Weekly Endocrinology Research Analysis
This week delivered cross-cutting advances: a mechanistic Science paper defines an intestinal FXR → GLP-1 gut–joint axis with therapeutic implications for osteoarthritis; a methodological Nature Communications paper (LEOPARD) provides a robust AI approach to complete missing views in longitudinal multi-omics enabling better temporal biomarker discovery; and a JCI physiology study shows meal timing drives ghrelin-dependent growth hormone pulsatility that preserves skeletal growth. Collectively th
Summary
This week delivered cross-cutting advances: a mechanistic Science paper defines an intestinal FXR → GLP-1 gut–joint axis with therapeutic implications for osteoarthritis; a methodological Nature Communications paper (LEOPARD) provides a robust AI approach to complete missing views in longitudinal multi-omics enabling better temporal biomarker discovery; and a JCI physiology study shows meal timing drives ghrelin-dependent growth hormone pulsatility that preserves skeletal growth. Collectively these studies highlight convergence of computational multi-omics, gut-hormone biology, and chronobiology with near-term translational paths in diagnostics and therapeutics.
Selected Articles
1. Osteoarthritis treatment via the GLP-1-mediated gut-joint axis targets intestinal FXR signaling.
This study identifies reduced microbial GUDCA and intestinal FXR signaling as modulators of osteoarthritis and shows that suppressing intestinal FXR alleviates joint disease via intestine-derived GLP-1 in mice; GLP-1R activation mitigated disease while blockade attenuated benefits. Human cohort bile-acid signatures aligned with the mechanistic findings.
Impact: Uncovers a causal gut–joint endocrine pathway (intestinal FXR → GLP-1) linking microbiome-bile acid changes to osteoarthritis and suggests repurposing or testing GLP-1R agonists and intestinal FXR modulators for joint disease.
Clinical Implications: Supports clinical evaluation of GLP-1 receptor agonists and gut bile-acid/FXR-targeted interventions as potential disease-modifying therapies for osteoarthritis; suggests biomarkers (GUDCA, bile-acid profiles) for patient selection.
Key Findings
- Osteoarthritis patients showed reduced glycoursodeoxycholic acid (GUDCA) and altered microbial bile-acid metabolism.
- Suppressing intestinal FXR alleviated osteoarthritis in mice via intestine-secreted GLP-1; GLP-1R activation mitigated disease and blockade attenuated benefits.
2. Meal-feeding promotes skeletal growth by ghrelin-dependent enhancement of growth hormone rhythmicity.
Cross-species experiments in rodents and short-term human feeding studies show that structured meal (bolus) feeding produces preprandial ghrelin surges that amplify GH burst height/frequency and preserve skeletal growth metrics despite reduced intake; continuous feeding flattens GH rhythms. The ghrelin–GHS-R pathway mediates these effects in rodents.
Impact: Links nutrition timing to endocrine pulsatility and tangible growth outcomes, challenging prevailing notions of grazing/snacking and suggesting meal timing as an actionable modifier of GH biology with pediatric implications.
Clinical Implications: Suggests structured meal timing could be considered in pediatric nutrition counseling and as an adjunct to GH therapy trials; randomized growth-outcome trials are needed before guideline change.
Key Findings
- Meal-feeding triggered preprandial ghrelin surges and increased GH burst height/frequency (≈3× GH secretion in rodents).
- Meal-fed rodents maintained body length and tibial epiphyseal plate width despite reduced intake via ghrelin/GHS-R signaling; continuous feeding flattened GH rhythmicity.
3. LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer.
LEOPARD is an AI method that disentangles content and temporal representations to impute missing views in longitudinal multi-omics data. Validated across four real-world cohorts (3 days to 14 years), it outperformed standard imputation methods and improved downstream detection of age-associated metabolites, eGFR-associated proteins, and CKD prediction.
Impact: Provides the first generalized, benchmarked solution for missing-view completion in longitudinal omics, enabling more reliable temporal biomarker discovery and trajectory modeling important for endocrine/metabolic research.
Clinical Implications: By maximizing usable longitudinal omics data, LEOPARD can accelerate biomarker identification and patient stratification in diabetes, obesity, and endocrine disorders — supporting precision prevention and targeted trials once linked to outcomes.
Key Findings
- Introduces representation-disentanglement and temporal knowledge transfer to impute missing omics views.
- Outperformed missForest, PMM, GLMM, and cGAN across four real-world datasets spanning 3 days to 14 years and improved downstream biomarker/CKD predictive tasks.