OSCR

Mixture of organic pollutants is associated with cognitive aging.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § RESULTS › Mixture analysis of association between chemicals and cognitive domains ↔ code/figures/Figure_S4_dotplot_pips_all_outcomes.R, lines 1–75 · score 0.91 · diethyl phthalate, trans Permethrin, cis Permethrin, acenaphthene, malathion, mirex
  2. [2] § RESULTS › Mixture analysis of association between pollutants and global cognitive score ↔ code/figures/Figure_S4_dotplot_pips_all_outcomes.R, lines 1–75 · score 0.79 · diethyl phthalate, cis permethrin, DDD, anthracene, safrole, bromacil
  3. [3] § RESULTS › Mixture analysis of association between pollutants and global cognitive score ↔ code/functions/function_bkmr_plots.R, lines 3–109 · score 0.70 · univariate response, indoor dust, green space, legacy PCBs, global score, posterior
  4. [4] § METHODS › Assessment of cognitive function ↔ code/data_exploration/demographics_table1.R, the whole file · a weak match · score 0.68 · episodic memory, processing speed, fluid reasoning, global score, American, sub
  5. [5] § METHODS › Assessment of cognitive function ↔ code/functions/function_bkmr_plots.R, lines 3–109 · score 0.65 · episodic memory, processing speed, fluid reasoning, global score, SD, vocabulary
  6. [6] § METHODS › Chemical source assignment ↔ code/summaries/supplemental_tables.R, the whole file · a weak match · score 0.61 · indoor dust, green space, legacy PCBs, household, detection, farming
  7. [7] § METHODS › Chemical source assignment ↔ code/figures/chemical_concentration_boxplot.R, the whole file · a weak match · score 0.58 · indoor dust, green space, legacy PCBs, household, farming, food
  8. [8] § METHODS › Exposure data processing ↔ code/data_preparation/chemical_exposure_data.R, lines 138–177 · score 0.56 · quartile variation, variability, CQV, coefficient, concentrations, filtered
  9. [9] § RESULTS › Correlations among pollutants ↔ code/figures/Figure1_B_chemical_correlation_heatmap.R, lines 1–40 · score 0.55 · indoor dust, green space, legacy PCB, Spearman, correlations, farming

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 101 lines · 6.6 KB · no license · 2 matches

