event from the initial acute phase reaction represented by an increase in serum CRP, and its actions appear to be explicitly proinflammatory (Clancy et al., 2013, Verma, Szmitko, & Yeh, 2004).

Dietary Assessment

We measured dietary information using multiple 24-hour dietary recalls across one menstrual cycle per participant. Recalls were completed on approximately the third, eighth, thirteenth, eighteenth, and twenty-third days following the first day of the first menses and start of sample collection. Participants completed anywhere from three to six dietary recalls (3 recalls: n=4; 4 recalls: n=9; 5 recalls: n=64; 6 recalls: n=3). Multiple recalls were collected per participant in order to assess for variability in CRP. Most publications suggest a minimum of five dietary recalls to establish an individual’s baseline nutrition profile (Mennen et al., 2002, Stote, Radecki, Moshfegh, Ingwersen, & Baer, 2011). Data obtained from dietary recalls were averaged across all available recall dates to calculate daily average nutritional values for each participant. Amount of energy, macro- and micronutrient intake, and fiber were assessed. Calculations for composition of food items were performed using Dieta 6.0 software (National Food and Nutrition Institute, Warsaw, Poland). Animal protein included protein measured from red meat, poultry, fish, dairy, and egg. Plant protein included protein measured from cereal, potato, fruits and vegetables, legumes, and nuts and seeds.

Statistical Analysis

Multiple linear regression in RStudio (1.2.5033) was used to analyze the association of log-transformed CRP and dietary protein (RStudio Team, 2019). Body fat percent, total energy intake (kJ), and fiber (g) were included in all models. Body fat percent and body mass index (BMI) are positively associated with CRP in some samples (Festa et al., 2001, Khera et al., 2009), including a sample of post-reproductive women from our study site (Galbarczyk et al., 2021). We used body fat percent to better account for variation in body size and composition since measuring obesity with BMI can result in inaccurate observations regarding obesity-related effects due to misclassification of body composition (Rothman, 2008). Total energy intake (kJ) was included to adjust for its effect on low-grade inflammatory response (Bertran et al., 2005). Fiber was included as it has been shown to be negatively correlated with CRP (King, Egan, & Geesey, 2003, King, Mainous III, Geesey, & Woolson, 2005, King et al., 2007, Ma et al., 2006). Age was not included, despite reports of association with CRP (Ferrucci et al., 2005, Wyczalkowska-Tomasik, Czarkowska-Paczek, Zielenkiewicz, & Paczek, 2016), since the Pearson correlation coefficient was low (r=0.003). In a sensitivity analysis, inclusion or exclusion of age did not change model outcomes.

We assessed the participants’ dietary animal and plant protein intake. The highest three food groups for average daily consumption of each animal protein (red meat, dairy, poultry) and plant protein (cereal, fruit and vegetable, potato) were included in analyses. In addition to including protein from individual food groups in analyses as separate variables, we also ran models that combined animal and plant protein from two, three, and four different foods into single variables. Grouped variables are not reported for plant protein since separate variables provided a better model fit. For foods measured for animal protein, one model is reported with a grouped variable consisting of the sum of red meat, dairy, and fish animal protein. Resulting p-values for the regression models were assessed with significance set at p<0.05. Additionally, cereal plant protein was divided into tertiles, and differences in log-transformed cycle-average CRP between tertiles was tested with ANOVA.

RESULTS

Descriptive Statistics

Women (n=80) were 33.67 ± 7.8 (mean ± SD) years old with body fat percentage of 28.6 ± 8.1% (Table 1). The highest average protein consumption was animal protein from dairy (19.05 g/day) followed by animal protein from red meat (17.55 g/day) and then plant protein from cereals (14.50 g/day, Figure 1). Cycle-average CRP for each woman ranged from 86.95 to 796.80 ng/ml with an average of 228.85 ng/ml (Table 1).

Table 1.

