DOI: 10.5061/dryad.2v6wwq00q
title: Inflammaging is minimal among forager-horticulturalists in the Bolivian Amazon

description: An increase in chronic systemic inflammation in later life, termed
 inflammaging, is implicated in health risk. However, it is unclear whether
 inflammaging develops in all human populations, or if it is the product of
 modern environmental mismatch. We assessed inflammaging in Tsimane
 forager-horticulturalists of the Bolivian Amazon, using serum cytokines in
 a primarily cross-sectional sample (1,134 samples from n = 714, ages
 39-94, 51.3% female). IL-6 was positively associated with age (β = 0.013,
 p &lt; 0.01). However, other pro-inflammatory markers, including IL-1β
 and TNF-α, did not increase with age (β = -0.005 and β = -0.001,
 respectively). We then compared the Moseten, a neighboring population that
 has experienced greater market integration (423 samples from n = 380, ages
 39-85, 48.2% female). The Moseten also showed a positive age association
 for IL-6 that attenuated at later ages (age β = 0.025, p &lt; 0.01;
 age2 β = -0.001, p &lt; 0.05). Further, IL-1β and TNF-α were both
 positively associated with age (β = 0.021, p &lt; 0.05, and β = 0.011,
 p &lt; 0.01, respectively). Our results demonstrate minimal
 inflammaging in the Tsimane, highlighting variation across populations in
 this age-related process. They also suggest inflammaging is exacerbated by
 lifestyle shifts.
description: # Inflammaging is minimal among forager-horticulturalists in the Bolivian
 Amazon Dataset DOI: [10.5061/dryad.2v6wwq00q](10.5061/dryad.2v6wwq00q) ##
 Description of the data and file structure The purpose of this study is to
 assess whether the Tsimane, forager-horticulturalists residing in the
 Bolivian Amazon, develop chronic inflammation in later life (termed
 inflammaging). The data collected to answer this question included a panel
 of cytokines measured from serum samples. Cytokines were measured in pg/mL
 using the Quansys 15-plex assay. Tsimane data were compared to a
 neighboring and closely related population, the Moseten, which has
 experienced greater market integration and lifestyle shifts.  The files
 include (1) the code used to produce the results, (2) a heading file
 showing the variable names used in the analysis, (3) data from each table
 in the manuscript in .csv format, and (4) data from regression models used
 to produce each figure. ### Files and variables #### File:
 inflammagingvariables.csv **Description:** A header file showing the
 variable names in the dataset ##### Variables * pid: person id *
 Collection.Date: date the serum sample was collected  * hsIL1a.pgmL: High
 sensitivity interleukin 1 alpha measured in pg/mL * hsIL1b.pgmL: High
 sensitivity interleukin 1 beta measured in pg/mL * hsIL2.pgmL: High
 sensitivity interleukin 2 measured in pg/mL * hsIL4.pgmL: High sensitivity
 interleukin 4 measured in pg/mL * hsIL5.pgmL: High sensitivity interleukin
 5 measured in pg/mL * hsIL6.pgmL: High sensitivity interleukin 6 measured
 in pg/mL * hsIL10.pgmL: High sensitivity interleukin 10 measured in pg/mL
 * hsIL12.pgmL: High sensitivity interleukin 12 measured in pg/mL *
 hsIL13.pgmL: High sensitivity interleukin 13 measured in pg/mL *
 hsIL15.pgmL: High sensitivity interleukin 15 measured in pg/mL *
 hsIL17.pgmL: High sensitivity interleukin 17 measured in pg/mL *
 hsIL23.pgmL: High sensitivity interleukin 23 measured in pg/mL *
 hsIFNy.pgmL: High sensitivity interferon gamma measured in pg/mL *
 hsTNFa.pgmL: High sensitivity tumor necrosis factor alpha measured in
 pg/mL * hsTNFb.pgmL.1: High sensitivity tumor necrosis factor beta
 measured in pg/mL * male: coded 1 = male, 0 = female * age: age in years *
 moseten: coded 1 = Moseten, 0 = Tsimane * female: coded 1 = female, 0 =
 male * age60: age in years centered at 60 years (60 subtracted from age) *
 Population: factor variable for plotting ("Tsimane" and
 "Moseten") * Sex: factor variable for plotting ("Male"
 and "Female") * PC1: The first principal component from all of
 the cytokines  * distance_town_log: The distance a person was living in
 from the nearest town of San Borja at the time of study participation
 (natural log transformed).  * analyzewbc: White blood cell count (natural
 log transformed).  * bmi: Body mass index (kg/m2) * waist: Waist
 circumference (centimeters) #### File: Tsimane_inflammaging_code.R
 **Description:** Code file used to produce the study results and figures
 #### File: inflammaging_procb_table1.csv **Description:** Descriptive
 statistics presented in Table 1 ##### Variables * descriptive: specifying
 the particular descriptive  * Tsimane: descriptives for the Tsimane *
 Moseten: descriptives for the Moseten #### File:
 inflammaging_procb_table2.csv **Description:** Data from Table 2 showing
 intra-class correlation estimates for the cytokines. Blank cells indicate
 that the cytokine (noted in the first column) was not measured in the
 previously published study (noted in the first row).  ##### Variables *
 cytokine: designating the cytokine below * Tsimane icc: the Tsimane
 intra-class correlation estimate * Tsimane icc lower: the Tsimane lower
 95% confidence interval estimate * Tsimane icc upper: the Tsimane upper
 95% confidence interval estimate * Moseten icc: the Moseten intra-class
 correlation estimate * Moseten icc lower: the Moseten lower 95% confidence
 interval estimate * Moseten icc upper: the Moseten upper 95% confidence
 interval estimate * Clendenen icc: the icc estimate from the cited
 Clendenen et al. study * Gu icc: the icc estimate from the cited Gu et al.
