OSCR

Early Adversity Imprints Multisystem Biological Aging: A Longitudinal Study of Adversity Dimensions and Aging Phenotypes Across Adolescence.

Code ↔ Paper

12 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 12 matches
  1. [1] § Results › Simultaneous Multiple Regression: Biomarker-Specific Associations ↔ code/05_sex_stratified_cca.py, lines 76–96 · score 0.82 · Low parental support, stress PCs, neighborhood disadvantage, telomere length, pubertal stage, mtDNA
  2. [2] § Methods and Materials › Participants and Study Design ↔ code/05_sex_stratified_cca.py, lines 76–96 · score 0.76 · brain age gap, neighborhood disadvantage, telomere length, pubertal stage, mtDNA, cumulative stress
  3. [3] § Results › Simultaneous Multiple Regression: Biomarker-Specific Associations ↔ code/07_structural_sensitivity_cca.py, lines 130–163 · score 0.75 · Low parental support, neighborhood disadvantage, telomere length, pubertal stage, mtDNA, cumulative stress
  4. [4] § Methods and Materials › Participants and Study Design ↔ code/fig3_forest_plot.py, lines 63–72 · score 0.74 · cortisol AUC, brain age gap, telomere length, pubertal stage, mtDNA, copy
  5. [5] § Results › CCA: Stress Dimensions and Biological Aging ↔ code/07_structural_sensitivity_cca.py, lines 130–163 · score 0.73 · low parental support, neighborhood disadvantage, telomere length, pubertal stage, mtDNA, cumulative stress
  6. [6] § Methods and Materials › Biomarkers of Aging ↔ code/fig3_forest_plot.py, lines 63–72 · score 0.70 · brain age gap, telomere length, pubertal stage, mtDNA, Tanner, copy
  7. [7] § Methods and Materials › Biomarkers of Aging ↔ code/03_theory_driven_cca.py, lines 51–77 · score 0.69 · brain age gap, telomere length, pubertal stage, mtDNA, Tanner, AUC
  8. [8] § Methods and Materials › Participants and Study Design ↔ code/02_main_analysis.py, lines 1–31 · score 0.67 · brain age gap, Biological aging, mtDNA, PCA, parallel, Tanner
  9. [9] § Methods and Materials › Statistical Analyses ↔ code/fig3_forest_plot.py, lines 1–42 · score 0.62 · low parental support, neighborhood disadvantage, cumulative stress, PC3, PC2, PC1
  10. [10] § Methods and Materials › Statistical Analyses ↔ code/06_sex_strat_psychopathology.py, lines 13–23 · score 0.59 · YSR internalizing, T4 YSR, CV scores, sex, externalizing, age
  11. [11] § Methods and Materials › Statistical Analyses ↔ code/04_theory_driven_psychopathology.py, lines 15–28 · score 0.59 · YSR internalizing, T4 YSR, CV scores, sex, externalizing, age
  12. [12] § Methods and Materials › Statistical Analyses ↔ code/02_main_analysis.py, lines 419–451 · score 0.58 · simultaneously regressed, biomarker intercept, covariates, OLS, PC3, PC2

