Anna A. Penn

Integration of early physiological responses predicts later illness severity in preterm infants.

Physiological parameters routinely and noninvasively collected in the first 3 hours of life can accurately predict morbidity in premature infants. Not All Preemies Are Alike Premature babies can be full of surprises. Although smaller and more premature babies generally experience more complications, the hospital course of individual infants can vary greatly. Preemies born the same size and at the same gestational age can have vastly different outcomes, ranging from death to healthy survival with minimal medical problems. Ideally, the infants who are likely to do well could stay in local hospitals where they are born, whereas their high-risk counterparts would be transferred to specialty referral centers for more aggressive treatment. Distinguishing these groups of patients has been the Holy Grail of neonatology for some time, however. Ranging from the old classic, the Apgar score, to the newest inventions such as SNAP, SNAPPE, and CRIB scores, these many different prediction methods attest to the difficulty of the task. Now, Saria et al. have developed a way to take advantage of the cardiorespiratory monitors that are ubiquitous in the neonatal intensive care unit and use routinely collected data to predict infants’ clinical outcomes more accurately than can be achieved with any of the scoring systems in use today. After infants are born prematurely, they are usually attached to a cardiorespiratory monitor within minutes of their delivery. The monitors continuously display and store each baby’s vital sign data, including heart rate, respiratory rate, and oxygen saturation. This continuous stream of vital sign data continues as each infant transfers from the delivery room to the neonatal intensive care unit, and then until the patient is discharged home, or longer as necessary. Saria et al. have found that physiologic data derived from routine monitoring in the first 3 hours of life can predict future outcomes. The authors used heart rate and respiratory rate, as well as variability in these parameters, and oxygen saturation and time of hypoxia in a computational model that was able to predict the patients’ risk of future morbidity. The model proved particularly accurate in predicting the risk of high morbidity due to infections and cardiopulmonary complications, even when these were not diagnosed until days or weeks later. PhysiScore, the new method developed by Saria et al. for assessing the prognosis of premature infants, is an important development given its improved specificity and sensitivity over preexisting scoring techniques. Moreover, it relies on readily accessible noninvasive data that are already routinely collected on all infants, and can be quickly calculated by computer as early as 3 hours into the infant’s life. PhysiScore is a timely and necessary invention and has the potential to optimize medical management for most premature infants. Physiological data are routinely recorded in intensive care, but their use for rapid assessment of illness severity or long-term morbidity prediction has been limited. We developed a physiological assessment score for preterm newborns, akin to an electronic Apgar score, based on standard signals recorded noninvasively on admission to a neonatal intensive care unit. We were able to accurately and reliably estimate the probability of an individual preterm infant’s risk of severe morbidity on the basis of noninvasive measurements. This prediction algorithm was developed with electronically captured physiological time series data from the first 3 hours of life in preterm infants (≤34 weeks gestation, birth weight ≤2000 g). Extraction and integration of the data with state-of-the-art machine learning methods produced a probability score for illness severity, the PhysiScore. PhysiScore was validated on 138 infants with the leave-one-out method to prospectively identify infants at risk of short- and long-term morbidity. PhysiScore provided higher accuracy prediction of overall morbidity (86% sensitive at 96% specificity) than other neonatal scoring systems, including the standard Apgar score. PhysiScore was particularly accurate at identifying infants with high morbidity related to specific complications (infection: 90% at 100%; cardiopulmonary: 96% at 100%). Physiological parameters, particularly short-term variability in respiratory and heart rates, contributed more to morbidity prediction than invasive laboratory studies. Our flexible methodology of individual risk prediction based on automated, rapid, noninvasive measurements can be easily applied to a range of prediction tasks to improve patient care and resource allocation.

Neonatal CSF oxytocin levels are associated with parent report of infant soothability and sociability

Oxytocin (OT) has been linked to social behavior in rodents, non-human primates, and adult humans, but almost nothing is known about brain OT activity in human newborns or its impact on social development. To better understand the role of OT biology in human social functioning, a multi-disciplinary, longitudinal study was conducted. Cerebral spinal fluid (CSF) OT levels from 18 human neonates were evaluated and examined in relationship to social-seeking behavior at term, at 3 months, and at 6 months of age. Higher neonatal CSF OT levels were consistently associated with solicitation of parental soothing and interest in social engagement with others. This is the first study to link CSF OT levels to normative human social functioning. Research is now required to test whether early OT levels serve as a biomarker for subsequent social abnormalities.