 study * Guo icc: the icc estimate from the cited Guo et al. study *
 Hofmann icc: the icc estimate from the cited Hofmann et al. study ####
 File: inflammaging_procb_table3.csv **Description:** Regression model
 results for the Tsimane (part 1) shown in Table 3. Blank cells indicate
 that the variable (specified in the first column) was not included in that
 particular regression model (specified in the first row).  ##### Variables
 * predictor: specifying the predictor variable in the model  * IL-1b 1:
 results for the first IL-1b model (linear age term) * IL-1b 2: results for
 the second IL-1b model (quadratic age term) * IL-1b 3: results for the
 third IL-1b model (linear age term adjusting for leukocytes) * IL-1b 4:
 results for the fourth IL-1b model (quadratic age term adjusting for
 leukocytes) * IL-6 1: results for the first IL-6 model (linear age term) *
 IL-6 2: results for the second IL-6 model (quadratic age term) * IL-6 3:
 results for the third IL-6 model (linear age term adjusting for
 leukocytes) * IL-6 4: results for the fourth IL-6 model (quadratic age
 term adjusting for leukocytes) * IL-10 1: results for the first IL-10
 model (linear age term) * IL-10 2: results for the second IL-10 model
 (quadratic age term) * IL-10 3: results for the third IL-10 model (linear
 age term adjusting for leukocytes) * IL-10 4: results for the fourth IL-10
 model (quadratic age term adjusting for leukocytes) * TNF-a 1: results for
 the first TNF-a model (linear age term) * TNF-a 2: results for the second
 TNF-a model (quadratic age term) * TNF-a 3: results for the third TNF-a
 model (linear age term adjusting leukocytes) * TNF-a 4: results for the
 fourth TNF-a model (quadratic age term adjusting for leukocytes)  ####
 File: inflammaging_procb_table4.csv **Description:** Regression model
 results for the Tsimane (part 2) shown in Table 4. Blank cells indicate
 that the variable (specified in the first column) was not included in that
 particular regression model (specified in the first row).  ##### Variables
 * predictor: specifying the predictor variable in the model  * IL-2 1:
 results for the first IL-2 model (linear age term) * IL-2 2: results for
 the second IL-2 model (quadratic age term) * IL-2 3: results for the third
 IL-2 model (linear age term adjusting for leukocytes) * IL-2 4: results
 for the fourth IL-2 model (quadratic age term adjusting for leukocytes) *
 IL-12 1: results for the first IL-12 model (linear age term) * IL-12 2:
 results for the second IL-12 model (quadratic age term) * IL-12 3: results
 for the third IL-12 model (linear age term adjusting for leukocytes) *
 IL-12 4: results for the fourth IL-12 model (quadratic age term adjusting
 for leukocytes) * IL-15 1: results for the first IL-15 model (linear age
 term) * IL-15 2: results for the second IL-15 model (quadratic age term) *
 IL-15 3: results for the third IL-15 model (linear age term adjusting for
 leukocytes) * IL-15 4: results for the fourth IL-15 model (quadratic age
 term adjusting for leukocytes) * IFN-g 1: results for the first IFN-y
 model (linear age term) * IFN-g 2: results for the second IFN-y model
 (quadratic age term) * IFN-g 3: results for the third IFN-y model (linear
 age term adjusting for leukocytes) * IFN-g 4: results for the fourth IFN-y
 model (quadratic age term adjusting for leukocytes) #### File:
 inflammaging_procb_table5.csv **Description:** Regression model results
 for the Moseten shown in Table 5. Blank cells indicate that the variable
 (specified in the first column) was not included in that particular
 regression model (specified in the first row).  ##### Variables *
 predictor: the predictor variables in the model * IL-1b 1: results for the
 first IL-1b model (linear age term) * IL-1b 2: results for the second
 IL-1b model (quadratic age term) * IL-6 1: results for the first IL-6
 model (linear age term) * IL-6 2: results for the second IL-6 model
 (quadratic age term) * IL-10 1: results for the first IL-10 model (linear
 age term) * IL-10 2: results for the second IL-10 model (quadratic age
 term) * TNF-a 1: results for the first TNF-a model (linear age term) *
 TNF-a 2: results for the second TNF-a model (quadratic age term) * IL-2
 1: results for the first IL-2 model (linear age term) * IL-2 2: results
 for the second IL-2 model (quadratic age term) * IL-12 1: results for the
 first IL-12 model (linear age term) * IL-12 2: results for the second
 IL-12 model (quadratic age term) * IL-15 1: results for the first IL-15
 model (linear age term) * IL-15 2: results for the second IL-15 model
 (quadratic age term) * IFN-g 1: results for the first IFN-y model (linear
 age term) * IFN-g 2: results for the second IFN-y model (quadratic age