Paper

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The authors' code

Python · 256 lines · 11 KB · MIT · 3 matches

  1. #!/usr/bin/env python3
  2. """
  3. Figure 3 — Forest plot: each biomarker regressed simultaneously on PC1, PC2, PC3.
  4. Standardized betas ± 95% CI.
  6. Style: matches Figure 2 aesthetic (Arial/sans-serif, CV phenotype colors for PCs).
  7. PC1 (Cumulative Stress & Trauma) → CV2 crimson #A63228 [threat]
  8. PC2 (Low Parental Support) → CV1 blue #1B6CA8 [deprivation]
  9. PC3 (Neighborhood Disadvantage) → neutral teal #2E7D6B
  11. Significance encoding (marker fill):
  12. p < .01 → full phenotype color (opaque)
  13. p < .05 → lighter shade
  14. p < .10 → even lighter
  15. ns → gray
  16. """
  17. import sys, numpy as np, pandas as pd, shutil
  18. sys.path.insert(0, '/sessions/brave-charming-carson/mnt/outputs/sci_viz')
  19. import matplotlib; matplotlib.use('Agg')
  20. import matplotlib.pyplot as plt
  21. import matplotlib.patches as mpatches
  22. from matplotlib.lines import Line2D
  23. import statsmodels.api as sm
  24. from figure_export import save_publication_figure
  26. plt.rcParams.update({
  27. 'font.family' : 'sans-serif',
  28. 'font.sans-serif' : ['Arial', 'Liberation Sans', 'Helvetica', 'DejaVu Sans'],
  29. 'font.size' : 9,
  30. 'axes.linewidth' : 0.6,
  31. 'xtick.major.width': 0.6,
  32. 'ytick.major.width': 0.6,
  33. 'xtick.major.size' : 3,
  34. 'ytick.major.size' : 3,
  35. 'figure.facecolor' : 'white',
  36. 'axes.facecolor' : 'white',
  37. 'figure.constrained_layout.use': False,
  38. })
  40. DATA = '/sessions/brave-charming-carson/mnt/BioAgingComposite'
  41. OUTDIR = '/sessions/brave-charming-carson/mnt/outputs'
  42. BIODIR = DATA
  44. # ── Colors ────────────────────────────────────────────────────────────────
  45. PC_COLORS = {
  46. 'PC1': '#A63228', # threat/cumulative stress → CV2 crimson
  47. 'PC2': '#1B6CA8', # deprivation/low parental support → CV1 blue
  48. 'PC3': '#2E7D6B', # neighborhood disadvantage → teal
  49. }
  50. PC_LABELS = {
  51. 'PC1': 'PC1: Cumulative Stress & Trauma',
  52. 'PC2': 'PC2: Low Parental Support',
  53. 'PC3': 'PC3: Neighborhood Disadvantage',
  54. }
  56. def sig_alpha(p):
  57. """Return (fill_alpha, edge_alpha) based on p-value."""
  58. if p < 0.01: return 1.00, 1.00
  59. elif p < 0.05: return 0.65, 0.80
  60. elif p < 0.10: return 0.35, 0.60
  61. else: return 0.00, 0.45 # ns: open circle, muted edge
  63. # ── Biomarkers to plot (display order top→bottom matches original Fig 3) ──
  64. BIO_SPECS = [
  65. ('t1_BMI', 'BMI'),
  66. ('t1_Tanner', 'Pubertal Stage'),
  67. ('t1_mtdna', 'mtDNA Copy Number'),
  68. ('t1_BrainAgeGap_Resid','Brain Age Gap'),
  69. ('t1_cort_AUC', 'Cortisol AUC'),
  70. ('t1_telo', 'Telomere Length'),
  71. ]
  72. PC_COLS = ['PC1', 'PC2', 'PC3']
  74. # ── Load data ─────────────────────────────────────────────────────────────
  75. df = (pd.read_csv(f'{DATA}/intercepts_final.csv')
  76. .merge(pd.read_csv(f'{DATA}/stress_pcs_final.csv'), on='SubID'))
  78. # ── Run regressions ───────────────────────────────────────────────────────
  79. results = []
  80. for col, label in BIO_SPECS:
  81. sub = df[[col] + PC_COLS].dropna()
  82. y_raw = sub[col].values
  83. X_raw = sub[PC_COLS].values
  85. # Standardize y by SD only (per figure note); leave PCs in raw units
  86. y_sd = y_raw.std(ddof=1)
  87. y = y_raw / y_sd
  89. X_df = pd.DataFrame(X_raw, columns=PC_COLS)
  90. X_fit = sm.add_constant(X_df)
  92. model = sm.OLS(y, X_fit).fit()
  93. r2 = model.rsquared
  94. n = len(y)
  96. for pc in PC_COLS:
  97. beta = model.params[pc]
  98. ci = model.conf_int(alpha=0.05).loc[pc]
  99. p = model.pvalues[pc]
  100. results.append({
  101. 'biomarker': label, 'col': col, 'PC': pc,
  102. 'beta': beta, 'ci_lo': ci[0], 'ci_hi': ci[1],
  103. 'p': p, 'r2': r2, 'n': n,
  104. })
  105. print(f'{label:28s} {pc} β={beta:+.3f} [{ci[0]:+.3f}, {ci[1]:+.3f}] p={p:.3f} R²={r2:.3f}')
  107. res_df = pd.DataFrame(results)
  109. # ── Layout ────────────────────────────────────────────────────────────────
  110. N_BIO = len(BIO_SPECS)
  111. N_PC = len(PC_COLS)
  113. # Each biomarker group occupies a "slot"; PCs are offset within it
  114. BIO_GAP = 1.20 # vertical spacing between biomarker groups (in plot units)
  115. PC_OFFSET = 0.22 # spacing between PC rows within a group
  117. FIG_W, FIG_H = 7.5, 8.5
  118. L, R = 0.28, 0.98
  119. T, B = 0.96, 0.10
  121. fig, ax = plt.subplots(figsize=(FIG_W, FIG_H))
  122. plt.subplots_adjust(left=L, right=R, top=T, bottom=B)
  124. # Y positions: biomarkers top→bottom
  125. bio_centers = {}
  126. for i, (col, label) in enumerate(BIO_SPECS):
  127. bio_centers[label] = (N_BIO - 1 - i) * BIO_GAP
  129. # ── Alternating row backgrounds ───────────────────────────────────────────
  130. for i, (col, label) in enumerate(BIO_SPECS):
  131. yc = bio_centers[label]
  132. ylo = yc - BIO_GAP / 2 + 0.02