Characteristics of healthy, rural Polish women participating in the study (n=80). Total energy intake is averaged across 3-6-day increments (3 recalls: n=4; 4 recalls: n=9; 5 recalls: n=64; 6 recalls: n=3). Urinary CRP concentrations are averaged across the full menstrual cycle.

Mean Standard Deviation Minimum Median Maximum
Age (years) 33.67 7.78 19.00 35.50 46.40
Body fat (%) 28.63 8.08 8.60 28.60 46.00
Average Total Energy Intake (kJ) 8193.34 2185.32 3129.53 8034.87 15206.34
Average Urinary CRP Concentration (ng/mL) 231.09 152.39 86.95 166.49 796.80
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Figure 1.

Figure 1.

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Box plot of protein intake for each food group. Within each box, black dot denotes median values. Light boxes represent plant protein intake and dark boxes represent animal protein intake. Boxes extend the interquartile range of protein consumption for each food group, and lines show minimum and maximum values. The food group from which the highest amount of animal protein was consumed per day was dairy (19.05 g/day). The food group from which the highest amount of plant protein was consumed per day was cereal (14.50 g/day).

Multiple Linear Regressions

We first ran a linear regression model for total protein intake with log-transformed CRP as the dependent variable. Total protein intake was not significantly associated with log-transformed CRP in this sample (β=−0.804, p=0.699). We then ran a model with total animal protein intake (sum of dairy, red meat, and poultry animal protein intake) and total plant protein intake (sum of cereal, fruit and vegetable, and potato plant protein intake) with log-transformed CRP as the dependent variable. When these variables were analyzed together in one multiple linear regression, neither variable was significantly associated to CRP (Animal: β=−1.851, p=0.390; Plant: β=8.414, p=0.152). Finally, we ran and are presenting detailed results for the following linear regression models with log-transformed CRP as the dependent variable: 1) red meat, dairy, and poultry animal protein and cereal, fruit and vegetable, and potato plant protein, 2) cereal, fruit and vegetable, and potato plant protein, 3) plant protein with isolated cereal plant protein, 4) red meat, dairy, and poultry animal protein, 5) animal protein with isolated red meat animal protein, and 6) sum of animal protein from red meat, dairy, and fish.

Plant protein from cereal foods was significantly positively associated with CRP (p=0.043) in the regression model that includes each cereal, potato, and fruit and vegetable plant protein intake and red meat, dairy, and poultry animal protein intake (r2-adjusted=0.126, p=0.027; Table 2 – Model 1). Cereal plant protein intake was also significantly positively associated with CRP in the model including only plant protein intake for cereal, potato, and fruit and vegetable and in the model with isolated cereal plant protein (Table 2 – Models 2, 3). When we compared log-transformed CRP in tertiles grouped by cereal plant protein intake, a single factor ANOVA displayed a significant difference (F2,77=5.085, p=0.008, supplemental Figure 2). Tukey’s test indicated that the tertile with the highest intake of cereal plant protein had significantly higher CRP (mean=293.80 ng/mL, log-transformed mean=2.39) than the tertile with the lowest intake (mean=168.75 ng/mL, log-transformed mean=2.18, p=0.006), but the middle tertile (mean=230.71 ng/mL, log-transformed mean=2.29) was not significantly different from either the high or low tertiles.

Table 2.

Multiple linear regressions with varying combinations of plant and animal protein foods included in each model. In the top portion of the table, Model 1 includes the top three food groups of the highest average values for each animal and plant protein consumed per day. Model 2 includes only the top three plant protein sources, and Model 3 displays isolated cereal plant protein. In the bottom portion of the table, Model 4 includes only the top three animal protein sources. Model 5 displays isolated red meat animal protein, and Model 6 contains animal protein from red meat, dairy, and fish summed in one variable. Beta values are multiplied by 1000 to standardize for scale.