  1. source("code/packages/packages_to_load.R")
  2. source("code/functions/function_bkmr_plots.R")
  4. # Get PIPs
  6. gcpip_mem <- get_pips(outcome_name = "memory")
  7. gcpip_vocab <- get_pips(outcome_name = "vocabulary")
  8. gcpip_reasoning <- get_pips(outcome_name = "reasoning")
  9. gcpip_speed <- get_pips(outcome_name = "speed")
  11. all_pip <- do.call(rbind, mget(ls(pattern="gcpip_"))) %>%
  12. select(label, PIP = condPIP, outcome, groupPIP, variable) %>%
  13. mutate(shape = "condPIP")
  15. group_pip <- all_pip %>%
  16. select(label, PIP = groupPIP, outcome) %>%
  17. distinct(.keep_all = TRUE) %>%
  18. mutate(shape = "groupPIP") %>%
  19. mutate(variable = "Group")
  21. pip_plot <- all_pip %>%
  22. select(label, PIP, outcome, shape, variable) %>%
  23. rbind(., group_pip) %>%
  24. arrange(desc(shape))
  26. levels(reorder(pip_plot$variable, pip_plot$PIP, median))
  28. pip_plot$variable_fct <- factor(pip_plot$variable, levels = c("PCB 49",
  29. "PCB 18",
  30. "PCB 81",
  31. "PCB 44",
  32. "p,p'-DDD",
  33. "PCB 128",
  34. "PCB 77",
  35. "p,p'-DDE",
  36. "Ethion",
  37. "PCB 33",
  38. "Benz[a]anthracene",
  39. "b-Hexachlorocyclohexane",
  40. "Endrin",
  41. "d-Lindane",
  42. "PCB 52",
  43. "Fonofos",
  44. "Chrysene",
  45. "Mirex",
  46. "2,4,5-Trichlorophenol",
  47. "2,3,7,8-Tetrachlorodibenzofuran",
  48. "Parathion",
  49. "Benzo[b]fluoranthene",
  50. "1,2,3,7,8-Pentachlorodibenzodioxin",
  51. "Pyrene",
  52. "1,2-Dichlorbenzene",
  53. "Phenanthrene",
  54. "Fluoranthene",
  55. "Anthracene",
  56. "Isosafrole",
  57. "Tri(2-chloroethyl) phosphate",
  58. "cis-Permethrin",
  59. "Diethyl Phthalate",
  60. "1,2,4,5-Tetrachlorobenzene",
  61. "Safrole",
  62. "Bromacil",
  63. "DEET",
  64. "PCB 203",
  65. "trans-Permethrin",
  66. "Lindane",
  67. "Etridiazole",
  68. "Malathion",
  69. "1,2,4-Trichlorobenzene",
  70. "Acenaphthene",
  71. "Simazine",
  72. "Triadimefon",
  73. "Hexachlorbutadiene",
  74. "PCB 60",
  75. "Group"))
  79. set.seed(2023)
  80. pips_graph <- pip_plot %>%
  81. ggplot(aes(x = PIP, y = variable_fct, color = factor(outcome), shape = factor(shape), alpha = factor(shape))) +
  82. geom_point(data = filter(pip_plot, shape == "condPIP"), size = 2.5) +
  83. geom_jitter(data = filter(pip_plot, shape == "groupPIP"), height = 0.25, size = 2.5) +
  84. facet_wrap(~label, scales = "free_y", nrow = 2) +
  85. xlim(0,1) +
  86. labs(color = "Outcome", y = "", shape = "PIP", x = "Posterior Inclusion Probability (PIP)") +
  87. theme_bw(base_size = 13) +
  88. theme(legend.position = "bottom",
  89. panel.border = element_rect(colour = "black", size=0.7),
  90. strip.placement = "outside",
  91. strip.background = element_rect(fill = "white")) +
  92. guides(alpha = "none") +
  93. scale_color_manual(values = c("#0B8EF8FF", "orange", "#681A15FF", "#DC5750FF"),
  94. labels = c(c("Episodic memory", "Fluid reasoning", "Processing speed", "Vocabulary"))) +
  95. scale_alpha_manual(values = c(0.6, 0.8)) +
  96. scale_shape_manual(values = c(20, 15), labels = c("Conditional PIP", "Group PIP")) +
  97. guides(color = guide_legend(ncol = 2),
  98. shape = guide_legend(ncol = 1))

Figure_S4_dotplot_pips_all_outcomes.R at commit deef88f, no license · at the source

Overview

Authors: Vrinda Kalia1, Katherine E Manz2, Jaime Benavides1, Suhang Song3, Brandi L Vollmer4, Beizhan Yan5, Jeff Goldsmith6, Kurt D Pennell7, Yaakov Stern8, Marianthi‐Anna Kioumourtzoglou1, Gary W Miller1, Christian Habeck9, Yian Gu10
ORCID iDs: Vrinda Kalia, Christian Habeck
  1. Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, USA
  2. Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
  3. Department of Health Policy and Management, College of Public Health, University of Georgia, Athens, Georgia, USA
  4. Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
  5. Lamont‐Doherty Earth Observatory, Columbia University, Palisades, New York, USA
  6. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA
  7. School of Engineering, Brown University, Providence, Rhode Island, USA
  8. Departments of Neurology, Psychiatry, G.H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
  9. Department of Neurology and Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
  10. Department of Neurology, G.H. Sergievsky Center and Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Vagelos College of Physicians and Surgeons, Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
Institutions: Columbia University (United States); University of Michigan (United States); Brown University (United States); University of Georgia (United States); Columbia University Irving Medical Center (United States); Lamont-Doherty Earth Observatory (United States)
Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association, volume 22, issue 4, article e71399
Dates: received 13 October 2025; accepted 19 March 2026; published online 19 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/alz.71399 · PMID 42002796 · PMCID PMC13092418 · OpenAlex W4415349216
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.10.17.25338236
Status: code verified
Categories: behavior only (modality), human (organism)
Methods: Statistics, Connectivity
Keywords: cognitive aging, exposome, polychlorinated biphenyls
MeSH: Cognitive Aging*, Environmental Exposure*, Persistent Organic Pollutants*, Adult, Aged, Aged, 80 and over, Bayes Theorem, Female, Humans, Latent Class Analysis, Male, Middle Aged, Neuropsychological Tests, Polychlorinated Biphenyls, Regression Analysis, Young Adult (* major topic)
Topic: Toxic Organic Pollutants Impact (Health, Toxicology and Mutagenesis, Environmental Science), according to OpenAlex
Funding: NIEHS NIH HHS (R01 ES023839, U24 ES036819, P30 ES009089, R01 ES028805); National Institute on Aging (R01 AG061008, P20 AG093975); NIA NIH HHS (P20 AG093975, R01 AG061008)
Citations: cited by 2 papers (Europe PMC); 60 references in the paper

