bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.03.12.711462

The Female Biomarker Challenge: Sex-Specific Network Robustness Constrains Biological Age Estimation and Geroscience Trial Design

Abstract

Geroscience has more candidate interventions than it can afford to test. Trials powered for mortality or functional decline require large cohorts and long follow-up, and the persistent female survival advantage could further increase sample-size requirements if women and men age through different biology. Existing ageing biomarkers -- including frailty indices, biological-age clocks, allostatic load and homeostatic dysregulation -- compress multidimensional physiology to a scalar, discarding which combination of biomarkers changed. In 29,053 US adults measured on 38 routine laboratory and examination markers, we decomposed displacement from sex-specific young-healthy physiology into total magnitude and alignment with a cross-fitted mortality-associated direction. Women were no closer to young-health than men but had substantially lower disease mortality. Adjustment for magnitude widened the female survival advantage; adjustment for mortality-directed projection attenuated 87% of the sex difference on the log-hazard scale. Sex-specific directions transferred almost without loss, indicating a substantially shared predictive geometry with different positions along it. Across the population, a median 68% of the positive mortality-associated contribution came from measurements lying within conventional clinical limits and 57% from within the young-healthy range; these shares reflect how numerous such measurements are, not greater information per measurement. Ten routine measurements reproduced the 38-marker direction, and seven of eight predictions registered before analysis were confirmed in NHANES III. High-sensitivity C-reactive protein was strongly prognostic alone but showed no incremental information beyond the routine panel, whereas measuring lung-function added more held-out prognostic information than 33 of 34 individual blood and urine markers in the replication cohort. Retrospective sorting on this geometry produced mortality-enriched and physiologically displaced candidate trial groups; pooled thresholds were strongly sex-imbalanced, whereas within-sex thresholds restored balance with only a 1.5-3.8% reduction in expected five-year events. These findings suggest that inexpensive routine biomarkers plus simple functional testing could support smaller, more informative and sex-balanced geroscience trials, but prospective validation of treatment enrichment is required.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Harding, A. S., Coward, J., Tian, T.. 2026-03-16. The Female Biomarker Challenge: Sex-Specific Network Robustness Constrains Biological Age Estimation and Geroscience Trial Design. https://doi.org/10.64898/2026.03.12.711462

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Large language models complement established workflows for mechanistic model construction and refinement in systems biology

Mechanistic models provide a framework for describing, understanding, and predicting biological processes, but their development requires an interdisciplinary team with expertise in biology, mathematics, and computation. Here we present Octomod, a multi-agent workflow based on large language models (LLMs) that integrates biological knowledge retrieval, model construction, parameter estimation, and hypothesis testing. Built on established software tools, including AMICI and pyPESTO, and community standards, including SBML and PEtab, Octomod supports model development and refinement through natural-language interaction with a user. The agents' proposals must pass defined checks and the user's decision before they are used. Every input, model, check, and decision is recorded, so that model building remains transparent and reproducible. We applied Octomod to four problems. Three used published data and differ in model size and in how much the user guided the run: Octomod constructed a model that matched a published model without user guidance, supported the interactive refinement of a gene-regulatory model, and built models of cell-cell communication that explained the responses of five immune cell types to different stimuli. In the fourth application, on newly generated and unpublished lipidomics data, Octomod helped to determine which processes the measurements could inform, identify unresolved label fluxes, and reduce a supplied model to those processes. Together, these results show how LLMs can strengthen and complement established workflows for constructing and refining biologically informed models.

systems biology↗

A Mathematical Model Connecting Appetite-Driven Food Intake to Metabolic Changes across Diet and GLP-1RA Treatments in Mice

Mouse models are widely used to study obesity and the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on appetite-driven meal responses and eating behaviours. However, connecting these behavioural changes to disease prevention remains limited. One reason is that eating behaviour is often summarised as daily or weekly energy intake, obscuring meal-level dynamics. Here we present a mathematical model of mouse appetite-driven food intake across diets and treatment with the GLP-1RAs semaglutide and exenatide. The model jointly described three studies and passed both qualitative assessments and a statistical {chi}2 test (disagreement<threshold, 348<1111). Predictive performance was subsequently validated using four pre-existing studies and one new study. Finally, the model was used to simulate mouse populations under dietary and GLP-1RA interventions, with predictions showing qualitative agreement with the literature. This work aids preclinical drug discovery, supports mouse study design, and improves understanding of how individual meals accumulate into long-term physiological changes.

systems biology↗

Generalized inference of noise-corrected state variability in single cells

Single-cell omics methods operate at their detection limit and are less conducive to technical controls than mini-bulk approaches, which combine cells of the same type before profiling. To integrate mini-bulk samples with paired split-pool controls more holistically, we built stochprofIC, an algorithm that considers data, noise, and covariates in a distributional-regression framework. We vet stochprofIC with synthetic datasets and compare against an adjusted-variance alternative using mini-bulk data in mouse brains and human breast cancers. The noise-batch corrections provided by stochprofIC enable inference of the single-cell populations comprising the mini-bulk, which are validated repeatedly by RNA FISH in different contexts. We use stochprofIC outputs as input to a systems model of nucleocytoplasmic transport and distinguish heterogeneous trafficking states. Pharmacologic inhibition of a MAP3K11-high state inhibits apoptosis in primary breast-cancer organoids, supporting relevance to phenotypes. By deconvolving changes in location and scale, stochprofIC supports mini-bulking when high-sensitivity and reliability profiles are important.

systems biology↗