Risk prediction's new inputs: urine, gut bugs, dominance effects, regulatory layers
Urine metabolites beat plasma for diabetes risk. Gut taxa plus clinical features flag prodromal Parkinson's. And two preprints remind us the modeling assumptions, not just the data, are what move the needle.
- In a preprint, a urine metabolomic risk score was independently associated with incident type 2 diabetes after adjusting for plasma MRS and clinical markers; the plasma score showed no significant predictive value after clinical adjustment. Urine provides a complementary perspective on metabolic variation associated with future type 2 diabetes compared to plasma. Self-reported m:…5818
- Six SCFA-producing gut taxa were depleted in both RBD and prodromal Parkinson's groups; microbiome-only models reached AUC 0.889, microbiome plus clinical features peaked at 0.917, and a reduced six-taxa model still hit 0.853. Self-reported m:…1066
- A dominance-aware polygenic score (PGSa,d) reduced prediction error versus additive-only scores where dominance is present; 141 variants in 39 regions showed significant dominance effects across 16 blood cell traits, height, and BMI in UK Biobank. Self-reported m:…5496
- Using AlphaGenome Atlas predictions, a computational study found predicted DNase (chromatin-accessibility) effects most informative for height and bone mineral density heritability, while predicted CAGE (transcription-initiation) effects won for Alzheimer's, brain traits, and cholesterol, remaining informative after conditioning on fine-mapped eQTLs. Self-reported m:…2801
Urine beats plasma for T2D risk
If you want to predict type 2 diabetes from metabolites, reach for urine, not plasma. In a preprint, a Japanese population-based cohort compared paired plasma and urine metabolomic risk scores for incident type 2 diabetes. The urine metabolomic risk score was independently associated with incident T2D after adjusting for plasma MRS and clinical markers. The plasma score was not significant after adjustment. m:…5818
The comparison is stark: after adjustment, only the urine signal survived — the plasma score showed no significant predictive value. The paper's read is that urine provides a complementary perspective on metabolic variation associated with future T2D. For a field built on plasma biobanks, that is a nudge to look in the other tube. m:…5818
The practical question is whether urine metabolomics can earn a place in screening workflows, where fasting blood draws are the incumbent. A non-invasive sample that adds independent signal on top of plasma and clinical markers does not have to replace anything to be useful. m:…5818
Gut bugs plus clinics flag prodromal Parkinson's
Prodromal Parkinson's may be partly a gut story. In a preprint, Gogoi and colleagues found six short-chain-fatty-acid-producing taxa significantly depleted in both RBD and prodromal PD groups. Hyperparameter-tuned models on gut microbiome data alone reached AUC 0.889 for prodromal classification, and integrating microbial with clinical features pushed the peak to 0.917. m:…1066
Even a reduced model using only the six differentially abundant taxa reached 0.853. The pattern is a familiar one: the new input does not replace the clinic, it completes it. Clinical features plus six taxa is a panel a screening program could actually contemplate, which is more than most omics signatures can say. m:…1066
The 0.917 is a research number, not a product, and a preprint does not constitute screening guidance. But the shape of the result is what matters: a six-taxa panel is a tractable measurement, not a whole-metagenome fishing expedition, and pairing it with clinical features a doctor already collects is how omics signatures stop being journal figures and start being tests. m:…1066
Dominance and the regulatory layer you pick
Polygenic scores have been leaving signal on the table by assuming every variant acts additively. In a preprint, Sun, Morgante, and Mackay's PGSa,d framework, additive plus dominance, reduced root mean squared error versus additive-only scores for complex traits with significant dominance effects. They mapped 141 variants across 39 genomic regions with significant dominance effects across 16 blood cell traits, height, and BMI in UK Biobank. m:…5496
A second preprint, from Choi and Jeong, asked which regulatory layer carries heritability, using AlphaGenome Atlas predictions. This is computational biology, not a clinical trial. The answer was trait-specific: predicted chromatin-accessibility (DNase) effects were most informative for height and bone mineral density, while predicted transcription-initiation (CAGE) effects won for Alzheimer's, most brain traits, and cholesterol, and the CAGE signal remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. The takeaway: the choice of regulatory layer is itself a modeling decision, with trait-dependent consequences. m:…2801
Together the two genetics papers argue the same point from opposite ends. Sun and colleagues say the score formula is leaving money on the table; Choi and Jeong say the input annotations matter as much as the formula. Prediction is not just data collection. The modeling assumptions are where the next gains hide, and both are cheaper to fix than recruiting a new cohort. m:…5496
What is still unresolved
- Does the urine MRS advantage replicate in non-Japanese cohorts and different mass-spec platforms?
- Is microbiome depletion causal in prodromal PD, or a downstream echo of the disease process?
- How much do dominance-aware scores gain on diverse ancestries outside UK Biobank?
- Do the AlphaGenome regulatory-layer rankings hold when tested against measured, not predicted, effects?
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