Abstract

INTRODUCTION: Environmental organic pollutants impact brain function and cognitive aging, but the effect of real‐world complex mixtures of these pollutants is unexplored.

METHODS: Using data collected at two time points from 170 cognitively normal adults, we used hierarchical Bayesian kernel machine regression to examine the association between joint exposure to 49 organic pollutants and latent variables derived from neuropsychological tests that capture key aspects of cognitive aging.

RESULTS: We observed a non‐linear, inverted U‐shaped relationship between the pollutant mixture and the global cognitive score. Polychlorinated biphenyls (PCBs) were the most important pollutant group in the mixture followed by industrial‐use pollutants.

DISCUSSION: Exposure to a mixture of organic pollutants was associated with poor cognitive aging. Even though many of these pollutants, like PCBs, have been banned for decades, they persist in our environment. Strategies to reduce exposure to these organic pollutants are needed to minimize their impact on cognitive aging.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

vrindakalia/chemicals_and_reference_abilities

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: deef88fdb2226d33b91685c0653346ca1bc64a33, 6 October 2025
Languages: R (23)
Size: 79 files, 23 scripts
Software Heritage: not archived
Found in: “Statistical software and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: cowplot (1 file), easystats (1 file), lme4 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
24 files
At the source: github.com/vrindakalia/chemicals_and_reference_abilities

Statistical software and code availability

All statistical analyses were conducted in R (version 4.4.1). The code used for analysis was reviewed by a co‐author and can be found on the first author's GitHub page: https://github.com/vrindakalia/chemicals_and_reference_abilities

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 23 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 3 keywords, 16 MeSH terms, 3 funders, 59 references.

Cite

This paper

Kalia, V., Manz, K. E., Benavides, J., Song, S., Vollmer, B. L., Yan, B., Goldsmith, J., Pennell, K. D., Stern, Y., Kioumourtzoglou, M., Miller, G. W., Habeck, C., & Gu, Y. (2026). Mixture of organic pollutants is associated with cognitive aging. Alzheimer's & dementia : the journal of the Alzheimer's Association, 22(4), e71399. https://doi.org/10.1002/alz.71399

BibTeX

@article{kalia2026mixture,
author = {Kalia, Vrinda and Manz, Katherine E and Benavides, Jaime and Song, Suhang and Vollmer, Brandi L and Yan, Beizhan and Goldsmith, Jeff and Pennell, Kurt D and Stern, Yaakov and Kioumourtzoglou, Marianthi‐Anna and Miller, Gary W and Habeck, Christian and Gu, Yian},
title = {{Mixture of organic pollutants is associated with cognitive aging}},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = apr,
volume = {22},
number = {4},
pages = {e71399},
publisher = {Wiley},
issn = {1552-5260},
doi = {10.1002/alz.71399},
url = {https://doi.org/10.1002/alz.71399},
pmid = {42002796},
pmcid = {PMC13092418}
}

RIS

TY - JOUR
AU - Kalia, Vrinda
AU - Manz, Katherine E
AU - Benavides, Jaime
AU - Song, Suhang
AU - Vollmer, Brandi L
AU - Yan, Beizhan
AU - Goldsmith, Jeff
AU - Pennell, Kurt D
AU - Stern, Yaakov
AU - Kioumourtzoglou, Marianthi‐Anna
AU - Miller, Gary W
AU - Habeck, Christian
AU - Gu, Yian
TI - Mixture of organic pollutants is associated with cognitive aging
T2 - Alzheimer's & dementia : the journal of the Alzheimer's Association
J2 - Alzheimers Dement
PY - 2026
DA - 2026/04/01
VL - 22
IS - 4
SP - e71399
SN - 1552-5260
PB - Wiley
DO - 10.1002/alz.71399
UR - https://doi.org/10.1002/alz.71399
LA - en
ER -

CSL-JSON

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