  133. yhi = yc + BIO_GAP / 2 - 0.02
  134. col_bg = '#F7F7F7' if i % 2 == 0 else 'white'
  135. ax.axhspan(ylo, yhi, facecolor=col_bg, edgecolor='none', zorder=0)
  137. # ── Zero reference line ───────────────────────────────────────────────────
  138. ax.axvline(0, color='#999999', lw=0.8, ls='--', zorder=1)
  140. # ── Plot each PC within each biomarker ────────────────────────────────────
  141. pc_y_offsets = {'PC1': +PC_OFFSET, 'PC2': 0, 'PC3': -PC_OFFSET}
  143. for _, row in res_df.iterrows():
  144. bio = row['biomarker']
  145. pc = row['PC']
  146. yc = bio_centers[bio] + pc_y_offsets[pc]
  147. beta = row['beta']
  148. ci_lo = row['ci_lo']
  149. ci_hi = row['ci_hi']
  150. p = row['p']
  151. col = PC_COLORS[pc]
  152. fa, ea = sig_alpha(p)
  154. # CI line
  155. ax.plot([ci_lo, ci_hi], [yc, yc],
  156. color=col, lw=1.6, alpha=max(ea, 0.35),
  157. solid_capstyle='round', zorder=3)
  159. # Point
  160. if fa == 0: # ns: open circle
  161. ax.plot(beta, yc, 'o', ms=6.5,
  162. mfc='white', mec=col, mew=1.2,
  163. alpha=ea, zorder=4)
  164. else:
  165. ax.plot(beta, yc, 'o', ms=6.5,
  166. mfc=col, mec=col, mew=0.6,
  167. alpha=fa, zorder=4)
  169. # ── Biomarker labels (left side) ──────────────────────────────────────────
  170. # Group header bold + R² inline, then PC sub-labels
  171. for col, label in BIO_SPECS:
  172. yc = bio_centers[label]
  173. r2 = res_df[res_df['biomarker'] == label]['r2'].iloc[0]
  174. n = res_df[res_df['biomarker'] == label]['n'].iloc[0]
  176. # Bold biomarker name + R²
  177. ax.text(-0.01, yc + PC_OFFSET + 0.16,
  178. f'{label} (R² = {r2:.3f})',
  179. ha='right', va='center', transform=ax.get_yaxis_transform(),
  180. fontsize=9.5, fontweight='bold', color='#1A1A1A')
  182. # PC sub-labels
  183. for pc, dy in pc_y_offsets.items():
  184. ax.text(-0.01, yc + dy,
  185. pc,
  186. ha='right', va='center', transform=ax.get_yaxis_transform(),
  187. fontsize=8, color=PC_COLORS[pc], fontweight='bold')
  189. # ── X axis ────────────────────────────────────────────────────────────────
  190. ax.set_xlim(-0.32, 0.32)
  191. ax.set_xticks([-0.3, -0.2, -0.1, 0, 0.1, 0.2, 0.3])
  192. ax.set_xlabel('Standardized β (± 95% CI)',
  193. fontsize=9.5, color='#333333', labelpad=6)
  195. # ── Y axis ────────────────────────────────────────────────────────────────
  196. ax.set_ylim(-BIO_GAP / 2 - 0.1,
  197. (N_BIO - 1) * BIO_GAP + BIO_GAP / 2 + 0.1)
  198. ax.yaxis.set_visible(False)
  200. # ── Spines ────────────────────────────────────────────────────────────────
  201. ax.spines['top'].set_visible(False)
  202. ax.spines['right'].set_visible(False)
  203. ax.spines['left'].set_visible(False)
  204. ax.spines['bottom'].set_color('#AAAAAA')
  205. ax.tick_params(axis='x', colors='#666666')
  207. # ── Legend ────────────────────────────────────────────────────────────────
  208. # PC color legend (top section)
  209. pc_handles = [
  210. Line2D([0],[0], color=PC_COLORS[pc], lw=2.0,
  211. marker='o', ms=5.5, mfc=PC_COLORS[pc], mec=PC_COLORS[pc],
  212. label=PC_LABELS[pc])
  213. for pc in PC_COLS
  214. ]
  215. # Significance legend (bottom section)
  216. sig_handles = [
  217. Line2D([0],[0], color='#555555', lw=1.5,
  218. marker='o', ms=6, mfc='#555555', mec='#555555', label='p < .01'),
  219. Line2D([0],[0], color='#555555', lw=1.5,
  220. marker='o', ms=6, mfc='#555555', mec='#555555', alpha=0.65, label='p < .05'),
  221. Line2D([0],[0], color='#555555', lw=1.5,
  222. marker='o', ms=6, mfc='#555555', mec='#555555', alpha=0.35, label='p < .10'),
  223. Line2D([0],[0], color='#888888', lw=1.2,
  224. marker='o', ms=6, mfc='white', mec='#888888', mew=1.2, label='ns'),
  225. ]
  227. leg1 = ax.legend(handles=pc_handles, loc='lower right',
  228. frameon=False, fontsize=8, title='Stress Dimension',
  229. title_fontsize=8.2,
  230. handlelength=1.6, labelspacing=0.5,
  231. bbox_to_anchor=(1.0, 0.22))
  232. leg1.get_title().set_fontweight('bold')
  233. ax.add_artist(leg1)
  235. leg2 = ax.legend(handles=sig_handles, loc='lower right',
  236. frameon=False, fontsize=8, title='Significance',
  237. title_fontsize=8.2,
  238. handlelength=1.6, labelspacing=0.5,
  239. bbox_to_anchor=(1.0, 0.0))
  240. leg2.get_title().set_fontweight('bold')
  242. # ── Caption note ──────────────────────────────────────────────────────────
  243. fig.text(L, 0.025,
  244. 'Each biomarker regressed simultaneously on PC1, PC2, and PC3. '
  245. 'Coefficients standardized by biomarker SD.',
  246. ha='left', va='bottom', fontsize=7.5,
  247. color='#777777', fontstyle='italic',
  248. transform=fig.transFigure)
  250. # ── Save ──────────────────────────────────────────────────────────────────
  251. save_publication_figure(fig, f'{OUTDIR}/fig3_forest.png',
  252. formats=['png', 'pdf'], dpi=300)
  253. for ext in ['png', 'pdf']:
  254. shutil.copy(f'{OUTDIR}/fig3_forest.{ext}',
  255. f'{BIODIR}/Fig3_Forest.{ext}')
  256. print('Saved fig3_forest')

