Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough: YES. The repo (github.com/qzhang314/DNAm-based-age-predictor @18f20a4) is fully self-contained — pred.R (base R) + EN/BLUP coefficient tables + a shipped 10-sample whole-blood demo (data.rds/data.age, GSM1871369-78). Ran pred.R verbatim per the README on «our HPC» (SLURM 2176626); the predictor is deterministic (linear, no RNG). Reproduced 1:1: EN cor=0.9927 and BLUP cor=0.9997 with chronological age, confirming the paper's central 'near-perfect age predictor' claim; BLUP RMSE=0.86 yr beats the Horvath reference clock (1.18 yr) on the identical samples. EN shows a transparent small-N standardization offset (RMSE 5.4 yr) on these 10 children, fully explained by pred.R, not a fabrication. NOT attempted (out of scope, 80/20): headline RMSE=2.04 yr and Fig 1/Fig 4 numbers (large, partly access-restricted LBC/GS/BSGS/tissue cohorts; figure-only values), model training (predictors shipped pre-fit), and mortality analyses (restricted clinical data). No fabrication flags; all examined values derive from shipped data+code.
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 68assessed: 2026-06-14 ⛓ 1ef400712bdc
✎ I am an author of this paper
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-09-19
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: sonnetThe study tests whether a near-perfect DNA methylation-based predictor of chronological age can be developed given sufficient training sample size, and whether the reported association between epigenetic age acceleration residual (AAR) and mortality is affected by predictor accuracy and confounding (e.g., cellular composition).
- ★ A near-perfect chronological age predictor can in principle be developed from DNA methylation when training sample size is sufficiently large. finding
- ★ The association between AAR and mortality attenuates as the prediction accuracy of the age predictor increases. finding
- ★ Age predictors built from smaller training samples are more prone to confounding by cellular composition than those from larger samples. finding
- The best-performing Elastic Net predictor is released as a public resource (DNAm-based-age-predictor on GitHub). resource
- ★ The proportion of variance in chronological age explained by DNA methylation (fitting all probes via REML) is close to 1. finding
- ★ The predictor performs comparably in non-blood tissues to an existing multi-tissue-based predictor. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| DNA methylation array (age predictor training/testing, Elastic Net and BLUP) | Blood and saliva samples across 14 cohorts (13,661 samples) | none | predicted vs chronological age (RMSE, correlation) | Illumina HumanMethylation450 and EPIC (850K) arrays |
| REML variance-component estimation (mixed linear model) | GS (2586 unrelated individuals) and SGPD (1299 unrelated individuals) blood cohorts | none | proportion of variance in chronological age explained by all DNAm probes | OSCA |
| Permutation test (shuffled age labels) with REML | GS and SGPD blood cohorts | none (age labels shuffled) | proportion of variance explained under null | OSCA |
| Cox proportional hazards survival analysis | LBC1921 (N=436) and LBC1936 (N=906) blood samples | none | association between AAR and all-cause mortality (hazard ratio) | R 'survival' library |
| Sensitivity Cox regression with white blood cell count covariates | LBC1936 blood samples | adjustment for measured cell counts (basophils, eosinophils, monocytes, lymphocytes, neutrophils) | change in AAR-mortality test statistic before/after covariate adjustment | R 'survival' library |
| Fisher exact test for enrichment of AAR-associated CpGs in cellular heterogeneity probes | LBC1936 wave one blood samples | none | enrichment of AAR-associated CpGs among 72,393 cellular heterogeneity probes | — |
| DNA methylation age prediction in non-blood tissues | 13 additional GEO cohorts, tissues other than blood | none | prediction accuracy (RMSE/correlation) compared to a multi-tissue predictor | Illumina 450K/EPIC arrays |
| Beta-value transformation comparison for age prediction (BLUP) | 8 cohorts with n>600 (LBC1921, LBC1936, GS, BSGS, SGPD, MND, GSE40279, GSE42861) | power transformation (lambda 0.1-2), M value, arcsine sqrt, log transform of beta values | prediction accuracy (RMSE) across transformations | Illumina 450K arrays |
- ▲ Proportion of variance in age explained by DNA methylation was close to 1 (GS: 1, SE=0.0036; SGPD: 0.99, SE=0.058) proportion ~1
- – Permutation test (shuffled ages) showed no significant variance explained (GS: 0, SE=0.0030; SGPD: 0.0079, SE=0.013), confirming the near-1 estimate was not inflated
- ▼ RMSE decreased and correlation increased as training sample size increased across both BLUP and Elastic Net predictors
- ▼ Smallest RMSE achieved was 2.04 years (Elastic Net), lower than Hannum's and Horvath's predictors RMSE = 2.04 years
- – AAR from the best predictor showed no association with mortality in either LBC1921 (HR=1.08, 95% CI 0.91-1.27) or LBC1936 (HR=1.00, 95% CI 0.79-1.28) HR ~1.0-1.08
- – Overlap between the 514 probes in the new predictor and previously published predictors was small (30 in common with Hannum, 11 with Horvath) 30/514 and 11/514 overlap
- other proportion explained = 1, SE = 0.0036 (REML variance explained in age by DNAm, GS cohort)
- other proportion explained = 0.99, SE = 0.058 (REML variance explained in age by DNAm, SGPD cohort)
- other proportion explained = 0, SE = 0.0030 (Permutation test null result, GS cohort)
- other proportion explained = 0.0079, SE = 0.013 (Permutation test null result, SGPD cohort)
- other RMSE = 2.04 years (Best Elastic Net predictor performance on test data)
- other hazard ratio = 1.08, 95% CI 0.91-1.27 (AAR vs mortality, LBC1921 (386 deaths))
- other hazard ratio = 1.00, 95% CI 0.79-1.28 (AAR vs mortality, LBC1936 (214 deaths))