Plant-Focused Models
Model 1 Model 2 Model 3
r-squared 0.226 0.189 0.154
Adjusted r-squared 0.126 0.123 0.097
Overall p value 0.027* 0.015* 0.027*
Variables Beta p value Beta p value Beta p value
Body fat (%) 2.611 0.442 2.219 0.508 3.490 0.308
Total energy (kJ) 0.121 0.187 0.110 0.176 0.085 0.324
Dietary fiber (g) −7.501 0.248 −7.893 0.195 −9.865 0.134
Cereal (plant) protein 12.73 0.043* 12.74 0.040* 15.09 0.029*
Potato (plant) protein −32.12 0.085 −30.31 0.079
Fruit and vegetable (plant) protein −0.982 0.943 0.454 0.973
Dairy (animal) protein −2.243 0.499
Red meat (animal) protein −1.796 0.507
Poultry (animal) protein 3.879 0.234
Plant protein (minus cereal) 3.217 0.749
Animal-Focused Models
Model 4 Model 5 Model 6
r-squared 0.140 0.130 0.132
Adjusted r-squared 0.069 0.071 0.085
Overall p value 0.080 0.063 0.030*
Variables Beta p value Beta p value Beta p value
Body fat (%) 3.140 0.359 2.966 0.384 3.087 0.360
Total energy (kJ) 0.224 0.007** 0.223 0.006** 0.230 0.003**
Dietary fiber (g) −9.404 0.083 −9.981 0.064 −8.732 0.094
Red meat (animal) protein −4.340 0.097 −4.288 0.097
Dairy (animal) protein 1.732 0.605
Poultry (animal) protein 1.993 0.541
Animal protein (minus red meat) 0.090 0.967
Red meat, dairy, fish (animal) protein −3.492 0.067
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Animal protein intake was not significantly associated with CRP in any model (Table 2). In all animal protein-focused models, total energy intake (kJ) was significantly positively associated with CRP (Table 2). Total energy intake (kJ) was not significant in the other models.

DISCUSSION

In this study, we examined whether and how dietary protein intake from plant and animal sources are associated with urinary CRP concentrations averaged across the full menstrual cycle in healthy, adult women. We hypothesized that plant protein intake would be negatively associated with CRP and animal protein intake would be positively associated with CRP. We based these hypotheses on prior work in populations where physical activity patterns and main plant protein sources differ from this sample of rural Polish women (Allès et al., 2017, Baum et al., 1998, Hall et al., 2005, Jenkins et al., 2003, McCarty, 2000). We found that plant protein intake from cereal foods is positively associated with CRP, but animal protein intake, including red meat, is not significantly associated with CRP. Therefore, neither initial hypothesis was supported.

Finding 1: Cereal plant protein is positively associated with CRP

Our results may be attributed to population-specific dietary habits, including plant protein largely comprised of cereals, and animal protein sourced from domestic chicken and pork (Jasienska, 2013). Since many plant-based diets consist of substitution of animal protein with soy (Allès et al., 2017), the positive relationship between plant protein and CRP for this sample may be due to a lack of soy protein in the rural Polish diet. Plant protein consumed by this sample is largely derived from wheat, where the main protein is gluten, which is associated with increased CRP in mice and pigs (Jönsson et al., 2005, Soares et al., 2013). Other studies on the association between grains and CRP in humans yield varying results with some evidence of a significant inverse association and some reporting no association (Jensen et al., 2006, Vitaglione et al., 2015). Differences in reported associations could be due to level of refinement of dietary grains as well as other population-specific lifestyle factors (De Punder & Pruimboom, 2013). Proteinaceous components of gluten-containing cereals are reportedly involved in inflammation and immune responses (Freed, 1991, Jönsson et al., 2005, Zevallos et al., 2017). Lectin wheat germ agglutinin, which is present in wheat seed in both the germ and the gluten part of endosperm, has exhibited effects related to autoimmunity, allergy and inflammation (Jönsson et al., 2005). Thus, the observed significant positive association between cereal plant protein and CRP in this study may be attributed to the presence of proteinaceous anti-nutritional factors, such as lectins and amylase trypsin inhibitors, in gluten-containing cereals consumed by this population.

