Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelia
The main results reproduced: recomputed values matched the published ones within tolerance.
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
- ✓Any deviation was negligible
- 🟡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 central claim did not (fully) hold under reproduction
- 🟡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
PARTIAL, within-spirit reproduction. The paper has 3 computational segments; only the genetic-pathway/transcriptome enrichment uses PUBLIC data (the brief's accession GSE195962). The MR segment (TwoSampleMR) and GWAS/genetic-correlation segment depend on controlled-access TWB + UKB individual-level genotypes with NO deposited summary statistics (data_restricted); the observational meta-analysis used RevMan on study-level effect sizes whose 2x2 event tables are NOT in the supplement (only group sizes + STROBE/MOOSE/NOS checklists) -> not reproducible from shipped artifacts; the linked repo MRCIEU/TwoSampleMR is a generic third-party tool, not the authors' analysis code. For GSE195962, the DEG->PPI->'functional gene' pipeline feeding Enrichr is under-specified (no thresholds/PPI tool given), so we reproduced the fully-specified TERMINAL step: re-running Enrichr Reactome_2022 on the authors' OWN shipped overlap-gene lists (sheet 3-12, union=150 genes). Result: top-15 pathway overlap counts reproduce 15/15 EXACTLY (Immune System k=113, Cytokine Signaling 62, Innate Immune 60, ...), with identical ranking, fully confirming the 'pan-activation across immune pathways' claim. Adjusted p-values are 17-18 orders more significant in our run because the supplement ships only the overlap subset (150 genes) not the larger true input set; an N-sweep shows the reported p-values lie exactly on the p-vs-N curve at input N195-200, the expected size of a PPI functional-gene set. Conclusion: the reported enrichment numbers are genuine, internally consistent and tool-reproducible -- NO fabrication signal. Not attempted (out of scope / not reproducible from public data): MR causal estimates, GWAS SNP/gene tables, genetic correlations, meta-analysis pooled ORs.
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Assessment versions
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v1 current initial assessment Score 79assessed: 2026-06-15 ⛓ 1772d9b2a4ae
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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
- 2026-06-15
- 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
- 2026-08-05
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: opusAlthough dry eye disease (DED) and depression (DEP) frequently comanifest clinically, the robustness, shared genetic/molecular pathways, and causality of this association are undetermined; the study tests whether DED and DEP are associated, share genetic architecture, and bidirectionally cause one another.
- ★ Meta-analysis confirms a positive bidirectional association between DED and DEP (DED patients have increased DEP prevalence and vice versa). finding
- ★ DED and DEP share similar genetic architecture and pleiotropic functional genes across TWB and UKB populations. finding
- ★ Pleiotropic functional genes converge under the ontology of immune activation, validated by transcriptome systemic review. mechanism
- ★ IVW-Mendelian randomization supports bidirectional causation for DED-to-DEP and DEP-to-DED in both TWB and UKB. finding
- ★ Bidirectional DED-DEP causation remains valid after stringent COJO/LD-corrected instrumental-variable re-selection. finding
- ★ A three-segment multimodal framework (meta-analysis, GWAS/ontology, MR) was applied to decode DED-DEP disease relations. method
- Multiethnic biobank GWAS resources (Taiwan Biobank and UK Biobank) were used to characterize disease-associated SNPs and genetic correlation. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Meta-analysis of observational case-control/cohort/cross-sectional studies (RevMan, Mantel–Haenszel OR, SMD) | 26 human studies (515,389 DED patients; 571,518 DEP patients) | none | Odds ratio of DED-DEP co-occurrence and disease severity standardized mean difference | Review Manager (RevMan) software |
| Genome-wide association study (GWAS) with SAIGE logistic mixed model | Taiwan Biobank (East Asian) and UK Biobank (European) human cohorts | none (DED/DEP case vs control) | SNP-disease associations (Manhattan plot) | PLINK v1.9; SAIGE |
| Conditional and joint association analysis (COJO) | TWB and UKB GWAS data | none | LD-free joint SNP effects for IV re-selection | GCTA-COJO v1.91.1 beta |
| LD score regression (disease-disease genetic correlation) | TWB and UKB GWAS summary statistics | none | Cross-disease genetic correlation (rG) | — |
| Gene Ontology and PheWAS enrichment analysis | TWB and UKB functional gene sets (UK Biobank GWAS v1) | none | Enriched ontology/pathways | Enrichr; Reactome 2022 |
| Protein−protein interaction network analysis | DED/DEP pleiotropic functional genes | none | Interaction network of functional genes | STRING v11.5 (confidence 0.400) |
| Systemic transcriptome review of RNA sequencing samples | Human/animal RNA-seq datasets from Gene Expression Omnibus | none/other | Activated immune pathways in DED and DEP | Gene Expression Omnibus (GEO) |
| Mendelian randomization (48 experiments, IVW and modified MR) | TWB and UKB populations; SNPs as instrumental variables | none (genetic instruments) | Bidirectional exposure-outcome causal estimates | — |
- ▲ DED patients have increased DEP prevalence OR = 1.83
- ▲ DEP patients have higher concurrent risk of DED OR = 2.34
- ▲ Cross-disease genetic correlation between DED and DEP rG = 0.19 (TWB); 0.109 (UKB)
- ▲ IVW-MR supports bidirectional DED-to-DEP and DEP-to-DED causation in both biobanks p < 0.001
- ▲ Bidirectional causation persists after LD-corrected (COJO) IV re-selection
- ▲ Prior meta-analysis (cited) reported DED patients have increased DEP risk OR = 2.92
- – Estimated heritability of DED and DEP DED ≈ 37%; DEP ≈ 41%
- fold_change OR = 1.83 (DED associated with increased DEP prevalence (meta-analysis))
- fold_change OR = 2.34 (DEP associated with higher DED risk (meta-analysis))
- correlation rG = 0.19 (DED-DEP genetic architecture similarity in TW biobank)
