Hypothesis

Applying manifold alignment techniques to harmonize transcriptomic, proteomic, metabolomic, and epigenomic datasets before training ensemble models will reduce cross‑cohort batch effects, improve the accuracy of multi‑omics biological age clocks, and uncover conserved nonlinear aging trajectories that are obscured by current stacking approaches.

Mechanistic Rationale

Recent work shows that ensemble models like LightGBM outperform linear methods by capturing nonlinear interactions across omics layers [1, 2]. However, these models still operate on raw or minimally corrected feature spaces, where technical variation (batch effects, platform differences) can distort the true biological signal. Manifold learning preserves the intrinsic geometry of high‑dimensional data by mapping samples to a lower‑dimensional space where geodesic distances reflect biological similarity rather than technical noise [4]. Aligning multiple omic manifolds via joint embedding (e.g., using diffusion maps coupled with Procrustes analysis or optimal transport) should create a unified latent representation where aging‑related variation is shared across cohorts while cohort‑specific artifacts are minimized. This unified space can then serve as input to gradient‑boosting ensembles, allowing the model to learn aging patterns that are genuinely conserved rather than artifacts of dataset composition.

Testable Predictions

1. Prediction Accuracy: Multi‑omics clocks trained on manifold‑aligned data will achieve a lower mean absolute error (MAE) than clocks trained on concatenated raw features when evaluated on an external hold‑out cohort (e.g., ClockBase [5]). We expect a reduction of MAE by at least 0.5 years relative to the current OMICmAge benchmark (MAE ≈ 4.97 years).
2. Cross‑Cohort Generalization: The aligned‑data clock will show smaller performance degradation when applied to cohorts with different demographic compositions or assay platforms, indicating improved robustness to batch effects.
3. Biological Interpretation: SHAP values derived from the aligned‑data model will highlight a consistent set of multi‑omics features (e.g., specific lipid metabolites and epigenetic biomarker proxies) across cohorts, whereas the raw‑feature model will show cohort‑dependent feature importance.
4. Discovery of Aging Archetypes: Clustering in the aligned latent space will reveal distinct aging trajectories (archetypes) that replicate across independent datasets, providing evidence for conserved nonlinear aging pathways.

Experimental Approach

• Data: Use transcriptomics, proteomics, metabolomics, and epigenomics from the 12,000‑person cohort described in [1] and external validation sets from ClockBase [5] and mouse multi‑omic studies [3].
• Manifold Alignment: For each omic layer, compute a diffusion map embedding. Align the embeddings across layers using joint Procrustes analysis followed by optimal transport to minimize cross‑omic divergence. Concatenate the aligned embeddings to form a unified feature matrix.
• Model Training: Train LightGBM ensembles on the aligned matrix to predict chronological age, employing the same hyper‑parameter search as in [1] and [2].
• Evaluation: Compute MAE, R², and mortality risk stratification (Cox hazard ratios) on held‑out test sets. Compare against baseline models trained on raw concatenated features and on OMICmAge alone. Use DeLong test for AUC differences and paired t‑test for MAE differences.
• Falsifiability: If the aligned‑data clock does not significantly outperform the raw‑feature baseline (p > 0.05) or shows no improvement in cross‑cohort stability, the hypothesis is falsified. Similarly, if identified aging archetypes fail to replicate in independent cohorts, the claim of conserved trajectories is not supported.

Implications

Success would demonstrate that geometric harmonization of multi‑omic data is a necessary preprocessing step for robust aging clocks, shifting focus from purely algorithmic ensemble improvements to data‑level manifold integration. This approach could lower the required sample size for clock development, facilitate clinical translation across heterogeneous populations, and provide a mechanistic bridge between multi‑omic correlations and underlying nonlinear aging dynamics.