Finding 2: Animal protein is not significantly associated with CRP

In samples of Iranian men and women, American men, and European men and women, red meat is associated with elevated levels of inflammatory biomarkers (Azadbakht et al., 2008, Fung et al., 2001, Montonen et al., 2013). However, consumption of red meat and poultry is not associated with elevated CRP in Australian or Dutch populations (Hodgson, Ward, Burke, Beilin, & Puddey, 2007, van Woudenbergh et al., 2012). Since our sample often consumes animal products sourced from domestic animals, and dairy and cereal grain products are often homemade (Jasienska, 2013), dietary components of this sample may differ from those of other studies. Additionally, industrially produced food products may be sources of exposure for endocrine disrupting chemicals, which have been linked to increased CRP as well as other inflammatory biomarkers (Erden et al., 2014, Martina, Weiss, & Swan, 2012). Recommendations regarding lowering exposure to endocrine disruptors include consuming homegrown food products (Martina et al., 2012). Thus, the domestic food items consumed by this sample may lack inflammatory-inducing elements found in the diets of other sample groups whose foods are more often commercially purchased.

Finding 3: Total energy intake is positively associated with CRP

Total energy intake (kJ) was significantly positively associated with CRP in all animal protein analyses but was not significant when plant protein was included (Table 2). Other studies report different associations between CRP and energetic intake in various populations, indicating that ranges of energetic intake may have effects on CRP levels in different populations. Some studies have noted decreased energetic intake to be associated with decreased levels of inflammatory biomarkers (Dixit, 2008, Imayama et al., 2012, Morgan, Wong, & Finch, 2007, Reed, De Souza, & Williams, 2010). For instance, Reed and colleagues (2010) observed caloric restriction coupled with exercise training to be associated with decreased inflammatory biomarkers in overweight, premenopausal women. However, the opposite has also been observed in older, hospitalized individuals where malnutrition was present (Pourhassan et al., 2020). These observations across different samples demonstrate the potential influence of range of energetic intake for persons of variable health conditions and ages on inflammatory biomarkers like CRP. Additionally, observations such as these suggest that the conditions under which caloric restriction is generated (e.g., without malnutrition versus with malnutrition) are influential on these trends (Meydani et al., 2016, Schaible & Kaufmann, 2007).

Our sample inhabits a rural environment with moderate energetic expenditures due to a moderately labor-intense and seasonal agrarian lifestyle different from the sedentary industrialized populations most often studied (e.g., Azadbakht et al., 2008, Ley et al., 2014, Montonen et al., 2013). A pattern of moderate physical activity, experienced by this sample especially during harvest season (Clancy et al., 2013, Jasienska & Ellison, 2004, Lee et al., 2019), is associated with anti-inflammatory effects (Kasapis & Thompson, 2005). While Poland is in central Europe and a member of the European Union, there is significant variation across European and Polish regions in the degree of sustenance agriculture, industrialization, and infrastructure development (Clancy & Davis, 2019). While many European countries and citizens experience economies that are largely characterized as “postindustrialized,” defined by more services, information, and research and less manufacturing, this is clearly not always the case (Clancy & Davis, 2019). This sample demonstrates the heterogeneity of the lived experiences of women and emphasizes the importance of accounting for this diversity in biological studies (Clancy & Davis, 2019).