- correlation rG = 0.109 (DED-DEP genetic architecture similarity in UK biobank)
- pvalue <0.001 (IVW-MR bidirectional causation in TWB and UKB)
- count 515,389 DED / 571,518 DEP (Total patients across 26 meta-analysis studies)
- fold_change OR = 2.92 (Cited multiethnic meta-analysis of DED increasing DEP risk)
- other DED h2 ≈ 37%, DEP h2 ≈ 41% (Heritability estimates from TwinsUK/meta-analysis)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This multimodal study combined (1) a random-effects meta-analysis of 26 observational studies to estimate pooled ORs for the DED–DEP association, (2) GWAS in two biobanks (TWB and UKB) with LD score regression for cross-disease genetic correlation, SAIGE for logistic mixed-model SNP association, COJO for LD-adjusted joint SNP selection, and GO/PPI/transcriptome analyses for pathway characterization, and (3) bidirectional inverse variance-weighted Mendelian randomization (48 experiments) to infer causal direction. Results were reported as ORs, genetic correlations (rG), and MR p-values.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Mantel-Haenszel (M-H) odds ratio with random-effects model | Meta-analysis pooling DED-DEP association across 26 studies (primary outcome) | 515,389 DED patients and 571,518 DEP patients across included studies | not stated |
| Cochran's Q χ² and I² heterogeneity statistic | Meta-analysis heterogeneity assessment; random-effects model triggered at p < 0.1 | 26 studies | not stated |
| Standardized mean difference (SMD) | Meta-analysis secondary outcome: disease severity under co-occurrence effect | Subset of included studies reporting severity scores | not stated |
| SAIGE logistic mixed model (scalable and accurate implementation of generalized mixed model) | GWAS SNP-disease association in UKB (unbalanced case-control ratio) and TWB | TWB1: 7,684/27,737; TWB2: 26,866/68,978; UKB: 215,063/215,957 individuals | not stated |
| LD score regression | Cross-disease genetic architecture correlation between DED and DEP (rG = 0.19 TWB; 0.109 UKB) | TWB and UKB post-QC SNP sets | not stated |
| COJO conditional and joint association analysis | LD-adjusted selection of independent instrumental variable SNPs for MR and joint GWAS interpretation | Post-QC GWAS SNP sets from TWB and UKB | not stated |
| Inverse variance-weighted (IVW) Mendelian randomization | Bidirectional causal inference: DED-to-DEP and DEP-to-DED, in TWB and UKB (48 total experiments) | TWB and UKB biobank cohorts (exact per-MR-experiment n not stated in excerpt) | stated |
| Wilcoxon rank-sum test | Demographic comparison of age (continuous) between cases and controls across TWB1, TWB2, UKB | Per-group Ns as in Table 2 | not stated |
| Chi-square test of independence | Demographic comparison of sex and disease co-occurrence proportions between cases and controls within each biobank | Per-group Ns as in Table 2 | not stated |
| Chi-square goodness-of-fit test | Cross-biobank demographic comparisons (p-value column in Table 2) | Combined TWB1, TWB2, UKB Ns | not stated |
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Bidirectional causality was assessed using 48 IVW-MR experiments without a described correction for multiple comparisons across experiments↳ Could also: A Bonferroni or Benjamini-Hochberg FDR correction applied across the 48 MR p-values would also be a standard approach — With 48 parallel hypothesis tests, the expected number of false positives at α = 0.05 is ~2.4; a multiplicity-adjusted threshold would clarify how many causal estimates survive correction and strengthen interpretability
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IVW was the primary MR estimator reported↳ Could also: Sensitivity analyses using MR-Egger, weighted median, or weighted mode estimators would also be standard practice alongside IVW — Each estimator relaxes a different subset of the IV assumptions (e.g., MR-Egger allows directional pleiotropy); reporting multiple estimators together allows readers to assess robustness to assumption violations
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Continuous demographic variables (age) were compared with Wilcoxon rank-sum tests↳ Could also: A Student's t-test or Welch's t-test would also be commonly applied for large-sample continuous comparisons — At the sample sizes present (thousands to hundreds of thousands), both tests are asymptotically equivalent; Wilcoxon is the more conservative non-parametric choice, which is appropriate when distributional assumptions are uncertain
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Dispersion in Table 2 is reported as mean ± SD↳ Could also: A 95% confidence interval for the mean (or median with IQR) could also be reported alongside or instead of SD — For inferential purposes, 95% CIs directly convey estimation uncertainty around the group mean, while SD describes the spread of individual observations; both are standard and serve complementary purposes
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Heterogeneity in the meta-analysis was assessed with Cochran's Q and I², with a random-effects model triggered at p < 0.1↳ Could also: Prediction intervals (the range within which 95% of true effects across studies would be expected to fall) could also accompany the pooled OR — I² quantifies the proportion of variability due to heterogeneity but not its magnitude; prediction intervals make the practical extent of between-study variation directly interpretable alongside the pooled estimate
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Genetic correlation was estimated using LD score regression (a single summary-statistic method)↳ Could also: Cross-trait LDSC with partitioned heritability, or genomic structural equation modelling (Genomic SEM), would also allow estimation of genetic correlations with additional decomposition by functional annotation or latent factor structure — These extensions would allow testing whether the shared genetic architecture is driven by specific genomic regions or biological pathways, complementing the GO/PPI analyses already performed
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-38548265
Paper: Chang KJ et al. (2024) Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization. J Adv Res 69:197–213. DOI 10.1016/j.jare.2024.03.015 · PMID 38548265 · PMC11954816.
