Comprehensive Multi-omics Approach to Investigate the Association Between Metabolic Syndrome and Pituitary Neuroendocrine Tumor Risk in UK Biobank: A Cohort Study.

OBJECTIVE

Metabolic syndrome (MetS) is a cluster of metabolic disorders linked to cancer development and progression. The pituitary gland plays a central role in systemic metabolic homeostasis. However, the contribution of metabolic disorders to pituitary neuroendocrine tumor (PitNET) pathogenesis remains poorly understood.

METHODS

We analyzed data of 355 139 participants from the UK Biobank with complete MetS data, with no prior PitNET at baseline. Cox proportional hazards models estimated associations between MetS and incident PitNET, whereas bidirectional Mendelian randomization assessed causality and minimized reverse-causation bias. Machine learning, multi-omics approaches and single-cell transcriptomic analyses were applied to identify consensus genes and determined cell-type enrichment shared between MetS and PitNET.

RESULTS

Over a median follow-up of 13.0 years, 392 PitNET cases occurred among 135 686 participants with MetS. MetS was associated with a higher risk of PitNET (hazard ratio 1.57; 95% confidence interval 1.28-1.93) after model adjustment, with findings in sensitivity analyses. Bidirectional Mendelian randomization provides suggestive evidence consistent with a causal effect of MetS on PitNET risk (odds ratio [OR] 1.38; 95% confidence interval 1.04-1.88; P = .041) and found no evidence of reverse causation (P > .05). Integrative multi-omics, machine learning and single-cell analyses, validated in clinical cohorts, revealed pronounced cellular heterogeneity across PitNET subtypes and the tumor microenvironment, with enrichment of metabolic pathways-including cholesterol, lipid, and glutamine metabolism.

CONCLUSION

These findings support an association of MetS and PitNET pathogenesis. The identified metabolic signatures provide insights into disease mechanisms, may facilitate early identification of high-risk individuals, and highlight potential targets for metabolism-oriented prevention and therapy.