Limitations

Isolating the effects of one macronutrient is challenging in nutritional studies. If participants consumed more of one type of protein, they may have consumed other nutrients in higher or lower quantities. Additionally, the presence of trace amounts of animal or plant protein in foods inhibited precise separation of animal and plant protein measurements. While trace amounts of protein were excluded from analyses, this leaves a margin for error in the estimation of the true amount of plant and animal protein from each food. Confounding variables may have affected the observed relationship between protein and CRP in this sample. In dietary analysis in a similar study, participants with higher cereal intake had generally healthier lifestyles characterized by lower BMI, increased physical activity, increased fruit and vegetable intake, and decreased alcohol, saturated fat, and meat intake (Lefevre & Jonnalagadda 2012). Thus, other dietary and lifestyle factors may modulate the observed association of dietary protein and CRP (De Punder & Pruimboom, 2013). Future studies are warranted to improve methods of isolating effects of nutrients and to further investigate the underlying mechanisms in the mediation of these observed associations in different populations experiencing varying energetic demands.

Conclusions

Cereal plant protein consumption was positively correlated with urinary CRP concentrations in this sample of premenopausal women under moderate energetic constraint in a rural environment. Evidence of a link between animal-sourced protein and inflammation in largely sedentary populations has been used to justify a push towards a plant-based diet (Azadbakht et al., 2007, Cao et al., 2017, Ley et al., 2014). However, this study suggests variation in the inflammatory effects of different plant protein sources by subsistence environment, emphasizing the importance of considering population-specific factors when exploring the relationship between nutritional factors and inflammation.

Supplementary Material

supinfo

ACKNOWLEDGEMENTS

We first and foremost wish to thank the women who participated in this study. We additionally would like to thank Pan Doktor Leszek Pieniążek, and Pani Położna Emilia Bulanda for their work on this project. Additional thanks to Meredith Wilson for mentoring HEB and assisting with code documentation. This work would not be possible without all of our research assistants, including (in alphabetical order) Kristina Allen, Vilimira Asenova, Priya Bhatt, Klaudia Dziewit, Sara Gay, Juliana Georges, Fatima Godfrey, Denise Herrera, Piotr Hutka, Szczepan Jakubowski, Ansley Jones, Jacob Kanthak, Monika Kukla, Karolina Miłkowska, Rachel Mitchell, Agata Orkisz, Kamila Parzonka, AnnaPawińska, BryanaRivera, Ohm Shukla, Aleksandra Starnawska, Zarin Sultana, Monika Szlachta, Katarzyna Szulc, Aleksandra Wojtarowicz, and Kevin Zavala. Thank you to Scholarly Commons Statistical Consulting at the University of Illinois Urbana-Champaign for assisting with statistical methods. The data collection at the Mogielica Human Ecology Study Site was possible thanks to National Science Centre grant UMO-2017/25/B/NZ7/01509 (GJ). This material is based upon work supported by the National Science Foundation under Grant Numbers 1317140 (KBHC), BCS-1732117 (KBHC, Polk, and KMNL), BCS-1650839 (KBHC, Malhi, and MPRL), and the Graduate Research Fellowships under Grant Number DGE-1144245 (KMNL and MPRL). Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This work was also supported by Wenner-Gren Foundation Dissertation Field Work Grants #084918 (MPRL) and #089812 (KMNL), The American Philosophical Society Lewis and Clark Fund for Exploration and Field Research (KMNL), the University of Illinois Department of Anthropology Summer Research Fund (KMNL and MPRL), The Beckman Institute Cognitive Science/Artificial Intelligence Award (KMNL and MPRL), the University of Illinois Graduate College Dissertation Travel Grant (KMNL and MPRL), and Grant-In-Aid of Research from Sigma Xi, The Scientific Research Society (MPRL). Finally, this research was supported by T32CA190194 (MPI: Colditz/James) and by the Foundation for Barnes-Jewish Hospital and by Siteman Cancer Center (KMNL). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

Footnotes

CONFLICT OF INTEREST

The authors do not have any conflicts of interest to disclose.

DATA AVAILABILITY

Data available from authors upon reasonable request.

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Supplementary Materials

supinfo
NIHMS1803946-supplement-supinfo.pdf (103.4KB, pdf)

Data Availability Statement

Data available from authors upon reasonable request.

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