From brain bioenergetics to hypothalamic glucoregulation: A shared-systems hypothesis for intrinsic dysglycemia in schizophrenia.

Dysglycemia and type 2 diabetes occur at markedly elevated rates in people with schizophrenia-spectrum disorders, contributing substantially to the 15–20 year mortality gap seen in this population. Although antipsychotic medications and lifestyle factors are important contributors, convergent data indicate that a component of this metabolic liability is intrinsic to the illness itself. In this review, we synthesize evidence that schizophrenia is associated with multi-level perturbations in brain bioenergetic function, including glucose transport, glycolytic flux, astrocyte–neuron lactate shuttling, insulin signalling, mitochondrial oxidative capacity, and redox/proteostatic stress pathways. Because fast synaptic transmission operates near energetic limits, these perturbations offer a mechanistically coherent route to NMDA-receptor-dependent network dysfunction and downstream dopaminergic dysregulation. We propose a unifying brain-to-periphery framework in which these same bioenergetic modules are redeployed in hypothalamic and brainstem circuits that regulate hepatic glucose production and peripheral glucose disposal. Deficits in central glucose/lactate sensing, insulin action, mitochondrial function, NMDA receptor signaling, and dopaminergic transmission are proposed as convergent routes through which impaired central glucoregulation may drive peripheral dysglycemia, a model reinforced by immunometabolic stress programs, pleiotropic signalling nodes, and emerging evidence of hypothalamic abnormalities in schizophrenia. In this integrated framework, dysglycemia can be understood not solely as a consequence of medication and lifestyle factors, but as a manifestation of shared intrinsic vulnerability linking the bioenergetics of information processing with whole-body metabolic control. Finally, we evaluate candidate “brain energy rescue” strategies and propose that early characterization of metabolic phenotype may help define patient subgroups most likely to benefit from targeted metabolic intervention.