fig3_forest_plot.py at commit 8d161e5, under MIT · at the source

Overview

Authors: Chase Antonacci1,2, Eugenia Giampetruzzi1, Jessica L Buthmann1, Sarah Rocha1, Xinyan Tao1, Ian H Gotlib1
ORCID iDs: Chase Antonacci, Eugenia Giampetruzzi, Jessica L Buthmann, Xinyan Tao, Ian H Gotlib
  1. Department of Psychology, Stanford University, Stanford, California
  2. Neurosciences Interdepartmental Program, Stanford University, Stanford, California
Institutions: Stanford University (United States)
Journal: Biological psychiatry global open science, volume 6, issue 5, article 100782
Dates: received 28 April 2026; accepted 18 June 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.bpsgos.2026.100782 · PMID 42602875 · PMCID PMC13475218 · OpenAlex W7165769036
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: developmental (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Adolescence, Biological aging, Developmental imprinting, Developmental timing, Early-life adversity, Pubertal development
Topic: Stress Responses and Cortisol (Behavioral Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R37 MH101495)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Background: Early-life stress (ELS) is associated with accelerated biological aging; however, it is unclear whether dimensions of adversity such as threat and deprivation produce distinct multisystem aging phenotypes. Here we characterize, for the first time, the multivariate copatterning of stress dimensions and aging biomarkers and examine whether such phenotypes predict psychopathology 6 years later.

Methods: In a longitudinal sample (N = 225; ages 9–13; 58.7% female) studied across 4 waves (approximately 6 years), 16 measures of stress exposure were reduced via principal component analysis to 3 orthogonal dimensions: cumulative stress & trauma, low parental support, and neighborhood disadvantage. Canonical correlation analysis (CCA) related these dimensions to baseline levels and developmental trajectories of 6 aging biomarkers (mitochondrial DNA [mtDNA], telomere length, cortisol, body mass index [BMI], pubertal stage, and brain age gap). Canonical variate (CV) scores predicted internalizing, externalizing, and total problems in late adolescence (n = 142).

