A preliminary examination of cortical neurotransmitter levels associated with heavy drinking in posttraumatic stress disorder.

Posttraumatic stress disorder (PTSD) patients have low cortical concentrations of γ-aminobutyric acid (GABA) and elevated glutamate (Glu) as measured by proton magnetic resonance spectroscopy ((1)H MRS). Alcohol use disorder (AUD) is highly comorbid with PTSD, but the neurobiological underpinnings are largely unknown. We wanted to determine if PTSD patients with AUD have normalized cortical GABA and Glu levels in addition to metabolite alterations common to AUD. We compared brain metabolite concentrations in 10 PTSD patients with comorbid AUD (PAUD) with concentrtations in 28 PTSD patients without AUD and in 20 trauma-exposed controls (CON) without PTSD symptoms. We measured concentrations of GABA, Glu, N-acetylaspartate (NAA), creatine- (Cr) and choline-containing metabolites (Cho), and myo-Inositol (mI) in three cortical brain regions using (1)H MRS and correlated them with measures of neurocognition, insomnia, PTSD symptoms, and drinking severity. In contrast to PTSD, PAUD exhibited normal GABA and Glu concentrations in the parieto-occipital and temporal cortices, respectively, but lower Glu and trends toward higher GABA levels in the anterior cingulate cortex (ACC). Temporal NAA and Cho as well as mI in the ACC were lower in PAUD than in both PTSD and CON. Within PAUD, more cortical GABA and Glu correlated with better neurocognition. Heavy drinking in PTSD is associated with partially neutralized neurotransmitter imbalance, but also with neuronal injury commonly observed in AUD.

Human linear growth trajectory defined.

OBJECTIVES: The purpose of this study was to assess the applicability of a simple mathematical formula for prediction of individual child linear growth. The formula describes a square root dependence of height on age with only two constants, k and C. METHODS: Retrospective serial height measurements of 137 healthy children (61 female), who attended clinic in the Pediatrics Department at the University of California, San Francisco were used. For each child, two of the initial measurements and their corresponding measurement times were used to determine the values of k and C. By substituting the determined values of k and C into the formula, the formula was then used to predict the trajectory of the child's growth. RESULTS: The 137 children were comprised of 20% Hispanic, 23% African-American, 27% Caucasian and 30% Asian. The formula predicted growth trajectories of 136 out of the 137 children with minimal discrepancies between the measured data and the corresponding predicted data. The mean of the discrepancies was 0.8 cm. CONCLUSIONS: Our proposed formula is very easy to use and predicts individual child growth with high precision irrespective of gender or ethnicity. The formula will be a valuable tool for studying human growth and possibly growths of other animals.