Plasma vasopressin concentrations positively predict cerebrospinal fluid vasopressin concentrations in human neonates

Central arginine vasopressin (AVP) plays a critical role in mammalian social behavior and has been hypothesized to be a biomarker of certain human neurodevelopmental disorders, including autism. However, opportunities to collect post-mortem brain tissue or cerebrospinal fluid (CSF) from children are extremely limited, and the use of less invasive peripheral assessments (e.g., blood, urine, or saliva) of AVP as a proxy for more invasive central measures has not been well validated. Further, almost nothing is known about AVP biology in very young infants. Therefore in the present study we concomitantly collected basal CSF and plasma samples from N = 20 neonates undergoing clinical sepsis evaluation (all were sepsis negative) and quantified AVP concentrations via well-validated enzyme-immunoassay methodology. Plasma AVP concentrations significantly and positively predicted CSF AVP concentrations (r = 0.73, p = 0.0021), and this relationship persisted when variance attributed to sex, gestational age, and sample collection time was controlled for in the statistical model (r = 0.75, p = 0.0047). These findings provide preliminary support for the use of basal plasma AVP measurement as a proxy for basal brain AVP activity in pediatric populations. Future studies are now required to determine the relationship between behavioral measures and AVP concentrations in both central and peripheral compartments in young infants and older children.

Global hormone profiling of murine placenta reveals Secretin expression

OBJECTIVE To elucidate and categorize the murine placental hormones expressed across gestation, including the expression of hormones with previously undescribed roles. STUDY DESIGN Expression levels of all genes with known or predicted hormone activity expressed in two separate tissues, the placenta and maternal decidua, were assessed across a timecourse spanning the full lifetime of the placenta. Novel expression patterns were confirmed by in situ hybridization and protein level measurements. RESULTS A combination of temporal and spatial information defines five groups that can accurately predict the patterns of uncharacterized hormones. Our analysis identified Secretin, a novel placental hormone that is expressed specifically by the trophoblast at levels many times greater than in any other tissue. CONCLUSIONS The characteristics of Secretin fit the paradigm of known placental hormones and suggest that it may play an important role during pregnancy.

Intrauterine Growth Restriction Alters Hippocampal Expression and Chromatin Structure of Cyp19a1 Variants

We evaluated the impact of uteroplacental insufficiency (UPI), and subsequent intrauterine growth restriction (IUGR), on serum testosterone and hippocampal expression of Cyp19a1 variants and aromatase in rats. Additionally, we determined UPI induced histone modification of the promoter regions of Cyp19a1 variants using chromatin immunoprecipitation. Cyp19a1 is the gene encoding the protein aromatase, that catalyzes the biosynthesis of estrogens from androgens and is necessary for masculinization of the brain. IUGR was induced via bilateral uterine artery. UPI increased serum testosterone in day of life 0 (D0) and day of life 21 (D21) IUGR males to 224% and 299% of control values, respectively. While there was no significant impact of UPI on testosterone in D0 females, testosterone in D21 IUGR females was 187% of controls. Cyp19a1 variant 1.f and variant II are expressed in the rat hippocampus at D0 and D21. UPI significantly reduced expression of Cyp19a1 variant 1.f in D0 males, with no impact in females. Similarly at D0, UPI reduced expression of aromatase, the protein encoded by Cyp19a1, in males. Dimethylation of H3K4 was increased in the promoter region of variant 1.f (P1.f) and trimethylation of H3K4 was decreased in the promoter region of variant II (PII). At D21, dimethylation of H3K4 is significantly reduced in PII of IUGR males. We conclude that UPI increases serum testosterone and reduces Cyp19a1 variant 1.f expression in the hippocampus of D0 IUGR males. Additionally, UPI alters the chromatin structure of CYP19a1 at both D0 and D21.