 term) #### File: inflammaging_procb_il6plotdat.csv
 **Description:** Regression model results used to create the IL-6 plot in
 Figure 1. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-6 in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_il10plotdat.csv
 **Description:** Model results data used to make the IL-10 plot in Figure
 1. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-10 in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_il1bplotdat.csv
 **Description:** Model results data used to make the IL-1b plot in Figure
 1. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-1b in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_tnfaplotdat.csv
 **Description:** Model results data used to make the TNF-a plot in Figure
 1. These metadata produce the age trend line (with 95% confidence
 intervals) for TNF-a in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_il2plotdat.csv
 **Description:** Model results data used to make the IL-2 plot in Figure
 2. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-2 in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_il12plotdat.csv
 **Description:** Model results data used to make the IL-12 plot in Figure
 2. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-12 in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_il15plotdat.csv
 **Description:** Model results data used to make the IL-15 plot in Figure
 2. These metadata produce the age trend line (with 95% confidence
 intervals) for IL-15 in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten #### File: inflammaging_procb_ifnyplotdat.csv
 **Description:** Model results data used to make the IFN-y plot in Figure
 2. These metadata produce the age trend line (with 95% confidence
 intervals) for IFN-y in the Tsimane and Moseten.  ##### Variables * fit:
 the fitted cytokine value derived from the regression model * se: the
 standard error for the fit value * lower ci: the lower 95% confidence
 interval for the fit value * upper ci: the upper 95% confidence interval
 for the fit value * age: age in years  * population: designating either
 Tsimane or Moseten ## Code/software R version 4.4.2 (2024-10-31) attached
 base packages: stats graphics grDevices utils datasets methods base other
 attached packages: ggExtra_0.10.1 effects_4.2-2 performance_0.13.0
 AER_1.2-14 survival_3.7-0 car_3.1-3 carData_3.0-5 ggpubr_0.6.0
 lmtest_0.9-40 zoo_1.8-13 sandwich_3.1-1 stargazer_5.2.3 lubridate_1.9.4
 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.4 readr_2.1.5
 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 ## Human subjects
 data Informed consent was given by the Tsimane Government (Gran Consejo
 Tsimane), village leaders, and all study participants directly. Methods
 and procedures were approved by the Institutional Review Boards of the
 University of New Mexico (HRRC #07-157; #15-133; #17-230), University of
 California Santa Barbara (HRRC #28-21-0788), and the Universidad San Simon
 Cochabamba, Bolivia. Individual-level data are stored in the Tsimane
 Health and Life History Project (THLHP) Data Repository, and are available
 through restricted access for ethical reasons. THLHP's highest
 priority is the safeguarding of human subjects and minimization of risk to
 study participants. The THLHP adheres to the “CARE Principles for
 Indigenous Data Governance” (Collective Benefit, Authority to Control,
 Responsibility, and Ethics), which assure that the Tsimane 1) have
 sovereignty over how data are shared, 2) are the primary gatekeepers
 determining ethical use, 3) are actively engaged in the data generation,
 and 4) derive benefit from data generated and shared for use whenever
 possible. The THLHP is also committed to the “FAIR Guiding Principles for
 scientific data management and stewardship” (Findable, Accessible,
 Interoperable, Reusable). Requests for individual-level data should take
 the form of an application that details the exact uses of the data and the
 research questions to be addressed, procedures that will be employed for
 data security and individual privacy, potential benefits to the study
 communities, and procedures for assessing and minimizing stigmatizing
 interpretations of the research results (see the following webpage for
 links to the data sharing policy and data request forms:
 https://tsimane.anth.ucsb.edu/data.html). Requests for individual-level
 data will require institutional IRB approval (even if exempt) and will be
 reviewed by an Advisory Council composed of Tsimane community leaders,
 community members, Bolivian scientists, and the THLHP leadership. The
 study authors and the THLHP leadership are committed to open science and
 are available to assist interested investigators in preparing data access
 requests.