Synopsis
1H-MRS non-invasively quantifies metabolites that play important roles in neurodevelopment. The physiological functions of these metabolites, however, are still debated. Examining the regional intercorrelations between metabolites such as NAA, creatine, choline and glutamate provides insight about the role of individual and coupled biochemicals in the developing brain. We examined correlations between pairs of metabolites in the midfrontal gray matter (MFGM), peritrigonal white matter (PWM), basal ganglia (BG) at 5, 7 and 9 years in a cohort of South African children. We found significant metabolite couplings in both the MFGM and PWM, however no significant couplings were observed in the BG. Purpose
1H-MRS measures localized brain metabolism. Throughout childhood the brain develops biochemically, and different metabolites may develop in tandem depending on the brain region. Understanding how metabolites are inter-related may provide insight into healthy brain growth, and create norms to aid in identifying pathology and abnormal development.
Relationships between individual metabolites in different brain regions may also provide physiological insight. For example, even though much literature exists on the role of N-acetyl-aspartate (NAA) in the brain, debate still surrounds its precise function. NAA is primarily interpreted as a marker of neuronal integrity. However, additional proposed roles of NAA include facilitating energy metabolism in neuronal mitochondria1 and a reservoir for glutamate.2
An MRS study in healthy adults examined intra-regional metabolite correlations.3 In the anterior cingulate, NAA-glutamate levels were coupled, whereas in the cerebellar vermis multiple correlations were reported including NAA-glutamate, NAA-choline (GPC+PCh) and glutamate-choline. In schizophrenia4,5, NAA and glutamate+glutamine levels are regionally decoupled in patients compared to control subjects.
Here we identify couplings of typically measured metabolites - NAA, creatine (Cr+PCr), glutamate, choline - in a representative pediatric population in South Africa (SA). Since 95% of HIV-positive pregnant women and 68% of HIV-exposed infants in SA receive antiretroviral therapy (ART)6,7, the population includes HIV-exposed, uninfected (HEU) children. Although the risks of ART and HIV in utero exposure to brain development are expected to be minimal, long-term consequences are not well-established8,9.
We hypothesize that within a South African pediatric population (followed longitudinally from 5 - 9 years) NAA-glutamate levels will be coupled across three different brain regions, independent of age and HIV exposure.
Methods
MRI. A high-resolution T1-weighted acquisition and single voxel 1H-MRS in the midfrontal gray matter (MFGM), peritrigonal white matter (PWM) and basal ganglia (BG) were performed on a Siemens 3T Allegra Head Scanner (Siemens, Erlangen, Germany) in Cape Town, South Africa. MRS used a real-time motion and B0 corrected10 PRESS (TR/TE 2000/30 ms, 64 averages, 2:16 min). SPM12 was used for voxel segmentation to determine tissue type percentages for water concentration and partial volume calculations. Eddy current compensation and frequency/phase correction were performed. Absolute metabolite levels calculated with LCModel.
Subjects. Children studied were a subset of a longitudinal neuroimaging study, including HEU and HIV-unexposed, uninfected (HUU) children. Twenty-nine 5-year-old, forty-nine 7-year-old, and twenty-nine 9-year-old children were successfully scanned. All HEU children were exposed to ART for prevention of mother-to-child transmission. Table 1 summarizes the demographics of children included in analysis.
The R-progamming language was used for statistical analysis. We used a linear mixed effects regression model to account for repeated measures in some children. Gender, HIV-exposure, ethnicity, and signal-to-noise ratio were included as confounds. The threshold for significance was Bonferroni corrected for 6 comparisons (p < 0.006).
Results
BG. No statistically significant associations were observed.
MFGM. We found significant couplings between NAA-creatine (slope = 0.68, p < 0.0001), choline-creatine (slope = 0.12, p = 0.0002), NAA-glutamate (slope = 0.26, p < 0.0001) and creatine-glutamate (slope = 1.2, p < 0.0001). (Figures 1 - 4).
PWM. We found significant couplings between NAA-creatine (slope = 0.86; p < 0.0001), NAA-glutamate (slope = 0.20; p = 0.004) and creatine-glutamate (slope = 1.3; p = 0.0001). (Figures 1, 3 and 4).
Discussion
Surprisingly, we found couplings between NAA, creatine and glutamate in MFGM and PWM. The overarching relations of NAA, glutamate and creatine suggest these metabolites are metabolically interconnected, however these relationships may be indirectly connected. Choline-creatine coupling was only observed in the MFGM indicating the relationship is region specific. The absence of metabolite couplings in the BG was unexpected, revealing different regional roles for individual metabolites. Our results support previous findings3-5 of glutamate-NAA couplings in healthy adults, extending this result to different regions and a pediatric population.
Gender, HIV exposure, ethnicity and age did not significantly confound the reported couplings, implying these results can be applied to a wide pediatric population. However, ART exposure has subsequently increased in duration and from 2 to 3 drugs in utero.
Lastly, since the observed couplings have regional dependence, are observed mostly between metabolites without spectral overlap and in metabolites with low standard deviations, we believe these relationships are not related to data collection or analysis.
Conclusion
A better understanding of the interdependence of metabolite levels in different brain regions allows for a better description of healthy brain growth, and provides additional norms against which to judge pathology and abnormal growth. Studies often report and interpret metabolite ratios in terms of creatine levels because of its expected stability. Our results indicate caution is required in interpreting ratios in pediatric populations.
Acknowledgements
Support for this study was provided by NRF/DST South African Research Chairs Initiative; US National Institute of Allergy and Infectious Diseases (NIAID) through the CIPRA network, Grant U19 AI53217; NIH grants R01HD071664 and R21MH096559; NRF grant CPR20110614000019421, and the Medical Research Council (MRC). We thank the CUBIC radiographers (Marie-Louise de Villiers, Nailah Maroof and Alison Siljeur), our research staff (Thandiwe Hamana and Rosy Khethelo), and Shabir A. Madhi for access to control participants on the CIPRA-SA04 trial. References
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