Back

Reduced glucose supply during neonatal infection attenuates neurological and renal pathology via modulation of innate and Th1 immunity

Zhong, J.; Baek, O.; Doughty, R.; Joergensen, B. M.; Jensen, H. E.; Thymann, T.; Sangild, P. T.; Brunse, A.; Nguyen, D. N.

2024-05-17 immunology
10.1101/2024.05.15.594288 bioRxiv
Show abstract

BackgroundPremature infants are highly susceptible to infections that can lead to sepsis with life-threatening organ dysfunctions. The clinical practice of high parenteral glucose supply in preterm infants can exacerbate infection outcomes through excessive glycolysis-induced inflammatory response. This in turn can affect the health of vital preterm organs, including the brain and kidneys. We hypothesized that reducing glucose supply in infected preterm newborns may help protect against pathology in these two key organs. MethodsCaesarean-delivered preterm pigs were nourished with high or low parenteral glucose levels, infected with Staphylococcus epidermidis or saline, and cared for until 22h. Blood, brain, and kidney samples were collected at the end of the study for analyses. ResultsInfection led to multiple pathological changes, increased inflammation and tissue injury and dysfunction in both brain and kidneys of preterm piglets. Reduced glucose supply in infected animals alleviated neurological degradation, hyperemia and enhanced M2 microglial phenotype in the brain. This intervention also reduced plasma creatinine, renal edema, tubular vacuolization and dilatation. Multiple genes related to innate and Th1 immunity in both organs were highly correlated and dampened by reduced glucose supply, but there were clear signs that renal inflammation was closely connected to systemic inflammation while neuroinflammation was likely driven by immune response to the bacteria translocated into the brain. ConclusionReduced glucose supply can protect brain and kidney health in infected preterm neonates.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.