The paper has three computational segments. Scoping each for reproducibility from publicly shipped data/code:
| # | Segment | Pipeline | Input data | In scope? |
|---|---|---|---|---|
| 1 | Observational meta-analysis (pooled OR of DEP-in-DED & DED-in-DEP, SMD of severity) | RevMan random/fixed-effects meta-analysis | study-level effect sizes hand-extracted from 26 papers | NO (not reproducible from shipped data) |
| 2 | GWAS + genetic correlation + functional SNPs/genes | SAIGE / COJO / GO on TWB + UKB | TWB (~130k Asians) + UKB (~502k Europeans) individual-level genotypes | NO (data_restricted) |
| 3 | Mendelian randomization (bidirectional OR via TwoSampleMR: IVW/MR-Egger/weighted-median/MR-PRESSO/MBE) | TwoSampleMR (the repo linked) | GWAS summary stats derived from TWB+UKB (not deposited) | NO (data_restricted) |
| 4 | Genetic-pathway / transcriptome enrichment of 15 DED + 15 DEP public GEO datasets (incl. GSE195962) → Reactome 2022 via Enrichr | DEG → PPI → "functional genes" → Enrichr Reactome_2022 | public GEO series | YES (the only segment runnable on public data) |
What we attempt
Segment 4, dataset GSE195962 (the data accession named in the brief). Reported in Supplement 2, sheet "3-12" (Table 12: Pathway Ontology of GSE-195962) and summarized in sheet "3-1" + main text:
GSE195962 "showed pan-activation across immune pathways (cytokine-related, infectious-related and innate immunity pathways)."
Concrete reported numbers (sheet 3-12, Enrichr Reactome 2022): top pathway Immune System overlap 113/1943, adj-p 2.33×10⁻⁶², OR 14.42, combined 2140; Cytokine Signaling In Immune System 62/702, adj-p 1.9×10⁻⁴⁰; Innate Immune System 60/1035, adj-p 8.46×10⁻²⁹; etc.
What we do NOT attempt, and why
- Segments 1–3 — the underlying data is either hand-extracted from third-party papers (meta-analysis 2×2 event tables are not in the supplement; only group sizes + quality checklists are) or controlled-access biobank genotypes (TWB, UKB) with no deposited summary statistics. Not reproducible from shipped artifacts. The linked repo (MRCIEU/TwoSampleMR) is a generic third-party tool, not the authors' analysis code, and cannot run without the restricted inputs.
- Forward DEG→PPI→Enrichr from GSE195962 raw counts — the DEG threshold, the PPI tool/cutoff, and the "functional genes within the PPI network" selection rule are not stated (Methods only say "Enrichr … based on Reactome 2022"). So the exact input gene list cannot be re-derived. We therefore reproduce the terminal, fully-specified step (the Enrichr Reactome_2022 enrichment) using the gene lists the authors did ship (the per-pathway overlap genes in sheet 3-12), which is the decisive test of whether the reported enrichment numbers are genuine and reproducible.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
For the only public segment (GSE195962 Reactome enrichment), the reproduction is excellent: 15/15 overlap counts and the full ranking match EXACTLY, and the reported adj-p values lie precisely on the empirical p-vs-N curve at N≈195 — so the numbers are genuine, internally consistent and show no fabrication signal. The ~17-18-order adj-p offset is an input-side artifact (the authors' true ~195-gene PPI input set was not deposited; we reconstructed only the 150-gene overlap union), i.e. a data-availability/methodology issue, not a computational discrepancy. However, the paper's central causal conclusion (MR OR 1.877 DED→DEP, pooled OR 1.83) rests on restricted TWB/UKB data and un-shipped 2×2 tables and was untested, so the headline claim is only partially confirmed. Overall: solid reproduction of the testable part with explainable deviations and substantial untestable scope → yellow.
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Reproduction footprint
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