Results: CCA identified 2 significant CVs at baseline. CV1 linked deprivation-related stress (low parental support/neighborhood disadvantage) to a cellular-metabolic aging profile (higher BMI, older brain age, and lower mtDNA) (r = 0.39, p < .001). CV2 linked cumulative stress and trauma to a threat-responsive neuroendocrine maturation profile (earlier pubertal stage, higher cortisol, higher BMI, and higher mtDNA) (r = 0.24, p = .003). ELS was not associated with biomarker trajectories (ps > .20), consistent with early embedding. Only the threat phenotype (CV2) predicted psychopathology (β = 6.98, p = .004, R2 = 0.12).

Conclusions: ELS is biologically embedded along adversity type-specific pathways detectable by late childhood, supporting a developmental imprinting model. A threat-related multisystem aging phenotype conferred transdiagnostic psychiatric risk in late adolescence, pointing to pre-adolescent development as an important window for prevention.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

cantonacci/stress-bioaging

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8d161e507d47223acf3c1661b2b0e2cee6234e32, 31 May 2026
Languages: Python (11), R (1)
Size: 15 files, 12 scripts
Software Heritage: not archived
Found in: the acknowledgements
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (11 files), pandas (8 files), Matplotlib (6 files), SciPy (4 files), statsmodels (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/cantonacci/stress-bioaging

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;
  • 12 scripts, each with its path and the digest of its content;
  • 12 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

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Xinyan Tao (0009-0006-4555-4957); removed Xinyan Tao

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 keywords, 1 funder, 52 references.

Cite

This paper

Antonacci, C., Giampetruzzi, E., Buthmann, J. L., Rocha, S., Tao, X., & Gotlib, I. H. (2026). Early Adversity Imprints Multisystem Biological Aging: A Longitudinal Study of Adversity Dimensions and Aging Phenotypes Across Adolescence. Biological psychiatry global open science, 6(5), 100782. https://doi.org/10.1016/j.bpsgos.2026.100782

BibTeX

@article{antonacci2026early,
author = {Antonacci, Chase and Giampetruzzi, Eugenia and Buthmann, Jessica L and Rocha, Sarah and Tao, Xinyan and Gotlib, Ian H},
title = {{Early Adversity Imprints Multisystem Biological Aging: A Longitudinal Study of Adversity Dimensions and Aging Phenotypes Across Adolescence}},
journal = {Biological psychiatry global open science},
year = {2026},
month = jun,
volume = {6},
number = {5},
pages = {100782},
publisher = {Elsevier},
issn = {2667-1743},
doi = {10.1016/j.bpsgos.2026.100782},
url = {https://doi.org/10.1016/j.bpsgos.2026.100782},
pmid = {42602875},
pmcid = {PMC13475218}
}

RIS

TY - JOUR
AU - Antonacci, Chase
AU - Giampetruzzi, Eugenia
AU - Buthmann, Jessica L
AU - Rocha, Sarah
AU - Tao, Xinyan
AU - Gotlib, Ian H
TI - Early Adversity Imprints Multisystem Biological Aging: A Longitudinal Study of Adversity Dimensions and Aging Phenotypes Across Adolescence
T2 - Biological psychiatry global open science
J2 - Biol Psychiatry Glob Open Sci
PY - 2026
DA - 2026/06/24
VL - 6
IS - 5
SP - 100782
SN - 2667-1743
PB - Elsevier
DO - 10.1016/j.bpsgos.2026.100782
UR - https://doi.org/10.1016/j.bpsgos.2026.100782
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.bpsgos.2026.100782",
"type": "article-journal",
"title": "Early Adversity Imprints Multisystem Biological Aging: A Longitudinal Study of Adversity Dimensions and Aging Phenotypes Across Adolescence",
"container-title": "Biological psychiatry global open science",
"author": [
{
"family": "Antonacci",
"given": "Chase"
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{
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"given": "Eugenia"
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{
"family": "Buthmann",
"given": "Jessica L"
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{
"family": "Rocha",
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},
{
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"given": "Xinyan"
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{
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"given": "Ian H"
}
],
"container-title-short": "Biol Psychiatry Glob Open Sci",
"volume": "6",
"issue": "5",
"page": "100782",
"DOI": "10.1016/j.bpsgos.2026.100782",
"PMID": "42602875",
"PMCID": "PMC13475218",
"ISSN": "2667-1743",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.bpsgos.2026.100782",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

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