Association Between Hypoglycemia and the Occurrence of Early Onset Sepsis in Premature Infants.

BACKGROUND: We examined the association between hypoglycemia and the occurrence of early onset sepsis (EOS) in premature infants admitted to the neonatal intensive care unit (NICU). METHODS: We included infants discharged from 358 NICUs between 1997 and 2020 with gestational age <34 weeks, ≥1 culture collected in the first 3 days of life, and ≥1 serum glucose value recorded on the day of or day prior to culture collection. We used multivariable logistic regression and inverse probability weighting (IPW) and constructed models for three definitions of hypoglycemia: American Academy of Pediatrics (AAP), Pediatric Endocrine Society, and a definition based on neurodevelopmental studies. We performed subgroup analysis in EOS episodes caused by Gram-negative and Gram-positive organisms. RESULTS: Of the 62,178 infants and 64,559 cultures that met study inclusion criteria, 739 (1%) cultures were positive. The median (25th, 75th percentile) glucose value was 75 mg/dL (50, 106) on the day of or day prior to a positive culture versus 70 mg/dL (50, 95) on the day of or day prior to a negative culture. We found that hypoglycemia was not associated with the occurrence of EOS for all organisms and Gram-positive organisms, whereas there was a small but significant association between the lower AAP glucose cutoff value and EOS due to Gram-negative organisms (logistic regression: risk difference [RD] 0.24% [95% CI, 0.01-0.47]; IPW: RD 0.22% [95% CI, 0.00-0.43]). CONCLUSIONS: Hypoglycemia may be an early marker of EOS, particularly in episodes caused by Gram-negative organisms and when using a stricter definition of hypoglycemia.

Generation of the Chemical and Social Stressors Integration Technique (CASS-IT) to identify areas of holistic public health concern: An application to North Carolina.

Due to structural racism and income inequality, exposure to environmental chemicals is tightly linked to socioeconomic factors. In addition, exposure to psychosocial stressors, such as racial discrimination, as well as having limited resources, can increase susceptibility to environmentally induced disease. Yet, studies are often conducted separately in fields of social science and environmental science, reducing the potential for holistic risk estimates. To tackle this gap, we developed the Chemical and Social Stressors Integration Technique (CASS-IT) to integrate environmental chemical and social stressor datasets. The CASS-IT provides a framework to identify distinct geographic areas based on combinations of environmental chemical exposure, social vulnerability, and access to resources. It incorporates two data dimension reduction tools: k-means clustering and latent profile analysis. Here, the CASS-IT was applied to North Carolina (NC) as a case study. Environmental chemical data included toxic metals - arsenic, manganese, and lead - in private drinking well water. Social stressor data were captured by the CDC's social vulnerability index's four domains: socioeconomic status, household composition and disability, minority status and language, and housing type and transportation. Data on resources were derived from Federal Emergency Management Agency (FEMA's) Resilience and Analysis Planning Tool, which generated measures of health resources, social resources, and information resources. The results highlighted 31 NC counties where exposure to both toxic metals and social stressors are elevated, and health resources are minimal; these are counties in which environmental justice is of utmost concern. A census-tract level analysis was also conducted to demonstrate the utility of CASS-IT at different geographical scales. The tract-level analysis highlighted specific tracts within counties of concern that are particularly high priority. In future research, the CASS-IT can be used to analyze United States-wide environmental datasets providing guidance for targeted public health interventions and reducing environmental disparities.

Chemical Mixtures in Household Environments: In Silico Predictions and In Vitro Testing of Potential Joint Action on PPARγ in Human Liver Cells.

There are thousands of chemicals that humans can be exposed to in their everyday environments, the majority of which are currently understudied and lack substantial testing for potential exposure and toxicity. This study aimed to implement in silico methods to characterize the chemicals that co-occur across chemical and product uses in our everyday household environments that also target a common molecular mediator, thus representing understudied mixtures that may exacerbate toxicity in humans. To detail, the Chemical and Products Database (CPDat) was queried to identify which chemicals co-occur across common exposure sources. Chemicals were preselected to include those that target an important mediator of cell health and toxicity, the peroxisome proliferator activated receptor gamma (PPARγ), in liver cells that were identified through query of the ToxCast/Tox21 database. These co-occurring chemicals were thus hypothesized to exert potential joint effects on PPARγ. To test this hypothesis, five commonly co-occurring chemicals (namely, benzyl cinnamate, butyl paraben, decanoic acid, eugenol, and sodium dodecyl sulfate) were tested individually and in combination for changes in the expression of PPARγ and its downstream target, insulin receptor (INSR), in human liver HepG2 cells. Results showed that these likely co-occurring chemicals in household environments increased both PPARγ and INSR expression more significantly when the exposures occurred as mixtures vs. as individual chemicals. Future studies will evaluate such chemical combinations across more doses, allowing for further quantification of the types of joint action while leveraging this method of chemical combination prioritization. This study demonstrates the utility of in silico-based methods to identify chemicals that co-occur in the environment for mixtures toxicity testing and highlights relationships between understudied chemicals and changes in PPARγ-associated signaling.