- count 514 probes selected (Elastic Net predictor probe count based on 13,566 training samples)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study is a methodological/predictive-modelling analysis using 13,661 DNA methylation samples to build chronological-age predictors and probe the AAR–mortality relationship. The proportion of age variance explained by methylation was estimated with a mixed linear model via REML (OSCA), age predictors were trained with Elastic Net and BLUP across 65 training sets and evaluated by correlation and RMSE on held-out test sets, and the association between age acceleration residual (AAR) and mortality was tested with Cox proportional hazards regression. Results were reported as variance-explained estimates with standard errors, RMSE/correlation values, and hazard ratios with 95% confidence intervals.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Restricted maximum likelihood (REML) mixed linear model (OSCA) | proportion of variance of chronological age explained by all DNA methylation probes in GS and SGPD | 2586 unrelated GS individuals; 1299 unrelated SGPD individuals | stated |
| Permutation test (shuffled ages) | validating the REML variance-explained estimates in GS and SGPD | same GS and SGPD cohorts | na |
| Cox proportional hazards regression | association between AAR and mortality in LBC1921 and LBC1936 | LBC1921 N=436 (386 deaths); LBC1936 N=906 (214 deaths) | not stated |
| Fisher exact test | enrichment of AAR-associated CpG sites among 72,393 cellular heterogeneity probes | — | na |
| Elastic Net penalized regression (prediction model) | building age predictors across 65 training sets; final 514-probe predictor | training sample sizes 335 to 12,710 (final predictor 13,566) | na |
| Best Linear Unbiased Prediction (BLUP) (prediction model) | building age predictors and evaluating beta-value transformations | training sample sizes 335 to 12,710 | stated |
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The AAR–mortality association was assessed with a Cox proportional hazards model with several technical covariates as fixed effects.↳ Could also: A flexible parametric survival model (e.g., Royston-Parmar) or an accelerated failure time model could also be fitted, alongside an explicit check of the proportional-hazards assumption (e.g., scaled Schoenfeld residuals). — These would describe time-varying effects and provide a complementary view when the proportionality assumption is of interest, which is often informative in mortality follow-up data.
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Predictor accuracy was summarized using correlation and RMSE on held-out test sets.↳ Could also: Mean absolute error and a calibration/Bland-Altman-style analysis of predicted-minus-actual age could also be reported. — These add a view of systematic bias and age-dependent error patterns that complement a single RMSE or correlation value.
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Enrichment of AAR-associated CpGs among cellular-heterogeneity probes was evaluated with a Fisher exact test.↳ Could also: A logistic regression or permutation-based enrichment test that adjusts for probe-level covariates (e.g., methylation variability or genomic context) could also be used. — A model-based or permutation approach would let one account for probe characteristics and the known correlation structure among CpGs.
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Cross-validation used five random cohort-level samples for each number of training cohorts (65 training sets).↳ Could also: Repeated k-fold or nested cross-validation with more replicates, plus confidence intervals on the RMSE/correlation curves, could also be reported. — More resampling and uncertainty bands would quantify the variability of the sample-size-versus-accuracy relationship.
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Cohort summaries reported mean age with SD, and REML estimates with SE.↳ Could also: Reporting 95% confidence intervals alongside the SE for variance-explained estimates could also be done. — An interval presentation conveys the plausible range directly, which is often preferred when communicating an estimate that is bounded near 1.
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Two outlier cohorts identified by PCA were retained because their prediction accuracy was not low.↳ Could also: A sensitivity analysis re-running key models with those cohorts excluded could also be presented. — A leave-out sensitivity check would document how robust the main conclusions are to those specific cohorts.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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Age acceleration residual from the Elastic Net clock is not associated with mortality in LBC1921microarray human blood none 2019×1papers★ This paper is the founder (earliest)
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DNA methylation across all probes explains essentially 100% of the variance in chronological age in GS blood samplesmicroarray human blood 2019×1papers★ This paper is the founder (earliest)
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Permutation with shuffled age explains no variance in GS, confirming no overestimation of age variance explainedmicroarray human blood none 2019×1papers★ This paper is the founder (earliest)
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Elastic Net DNA methylation age predictor achieves lower RMSE (2.04 years) than Hannum and Horvath clocksmicroarray human blood down 2019×1papers★ This paper is the founder (earliest)
Citation network
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Data lineage
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Downstream reach in the literature
99 downstream papers · 1 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The shipped, deterministic predictor (pred.R + EN/BLUP coef tables + 10-sample blood demo) reproduces 1:1, confirming the paper's central near-perfect age predictor claim (BLUP cor=0.9997, EN cor=0.9927); no fabrication flags and every examined value is derivable from shipped data+code. The only deviation — EN RMSE=5.4 yr on N=10 — sits on our side (a small-sample within-individual standardization effect of the 514-probe EN set), and is explained, not a paper contradiction. The exact headline numbers (RMSE=2.04 yr, Fig 1/4) rest on large, partly access-restricted LBC/GS/BSGS/tissue cohorts and were intentionally not attempted (data availability, 80/20 scope), so the quantitative headline is unverified rather than refuted.
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Reproduction footprint
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